U.S. patent application number 13/772825 was filed with the patent office on 2013-07-04 for multi-feature product inventory management and allocation system and method.
This patent application is currently assigned to Ford Motor Company. The applicant listed for this patent is Ford Motor Company. Invention is credited to Robbie J. Aniol, William R. Fravel, Bryan R. Goodman, Melinda K. Hunsaker, He Li, Yu-Ning Liu, Barbara Nance, Gintaras V. Puskorius, Shigeru Sadakane.
Application Number | 20130173330 13/772825 |
Document ID | / |
Family ID | 48094949 |
Filed Date | 2013-07-04 |
United States Patent
Application |
20130173330 |
Kind Code |
A1 |
Puskorius; Gintaras V. ; et
al. |
July 4, 2013 |
MULTI-FEATURE PRODUCT INVENTORY MANAGEMENT AND ALLOCATION SYSTEM
AND METHOD
Abstract
A data store includes, for a dealer of interest, a target
inventory mix rate for an item having a feature. A computing device
is configured to perform an optimization to obtain a recommended
feature allocation of the item including the feature by minimizing
a difference between a projected inventory mix rate for the feature
and the target inventory mix rate for the feature.
Inventors: |
Puskorius; Gintaras V.;
(Novi, MI) ; Goodman; Bryan R.; (Northville,
MI) ; Aniol; Robbie J.; (Troy, MI) ; Fravel;
William R.; (Ann Arbor, MI) ; Liu; Yu-Ning;
(Ann Arbor, MI) ; Hunsaker; Melinda K.; (Canton,
MI) ; Nance; Barbara; (Grosse lle, MI) ;
Sadakane; Shigeru; (Northville, MI) ; Li; He;
(Novi, MI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Ford Motor Company; |
Dearborn |
MI |
US |
|
|
Assignee: |
Ford Motor Company
Dearborn
MI
|
Family ID: |
48094949 |
Appl. No.: |
13/772825 |
Filed: |
February 21, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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12554643 |
Sep 4, 2009 |
8428985 |
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13772825 |
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Current U.S.
Class: |
705/7.25 |
Current CPC
Class: |
G06Q 10/06315 20130101;
G06Q 10/00 20130101 |
Class at
Publication: |
705/7.25 |
International
Class: |
G06Q 10/06 20120101
G06Q010/06 |
Claims
1-41. (canceled)
42. A method, comprising: determining, for a dealer of interest, a
target inventory mix rate for an item having multiple features by
using dealer-level historical sales mix rates and market-level
historical sales mix rates for the features; determining a
projected inventory mix rate for the features based at least in
part on projected dealer sales for the features; and performing, in
a computing device having a processor and a memory, a mathematical
programming optimization that includes an objective function and
constraints to obtain a recommended feature allocation of the item
including the features, based at least in part on at least one
weighting factor associated with the dealer of interest, by
minimizing a difference between a projected inventory mix rate for
the features and the target inventory mix rate for the
features.
43. The method of claim 42, wherein the constraints include at
least one of a production constraint, a material availability
constraint, and an item definition constraint, and further wherein
the optimization compares output of the optimization relative to an
optimal value of the objective function.
44. The method of claim 42, further comprising: for the dealer of
interest, calculating each of: (a) dealer-level feature-related
sales of an item over a set of times where the sales are related to
a feature of the item, (b) dealer-level total sales for the item
over the set of times, and (c) a dealer-level historical sales mix
rate for the feature based at least in part on the dealer-level
total sales and the dealer-level feature sales; for a market
related to the dealer of interest, calculating each of: (d)
market-level feature-related sales of an item over the set of times
where the sales are related to a feature of the item, (e)
market-level total sales for the item over the set of times, and
(f) a market-level historical sales mix rate for the feature based
at least in part on the market-level total sales and the
market-level feature sales; using the dealer-level historical sales
mix rates and the market-level historical mix rates for the feature
at least in part in determining the target inventory mix rate; and
calculating an amount of dealer-level item-related inventory and
market-level item-related inventory related to the item over the
set of the times.
45. The method of claim 44, further comprising: calculating a
historical dealer sales rate for the item by dividing the
dealer-level item-related inventory into the dealer-level
item-related sales; calculating a historical market sales rate for
the item by dividing the market-level item-related inventory into
the market-level item-related sales; calculating a relative dealer
total sales rate by dividing the historical market sales rate for
the item into a difference between the historical dealer sales rate
for the item and the historical market sales rate for the item;
determining the target inventory mix rate for the item based at
least in part on the relative dealer total sales rate in addition
to the historical sales mix rate for the features; and adjusting
the target inventory mix rate for at least one of the features
based on at least one of the dealer historical sales mix rate for
the feature, the market historical sales mix rate for the feature,
and a relative dealer total sales rate for the item.
46. The method of claim 44, further comprising adjusting the target
inventory mix rate based on anticipated changes to future sales mix
rates.
47. The method of claim 44, further comprising: defining the market
according to a location of the dealer of interest; and applying a
weight to at least one of sales and inventories associated with a
dealer in the market based upon a distance between the location of
the dealer of interest and a location of the dealer in the market;
wherein defining the market includes determining an area according
to a radius around the location, the radius being determined
according to at least one of a predetermined distance, a
predetermined time of travel, a predetermined distance of travel,
and a distance necessary to encompass a predetermined number of
dealers.
48. The method of claim 42, wherein the item definition constraint
is defined by at least one of a set of all possible item
configurations and a set of inequalities derived from the set of
all possible item configurations.
49. The method of claim 42, wherein the recommended feature
allocation is based at least in part one of at least one weighting
factor associated with the dealer of interest, the at least one
weighting factor including at least one of a weight based on
expected dealer inventory and expected dealer item sales, and a
feature parameter to weight a relative importance of a chosen
feature relative to other features for which a recommended order is
being determined.
50. The method of claim 42, further comprising: calculating an
amount of projected sales at a specified time for the item based at
least in part on a dealer's inventory and sales rate for the item;
determining a projected inventory of the item at the specified time
based at least in part on actual dealer inventory, the projected
sales rate for dealer inventory, and an amount of expected arriving
inventory of the item including the feature; using the projected
inventory at least in part to determine the projected inventory mix
rate. determining if the amount of projected inventory for the
feature at the specified time is negative and, if the amount of
projected inventory for the feature at the specified time is
negative: determining a number of units projected to have been sold
that are in excess of inventory for a feature family in which the
feature is included; setting the projected sales for the feature to
available inventory for the feature at the specified time; and
redistributing the number of units projected to have been sold that
are in excess of available inventory for the feature family to
other features within the feature family.
51. A system, comprising: a data store that includes, for a dealer
of interest, a target inventory mix rate for an item having
multiple features by using dealer-level historical sales mix rates
and market-level historical sales mix rates for the features; and a
computing device including a processor and a memory that is
configured to: determine a projected inventory mix rate for the
features based at least in part on projected dealer sales for the
features; and perform a mathematical programming optimization that
includes an objective function and constraints to obtain a
recommended feature allocation of the item including the features,
based at least in part on at least one weighting factor associated
with the dealer of interest, by minimizing a difference between a
projected inventory mix rate for the features and the target
inventory mix rate for the features.
52. The system of claim 51, wherein the constraints include at
least one of a production constraint, a material availability
constraint, and an item definition constraint, and further wherein
the optimization compares output of the optimization relative to an
optimal value of the objective function.
53. The system of claim 51, the computing device further configured
to: for the dealer of interest, calculate each of: (a) dealer-level
feature-related sales of an item over a set of times where the
sales are related to a feature of the item, (b) dealer-level total
sales for the item over the set of times, and (c) a dealer-level
historical sales mix rate for the feature based at least in part on
the dealer-level total sales and the dealer-level feature sales;
for a market related to the dealer of interest, calculate each of:
(d) market-level feature-related sales of an item over the set of
times where the sales are related to a feature of the item, (e)
market-level total sales for the item over the set of times, and
(f) a market-level historical sales mix rate for the feature based
at least in part on the market-level total sales and the
market-level feature sales; and use the dealer-level historical
sales mix rates and the market-level historical mix rates for the
feature at least in part in determining the target inventory mix
rate.
54. The system of claim 53, the computing device further configured
to: calculate an amount of dealer-level item-related inventory and
market-level item-related inventory related to the item over the
set of the times; calculate a historical dealer sales rate for the
item by dividing the dealer-level item-related inventory into the
dealer-level item-related sales; calculate a historical market
sales rate for the item by dividing the market-level item-related
inventory into the market-level item-related sales; calculate a
relative dealer total sales rate by dividing the historical market
sales rate for the item into a difference between the historical
dealer sales rate for the item and the historical market sales rate
for the item; determine the target inventory mix rate for the item
based at least in part on the relative dealer total sales rate in
addition to the historical sales mix rate for the features; and
adjust the target inventory mix rate for at least one of the
features based on at least one of the dealer historical sales mix
rate for the feature, the market historical sales mix rate for the
feature, and a relative dealer total sales rate for the item.
55. The system of claim 53, the computing device further configured
to adjust the target inventory mix rate based on anticipated
changes to future sales mix rates.
56. The system of claim 53, the computing device further configured
to: define the market according to a location of the dealer of
interest, and apply a weight to at least one of sales and
inventories associated with a dealer in the market based upon a
distance between the location of the dealer of interest and a
location of the dealer in the market; wherein defining the market
includes determining an area according to a radius around the
location, the radius being determined according to at least one of
a predetermined distance, a predetermined time of travel, a
predetermined distance of travel, and a distance necessary to
encompass a predetermined number of dealers.
57. The system of claim 51, wherein the item definition constraint
is defined by at least one of a set of all possible item
configurations and a set of inequalities derived from the set of
all possible item configurations.
58. The system of claim 51, wherein the recommended feature
allocation is based at least in part one of at least one weighting
factor associated with the dealer of interest, the at least one
weighting factor including at least one of a weight based on
expected dealer inventory and expected dealer item sales, and a
feature parameter to weight a relative importance of a chosen
feature relative to other features for which a recommended order is
being determined.
59. The system of claim 51, the computing device further configured
to: calculate an amount of projected sales at a specified time for
the item based at least in part on a dealer's inventory and sales
rate for the item; determine a projected inventory of the item at
the specified time based at least in part on actual dealer
inventory, the projected sales rate for dealer inventory, and an
amount of expected arriving inventory of the item including the
feature; use the projected inventory at least in part to determine
the projected inventory mix rate; and determine if the amount of
projected inventory for the feature at the specified time is
negative and, if the amount of projected inventory for the feature
at the specified time is negative: determine a number of units
projected to have been sold that are in excess of inventory for a
feature family in which the feature is included; set the projected
sales for the feature to available inventory for the feature at the
specified time; and redistribute the number of units projected to
have been sold that are in excess of available inventory for the
feature family to other features within the feature family.
60. A computer-readable medium having computer-executable
instructions tangibly embodied thereon, the instructions comprising
instructions for: determining, for a dealer of interest, a target
inventory mix rate for an item having multiple features by using
dealer-level historical sales mix rates and market-level historical
sales mix rates for the features; determining a projected inventory
mix rate for the features based at least in part on projected
dealer sales for the features; and performing, in a computing
device having a processor and a memory, a mathematical programming
optimization that includes an objective function and constraints to
obtain a recommended feature allocation of the item including the
features, based at least in part on at least one weighting factor
associated with the dealer of interest, by minimizing a difference
between a projected inventory mix rate for the features and the
target inventory mix rate for the features;
61. The medium of claim 60, wherein the constraints include at
least one of a production constraint, a material availability
constraint, and an item definition constraint, and further wherein
the optimization compares output of the optimization relative to an
optimal value of the objective function.
62. The medium of claim 60, the instructions further comprising
instructions for: for the dealer of interest, calculating each of:
(a) dealer-level feature-related sales of an item over a set of
times where the sales are related to a feature of the item, (b)
dealer-level total sales for the item over the set of times, and
(c) a dealer-level historical sales mix rate for the feature based
at least in part on the dealer-level total sales and the
dealer-level feature sales; for a market related to the dealer of
interest, calculating each of: (d) market-level feature-related
sales of an item over the set of times where the sales are related
to a feature of the item, (e) market-level total sales for the item
over the set of times, and (f) a market-level historical sales mix
rate for the feature based at least in part on the market-level
total sales and the market-level feature sales; and using the
dealer-level historical sales mix rates and the market-level
historical mix rates for the feature at least in part in
determining the target inventory mix rate.
63. The medium of claim 62, the instructions further comprising
instructions for: calculating an amount of dealer-level
item-related inventory and market-level item-related inventory
related to the item over the set of the times; calculating a
historical dealer sales rate for the item by dividing the
dealer-level item-related inventory into the dealer-level
item-related sales; calculating a historical market sales rate for
the item by dividing the market-level item-related inventory into
the market-level item-related sales; calculating a relative dealer
total sales rate by dividing the historical market sales rate for
the item into a difference between the historical dealer sales rate
for the item and the historical market sales rate for the item;
determining the target inventory mix rate for the item based at
least in part on the relative dealer total sales rate in addition
to the historical sales mix rate for the features; and adjusting
the target inventory mix rate for at least one of the features
based on at least one of the dealer historical sales mix rate for
the feature, the market historical sales mix rate for the feature,
and a relative dealer total sales rate for the item.
64. The medium of claim 60, wherein the item definition constraint
is defined by at least one of a set of all possible item
configurations and a set of inequalities derived from the set of
all possible item configurations.
65. The method of medium of claim 60, wherein the recommended
feature allocation is based at least in part one of at least one
weighting factor associated with the dealer of interest, the at
least one weighting factor including at least one of a weight based
on expected dealer inventory and expected dealer item sales, and a
feature parameter to weight a relative importance of a chosen
feature relative to other features for which a recommended order is
being determined.
Description
RELATED APPLICATIONS
[0001] Some or all of the subject matter disclosed in this
application is also disclosed in co-pending applications having
Ser. Nos. ______ and ______ (attorney docket numbers 65080-0099 and
65080-0100), entitled "TURN RATE CALCULATION" and "ORDER
RECOMMENDATIONS," respectively, each of which is filed the same day
as the present application and incorporated herein by reference in
its entirety.
BACKGROUND
[0002] Items for sale may be pre-ordered by a dealer in various
configurations based on a forecast of item demand, i.e., based on
the number of items of a particular configuration that the dealer
thinks it can sell. Once received, pre-ordered items are included
in dealer inventory, and may be made available for sale. For many,
if not most, manufactured items, a large percentage of retail sales
are delivered from available dealer inventory. Therefore, sales
rates for items at a dealer are often largely determined based on
the configuration of items that the dealer has in inventory.
[0003] It is possible that an item may be produced in only one
configuration. Accordingly, a forecast of demand would include only
the number of units to order. However, many items are produced in
multiple configurations of features. In such cases, items may be
pre-ordered by a dealer based on a forecast of item demand, where
forecasted item demand may be determined from rates at which items
having a similar feature or features have recently sold. However,
often an item may be ordered with many different combinations of
features. This variety in configuration complicates forecasting of
item demand because sold items may have configurations including
many different features, making it difficult to determine what
features contributed to past sales and therefore should be ordered,
and in what combination. Variety in configurations for an item also
adds difficulties to the parts supply chain for the item, because
different configurations may require different materials for
manufacture of the items.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 is a block diagram illustrating an exemplary
implementation of a feature selection system.
[0005] FIG. 2 is a block diagram illustrating further details of
the feature selection system.
[0006] FIG. 3 illustrates exemplary sales data elements.
[0007] FIG. 4 illustrates an exemplary dealer market area.
[0008] FIG. 5 illustrates an exemplary expanded dealer market
area.
[0009] FIG. 6 illustrates an exemplary dealer market weighting.
[0010] FIG. 7 illustrates an exemplary relationship between actual
inventory and weighted inventory.
[0011] FIG. 8 illustrates an exemplary relationship between
weighted sales, weighted inventory, and weighted turn rates.
[0012] FIG. 9 illustrates exemplary relationships among certain
sales metrics.
[0013] FIG. 10 illustrates exemplary relationships relating to the
projection of dealer inventory.
[0014] FIG. 11 illustrates exemplary relationships relating to the
calculation of a target feature level inventory mix rate.
[0015] FIG. 12 illustrates exemplary relationships relating to
feature allocation.
[0016] FIG. 13 illustrates exemplary relationships relating to
generation of inventory turn rate calculators.
[0017] FIG. 14 illustrates an exemplary mix rate modulation
encoding.
[0018] FIG. 15 illustrates exemplary data relating to principal
component analysis.
[0019] FIG. 16 illustrates exemplary neural network context
variables relating to neural network data set records.
[0020] FIG. 17 illustrates an exemplary neural network processing
of mix rate modulation encoded data.
[0021] FIG. 18 illustrates exemplary construction of a
configuration matrix for the evaluation of neural network inventory
turn rate calculators.
[0022] FIG. 19 illustrates an exemplary calculation of
risk-adjusted turn rates based on inventory turn rate
calculators.
[0023] FIG. 20 illustrates exemplary data relationships relating to
order selection.
[0024] FIG. 21 illustrates an exemplary data relationship relating
to information used in report generation.
[0025] FIGS. 22-24 illustrate pages of an exemplary dealer
report.
[0026] FIG. 25 illustrates an exemplary feature selection system
setup process.
[0027] FIG. 26 illustrates an exemplary overall periodic process
for a feature selection system.
[0028] FIG. 27 illustrates an exemplary process for data
preprocessing.
[0029] FIG. 28 illustrates an exemplary process for inventory
projection.
[0030] FIG. 29 illustrates an exemplary process for establishing
target inventory mix rates.
[0031] FIG. 30 illustrates an exemplary process for feature
allocation.
[0032] FIG. 31 illustrates an exemplary process for creating turn
rate calculators.
[0033] FIG. 32 illustrates an exemplary process for evaluating turn
rate calculators.
[0034] FIG. 33 illustrates an exemplary process for generating
recommended orders.
DETAILED DESCRIPTION
I. Introduction
[0035] A. System Overview
[0036] An order generation system utilizes a feature selection
subsystem to generate order recommendations for multiple dealers
which result in a balanced inventory. Generally, the order
recommendations relate to a particular item, and specify a
configuration of a feature or features recommended to be included
in the item. A "feature," as that term is used herein, means any
characteristic or attribute of an item. A "configuration," as that
term is used herein, means a combination of one or more features
attributed to an item.
[0037] One exemplary use of an order generation system is for
automobile dealerships. In the context of automobile dealerships,
"retail orders" are orders for vehicles that are specially ordered
for customers, while "stock orders" are orders made to replenish
dealer inventory. Accordingly, an order generation system may
generate recommended stock orders, i.e., orders for vehicles that
may be used to replenish dealer stock. In such an example, the item
with respect to which an order recommendation is generated may be a
particular make and model of a vehicle. Features that may be
recommended to be included in vehicles ordered by a dealer could
then be determined, such as exterior paint color, transmission
type, presence or absence of a moon-roof, wheel size, brake
packages, interior trim packages, etc. Specific configurations of
the recommended features may then be determined by the order
generation system and sent to the dealer as a recommendation of
what to order for dealer stock.
[0038] In one implementation, the system projects sales and
inventory based on reported data and/or assumptions that various
quantities of items having various configurations of features have
been or will be ordered. The system then performs an optimization
to determine quantities of items with various features that the
dealer should order. The system then may recommend quantities of
items containing various features, i.e., quantities of recommended
configurations, to be ordered. The optimization may include
minimizing a function of the difference between projected inventory
mix rates and target inventory mix rates, where the target may be
based on expected sales.
[0039] Generally, a "mix rate" is the ratio of items in a
particular subset compared to all items regardless of membership in
the subset. For example, a sales mix rate for a particular model of
vehicle configured with a moonroof would be the ratio of sales for
the model with moonroofs (a subset of all units for the model) to
the total sales of the model regardless of whether it was sold with
a moonroof. For further example, an "inventory mix rate" for an
item having a particular feature means the number of the item in
inventory having the feature divided by the total number of the
items in inventory, regardless of whether the feature is
present.
[0040] Minimizing the difference between projected inventory mix
rates and target inventory mix rates means that the order
generation system may attempt to provide a dealer with what is
referred to as a "balanced inventory." One meaning of a balanced
inventory is that projected sales mix rates and projected inventory
mix rates are equal, or as close to it as possible. (Other ways of
understanding the concept of balanced inventory are discussed
below.) The order generation system may generate both projected
sales mix rates and projected inventory mix rates for a dealer, and
uses these projections to attempt to balance inventory.
[0041] In general, once the order generation system determines
feature allocations that are used to arrive at balanced inventory,
the order generation system may then recommend that a dealer order
configurations including the allocated features. Which
configurations to choose, and how many of each configuration, may
be determined based on a measure of the goodness of each particular
configuration, i.e., how much better one configuration is over
other configurations. For example, a goal of determining
recommended configurations may be to create a fast turning
inventory, i.e., an inventory in which items remain for a
relatively short time before being sold. Where fast turning
inventory is a goal, a better configuration may be defined as a
configuration that is more likely to sell quickly.
[0042] Accordingly, the system may determine inventory turn rates
associated with items and features. An "inventory turn rate" for an
item is essentially the rate of sale for the item, i.e., the rate
at which the item is turned from inventory. Factors that may affect
the inventory turn rate other than sales, such as dealer trades,
may also be taken into account. Other measures of how good one
configuration of features is compared to other configurations of
features are possible (i.e., relative goodness). However, in some
instances the order generation system may use what are referred to
as "inventory turn rate calculators" to measure the relative
goodness of one configuration over other configurations in terms of
inventory turn rate. The system may use various processes such as
those described below to develop the inventory turn rate
calculators.
[0043] In general, inventory turn rates for an item with a
configuration of features are a function of (i) the demand for the
item with the configuration and (ii) its availability in inventory.
Inventory turn rates for an item may further be a function of the
availability of other similar items in inventory at a dealer of
interest and also in the market area of the dealer. However, in
practice demand is generally not a linear function, and other
factors can affect inventory turn rates as well. For example,
attempting to estimate demand for an item associated with a
particular feature can be affected by other features with which the
particular feature is bundled. In other words, it can be difficult
to look at features in isolation. Moreover, some factors relevant
to turn rates may not be observed, or observable, and therefore are
not knowable to the system. Such factors may present disturbances
affecting the usefulness of inventory turn rate functions derived
from past inventories and sales rates. Therefore, the system may
use neural networking technology or another suitable technology to
generate turn rate calculators providing projected turn rates that
in turn are used to maximize relative goodness for recommended
configurations, and thus generate order recommendations.
[0044] Order recommendations may take into account history and
projections concerning multiple order cycles. For example, if an
order cycle is weekly, meaning that dealer orders are submitted
once per week, item orders may be managed for multi-week periods.
Managing orders over a span of multiple order cycles can be useful
in managing a supply chain for materials required in item
manufacture, in addition to providing order recommendations that
will assist dealers in maximizing their sales.
[0045] B. Configuration
[0046] An item configuration is a unique or substantially unique
combination of features associated with, i.e., attributable to, an
item. Generally, but not necessarily, an item configuration
includes all of the features that may be attributed to the item.
For example, if the item is a vehicle, a configuration may include
a combination of all selectable features with which the vehicle is
sold, e.g., exterior paint color, interior trim color, transmission
type, presence or absence of a moon-roof, wheel size, brake
packages, interior trim packages, etc.
[0047] An item may be manufactured in one or more configurations of
features, i.e., for an item, one or more configurations may be
possible. For some items, the potential number of unique
configurations may number in the hundreds. For more complex items
with many available features, the number of configurations of
features may number in the tens-of-billions.
[0048] C. Constraints and Realizable Configurations
[0049] Feature allocations, order recommendations, turn rate
calculations, etc. may be subject to various constraints. One type
of constraint is a production constraint. A production constraint
is a constraint on the ordering of items based on a limit in
production. For example, if a certain part is required for a highly
desirable feature, and the part is not available, then the order
generation system may be configured not to recommend orders or to
limit the number of recommended orders including that feature.
Thus, a production constraint may prevent a combination of features
from including an all-wheel drive system due to a temporary
shortage or a limited supply of a particular part or component
required for production of items including the all-wheel drive
system.
[0050] Additional types of constraints may include, for example, a
business decision to limit production including a particular
feature, and a constraint on production related to a particular
region, among others.
[0051] In addition, although a large number of possible
combinations of features may exist for an item, not all possible
combinations of features may be capable of being manufactured. Such
a limit on the combination of one or more features may be referred
to as a product definition constraint. An example of a product
definition constraint may be that for some vehicles a manual
transmission may only be available with certain engine choices. As
another example, for some vehicles heated seats are only available
with leather seats, but never with cloth seats. In some instances,
product definition constraints may be the result of lack of
engineering plans to account for a possibility, or may be based on
a business decision to disallow one or more combinations.
[0052] A "realizable" configuration is a combination of features
that is possible to be built, given various product definition
constraints. An order generation system preferably will not
recommend for a dealer to order a configuration that is not
realizable.
[0053] It is important to note that a configuration may be
realizable based on product definition constraints, but may not be
buildable at a particular time due to another constraint, such as a
production constraint, related to a shortage of a particular
required part.
[0054] D. Feature Families
[0055] A "feature family" is a set of values that comprise all
possible values for a feature. An item may possess exactly one
feature from a feature family. For example, in general, a vehicle
is provided with only one exterior paint color; therefore, a
feature family could include a set of possible vehicle colors,
where a feature for the vehicle must include only one color.
Logically, every feature family must contain at least two values,
because if there is only one value in a feature family, i.e., for a
feature, then all items with regard to that feature are the same.
For example, for features that are either present or not (i.e.,
binary features), the two features in the family will be the
presence of the feature and the absence of the feature. An example
of a binary feature is a vehicle rear spoiler. An example of a
family including more than two features would be a vehicle paint
color feature family, mentioned above. Such a feature family may
include many different paint colors, including red, green, blue,
white, silver, and black, etc.
[0056] A "superfamily" is a logical grouping of two or more feature
families. Thus, a superfamily may consist of all possible
combinations of features from the different feature families that
are included in the superfamily. In other words, a superfamily is
comprised of multiple features families, and contains exactly one
feature selected from each contributing feature family.
Superfamilies are predefined constructs, and are used to allow
multiple features to be considered in combination, e.g., where the
features are considered to be strongly related. Superfamilies allow
for the system to better handle feature family interactions at the
feature allocation stage. Without superfamilies, individual feature
families would otherwise be considered independently. Generally,
superfamilies are created by a user as a logical grouping based on
operator knowledge of features that should be considered in
combination.
[0057] Care must be taken in defining superfamilies, however,
because superfamilies introduce additional complexity into feature
allocation. For example, for a set of three binary features, there
are eight ways to combine the features when put together as a
superfamily. However, when considered separately as a sequence of
three binary features, there are only six elements to consider, not
eight.
[0058] E. Metrics
[0059] The system determines and utilizes various sales and
inventory metrics. These metrics may be determined for a dealer or
for a market that, as explained in detail below, is determined with
reference to the dealer. Such metrics are sometimes referred to as
a dealer-level metrics or market-level metrics, respectively.
Further, sales and inventory metrics, and other metrics, may be
determined with respect to a feature, or with respect to all
configurations for an item. Accordingly, metrics may also be
referred to as feature-level, or item-level, as warranted. Note
that item-level and configuration-level are sometimes used
interchangeably. Mix rates typically represent feature-level or
super-feature-level data, while turn rates may represent
feature/super-feature-level as well as item-level data. Further, as
used herein, a reference to a metric as being on a per-dealer,
per-market, etc., basis means the same thing, respectively, as a
metric that is dealer-level, market-level, etc.
[0060] Some metrics compare feature-level metrics with item-level
metrics. For example, a sales mix rate may be a percentage
calculated by comparing feature-level sales with item-level sales.
Similarly, inventory mix rates may be a percentage of inventory at
a given point in time that includes a feature. In general, sales
rates may be referred to as inventory turn rates, i.e., the rate at
which an item is removed from inventory because it has been sold.
As discussed below, projecting and estimating inventory turn rates
can be an important component for generating order
recommendations.
[0061] As mentioned above, the system generally generates order
recommendations for a specific dealer for a particular item. Thus,
although order recommendations are based both on dealer-level and
market-level data, order recommendations are generally tailored for
the specific dealer. Further, the system may generate reports for
the dealer concerning the dealer's past performance to explain the
order recommendations.
[0062] F. Sales and Inventory Mix Rates
[0063] As explained above, a sales mix rate is the ratio of items
sold with a specific feature relative to all sales. A sales mix
rate may be determined based on sales data for a particular period
of time.
[0064] As also discussed above, an inventory mix rate is a ratio of
the amount of inventory that has a particular feature compared to a
measure of the total inventory of an item with which the feature is
associated.
[0065] Intuitively, dealers can only sell what they have in
inventory, which may lead to the assumption that observed sales mix
rates are strongly biased by corresponding production and inventory
mix rates. Over an entire run of items, such as an entire model
year for a vehicle, this may be true, and over a vehicle model year
sales may be a measure of supply. However, it has been discovered
that, over a short period of time where only a small fraction of
inventory is sold, sales mix rates and inventory mix rates may
differ substantially, and accordingly may serve as a measure of
demand.
[0066] Inventory mix rates and sales mix rates may bear some
relationship. For example, if there are no items in inventory with
a particular feature, then the inventory mix rate for that feature
would be zero. Likewise, the sales mix rate for that feature would
also be zero. On the other hand, if all items in inventory have a
particular feature, then 100% of the sales will be of items that
have that feature.
[0067] G. Inventory
[0068] 1. Balanced Inventory
[0069] The order generation system uses a feature selection
(sometimes also referred to as feature allocation) subsystem to
attempt to provide a dealer with a balanced inventory, referred to
above. In a balanced inventory, projected inventory mix rates are
roughly equal to historical sales mix rates, possibly adjusted to
account for recent trends and/or other external factors. Thus, to
balance inventory, the historical sales mix rate may be used to
derive a target inventory mix rate, where the target inventory mix
rate is a measure of desired inventory based on historical sales
mix rates.
[0070] Another way of describing balanced inventory is to state
that any given item in inventory, regardless of the item's
configuration, should have the same probability of sale as any
other item in inventory. Yet another way of describing balanced
inventory is to state that all items in inventory should have the
same inventory turn rate, which may be defined as the number of
items, e.g., associated with a particular feature or configuration,
that have been sold in a given time period divided by the number of
items associated with the feature or configuration that are in
inventory during the time period. In other words, a balanced
inventory implies that the mix of features for an item in inventory
matches what customers are likely to purchase.
[0071] A balanced inventory may be created through targeted
ordering of items with certain features or combinations of
features, incentives or price discounts, or a combination of these
strategies. Targeted ordering is often preferable because it leads
to higher profits than incentives or discounts.
[0072] Targeted ordering may include measuring inventory levels for
an item associated with various features, further measuring sales
rates of the items including the various features, and then
ordering items having feature configurations intended to lead to a
balanced inventory in the future. However, because items take time
to manufacture, there may be substantial delays associated with
such an inventory rebalancing approach through targeted ordering.
Accordingly, manufacturing delay must be taken into account when
recommending orders in order to create a balanced inventory through
inventory projection, discussed in detail below.
[0073] 2. Fast Turning Inventory
[0074] Fast turning inventory is a distinct concept from balanced
inventory. Fast turning inventory means that each unit in inventory
should have a relatively high probability of sale. For a given
dealer, the order generation system may maximize the overall
inventory turn rate, i.e., maximizing a risk adjusted rate of
sales, by choosing an appropriate mixture of configurations of
features. For example, even if red is the best selling vehicle
color, the system may determine that, in a given time period, a
dealer is likely to sell a small number of white cars even as the
dealer sells a larger number of red cars. Thus, to provide each car
with a relatively high probability of sale, the system may
recommend that the dealer order a relatively large number of red
cars but also a smaller number of white cars.
[0075] Although promotion of fast turning inventory may lead to an
inventory imbalance, such imbalance may be desirable if the
resultant inventory includes particular items that turn faster than
average. Additionally, a dealer's inventory may be balanced, but
slow-turning, in which case additional measures may be required to
guide targeted ordering to improve the item turn rate for the
dealer.
[0076] Fast turning inventory may allow a dealer to maximize
profits and minimize the costs of carrying an item in inventory.
Further, with a higher inventory turn rate, a dealer or a
manufacturer may obtain timely feedback from the marketplace
relevant to an inventory stocking strategy, as well as pricing
decisions, which then may place the dealer or manufacturer in a
better position to adapt to the market as it evolves.
[0077] 3. Diverse Inventory
[0078] While the feature selection subsystem of the order
generation system generally attempts to provide dealers with
balanced inventories, it may also generate recommendations that
attempt to provide a diverse inventory, i.e., an inventory in which
items have a diverse set of features. For example, a vehicle dealer
may offer a particular make and model of a vehicle where one color
is a best seller, but nonetheless may wish to stock a range of
colors. In terms of diversity, it may be desirable for a dealer to
order a quantity of vehicles with a particular feature. The
recommended orders may for example, all be for red vehicles, but a
dealer may prefer to order different colored vehicles to have a
more diverse inventory of colors. Indeed, the concept of diverse
inventory may lead to promoting somewhat less popular features
based on a measure between target and actual inventory mix rates in
addition to more popular features, because even if a feature is
less popular, e.g., a less popular color, there is still a
probability that a certain number of items with the less popular
feature will be sold. That is, as an example of promoting a diverse
inventory, a dealer may prefer to order a variety of configurations
even if some of the configurations will lead to slower-turning
inventory.
II. Exemplary System Elements
[0079] FIG. 1 is a block diagram illustrating an exemplary order
generation system 100. The system 100 may include a dealer computer
110, an ordering server 120, a data server 130, a data store 140,
an inventory management server 150, and a supercomputer 170. System
elements including without limitation the foregoing may be in
selective communication with one another over a network 160.
[0080] A. Dealer Computer
[0081] As illustrated in FIG. 1, feature configuration system 100
includes a dealer computer 110. A dealer may offer merchandise for
sale, barter, or trade. For example, a dealer may purchase goods or
services from consumers or businesses, and may offer those goods
and services for resale, such as to end consumers. The dealer may
use dealer computer 110 for various operations, including receiving
order recommendations and placing orders in system 100.
[0082] A dealer may be a franchisee or may be otherwise authorized
by a manufacturer to deal goods of a particular model line or
brand. For example, an automobile dealer may be authorized by an
automotive manufacturer to sell vehicles from that manufacturer.
Moreover, a dealer may be authorized by a manufacturer to operate
only in one or more particular markets and/or geographic areas. To
allow for the goods and services to be sold, a dealer may maintain
an establishment within the authorized market area for the
dealer.
[0083] As illustrated in FIG. 1, dealer may use a dealer computer
110 to communicate inventory orders to ordering server 120, e.g.,
via network 160. Although only one dealer computer 110 is shown in
FIG. 1, system 100 may include many dealers, and generally
does.
[0084] B. Dealer Inventory
[0085] A dealer may hold a supply or stock of goods in order to
offer the goods for sale. Information regarding this supply or
stock of goods may be represented by dealer computer 110 as records
of dealer inventory 115. Although not shown in FIG. 1, information
concerning inventory 115 for a dealer may also be stored in data
store 140. Because a dealer may take ownership of goods and/or
other assets in dealer inventory 115, the dealer may be exposed to
risk if the dealer orders items that sell slowly or not at all. In
the case of a vehicle dealer, for example, the longer a vehicle is
in dealer inventory 115, the more money the dealer may have to pay
in interest on a loan for the vehicle, which may reduce dealer
profits. To mitigate this risk, a dealer may endeavor to order an
optimal mix and amount of dealer inventory 115. Specifically a
dealer may endeavor to have a dealer inventory 115 that is
balanced, fast-turning, and diverse.
[0086] C. Ordering Server
[0087] Ordering server 120 may be implemented as a combination of
hardware and software, and may include instructions stored on one
or more computer-readable media, the instructions for causing one
or more computer processors to perform the operations of the
ordering server 120 described herein.
[0088] Ordering server 120 may receive orders, and may forward the
orders to be built by a manufacturer. For example, ordering server
120 may be in selective communication with one or more dealer
computers 110, e.g., via network 160, and may receive item orders
from at least a subset of the one or more dealer computers 110.
Thus, in some examples, orders are placed via dealer computer 110
and sent to ordering server 120 for further processing. In other
examples, dealer computer 110 may use a terminal connection to a
mainframe computer to enter orders, where the software that
generates the orders runs on the mainframe computer, not the dealer
computer 110.
[0089] In any event, ordering server 120 or the mainframe may be
configured to provide order options or an order form to dealer
computer 110 through a user interface, e.g., a graphical user
interface (GUI), over a terminal session, etc. The interface may
include information relating to features available for one or more
items. The interface may further present ordering options according
to availability of features for an item and/or according to
dependencies between various features.
[0090] D. Data Server
[0091] Data server 130 may be implemented as a combination of
hardware and software, and may include instructions stored on one
or more computer-readable media, the instructions for causing one
or more computer processors to perform the operations of the data
server 130 described herein.
[0092] Data server 130 is capable of receiving information from one
or more sources. Data server 130 generally includes a set of
instructions needed to provide data storage and retrieval
functionality for the system 100. Data server 130 may include, or
be communicatively coupled to, one or more data stores 140.
Although only one data server 130 is illustrated in FIG. 1, systems
may include multiple data servers 130. In some systems including
multiple data servers 130, each data server 130 may be configured
to store and retrieve a particular subset of data, or data of a
particular type or types.
[0093] E. Data Store
[0094] Data store 140 may include one or more data storage mediums,
devices, or configurations, and may employ various types, forms,
and/or combinations of storage media, including but not limited to
hard disk drives, flash drives, read-only memory, and random access
memory. Data store 140 may include various technologies useful for
storing and accessing information and may be integrated within or
external of data server 130. Data server 130 may be configured to
communicate with data store 140, including searching, accessing,
and retrieving data from host data store 140. Data store 140 may be
implemented as a combination of hardware and software, and may
include instructions stored on one or more computer-readable media,
the instructions for causing one or more computer processors to
perform the operations of the data store 140 described herein.
[0095] Data store 140 may be configured to store any suitable type
or form of electronic data, which may be referred to as content.
Content may include computer-readable data in any form, including,
but not limited to video, image, text, document, audio,
audiovisual, metadata, and other types of files or data. Content
may be stored in a relational format, such as via a relational
database management system (RDBMS).
[0096] As illustrated in FIG. 2, content stored in data store 140
may include information concerning dealers, including dealer name
202 and dealer location 204, dealer region 206, detailed data
regarding pending historical item orders/configurations 208 (e.g.,
vehicle features and order type), as well as dates of relevant item
transactions 210 (e.g., order, production, gate release, shipment,
arrival, trade-away, sales dates, etc.); dealer allocations 212
(i.e., how many vehicle orders a dealership has committed to place
for a given time period); production constraints 222 that may limit
the number of units that can be produced with a given feature;
records for each item produced including the total item
configuration; item sales and trade data for each item including
production date, trade date, and sales date; item delivery
timelines; and groupings such as feature families 214,
superfamilies 216, and features 218; other information regarding
item features such as product definition constraints 224 and
material availability constraints 226 that may limit realizable
feature combinations; and other information relating to market
conditions, such as feature bundling 220 and incentives 228. These,
and other data elements stored in the data store 140, are discussed
in greater detail below.
[0097] F. Inventory Management Server
[0098] Returning to FIG. 1, inventory management server 150 may be
implemented as a combination of hardware and software, and may
include instructions embodied on one or more computer-readable
media, the instructions for causing one or more servers to perform
the operations of the inventory management server 150 described
herein.
[0099] Generally, inventory management server 150 may be configured
to retrieve data from data store 140 via data server 130, and to
generate order recommendations based on the received data.
Exemplary data elements utilized by the inventory management server
150 are discussed below, as are details of the operation of the
inventory management server 150.
[0100] G. Supercomputer
[0101] Supercomputer 170 is a computing device that is generally
capable of performing parallel, processing-intensive, and/or
memory-intensive calculations. Some examples of supercomputers 170
are a specially designed custom computing device, or a cluster or
collection of off-the-shelf processing components, or nodes,
operated together through specialized interconnections.
Supercomputers may be used for highly calculation-intensive tasks,
such as weather and climate prediction, molecular modeling, and
other types of simulation that allow for a task to be broken up
into smaller units that may be processed in parallel.
[0102] Supercomputer 170 may be used to carry out many tasks in
parallel, such as treating vehicle lines in parallel, further
parallelizing by region, and also parallelization for training of
neural network rate of sale calculators. This ability to perform
tasks in parallel may allow for quicker computations of complex
statistical analyses such as singular value decomposition (SVD) and
principal components analysis (PCA). To aid in computation of the
SVD and the PCA, a supercomputer 170 may perform many independent
computations in parallel, such as computations relating to multiple
types of items, or calculations for multiple geographic regions.
SVD is a factorization of rectangular real or complex matrixes,
with applications in signal processing and statistics, such as
computing a pseudoinverse, a least squares fitting of data, a
matrix approximation, and determining a rank, range and null space
of a matrix. PCA is a mathematical procedure that transforms a
number of possibly correlated variables into a smaller number of
uncorrelated variables called principal components. The first
principal component accounts for as much of the variability in the
data as possible, and each succeeding component accounts for as
much of the remaining variability as possible. Depending on the
field of application, it may also be referred to as a discrete
Karhunen-Loeve transform (KLT), a Hotelling transform or proper
orthogonal decomposition (POD).
[0103] Supercomputer 170 may be in selective communication with
inventory management server 150, and may be used to aid in or
otherwise perform SVD and PCA for the inventory management server
150.
[0104] H. Summary of Exemplary System Configuration
[0105] The elements shown in FIG. 1 may be implemented as software,
hardware, firmware, etc., or combinations thereof. In many
examples, some of which were identified above, components shown in
FIG. 1 include or are in the form of software or firmware modules
stored on computer-readable media for execution in one or more
computing devices. Thus, system 100 may be implemented on one or
more than one physical computing device. In particular, ordering
server 120, data server 130, data store 140, and inventory
management server 150 may be included in or implemented on one or
more computing devices, such as one or more servers. Computing
devices of system 100 may include, but are not limited to, servers,
personal and laptop computers, as well as virtual computing
devices, for example virtual servers implemented on rack-mount
virtualization systems.
[0106] In general, the ordering server 120, data server 130, data
store 140, inventory management server 150, and supercomputer 170
may employ any of a number of computer operating systems,
including, but by no means limited to known versions and/or
varieties of mobile operating systems, the Microsoft Windows.RTM.
operating system, the Unix operating system (e.g., the Solaris.RTM.
operating system distributed by Sun Microsystems of Menlo Park,
Calif.), the AIX UNIX operating system distributed by International
Business Machines of Armonk, N.Y., an operating system distributed
by Apple, Inc. of Cupertino, Calif., and the Linux operating
system.
[0107] The ordering server 120, data server 130, data store 140,
inventory management server 150, and supercomputer 170 may further
include instructions executable by one or more computing devices
such as those listed above. Computer-executable instructions may be
compiled or interpreted from computer programs created using a
variety of well known programming languages and/or technologies,
including, without limitation, and either alone or in combination,
Java.TM., C, C++, Visual Basic, Java Script, Perl, Fortran, etc. In
general, a processor (e.g., a microprocessor) receives
instructions, e.g., from a memory, a computer-readable medium,
etc., and executes these instructions, thereby performing one or
more processes, including one or more of the processes described
herein. Such instructions and other data may be stored and
transmitted using a variety of known computer-readable media.
[0108] A computer-readable medium (also referred to as a
processor-readable medium) includes any tangible medium that
participates in providing data (e.g., instructions) that may be
read by a computer (e.g., by a processor of a computer). Such a
medium may take many forms, including, but not limited to,
non-volatile media and volatile media. Non-volatile media may
include, for example, optical or magnetic disks and other
persistent memory. Volatile media may include, for example, dynamic
random access memory (DRAM), which typically constitutes a main
memory. Common forms of computer-readable media include, for
example, a floppy disk, a flexible disk, hard disk, magnetic tape,
any other magnetic medium, a CD-ROM, DVD, any other optical medium,
punch cards, paper tape, any other physical medium with patterns of
holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory
chip or cartridge, or any other medium from which a computer can
read.
[0109] Databases, data repositories or other data stores described
herein, such as the data store 140 may include various kinds of
mechanisms for storing, accessing, and retrieving various kinds of
data, including a hierarchical database, a set of files in a file
system, an application database in a proprietary format, a
relational database management system (RDBMS), etc. Each such data
store is generally included within a computing device employing a
computer operating system such as one of those mentioned above, and
are accessed via a network in any one or more of a variety of
manners, as is known. A file system may be accessible from a
computer operating system, and may include files stored in various
formats. An RDBMS generally employs the known Structured Query
Language (SQL) in addition to a language for creating, storing,
editing, and executing stored procedures, such as the PL/SQL
language mentioned above.
[0110] In some examples, system elements and/or modules may be
tangibly implemented as computer-readable instructions (e.g.,
software) on one or more computing devices (e.g., the ordering
server 120, data server 130, data store 140, inventory management
server 150, supercomputer 170, etc.), and stored on computer
readable media associated therewith (e.g., disks, memories,
etc.).
[0111] While FIG. 1 illustrates an exemplary system 100, other
implementations may be used. In other embodiments, additional
elements may be included, or the elements shown in FIG. 1 may be
omitted or modified. For example, ordering server 120 and inventory
management server 150 may be combined in certain implementations.
As another example, data server 130 and data store 140 may be
implemented as a plurality of heterogeneous database systems. As
yet another example, supercomputer 170 may be omitted and/or
combined with inventory management server 150.
III. System Data
[0112] The system 100 uses and/or generates various data elements
in performing processes to generate order recommendations and other
information. Exemplary data elements are described below with
respect to FIGS. 2-3 and 7-9. Further, before turning to the
descriptions of specific data elements in FIGS. 2-3 and 7-9,
preliminary concepts of weighting factors and market area
definition are discussed. In general, some or all of the data
elements illustrated in FIGS. 2-3 and 7-8 may be stored in data
store 140, and may be accessible from data store 140 by the
inventory management server 150 through use of the data server
130.
[0113] As will be clear from the explanation that follows, some of
these data elements are directly based on physical phenomena or
reported facts, while others are derived from the elements that are
directly based on physical phenomena, e.g., via various
computations and/or processes. For example, data store 140 may
store data representative of actual physical phenomena such as
actual dealer sales 302 (discussed below with reference to FIG. 3)
or actual dealer inventory 702 (discussed below with reference to
FIG. 7). Further for example, data store 140 may store, and/or
inventory management server 150 may compute, derived data such as
weighted dealer sales 306 or weighted dealer inventory 706.
[0114] In general, system 100 operates on data that pertains to a
specific item. For example, as explained in detail below, the
system 100 may generate feature allocations and/or order
recommendations for different configurations. However, the
configurations are all of a specific item. For example, the
particular item may be a particular make and model of a motor
vehicle, i.e., a vehicle line. The system 100 may then work with
data relating to that particular vehicle line, and with various
features and superfeatures relative to that vehicle line. Thus, in
general, data and metrics discussed herein, whether at a dealer
level, a market level, etc., generally pertain to a specific
item.
[0115] A. Market Definition
[0116] Certain operations of system 100 may require comparing
metrics for a dealer, e.g., sales, to metrics for a market in which
the dealer is included. In some instances, a sales region may be
defined based on the geographic area in which the dealer is
located, e.g., a city, a postal code, etc., where all dealers
included within the sales region are considered to be a part of
that region. However, there are advantages to defining a market
with reference to the specific dealer for whom comparisons are
being performed, rather than simply grouping a dealer according to
the geographic area in which the dealer may be located. That is, a
market may be defined that is unique or substantially unique for
each dealer processed by the system 100 that takes into account the
dealer's location relative to the other dealers.
[0117] FIG. 4 illustrates an exemplary dealer market area 410
defined for target dealer 420. The market area 410 is substantially
a circle determined according to a predefined radius, e.g., 25
miles, around the target dealer 420. Dealer market area 410
includes a plurality of competing dealers 430. The purpose of
defining the market area 410 is to include other dealers that are
in competition with the target dealer 420, e.g., competing dealers
430. The advantage of defining a unique or substantially unique
market area 410 for each target dealer 420 is to maximize the
likelihood that sales and inventory metrics and other metrics for
competing dealers 430 are most relevant to the target dealer 420.
Where market areas are defined in other ways, e.g., according to
pre-existing geographic boundaries, there is a greater likelihood
of including less relevant competing dealers 430 and also of
excluding more relevant competing dealers 430.
[0118] A dealer market area 410 may be used to compare the sales
and inventory performance of a target dealer 420 to that of
competing dealers 430. For example, a dealer market area 410 may be
used to determine when the target dealer 420 performs better or
worse than an average of competing dealers 430 for a given time
period and/or feature or configuration. The dealer market area 410
calculated in this manner also may allow the inventory for a target
dealer 420 to be viewed in context of other inventory available in
a dealer market area 410. Determination of market area inventory
may provide a basis or a factor to aid in the estimation of
likelihood of sales for a specific configuration at a target dealer
420, because the number of items sold at a target dealer 420 may be
influenced by what inventory may be available at competing dealers
430.
[0119] FIG. 5 illustrates an exemplary expanded dealer market area
410 for a target dealer 420 in a more sparsely populated area. In
some cases, a predefined radius, e.g., 25 miles, around a target
dealer 420 may encompass a relatively low number of competing
dealers 430. Therefore, dealer market area 410 may also include, in
one exemplary implementation, dealerships beyond a predefined
radius, e.g., 25 miles, until either a predefined number of
dealerships, e.g., 25 dealerships, are identified, including the
target dealer 420, or until a predefined distance, e.g., 200 miles
(or 322 kilometers), is reached. As illustrated in FIG. 5, only two
competing dealers 430 are included within a 25 mile radius of
target dealer 420, as indicated by the inner circle within the
dealer market area 410. Accordingly, dealer market area 410 is
illustrated as being expanded to further include additional
competing dealers 430, such that the dealer market area 410
includes a total of 25 dealers.
[0120] While the distance calculations for defining market areas
410 may be based on a radial distance, i.e., by defining a circle
or a substantially circular shape with the target dealer 420 in the
center, in other examples alternative dealer market area
characterizations may be based on actual distance, or actual travel
distance, between dealerships, or on expected travel time between
dealerships. Such distances or travel times may be determined, for
example, by reference to online services, such as Google Maps,
Yahoo! Maps, MapQuest, etc.
[0121] In other examples, postal codes, e.g., zip codes, associated
with physical locations of dealers may be used to define dealer
market areas 410. Alternately, a region or zone may be defined by a
manufacturer without reference to a particular target dealer 420.
Exemplary dealer market areas 410 of this type are a metro area,
and a postal zip code. Such a dealer market area 410 may provide
for a logical grouping for the manufacturer, and may be used, for
example, by the manufacturer to provide for market area
promotions.
[0122] However, as mentioned above, such use of predefined
geographic boundaries may be less advantageous than defining a
market area 410 with reference to a specific target dealer 420. For
example, a target dealer 420 at the edge of a particular zip code
or metro area may in fact be closer to competing dealers 430 in an
adjacent manufacturer-defined market area 410. From the target
dealer's 420 perspective, the dealer market area 410 is comprised
of those competing dealers 430 that are likely to share a customer
base and can thus be thought of as competing dealers 430.
Accordingly, the target dealer 420 may consider those competing
dealers 430 in an adjacent market area 410 as more relevant
competition than the other competing dealers 430 included within a
manufacturer-defined market area 410. Thus, use of a target dealer
420 to define a market area 410 presents advantages, including the
advantage of providing relevant and pertinent comparisons for the
target dealer 420.
[0123] B. Weighting Factor Vectors
[0124] FIG. 3 illustrates exemplary data elements related to sales
metrics, including actual and weighted sales metrics on both a
per-dealer and a per-market area basis. Some or all of the data
elements illustrated in FIG. 3 may be stored in data store 140, and
may be accessible from data store 140 by the inventory management
server 150, either directly or by querying through data server 130.
Alternatively or additionally, some of these data elements may be
computed by a device such as inventory management server 150.
[0125] A weighting factor is a factor that may be used to affect
the relative influence of a quantity in a value being computed.
System 100 may use weighting factors in computing various
quantities, such as sales and inventory volumes. A weighting factor
vector 304 may include a set of weighting factors applied to
measurements associated with different periods of time. For
example, an exemplary set of sales data may include 16 weeks of
sales history, where the most recent 4 weeks are weighted at 100%,
the four week before that are weighted at 75%, the four weeks
before that are weighted at 50% and the four weeks before that are
weighted at 25%. These weighting factors may be placed in a
weighting factor vector 304, e.g., vector .omega.=[1.00 0.75 0.50
0.25]. The exemplary weighting factor vector 304 may provide
weighted values for use in evaluating historical sales and
historical inventory, insuring that sales and inventory in more
recent time periods are given greater consideration, or weight.
[0126] C. Weighted Dealer Sales
[0127] As used herein, data elements that reference quantities
related to a target dealer 420 are indicated throughout by use of
the modifier "dealer." In contrast, data elements that reference
quantities related to a market area 410 including the target dealer
420 are indicated throughout by use of the modifier "market." As an
example, actual dealer sales 302 refers to sales of items made by a
target dealer 420, while actual market sales 308 refers to sales of
items made by a target dealer 420 and also by competing dealers 430
in the market area 410 defined by the target dealer 420.
[0128] With regard to the sales of items by a target dealer 420,
actual dealer sales 302 may include sales reported for the
particular dealer during a particular time period. Weighted dealer
sales 306 is then defined as the actual dealer sales 302 made by a
dealer during a particular time period, multiplied by a weighting
factor vector 304. An exemplary weighting for historical sales and
inventory is shown in Table 1:
TABLE-US-00001 TABLE 1 Example of sales weighting scheme Dates:
12/03-12/30 12/31-01/27 01/28-02/24 02/25-03/23/ TOTALS Actual
Sales 8 8 8 8 32 Weighting Factor Vector 25% 50% 75% 100% --
Weighted Sales 2 4 6 8 20
[0129] As illustrated in the example of Table 1, while the actual
dealer sales 302 over the exemplary 16 week period number 32 sales,
the weighted dealer sales 306 over the illustrated 16 weeks is
computed to be 20 sales.
[0130] D. Weighted Market Sales
[0131] Continuing with FIG. 3, actual market sales 308 include a
sum of actual sales within a given time period by a target dealer
420 and competing dealers 430 in the market area 410 defined by the
target dealer 420. Weighted market sales 312 may be defined as the
actual market sales 308 multiplied by an appropriate element from
weighting factor vector 304.
[0132] Actual market sales 308 may be weighted for each competing
dealer 430 in a dealer market area 410 according to a dealer market
weighting function 310, e.g., according to a distance of competing
dealer 430 from target dealer 420. Such weighting according to a
dealer market weighting function 310 may be performed instead of or
in addition to multiplying actual market sales 308 by a weighting
factor vector 304. Thus, weighted market sales 312 may be defined
as the actual market sales 308 as multiplied by at least one of a
weighting factor vector 304 and a dealer market weighting function
310.
[0133] FIG. 6 illustrates an exemplary dealer market weighting
function 310. As illustrated in FIG. 6, metrics associated with
competing dealers 430 are weighted according to a distance of the
competing dealer 430 from the target dealer 420. Generally,
competing dealer 430 that are closer to a target dealer have a
greater weighting factor applied than competing dealers 430 that
are farther from the target dealer 420. This is because competing
dealers 430 that are closer to a target dealer 420 are determined
to be more relevant than competing dealers 430 that are farther
away. When computing weighted market sales 312, the sales of each
competing dealer 430 may be weighted according to the dealer market
weighting function 310, and then added to the total weighted market
sales 312. As shown in FIG. 6, the dealer market weighting function
is a monotonically decreasing function, with a value of 100% for a
distance of zero (corresponding to the target dealer), with values
slightly less than 100% for dealers within a few miles of the
target dealer, and with a value of approximately 5% for distances
ranging from 140 to 200 miles.
[0134] Although only one dealer market weighting function 310 is
illustrated, other weighting functions are possible. For example, a
linearly decreasing dealer market weighting function 310 could
alternately be used in part to determine weighted market sales
312.
[0135] E. Weighted Feature Level Dealer Sales
[0136] Returning to FIG. 3, feature level actual dealer sales 314
may include the actual, i.e., unweighted, sales made by a dealer
that include a particular item feature. For example, if a vehicle
sold by a dealer is painted a red color, then that vehicle may
count as a feature level actual dealer sale 314 of a vehicle with
the feature of being painted red. Weighted feature level dealer
sales 316 may likewise be determined as the feature level actual
dealer sales 314 for a particular time period, multiplied by an
appropriate element from weighting factor vector 304.
[0137] F. Weighted Feature Level Market Sales
[0138] Feature level actual market sales 318 includes the sales of
a feature, i.e., a particular item having one or more specified
features, by a target dealer 420 and competing dealers 430 in a
market area 410. Weighted feature level market sales 320 may be
determined as the feature level actual market sales 318 made by
target dealer 420 and competing dealers 430 during a particular
time period, multiplied by an appropriate element from weighting
factor vector 304 and/or according to a dealer market weighting
function 310.
[0139] G. Weighted Dealer Inventory
[0140] FIG. 7 illustrates exemplary inventory-related data
elements, including an exemplary relationship between actual and
weighted inventory, both per-dealer and per-market area. Actual
dealer inventory 702 may include the inventory that a target dealer
420 has in stock at a particular time. Weighted dealer inventory
704 may be defined as the actual dealer inventory 702 multiplied by
an appropriate element from a weighting factor vector 304.
[0141] H. Weighted Market Inventory
[0142] Actual market inventory 706 includes the inventory available
in stock by a target dealer 420 and competing dealers 430 in a
market area 410. Weighted market inventory 708 is the actual market
inventory 706 multiplied by an appropriate element from weighting
factor vector 304 and/or based on a dealer market weighting
function 310.
[0143] I. Weighted Feature Level Dealer Inventory
[0144] Feature level actual dealer inventory 710 includes inventory
for a particular time period of a target dealer 420 that include a
particular item feature. Weighted feature level dealer inventory
712 may be determined as the feature level actual dealer inventory
710 multiplied by an appropriate element from a weighting factor
vector 304.
[0145] J. Weighted Feature Level Market Inventory
[0146] Feature level actual market inventory 714 may include
inventory of a target dealer 420 and the competing dealers 430
including a particular item feature. Weighted feature level market
inventory 716 may be determined as feature level actual market
inventory 714 multiplied by an appropriate element from a weighting
factor vector 304 and/or based on a dealer market weighting
function 310.
[0147] K. Weighted Dealer Turn Rate
[0148] FIG. 8 illustrates an exemplary relationship between
weighted sales, weighted inventory, and weighted inventory turn
rates. As mentioned above, an inventory turn rate for a feature in
a given time period may be determined by dividing sales related to
the feature by the total amount of inventory related to the feature
in the time period. Inventory turn rates may be determined for the
overall inventory of a target dealer 420, for the overall inventory
of a dealer market area 410, and/or for a feature either on a
per-dealer or per-market basis.
[0149] A weighted dealer turn rate 802 may be defined as a rate of
sale of an item, regardless of its feature configuration, from
inventory of a target dealer 420. The weighted dealer turn rate 802
may be calculated as weighted dealer sales 306 divided by weighted
dealer inventory 704.
[0150] L. Weighted Feature Level Dealer Turn Rate
[0151] A weighted feature level dealer turn rate 804 may be defined
as a rate of sale of an item, having a particular feature, from
inventory including the particular feature of a target dealer 420.
A weighted feature level dealer turn rate 804 may be calculated as
weighted feature level dealer sales 316 divided by feature-level
weighted dealer inventory 704.
[0152] M. Weighted Market Turn Rate
[0153] A weighted market turn rate 806 may be defined as a rate of
sale of an item, regardless of its feature configuration, from
inventory of target dealers 420 and competing dealers 430 in a
dealer market area 410. The weighted market turn rate 806 may be
calculated as weighted market sales 312 divided by weighted market
inventory 708.
[0154] N. Weighted Feature Level Market Turn Rate
[0155] A weighted feature level market turn rate 808 may be defined
as a rate of sale of items in inventory of a dealer market area 410
that possess a particular feature. The weighted feature level
market turn rate 808 may be calculated as weighted feature level
market sales 320 divided by weighted feature level market inventory
716.
[0156] O. Weighted Feature Level Dealer Sales Mix Rate
[0157] FIG. 9 illustrates an exemplary relationship between
weighted sales and weighted inventory used in the generation of
weighted sales mix rates and weighted inventory mix rates. A
weighted feature level dealer sales mix rate 902 is a measure of
the sales from dealer stock including a particular feature compared
to the overall amount of sales from stock at the dealer. For
example, weighted feature level dealer sales mix rate 902 may be
determined by dividing weighted feature level dealer sales 316 by
weighted dealer sales 306.
[0158] P. Weighted Feature Level Market Sales Mix Rate
[0159] A weighted feature level market sales mix rate 904 is a
measure of the sales from stock of a target dealer 420 and
competing dealer 430 in a dealer market area 410, where the sales
including a particular feature is compared to the overall amount of
sales in the dealer market area 410. A weighted feature level
market sales mix rate 904 may be determined by dividing weighted
feature level market sales 320 by weighted market sales 312.
[0160] Q. Weighted Feature Level Dealer Inventory Mix Rate
[0161] A weighted feature level dealer inventory mix rate 906 is a
measure of the inventory of a dealer including a particular feature
compared to the overall inventory of the dealer. For example,
weighted feature level dealer inventory mix rate 906 may be
determined by dividing weighted feature level dealer inventory 712
by weighted dealer inventory 704.
[0162] R. Weighted Feature Level Market Inventory Mix Rate
[0163] A weighted feature level market inventory mix rate 908 is a
measure of the inventory of a target dealer 420 and competing
dealer 430 in a dealer market area 410, where the inventory
including a particular feature is compared to the overall inventory
in the dealer market area 410. A weighted feature level market
inventory mix rate 908 may be determined by dividing weighted
feature level market inventory 716 by weighted market inventory
708.
[0164] S. Dealer Projected Sales
[0165] FIG. 10 illustrates exemplary relationships relating to the
projection of dealer inventory. Dealer projected sales 1002 is a
measure of sales that are projected to be made by a dealer over a
future time period, generally for an item regardless of its feature
configuration. For example, dealer projected sales 1002 may be
calculated beginning at the most recent week of available sales
data, proceeding up to the point in time for which items that are
ordered in the current week are expected to arrive at
dealerships.
[0166] In many instances, inventory turn rate is assumed to be
relatively constant, and accordingly weighted dealer turn rates 802
may be used to determine dealer projected sales 1002. Importantly,
if inventory turn rates are not relatively constant and instead
fluctuate wildly, then the ability to estimate future inventory
turn rates may be markedly reduced. Accordingly, dealer projected
sales 1002 may be computed by multiplying actual dealer inventory
702 by weighted dealer turn rate 802.
[0167] T. Feature Level Dealer Projected Sales
[0168] Feature level dealer projected sales 1008 is a measure of
the number of sales that are projected to be made by a dealer
including a particular feature over a future period of time.
Feature level dealer projected sales 1008 may be computed by
multiplying dealer projected sales 1002 by weighted feature level
dealer sales mix rate 902.
[0169] U. Dealer Projected Inventory
[0170] Dealer projected inventory 1006 is a measure of the
inventory for a dealer, generally for an item regardless of its
feature configuration, at a future point in time. Generally,
inventory by dealer may be calculated by taking the prior-period
inventory, subtracting the projected prior-period sales, and adding
the expected arrivals. Dealer expected arrivals 1004 are items that
were previously ordered, and are expected to leave the supply
pipeline and reach dealer inventory 115 during the week being
projected. For example, dealer projected inventory 1006 may be
computed by adding dealer expected arrivals 1004 to actual dealer
inventory 702, and subtracting dealer projected sales 1002.
[0171] V. Feature Level Dealer Projected Inventory
[0172] Feature level dealer projected inventory 1012 is a measure
of the inventory for a dealer that includes a particular feature at
a future point in time. Feature level dealer expected arrivals 1010
are items that were previously ordered including the particular
feature, and are expected to leave the supply pipeline and reach
dealer inventory 115 during the week being projected. Therefore,
feature level dealer projected inventory 1012 may be computed by
adding feature level dealer expected arrivals 1010 to feature level
actual dealer inventory 710, and subtracting feature level dealer
projected sales 1008.
[0173] W. Relative Total Weighted Dealer Turn Rate
[0174] FIG. 11 illustrates exemplary relationships relating to the
calculation of a target feature level inventory mix rate 1104
through use of a relative total weighted dealer turn rate 1102. A
relative total weighted dealer turn rate 1102 is a measure of
whether a target dealer 420 is over or underperforming compared to
other competing dealers 430 in the dealer market area 410 for the
target dealer 420. Relative total weighted dealer turn rate 1102
may be measured in terms of weighted dealer turn rate 802 compared
to a weighted market turn rate 806 including competing dealers 430
in the dealer market area 410. For example, weighted market turn
rate 806 may be subtracted from weighted dealer turn rate 802, and
the result may in turn be divided by weighted market turn rate 806,
yielding relative total weighted dealer turn rate 1102.
[0175] Then, if relative total weighted dealer turn rate 1102 is
positive, the target dealer 420 may be determined to be
outperforming other competing dealers 430 in the dealer market area
410. If relative total weighted dealer turn rate 1102 is negative,
the target dealer 420 may be determined to be underperforming.
[0176] X. Target Feature Level Inventory Mix Rate
[0177] In order to balance inventory at the feature level, a target
feature level inventory mix rate 1104 may be established based at
least in part on weighted feature level dealer sales mix rate 902.
In other words, inventory targets may be established at least in
part based on sales history. In addition, a target feature level
inventory mix rate 1104 may be established based at least in part
on a predetermined turn rate for dealer inventory, and/or based on
a turn rate that is favorable compared to the turn rates for the
relevant feature in the relevant dealer market area 410.
[0178] Accordingly, where a target dealer 420 is outperforming its
dealer market area 410 in terms of overall weighted dealer turn
rate 802 (i.e., positive relative total weighted dealer turn rate
1102), then target feature level inventory mix rates 1104 may be
determined based on the performance of the target dealer 420 (i.e.,
according to the weighted feature level dealer sales mix rates 902
of the target dealer 420).
[0179] On the other hand, when the target dealer 420 is
underperforming the dealer market area 410 (i.e., negative relative
total weighted dealer turn rate 1102), target feature level
inventory mix rate 1104 may based on a combination of weighted
feature level dealer sales mix rate 902 and weighted feature level
market sales mix rate 904. Specifically, a target feature level
inventory mix rate 1104 may be determined by adding the number one
to the relative total weighted dealer turn rate 1102 and
multiplying this quantity by the weighted feature level dealer
sales mix rate 902, and from that result subtracting relative total
weighted dealer turn rate 1102 multiplied by weighted feature level
market sales mix rate 904.
[0180] Weighted feature level dealer sales mix rates 902 and
weighted feature level market sales mix rates 904 may be weighted
relative to relative total weighted dealer turn rate 1102, such
that a dealer that is only slightly underperforming may have a
target feature level inventory mix rate 1104 based mostly on
weighted feature level dealer sales mix rates 902, whereas a target
dealer 420 that is greatly underperforming the dealer market area
410 may have a target feature level inventory mix rate 1104 based
in greater part on weighted feature level market sales mix rate
904. For example, for a dealer having no sales related to a feature
in a relevant time period, e.g., the past 16 weeks, target feature
level inventory mix rate 1104 would be determined entirely or
almost entirely based on the weighted feature level market sales
mix rate 904.
[0181] Y. Dealer Initial Total Inventory
[0182] FIG. 12 illustrates exemplary relationships relating to
feature allocation. Initial total dealer inventory 1202 is a
measure of the inventory at the beginning of a time period, before
any sales from inventory or deliveries to inventory occur. For
example, for a current week, initial total dealer inventory 1202
may be determined based on actual dealer inventory 702. As another
example, when inventory is projected one week into the future,
initial total dealer inventory 1202 for the projected week may be
based on the dealer projected inventory 1006 for the next week,
before any sales or deliveries occur that week.
[0183] Z. Expected Total Dealer Inventory
[0184] Expected total dealer inventory 1204 is a measure of dealer
inventory available for sale during a particular time period.
Accordingly, expected total dealer inventory 1204 may be determined
by calculating the sum of initial total dealer inventory 1202 and
dealer expected arrivals 1004. Thus, this measure of expected total
dealer inventory 1204 assumes that if an item is for sale at any
time during a time period, then that item is part of expected total
dealer inventory 1204 for that entire time period, even if it, for
example, arrives late in the week or is sold early in the week.
[0185] AA. Initial Feature Level Total Dealer Inventory
[0186] Initial feature level total dealer inventory 1206 is a
measure of the inventory including a particular feature that is
available at the beginning of a time period, before any sales from
inventory or deliveries to inventory occur. For example, for the
current week, initial feature level total dealer inventory 1206 may
be based on the feature level actual dealer inventory 710. When
inventory is projected one week into the future, initial feature
level total dealer inventory 1206 for the projected week may be
based on the feature level dealer projected inventory 1012 for the
next week.
[0187] BB. Expected Feature Level Total Dealer Inventory
[0188] Expected feature level total dealer inventory 1208 is a
measure of dealer inventory for a feature that is available for
sale during a particular time period. Accordingly, expected feature
level total dealer inventory 1208 may be determined by calculating
the sum of initial feature level total dealer inventory 1206 and
feature level dealer expected arrivals 1010. Thus, this measure of
expected feature level total dealer inventory 1208 assumes that if
an item is for sale at any time during a time period, then that
item is part of expected feature level total dealer inventory 1208
for that entire time period.
[0189] CC. Expected Feature Level Dealer Inventory Mix Rate
[0190] An expected feature level dealer inventory mix rate 1210 is
a measure of the inventory mix rate related to a feature at a
future point in time, after items that are expected to be delivered
during that future week have arrived. The expected feature level
dealer inventory mix rate 1210 may be defined as the expected
feature level total dealer inventory 1208 for a particular feature
at a future time period, divided by the expected total dealer
inventory 1204 for the same future time period. In other words, the
expected feature level dealer inventory mix rate 1210 may also be
defined as the sum of inventory for that feature at the beginning
of a week plus expected deliveries of items with that feature for
the week, divided by the expected total dealer inventory 1204 for
the week.
[0191] DD. Feature Allocations
[0192] Feature allocations (or feature selections) 1212 include
quantities of each feature, i.e., a number of an item having a
particular feature, that are recommended to be ordered, without
regard for other features that may be included in an item
configuration. In other words, feature allocations 1212 do not
generate configurations, although feature allocations are
consistent with possible configurations because feature allocations
take into account constraints such as product definition
constraints 224. For example, a feature allocation 1212 may be
determined to include three red vehicles, seven blue vehicles and
two vehicles with a moonroof. That is, the features for which
quantities are provided are for a particular vehicle make and
model, specifically where the vehicle is red (three vehicles
allocated), a vehicle that is blue (seven vehicles allocated), and
a vehicle with a moonroof (two vehicles allocated). However, the
feature allocation 1212 does not indicate whether any of the blue
vehicles or the red vehicles are to have a moonroof, or whether the
vehicles with a moonroof are some other color.
[0193] An objective of feature allocation 1212 is to select orders
for items with features such that the inventory mix rates when the
ordered items are expected to arrive at the dealer match, as
closely as possible, historically based inventory mix rate targets.
Feature allocations 1212 may be calculated in various ways, and may
be calculated based on various data elements. As illustrated in
FIG. 12, feature allocations 1212 may be a function of numerous
data elements stored within data store 140. For example, feature
allocations 1212 may be based on features 218, production
constraints 222, material availability constraints 226, a parameter
1214 (discussed below), expected total dealer inventory 1204,
expected feature level dealer inventory mix rate 1210, target
feature level inventory mix rate 1104, dealer allocations 212,
superfamilies 216, dealer names 202, dealer locations 204, and
feature families 214.
[0194] 1. Feature Allocation Function
[0195] A function for feature allocation 1212 may be structured as
a minimization of the squared difference between the target feature
level inventory mix rate 1104 and expected feature level dealer
inventory mix rates 1210 at a future time for all item features
that can be ordered by a target dealer 420. Although the objective
of the minimization is to minimize the squared difference, it may
not be possible to obtain a difference of zero in one iteration.
However, it may certainly be possible to approach the target
feature level inventory mix rate 1104.
[0196] An exemplary objective function is as follows:
min k = 1 Nd S t ' , k j = 1 Nj .phi. j i = 1 lj ( p t ' , k j , i
- a t ' , k j , i ) 2 ##EQU00001##
[0197] where items are expected to arrive at the k.sup.th dealer at
time t', where p is the target feature level inventory mix rate for
feature i in feature family j, where a is the inventory mix rate
for items with feature i in feature family j at dealership k, where
N.sub.d is the number of dealers within a sales region, where S is
the expected sales at the k.sup.th dealer, Nf is the number of
feature families which completely define a vehicle order, l.sub.j
is the number of different feature values associated with the
j.sup.th feature family, and O.sub.j is a predefined parameter,
generally having a default value of unity. While an exemplary
objective function is given above, other functions may be used to
perform the minimization. For example, in some example the
objective function may scale by inventory, rather than by expected
sales.
[0198] When planning is conducted in the presence of production
constraints 222 or material availability constraints 226, then
feature allocations 1212 across all dealers within a dealer market
area 410, who are collectively subject to these constraints, must
be optimized together. In that case, the objective function may be
scaled by each respective expected total dealer inventory 1204
level, thereby giving greater weight to dealers with larger
inventories. In other examples, expected sales, e.g., computed
based on inventory multiplied by inventory turn rate, may be used
to scale the objective function. In still other examples, yet
another scaling factor may be used.
[0199] Additionally, a feature-family specific parameter 1214 may
be used to weight the relative importance of particular feature
families 214 in relation to one another. For example, the feature
allocation optimization may use a parameter 1214 to give greater
consideration to balancing inventory at the base feature level
(i.e., a feature composed of vehicle series, engine transmission,
drive, and optionally cab and body style for trucks), at the
expense of balancing other feature families 214.
[0200] As mentioned above, multiple classes of constraints must be
satisfied in an optimization that generates feature allocations,
including dealer allocations 212, production constraints 222 and
material availability constraints 226, and product definition
constraints 224. First, the sum of orders across all features
within a feature family 214 must equal the total dealer allocation
212 for all target dealers 420 and feature families 214. Second,
production constraints 222 and material availability constraints
226 must be satisfied. In some cases, these production constraints
222 and material availability constraints 226 may be specified as
do-not-exceed constraints. For example, a constraint may indicate
that total allocation for a feature within a dealer market area 410
cannot exceed a particular limit. Production constraints 222 and
material availability constraints 226 may also, in another example,
be specified as equality constraints or must-not-be-less-than
constraints. Third, the feature allocation 1212 must also satisfy
product definition constraints 224 that must be satisfied
separately for each target dealer 420. These constraints may be
provided as inequality constraints, where the left and right sides
of any inequality constraint are sums of various feature
allocations 1212.
[0201] As an example of optimization of feature allocations 1212
based on a constraint, a moonroof feature may be ordered with two
other vehicle features, e.g., "Series 1" and "Series 2," but not
with "Series 3." Thus, for each dealer, a constraint may be
specified that the number of vehicles with a moonroof ordered by
the dealer must be less than or equal to the number of vehicles
ordered with Series 1 and Series 2 by that same dealer. These
product definition constraints 224 are generated once in the setup
process, and may be re-used period-over-period, until product
definition or order guide changes occur, at which point the product
definition constraints 224 must be regenerated.
[0202] Constraints are used to account for configurations that are
available to a target dealer 420, and also to account for the
relationships between features, e.g., product definition
constraints 224. Without constraints, attempts to recommend orders
may not have a feasible solution for some target dealers 420. While
constraints involving relationships between features are often
expressed logically (e.g., heated seats must be leather),
generating order recommendations requires that constraints be
expressed arithmetically. For computational purposes, it is also
important to represent these arithmetic relationships compactly.
Possible ways of generating vehicle feature relationship
constraints are respectively based on set theory and computational
geometry. In each case, a binary vehicle configuration matrix such
as discussed above is provided input and used to generate product
definition constraints 224.
[0203] Under a set theory approach, items with different features
in a feature family are divided into subsets of a universal set
that contains all item orders for a certain target dealer 420. Then
empty intersections of subsets are identified that contain only
orders with a specific feature. From the identification of empty
intersections, product definition constraints 224 can be
generated.
[0204] A computational geometry approach identifies a sub-space in
an n-dimensional space that only contains a feasible solution
domain of the order selection optimization, where n is the number
of features in an item order. The configuration matrix provides
vertices (extreme points) of the convex hull. Then, computer
software is used to translate the vertices into a set of algebraic
inequalities that describe the n-dimensional polyhedron. These
inequalities may be, for example, product definition constraints
224.
[0205] In addition to optimizing feature allocation 1212 through
use of constraints, variety in feature allocation 1212 may be
encouraged during feature allocation 1212 through a controlled
decrease in the target feature level inventory mix rate 1104 for
those features that are expected to receive allocation, in the
absence of production constraints 222 and product definition
constraints 224.
[0206] 2. Uses of Feature Allocation
[0207] Feature allocation is often used to support the objective of
balancing inventory for a particular dealer. For example, a use for
feature allocation is to identify a number of items having a
certain feature that each dealer will order, this number of items
being included as an input to the execution of turn rate
calculators to determine the relative goodness of various
combinations (i.e., configurations) of the allocated features, as
discussed below. However, feature allocation may have other
uses.
[0208] Feature allocation can also be used to allocate features
without subsequently performing order recommendations. This could
be done either when there is a scarcity of one or more desirable
features or when there is a requirement to use more than the
desired number of one or more features.
[0209] For example, feature allocation may be used to balance
inventory across dealers in a market or some other set of dealers
by providing inventory to those dealers with greatest need. In this
case the feature family is the group of dealers, and feature
allocation represents the number of units to allocate to the group
of dealers. Using the expected inventory mix rates of the dealers,
a target for feature allocation may be determined based on
historical sales of the dealers, where the output of feature
allocation is the number of items to allocate to a particular
dealer. The faster a particular dealer sells its inventory, the
more the target level for that dealer is increased. Additionally,
the faster a dealer sells its inventory, the more the dealer is
losing inventory. This gap between inventory mix rate and sales
turn rate may thus be measured and used by feature allocation to
balance inventory to those dealers with a greater need for
inventory. Such a system may be characterized as a "turn-and-earn"
feature allocation system.
[0210] It may further be that there are material or production
constraints related to the production of the items that also have
to be taken into account. Feature allocation may be used to
determine the number of each item for a dealer to agree to order
over a future time period based on these constraints on what the
dealer wishes to order and based on what is currently possible to
be manufactured.
[0211] As another example of the use of feature allocation, for
some franchises, dealers are required to negotiate with a
manufacturer for how many items they are committed to buy in a
future time period, such as over the next one or more months. When
dealers enter into the negotiation, the dealers may want an
estimate of the number of items including a particular feature they
will be able to obtain. This estimate of the number of items
including a particular feature may guide the dealer's decision on
how many items to order. For example, if a dealer can obtain 83% of
the items they order with a scarce feature, then the dealer may
want to order twelve items total. However, if the dealer can only
obtain 50% of the items they order with the scarce feature, the
dealer may want to order fewer total items, e.g., eight total
items.
[0212] Feature allocation may further be used to estimate, before
the numbers of items to order for each target dealer 420 are known,
the number or percentage of each feature each dealer will be
allocated in the future. Feature allocation will either estimate a
number of items each dealer is likely to order or feature
allocation will receive from another data source an estimate of the
number of items each dealer is likely to order. Feature allocation
will use these item count estimates to provide output that may be
used as an estimate of feature counts, or item counts are divided
into the estimated feature counts to produce estimated percentages
of each feature.
[0213] EE. Turn Rate Calculators
[0214] Optimization of feature allocations 1212 allows the order
generation system 100 to determine what features to recommend to be
ordered and in what quantities. However, feature allocations 1212
do not include configurations of the allocated features. In other
words, feature allocations 1212 do not indicate how the features in
the feature allocations 1212 are to be combined into configurations
to be recommended to a target dealer 420 to order.
[0215] For example, feature allocations 1212 may indicate that
three red vehicles, seven blue vehicles, and one vehicle with a
moonroof should be ordered. However, the feature allocation 1212
does not indicate whether any of the blue vehicles or the any of
the red vehicles is to have the allocated moonroof, or whether the
vehicle with a moonroof should be ordered in yet another color.
[0216] Accordingly, the system 100 may use trained inventory turn
rate calculators 1302 to determine which configurations of the
features to use, given the feature allocations 1212, dealer
allocations 212, and other constraints (e.g., product definition
constraints 224, material availability constraints 226, etc.).
Through this process, the system 100 determines, for example, that
a red vehicle should be ordered with the moonroof.
[0217] 1. Overview of Generating Turn Rate Calculators
[0218] Before determining configurations to order, the neural
network models included in the inventory turn rate calculators 1302
are first trained based on records of inventory and sales of items,
including configuration information regarding the specific features
in the item configurations, contextual information regarding dealer
location, dealer inventory level as a fraction of market area,
average turn rate during the time period, and inventory mix rate
information regarding the specific item configurations compared to
what else was in dealer and market inventory.
[0219] The purpose of the neural network training is to create
inventory turn rate calculators 1302 that will estimate the
expected turn rates of all possible configurations of features. The
neural network includes a plurality of models, e.g., 100 models in
one exemplary implementation, that are trained with the input data,
and the result of the training of the neural network is a set of
inventory turn rate calculators 1302. Each inventory turn rate
calculator 1302 is thus trained to determine a set of dealer
configuration turn rates 1902 for a particular configuration of
features, one for each model in the inventory turn rate calculator
1302, discussed in detail below. In some implementations, a set of
inventory turn rate calculators 1302 are common to all dealers in a
particular sales region.
[0220] Feature allocations 1212 may be based on linear assumptions
concerning sales rates and available inventory. However, as noted
above, demand and other factors affecting inventory turn rates may
be nonlinear, or may be subject to non-unobservable disturbances.
Thus, feature allocations 1212 alone are generally not optimal for
determining which item configurations should include which
features, i.e., to determine what feature combinations should be
recommended for ordering by a target dealer 420. In addition to
feature allocations 1212, it is generally useful to employ a
mechanism that provides estimates of future turn rates for various
features, e.g., inventory turn rate calculators 1302.
[0221] FIG. 13 illustrates exemplary data used in generation of
inventory turn rate calculators 1302, each of data 202, 204, 216,
216, 218, 222, 224, and 226 each having been discussed above. A set
of inventory turn rate calculators 1302 may created for providing
estimates of inventory turn rates for any or all particular
configurations. In some instances, inventory turn rate calculators
1302 are determined for each item configuration that a target
dealer 420 may order to stock or replenish inventory. Using the
turn rate calculators 1302 and a matrix of all possible
configurations that may be built using feature allocations 1212,
the system 100 may determine what configurations of items to
recommend to a target dealer 420. Because this matrix can be
extremely large, it generally cannot be processed manually, and
generally some or all of the processing involving this
configuration matrix is performed by one or more computing
devices.
[0222] Turn rate calculators are created applying principles of
statistical survival analysis with neural network methodologies.
Turn rate calculators are designed to model a conditional
probability that a specific item configuration, i.e., an item
associated with a collection of features, at a given target dealer
420, under pre-specified dealer and market inventory mix rates,
such as target feature level inventory mix rates 1104, will sell
within a given time period, given that the item with the specific
configuration has not been sold up to that point in time.
[0223] As mentioned above, turn rate calculators 1302 are designed,
among other things, to address the problem that unobservable
factors are preferably accounted for when estimating turn rates.
For example, actual items available in inventory, and sales of
items, are likely to account for only a fraction of possible item
configurations. Therefore, turn rate calculators generalize and
create inventory turn rate calculators 1302 based on observing
multiple configurations with various combinations of features.
[0224] 2. Mix Rate Modulation Encoding
[0225] FIG. 14 illustrates an exemplary mix rate modulation
encoding. This encoding may later be used for facilitating neural
network processing, among other uses. As illustrated in FIG. 14,
historical item orders/configurations 208 in combination with
weighted feature level market inventory mix rates 904 may be used
to construct a compact representation of configurations of
individual items in relation to the mix rates for the items. The
representation may take historical configuration data (e.g.,
vehicle color) and modulate that data according to a number or a
percentage of inventory (e.g., fraction of red items in inventory)
that the configuration represented.
[0226] For each item, a configuration for the item may be created
in a binary fashion. Specifically, a data record for the item may
be encoded which includes a list of features associated with an
item by, for each feature in the list of features, placing the
value "1" in a field associated with a feature if the feature
exists with respect to the item, and placing the value "0" in the
field associated with the feature if the feature does not exist
with respect to the item.
[0227] Then, this representation may be converted into mix rate
modulated item configuration data 1402. Specifically, for each
feature in the list of features, a percentage of items in inventory
for which the feature exists is determined. For each field
associated with a feature in the data record, the percentage of
items in inventory for which the feature exists is subtracted from
the one or the zero encoded in the field, thereby generating a
matrix of inventory mix-rate modulated configurations.
[0228] The exemplary mix rate modulation encoding has non-negative
values for those features that are present in a configuration, and
non-positive values for the features that are not present in a
current configuration, because the mix rates are always bounded
between zero and unity. Furthermore, it is almost always possible
to recover the original information with this encoding, except for
the case where all items for a feature are of the same value.
However, for this special case where all items have the same value
for a feature, no estimates may be made with regard to an item that
has a different value for the feature, because no variation in
vehicle configurations with the indicated features is observed.
[0229] Other properties of the mix rate modulation are that the sum
of the values across all features within a feature family for a
given record are equal to zero, and average value across all
records for any given feature in any given time interval must be
equal to zero.
[0230] It is important to note that other encodings of item
configuration data and inventory mix rates are possible instead of
the mix rate modulated item configuration data 1402. One
alternative encoding is similar to the encoding described above in
that it uses the binary representation of features coupled with the
feature-level market inventory mix rates. However, rather than
subtracting the inventory mix rate from the binary encoding, this
alternative encoding operates as follows: if a feature is present
in a configuration, then the binary signal "1" is multiplied by the
inventory mix rate; however, if the feature is not present, then
the binary signal "-1" is multiplied by the inventory mix rate.
[0231] Note that in the case of the absence of the feature, the
original encoding discussed above (using 1s and 0s) and this
alternative encoding (using 1s and -1s) yield similar results.
However, in the case of the presence of the feature, in this
alternative encoding, as the inventory mix rate approaches unity,
so does the mix rate modulated encoding. In the encoding that uses
1s and 0s, as the inventory mix rate approaches unity, the mix rate
modulated encoding approaches zero. Further, in this alternative
representation, the characteristic is lost that the sum across all
features of a feature family of the individual mix rate modulated
configuration values approaches zero.
[0232] Also, for data sets prepared for training of turn rate
calculators 1302, this alternative encoding does not provide the
property that the sum of the mix rate modulated encoding across all
records for a given feature equals zero. Hence, application of PCA
would require some normalizing of the mix rate modulated data in
the alternative encoding. On the other hand, the use of the
alternative encoding makes it possible to always recover the
original information, even in the case where the inventory mix rate
for a feature is 100%. The encoding discussed above that uses 1s
and 0s, on the other hand, does not allow recovery of which feature
was always present (since the mix rate modulated encoding is zero
everywhere), whereas the alternative encoding allows for the full
recovery of information even in this limit.
[0233] 3. Principal Components Analysis
[0234] For some items, there may be few possible combinations of
features and therefore the dimensionality of the mix rate modulated
item configuration data 1402 matrix may be acceptable for further
processing as-is. However, in other cases, such as for an item with
many possible configurations, e.g., a vehicle with a large number
of different possible features, it may be desirable to reduce the
dimensionality of the matrix to a more manageable level.
[0235] Mix rate modulated item configuration data 1402 is a
redundant representation, as can be seen in the repetition of
various quantities in FIG. 14. Further, the sum of the number of
features across all feature families can be quite large for items
with a great quantity of features. However, in some cases, the
effective dimensionality can be substantially less, e.g., at a
minimum, the number of actual feature families. Because the neural
network training methods may have computational complexity that
scales with the square of the number of inputs (or feature space
dimensionality), it is may be useful in some cases to take steps to
reduce the input space dimensionality while preserving as much
information as is reasonably possible.
[0236] PCA may be used to reduce the dimensionality of the matrix
including the mix rate modulated item configuration data 1402
created above. One aspect of the mix rate modulated item
configuration data 1402 encoding is that the encoded data is
already in a form natural for application of PCA, where no further
transformations may be required. For example, the average value of
mix rate modulated feature indices are nearly zero, and would be
zero in the absence of cross sales region contributions to the mix
rate calculations. Furthermore, the values of each of these indexes
are bounded by plus and minus one.
[0237] FIG. 15 illustrates exemplary data relating to principal
component analysis. As seen in FIG. 15, the input to the PCA is the
mix rate modulated item configuration data 1402. More specifically,
the mix rate modulated item configuration data 1402 may be
organized into a matrix A of size n.sub.r rows by n.sub.c columns,
where n.sub.r is the number of records that have previously been
assembled for the 8-week period, and n.sub.c is the number of
features across all feature families. In many cases
n.sub.r.gtoreq.n.sub.c. SVD may be used to perform the PCA, as due
to the nature of the mix rate modulated item configuration data
1402, no scaling or centering of the data may be necessary.
[0238] Generally, SVD decomposes the matrix A into three
matrices:
A=UWV.sup.T;
[0239] where U is a column-orthogonal matrix of size n.sub.r by
n.sub.c; W is a diagonal matrix of size n.sub.c by n.sub.c and
containing a vector of scaled standard deviations along the matrix
diagonal; and V is an orthogonal matrix of size n.sub.c by n.sub.c,
also known as the rotation matrix 1506.
[0240] The transformed values may be recovered from the
untransformed data as follows:
X=AV=UW;
[0241] because of the fact that V.sup.TV=I. Accordingly, principal
component techniques may be performed to generate three outputs
from the mix rate modulated item configuration data 1402: a
transformed mix rate modulated matrix 1502 (i.e., matrix X), a
number of components to keep 1504 (based on analysis of matrix W),
and a rotation matrix 1506 (i.e., matrix V.sup.T),
[0242] The sorted list of standard deviations from the diagonal
matrix of standard deviations W may be analyzed to determine the
minimum number of transformed components to retain that account for
a certain percentage, e.g., 98%, of the variance in the original
data set. The concept of "variance" is discussed further below; for
the moment, note that "variance" may be defined as the square of
the standard deviation. The number of components retained is given
by n.sub.x, or the number of components to keep 1504. Only the
first n.sub.x components are retained from the transformed mix rate
modulated matrix 1502 (i.e., the transformed variables in matrix X)
for training the turn rate calculators 1302. This reduction in the
input data accordingly allows for the dimensionality of the later
calculations to be reduced. Note that the vector of standard
deviations encoded in the matrix W must be retained, as well as the
rotation matrix 1506 (i.e., matrix V), so that the same
transformation can be applied to the evaluation of mix rate
modulated item configuration data 1402 in the next step, where
expected turn rates for different configurations are evaluated.
[0243] 4. Neural Network Models
[0244] FIG. 16 illustrates exemplary context variables relating to
neural network data set records. Context variables 1602 are
variables that include information regarding particular items
unrelated to the configuration of the items, such as a location of
a dealer at which an item was in inventory. As illustrated, context
variables 1602 are added to the transformed mix rate modulated
matrix 1502. In other instances, where PCA is not used to reduce
dimensionality, the context variables 1602 may instead be added to
the to the mix rate modulated item configuration data 1402 itself.
In any event, providing configuration information for the items as
input to the neural network training modules allows for the order
generation system 100 to generate neural network models that
further take into account variables that are not related to item
configuration. As an example of the importance of contextual
information, a vehicle with a moonroof may be more popular in a
first dealer market area 410, but may be far less popular in a
second dealer market area 410.
[0245] Inputs to neural network training modules may include, but
are no means limited to, records relating to the configuration of
items that have sold or are in inventory, combined with contextual
information for the items. Merely by way of example, inputs to the
neural network training modules may include historical item
orders/configurations 208, item transactions 210, dealer locations
204, feature families 214, actual market inventory 706, actual
dealer inventory 702, weighted feature level market turn rates 808,
and weighted feature level market inventory mix rates 906. Inputs
may further include other market factors, including feature
bundling 220 and incentives 228 that should be taken into account
to prevent sales rates from being skewed based on temporary dealer
or manufacturer promotions.
[0246] As mentioned above, inputs to the neural network training
modules may include information relating to configurations of items
that have sold within a given time period, or that are in
inventory, combined with contextual information for the items. As
illustrated in FIG. 16, context variables 1602 may be included in
the inventory turn rate calculators 1302 to allow for the
contextual information relating to an item to be taken into
account, in addition to the configuration of the items that are
sold or in inventory. Specifically, exemplary context variables
1602 may include dealer latitude 1604, dealer longitude 1606,
dealer item market inventory 1608, retail/stock order type
indicator 1610, number of weeks in inventory 1612, dealer fraction
of item market inventory 1614, and market turn rate 1616.
[0247] In other implementations, more, fewer, or different context
variables 1602 may be used. As an example, as illustrated, dealer
latitude 1604 and dealer longitude 1606 are determined based on
dealer locations 204. However, in other examples, dealer location
may be omitted entirely from the set of context variables 1602. As
another example, as illustrated, dealer item market inventory 1608
is determined based on dealer projected inventory 1006 and a dealer
allocation 212 for items, such as items that could be ordered and
in inventory. However, in yet another example, context variables
1602 may be utilized that do not include contextual information
relating to other available inventory. Regardless of the context
variables 1602 that may be used, each row of the matrix includes
mix rate modulated item configuration data 1402, and may be
combined with context variables 1602 for that row.
[0248] Generally, neural network modules may develop predictive
capabilities by being trained on a set of historical inputs and
known resulting outputs. As one example, each of a set of neural
network modules may initially estimate a set of parameters, where a
parameter directly or indirectly connects one or more of the
designated input variables to one or more of the processing
elements of the neural network module. The neural network training
process may then adjust these parameters in response to the applied
input variables so that the output values of the neural network
module more closely match the known resulting outputs associated
with the set of historical input data. Once appropriate parameters
have been optimized to minimize the difference between expected and
actual output from the neural network module, the neural network
module may then be used to predict output values for new input
data.
[0249] Accordingly, the matrix of mix rate modulated item
configuration data 1402 and context variables 1602 may thus be used
as input to neural network modules. Based on these inputs, the
neural network training modules may produce multiple neural network
models that can be used to obtain expected inventory turn rates at
the configuration level, as well as a measure of variance for the
expected inventory turn rates.
[0250] Expected inventory turn rates and the variance may be used
to calculate risk-adjusted turn rates, i.e., turn rates that take
into account the relative reliability of the estimate. Turn rates
may have a high variance because of insufficient information
available in the inputs to the neural network models to determine
an inventory turn rate calculator 1302 with much reliability.
Accordingly, a high turn rate accompanied with high uncertainty may
reflect an undesirable configuration to use, because the risk of
error may be too high. Instead, a configuration with a lower
estimated turn rate may be preferable if that lower estimated turn
rate is based on a turn rate having a lower variance.
[0251] 5. Exemplary Neural Network Processing of Mix Rate
Modulation Encoded Data
[0252] FIG. 17 illustrates an exemplary neural network processing
of mix rate modulation encoded data to generate inventory turn rate
calculators 1302.
[0253] The data set for neural network training on sales and
inventory data for an item in a dealer market area 410 includes of
a plurality of records, each including context variables 1602, mix
rate modulated item configuration data 1402, and a sold status
1702. The sold status 1702 indicates whether or not the item
corresponding to the data row was sold.
[0254] For training of the neural network training models, such as
mentioned above, the resulting data set including mix rate
modulated item configuration data 1402, context variables 1602, and
sold status 1702 may be processed by a neural network training
program to develop neural network inventory turn rate calculators
1302 for each item and sales region. This step is particularly
well-suited to parallelization, and many separate neural network
models for different items and sales regions may be computed in
parallel, for example by supercomputer 170.
[0255] In other examples, turn rate calculators 1302 may be
generated from the mix rate modulated data through techniques other
than neural networks. For example, turn rate calculators 1302 may
be developed through use of regression analysis methods such as
simple logistic regression, in which prediction of the probability
of occurrence of an event is determined by fitting data to a
logistic curve. As another example, turn rate calculators 1302 may
be developed through statistical survival models, such as Cox
proportional hazards models.
[0256] 6. Configuration Matrix
[0257] FIG. 18 illustrates exemplary construction of a
configuration matrix for the evaluation of neural network inventory
turn rate calculators. Evaluation of the trained neural network
inventory turn rate calculators 1302 is performed to determine turn
rates for various configurations of features based on the
training.
[0258] Once the neural network is trained, and the inventory turn
rate calculators 1302 are generated, the system 100 uses the
inventory turn rate calculators 1302 to determine turn rates for
the possible configurations of features in feature allocations
1212. Specifically, the order generation system 100 may create a
matrix of all realizable configurations, which may then be further
limited to a possible configurations matrix 1802 based on feature
allocations 1212, which are affected by constraints such as product
definition constraints 224, dealer allocations 212, material
availability constraints 226, etc As illustrated, the possible
configurations matrix 1802 includes N possible configurations. Any
configuration that is precluded from being considered due to zero
allocation of certain features may be pruned from the matrix at
this point since no such configuration will be recommended. As a
result, it is not necessary to evaluate the pruned configuration
expected turn rate for that dealer, reducing the necessary
processing.
[0259] The possible configurations matrix 1802 may be mix rate
modulated into a mix rate modulated possible configurations matrix
1804. Specifically, for each possible configuration in possible
configurations matrix 1802, mix rate modulated possible
configurations matrix 1804 indexes may be encoded by subtracting
determined market-level projected inventory mix rates from the
binary feature encoding.
[0260] To allow the inventory mix rates needed to account for
projected inventory at the point in time when the items may be
added to dealer inventory, the market-level projected inventory mix
rates may be determined based on the feature allocations 1212, and
also based on the feature level projected dealer inventory 1012.
Weighted feature level market allocations 1806 may be determined
based on the feature allocations 1212 for a target dealer 420 and
competing dealers 430 in the dealer market area 410 weighted by a
dealer market weighting function 310. Additionally, projected
weighted feature level market inventory 1808 may be determined from
the feature level dealer projected inventory 1012 based on a dealer
market weighting function 310. Weighted feature level market
allocations 1806 and projected weighted feature level market
inventory 1808 may be used to determine market-level mix rates for
the possible configurations matrix 1802 for a future time, based on
the projected level of market inventory at that time and also based
on the feature allocations 1212 for the market area.
[0261] The mix rate modulated possible configurations matrix 1804
may be transformed according to the rotation matrix 1506 previously
computed during the PCA of the mix rate modulated item
configuration data 1402. Specifically the rotation matrix 1506 from
the PCA performed in the neural network training step may be
applied to the mix rate modulated possible configurations matrix
1804 to transform the matrix 1804 into a transformed mix rate
modulated possible configurations matrix 1810. Importantly, the
resulting transformed mix rate modulated possible configurations
matrix 1810 is transformed into a reduced space of size n.sub.x as
was done above to reduce the dimensionality of the data used to
train the turn rate calculators 1302. Also, the transformed mix
rate modulated possible configurations matrix 1810 includes the
minimum number of components that account for a predetermined
percentage, e.g., 98%, of the variance of the original data used to
train the turn rate calculators 1302.
[0262] 7. Evaluation of Turn Rate Calculators
[0263] FIG. 19 illustrates an exemplary calculation of
risk-adjusted turn rates based on evaluation of inventory turn rate
calculators 1302. In general, inventory turn rate calculators 1302
should be evaluated for each target dealer 420 in isolation from
other dealers, because each target dealer 420 will have particular
characteristics, e.g., as captured by the particular context
variables 1602 and weighted feature level market inventory mix
rates 908 associated with the target dealer 420.
[0264] As discussed above, feature allocations for the target
dealer 420 are used to create a transformed mix rate modulated
possible configurations matrix 1810. Context variables 1602 may
then be added to the transformed mix rate modulated possible
configurations matrix 1810. Exemplary context variables 1602 may
include, but are by no means limited to, dealer latitude 1604,
dealer longitude 1606, retail/stock indicator 1610, number of weeks
in inventory 1612, dealer fraction of item market inventory 1614,
and market turn rate 1616.
[0265] The matrix including context variables 1602 and transformed
mix rate modulated possible configurations matrix 1810 may then be
evaluated by the inventory turn rate calculators 1302 through use
of the neural network. The neural network may then produce dealer
configuration turn rates 1902 that indicate the expected turn rate
for each configuration in the matrix. For example, the neural
network may run 100 independent models on each configuration in the
matrix, and may determine the dealer configuration turn rates 1902
based on an average of those 100 results.
[0266] The neural network may further produce dealer configuration
turn rate variances 1904 for each dealer configuration turn rates
1902 that indicate a confidence factor for the associated dealer
configuration turn rates 1902. For example, using the same
one-hundred independent models run on each configuration in the
matrix, the neural network may determine dealer configuration turn
rate variances 1904 and the standard deviation as the square root
of the variance 1904.
[0267] The dealer configuration turn rates 1902 and dealer
configuration turn rate variances 1904 may then be used to
determine risk-adjusted dealer configuration turn rates 1906, which
are estimated turn rates for a configuration in the matrix adjusted
according to the dealer configuration turn rate variances 1904. For
example, risk-adjusted dealer configuration turn rates 1906 may be
computed by subtracting dealer configuration turn rate variances
1904 multiplied by a constant from dealer configuration turn rates
1902.
[0268] Because the inventory turn rate calculators 1302 each may
include a plurality of varying models (e.g., one-hundred models),
each model in an inventory turn rate calculator 1302 may produce a
somewhat different turn rate. These differences may be measured,
and may be used to determine an average dealer configuration turn
rate 1902 for the configuration.
[0269] Additionally, the variance of the calculated turn rates for
an inventory turn rate calculator 1302 may also be calculated.
Risk-adjusted dealer configuration turn rates 1906 may then be
generated from the average dealer configuration turn rates 1902, by
adjusting the average dealer configuration turn rates 1902 based on
the dealer configuration turn rate variances 1904. In some
examples, risk-adjusted dealer configuration turn rates 1906 may be
computed by subtracting the dealer configuration turn rate variance
1904 multiplied by a scaling factor from the average dealer
configuration turn rates 1902. Accordingly, an average calculated
turn rate with a greater dealer configuration turn rate variance
1904 may be reduced by a greater amount than an average calculated
turn rate with a lower dealer configuration turn rate variance
1904.
[0270] FIG. 20 illustrates exemplary data relationships relating to
order selection. Based on the feature allocations 1212 and the
computed risk-adjusted dealer configuration turn rates 1906, dealer
recommended order configurations 2004 can be generated.
[0271] More specifically, the risk-adjusted dealer configuration
turn rates 1906 may be used to determine which configurations of
features to recommend through a maximization of risk-adjusted
dealer configuration turn rates 1906. The maximization further
takes into account feature allocations 1212 for the target dealer
420.
[0272] The set of configurations resulting from maximizing
risk-adjusted dealer configuration turn rates 1906 may then be
recommended to a target dealer 420 to order. That is, specific
dealer orders recommendations may be generated, including
quantities, such that the sum of risk-adjusted turn dealer
configuration turn rates 1906 of the recommended configurations is
maximized. A penalty 2002 for selecting the same configuration
multiple times may further be imposed on the optimization to
introduce greater diversity in the inventory of a target dealer
420. The penalty 2002 term may be the scaled sum of the square of
the number of items to order across all configurations; this will
penalize choosing any one configuration more than once. Alternative
schemes for promoting diversity that are linear in the additional
terms for the objective function can be formulated. Similarly,
rewards may be conferred for greater diversity, e.g., the score of
a configuration may be adjusted up or down based on whether it
promotes diversity, the degree to which diversity is desired even
at the possible expense of maximizing sales, etc. In addition, the
order selection step must also satisfy feature allocations 1212 for
the target dealer 420, as well as dealer allocations 212.
[0273] 8. Uses of Turn Rate Calculators
[0274] One use of neural network turn rate calculators disclosed
herein is to provide a basis for evaluating the fitness of specific
configurations, under specific inventory conditions, in order to
guide the selection of configurations for dealers to order. That
is, turn rate calculators may be used to score configurations that
may be used in order recommendations. However, alternative
applications of such turn rate calculators are possible, and
contemplated. Two examples of such alternative applications are
provided herein, although other examples are possible and even
likely.
[0275] In a first example, inventory turn rate calculators can be
used as a basis for trading items between dealers. Consider a
situation in which Dealer A has a customer who desires a particular
item that Dealer A does not currently have in inventory. Dealer A
performs a search of available inventory at other dealerships, and
finds that Dealer B currently has the item of interest in
inventory. Dealer A inquires of Dealer B if Dealer B would be
interested in trading the item of interest, in exchange for another
item from Dealer A's inventory (plus any differential associated
with the costs of the two items being exchanged). Dealer B is
interested in such a trade, provided that the unit which would be
provided in return from Dealer A is expected to have sales
performance that is as good or better, in terms of expected
risk-adjusted turn rate, as the item being traded away.
[0276] In one scenario, Dealer A could offer that Dealer B may
select one of a group of vehicles for trade. Dealer A could select
which items to offer for trade by evaluating the expected inventory
turn rate for all items in Dealer A's inventory, and then select
that group of items which is expected to be the worst performing
units for Dealer A. In turn, Dealer B could evaluate the expected
turn rate of the vehicles being offered by Dealer A, as well as
that of the item being requested for trade. After evaluation of the
respective expected turn rates, Dealer B could rank order each of
the items, and if the highest ranked item has a higher expected
turn rate than the item being traded away, then Dealer B might
agree to the trade, and request that item which was highest ranked
in exchange.
[0277] In a variation on this scenario, Dealer B could also
evaluate other items that Dealer A has not offered in exchange to
see if there are suitable candidates, and provide a counter-offer
in case the group of items originally offered by Dealer A was found
to be unacceptable. In yet another variation of this scenario, a
manufacturer (sometimes referred to as the original equipment
manufacturer, or OEM) could provide to dealers who regularly
exchange items a mechanism for identifying items that are suitable
for trade, such that items which are expected to be poor performers
at one dealership would be good performers at the second, and
vice-versa.
[0278] In a second alternative exemplary use of inventory turn rate
calculators, an OEM could use evaluation of the turn rate
calculators to determine which combinations of features should or
should not be offered for sale. Turn rate calculators could thereby
provide the basis of reducing orderable complexity, which has
significant cost savings for the OEM (engineering, manufacturing,
marketing, logistics, etc.), while at the same time improving
dealer and customer satisfaction by insuring that undesirable items
cannot be ordered and stocked by dealers, and ultimately purchased
by end customers.
[0279] 9. Alternatives to Turn Rate Calculators
[0280] An alternative to using turn-rate calculators 1302 to score
configurations for order recommendation is to use survey data. For
example, a paper survey or a survey application on a website, or
through some other electronic mechanism, may be used to collect
data from potential customers concerning preferred configurations.
This data may then be used to rank possible configurations, and to
serve as a basis for recommending orders to customers.
Alternatively or in addition, dealers could be surveyed through
paper or electronic mechanisms to score configurations, enter a
preferred set of configurations, or enter an example set of
configurations in a desirable inventory state. The frequency of
observation of each configuration, or partial configuration, may
then be used as a score for the configuration. Such use of survey
data may be particularly useful when sales data for an item of
interest does not exist, such as when new items are introduced into
a marketplace.
[0281] Additional or alternative ways to score configurations could
include evaluating the profitability of various configurations for
one or both of dealers and suppliers, or scoring based on customer
surplus utility. Customer surplus utility is the monetary value
that a customer assigns to an item, minus the price paid for the
item. In general, rational customers and dealers will want to
maximize their surplus.
[0282] FF. Reporting
[0283] FIG. 21 illustrates exemplary data relationships relating to
report generation.
[0284] Dealer recommended order configurations 2004 for a target
dealer 420 may be included in a dealer report 2102 that is sent to
the target dealer 420.
[0285] For example, dealer reports 2102 may be communicated to
target dealers 420 through reports in a PDF (portable document
file) format, where the dealer reports 2102 are customized for each
dealer. The reports may, for example, be made available through a
website and/or are e-mailed to a designated contact at each target
dealer 420.
[0286] In addition to dealer recommended order configurations 2004,
the dealer report 2102 may further include information used to
arrive or justify the dealer recommended order configurations 2004.
For example, a dealer report 2102 may include one or more of the
following: weighted feature level dealer turn rates 804, weighted
feature level dealer sales mix rates 902, feature level dealer
projected sales 1008, feature level dealer projected inventory
1012, as well as historical sales, sales mix graphs 2104, projected
inventory mix graphs 2106, and report time periods 2108, among
other things.
[0287] FIGS. 22-24 illustrate various exemplary dealer reports
2102. As illustrated in FIG. 22, a dealer report 2102 may include a
cover page including general information, such as the title of the
report, the reporting period, general findings regarding the dealer
turn rate, the date of the report, etc. As illustrated in FIG. 23,
a dealer report 2102 may include a set of recommended orders for
the dealer to make for a particular time period, such as for a
particular week. As illustrated in FIG. 24, a dealer report 2102
may include data regarding sales volumes, and dealer and market mix
rates. In other examples, dealer report 2102 may include more,
fewer, or different data elements.
IV. System Operation
[0288] A. Setup Process
[0289] FIG. 25 illustrates an exemplary setup process 2500. Process
2500 details exemplary steps for storing and processing data,
related to possible configurations of an item, for periodic
processing by inventory management server 150. Accordingly, the
setup process 2500 may only be required to be executed initially,
not each time a new set of suggested orders are generated for the
item, or each time new sales data inventory data, etc. are reported
for the item. However, the setup process 2500 may be required to be
executed more than once, for example, when there are changes in
possible configurations for an item. For example, in the case of
vehicles, process 2500 may need to be re-run in the event of
mid-model year model changes, or for a new model year.
[0290] The process 2500 begins in a step 2510, in which data
mappings between different data sources are generated. For example,
a data mapping may be required for decoding a manufacturer-specific
representation of item features. As an example, a manufacturer may
represent item features as a concatenated string of codes that
represent the features included in the item. If necessary, a
translation table may be created which maps elements of the
manufacturer-specific representation into a format more usable for
the inventory management server 150, such as in a family feature
format.
[0291] One reason such a translation may be necessary is because
the manufacturer may include one or more compound descriptors in
the representation of item features, which are descriptors in which
multiple features are encoded. For example, the Rear Spoiler and
Moonroof feature families for a vehicle might share a character
field with the following descriptors: "blank"=No Spoiler or
Moonroof; "M"=Moonroof without a Spoiler; "S"=Spoiler without a
Moonroof; and "1"=Moonroof with a Spoiler. Because two separate
feature families are represented by the one descriptor, the data
mapping needs to map from the exemplary compound descriptor into a
feature family for spoiler, and a second feature family for
moonroof. Table 2 illustrates an exemplary mapping of the compound
descriptor into two individual feature families.
TABLE-US-00002 TABLE 2 Exemplary Mapping of a Compound Descriptor
Into Feature Families VOI Descriptor Moonroof Feature Family
Sunroof Feature Family " " Moonroof.null Sunroof.null M
Moonroof.Moonroof Sunroof.null S Moonroof.null Sunroof.Sunroof 1
Moonroof.Moonroof Sunroof.Sunroof
[0292] Next, in step 2520, features and feature families are
defined. As discussed above, superfamilies may group families that
should be considered together according to business judgments, and
may accordingly allow for multiple features to be considered in
combination to manage important interactions. For example,
superfamilies may be defined to allow for two or more related sets
of feature families to be grouped together. Each superfamily that
is defined may consist of all possible ways of bundling features
for the feature families that are included in the superfamily. The
resulting superfamilies accordingly contain exactly one feature
from each contributing feature family. Defining superfamilies
allows the inventory management server 150 to treat features in
combination, where they would otherwise be considered
independently. Accordingly, these superfamilies may allow the
inventory management server 150 to consider multiple features
together, at the expense of potentially increasing the
dimensionality of the problem of recommending orders.
[0293] In general, construction and use of superfamilies is most
appropriate when there are relatively strong correlations or
interactions among features from different feature families. Care
should be exercised in the construction of superfamilies, because a
superfamily may have a substantially greater number of possible
feature combinations than the sum of the component features
themselves. When considering inventory and sales turn rates over a
superfamily, inventory and sales at the feature level may be more
broadly distributed, which may lead to noisier estimates of mix and
turn rates. Moreover, it is possible that order recommendations
resulting from explicit consideration of feature family
interactions may be different than the order recommendations
obtained when feature families are considered independently.
Accordingly, features that are estimated to have a strong
correspondence with one another may be good features to include in
a superfamily.
[0294] For example, for a vehicle line, a superfamily may be
created for a base-vehicle super-feature which may include a body
style, trim level or marketing series, wheelbase, engine,
transmission, and drive type (e.g., 4.times.2 or 4.times.4), as
applicable. As another example, a superfamily may be created as the
combination of primary exterior paint family with two-tone accent
color family, to create a Paint/Two-Tone superfamily. Since every
accent color is generally not available with every primary paint
color, and since the two feature families are expected to interact
strongly, then the grouping of these feature families into a
superfamily may be appropriate.
[0295] Once superfamilies are defined, those feature families that
belong to the superfamily may be ignored during the remaining
inventory management server 150 process steps until specific orders
are constructed, which may require that a superfamily once again be
decomposed into its constituent components. However, in other
examples, it may be advantageous to additionally include the
constituent components in the feature allocation step. For example,
a superfamily defining a base of a vehicle may be included in
feature allocation. Although cab style may be included in the
superfamily, cab style may additionally be broken out separately in
the feature allocation. This may be done because cab style may
result in a greater turn rate than the turn rate shown when cab
style is included in the base vehicle superfamily, or as another
example, due to a production constraint on cab style. For example,
a superfamily may include ten different features, wherein each of
the different features has a slightly low inventory mix rate (e.g.,
0.01 units low). However, when each of the different features of a
cab style is considered individually, each of the different
features may still have a slightly low inventory mix rate (e.g.,
0.01 units low) that when summed yields a more significant mix rate
(e.g., 0.01*10=0.1 units).
[0296] Next, in step 2530, report formats are defined. For example,
a report format including one or more of weighted feature level
dealer turn rates 804, weighted feature level dealer sales mix
rates 902, feature level dealer projected sales 1008, feature level
dealer projected inventory 1012, as well as historical sales, sales
mix graphs 2104, projected inventory mix graphs 2106, and report
time periods 2108 may be established. As discussed above, exemplary
reports are illustrated in FIGS. 22-24.
[0297] Next, in step 2540, product information is encoded to
generate a list of all possible configurations. In this step, the
inventory management server 150 may encode the product definition
into a standard format, and may generate a list of all possible
item configurations based on the encoding. An exemplary portion of
a listing of all possible configurations for a vehicle is
illustrated in Table 3; in practice, an actual such listing would
likely be much larger.
TABLE-US-00003 TABLE 3 Exemplary portion of a listing of all
possible configurations for a vehicle Audio.Ap Audio.null Navig.N
Navig.null Paint.BriSil Paint.DarBlu Paint.Ebo 0 1 0 1 1 0 0 0 1 0
1 0 1 0 0 1 0 1 0 0 1 0 1 1 0 1 0 0 0 1 1 0 0 1 0 0 1 1 0 0 0 1 1 0
0 1 1 0 0
[0298] Next, in step 2550, product definition relationship
constraints are generated. Such constraints may represented by a
set of one or more inequalities. For example, a product dependency
may be as simple as Feature A requiring Feature B, which implies
that the allocation of Feature A has to be less than or equal to
the allocation of Feature B. As an example, for some manufacturers,
in order to have a heated seats feature, a vehicle also has to have
leather seats. Therefore, the allocation of heated seats must be
less than or equal to the allocation of leather seats. However,
other features dependencies can be substantially more complicated,
and may involve multiple features from multiple feature families or
superfamilies.
[0299] Once the inventory management server 150 has generated the
list of all possible configurations, the inventory management
server 150 analyzes the configurations to generate a list of
product definition relationship constraints that define how to
constrain the feature allocation process when there are feature
dependencies. That is, some features cannot be included in an item
with certain other features, or can only be included when other
features are present. Feature allocation needs to respect such
constraints, or the system 100 could end up recommending that a
dealer order items that could not or would not actually be
built.
[0300] Following step 2550, the process 2500 ends.
[0301] B. Periodic Order Selection Process
[0302] FIG. 26 illustrates an order selection process 2600 for an
order generation system. Once the inventory management server 150
has completed the setup process 2500, the overall process 2600 may
be run one or multiple times for the selection of features to
include in recommended orders to send to dealers. Generally,
process 2600 is run on a periodic basis, e.g., once per week.
[0303] 1. Order Cycles
[0304] Generally, order recommendations relating to an item are
generated based on the item's historical sales and inventory.
Further, the process 2600 takes into account the delay between the
time an item is ordered and the time at which it is actually
delivered into the dealer's inventory. Such delay may occur because
of the time it takes for an item to be manufactured, processed
through a fulfillment system, etc.
TABLE-US-00004 TABLE 4 Exemplary time line for periodic processing
on the weekend of Jan. 31, 2009 Oct. 6, 2008 . . . Jan. 19, 2009
Jan. 26 Feb. 2 Feb. 9 Feb. 16 Feb. 23 Mar. 2 Mar. 9 Mar. 16 Start
Last Orders Allocation Item Observed Observed Placed & and Item
Arrival Sales Sales Scheduled Production in Week Week Inventory
[0305] Before discussing exemplary steps of process 2600, Table 4
illustrates an exemplary time line illustrating the relative timing
of, the inputs to, processing time of, and outputs of process 2600.
In this example, the order generation system 100 considers the most
recent 16 weeks of complete data, and estimates that a four week
period that elapses before items in a recommended order are built,
and an additional two week period will elapse before the items are
delivered into dealer inventory 115. These time periods are merely
exemplary, and different time periods for measuring past sales and
inventory, order production time, and order transit time are
possible, and likely.
[0306] The dates as illustrated in Table 4 assume that Mondays are
the first day of each weekly time period. Historical sales and
inventory are observed from Oct. 6.sup.th, 2008 through the week of
Jan. 22.sup.nd, 2009 Based on these exemplary 16 weeks of sales and
inventory data, the inventory management server 150 performs the
overall periodic process 2600 between Friday, January 30.sup.th,
and Monday February 2.sup.nd. These orders are then recommended to
a target dealer 420, and may be ordered by the target dealer 420,
for example by Wednesday, February 4.sup.th. Scheduling of the
orders may then be performed later that same week, such as on
February 5.sup.th-6.sup.th. The scheduled items are then produced,
for example, during the week of March 2.sup.nd. Including an
approximate transit time of two weeks from factory to dealer, the
items are expected to arrive in inventory the week of Mar.
16.sup.th, 2009
[0307] 2. Process Description
[0308] With this background regarding the time at which overall
periodic process 2600 may be run, exemplary steps of process 2600
are discussed in more detail. First, in a step 2610, the inventory
management server 150 retrieves pertinent data, e.g., from data
store 140 via data server 130. The received data may then be
pre-processed into a periodic time series in a consistent form
suitable for further processing.
[0309] Generally, data preprocessing includes determining weighted
dealer sales 306, weighted feature level dealer sales 316, weighted
market sales 312, weighted feature level market sales 320, weighted
dealer inventory 704, weighted market inventory 708, weighted
feature level dealer inventory 712, and weighted feature level
market inventory 716. Data preprocessing is discussed in more
detail with regard to FIG. 27.
[0310] After step 2610 is executed and the data is in a suitable
format, steps 2620, 2630, and 2650 each may be executed next,
either sequentially or in parallel, or in some combination of
sequentially and in parallel. With regard to the parallelization,
supercomputer 170 may be used to perform at least portions of steps
2620, 2630, and 2650 in parallel. Additionally, supercomputer 170
may further be used to calculate data for different item types in
parallel, and for different sales regions in parallel.
[0311] In step 2620, future inventory is projected. For example,
dealer projected inventory and feature level dealer projected
inventory 1012 may be computed as discussed above. Inventory
projection, as discussed above, determines the composition of
dealer inventory 115 for a dealer at some future point in time when
orders for items being placed in the current week begin to arrive
at the dealer, e.g., 6 to 10 weeks in the future. The projection
considers what is currently in the dealer's inventory 115, what is
in the dealer's pipeline (i.e., items that have been ordered but
have not yet arrived at the dealership), and items that are
expected to be sold in the intervening time periods. In some
examples, inventory projection may further take into account
disturbances, such as shipment delays, item trades between dealers,
and inaccurate sales forecasts.
[0312] Due to the large possible number of orderable configurations
compared to the number of features, in many examples inventory is
projected at a feature level, as opposed to a configuration level,
so as to reduce the computational complexity of the inventory
projection.
[0313] In addition to projecting future inventory, also in step
2620 historical inventory turn rates are determined. Historical
sales mix rates, and historical feature level inventory mix rates
may be determined at both the dealer and the market area levels.
For example, the inventory management server 150 may determine
weighted dealer turn rate 802, weighted feature level dealer turn
rate 804, weighted market turn rate 806, weighted feature level
market turn rates 808, weighted feature level dealer sales mix rate
902, and weighted feature level market sales mix rate 904. The
historical sales mix rates provide a target level for allocating
features, as discussed above. Details of the inventory projection
step are further discussed below with regard to FIG. 28.
[0314] In step 2630, target feature level inventory mix rates 1104
are determined. For example, inventory management server 150 may
determine the relative total weighted dealer turn rates 1102 for a
target dealer 420. Then, based on the relative total weighted
dealer turn rates 1102, the inventory management server 150 may
determine target feature level inventory mix rates 1104 based on
weighted feature level dealer sales mix rate 902, or further based
on weighted feature level market sales mix rates 904 and relative
total weighted dealer turn rates 1102. Further details of
determining target feature level inventory mix rates 1104 are
discussed with regard to FIG. 29.
[0315] Once steps 2620 and 2630 have been executed, step 2640 may
be executed next. In step 2640, feature allocation optimization is
performed. To perform feature allocation optimization, as discussed
above, the inventory management server 150 may simultaneously or
nearly simultaneously consider the allocation of features for all
dealers 430 within a sales region.
[0316] Further, a target dealer 420 may have competing dealers 430
that are not in the same sales region as the target dealer 420.
This may occur, for example, when a target dealer 420 is located
near a boundary of a sales region. In such a situation, the target
dealer 420 may have competing dealers 430 that are located outside
of the dealer's sales region that are a part of the target dealer's
420 dealer market area 410. Although the system may not perform
feature allocation simultaneously with the target dealer 420 and
any potential competing dealers 430 that are located outside the
sales region, those competing dealers 430 outside the dealer market
area 410 may still contribute to the dealer market area 410
calculations including inventory and sales. Thus, while sales
region boundaries may be explicit, dealer market areas 410 may
overlap and may still be taken into account during feature
allocation.
[0317] In any event, the inventory management server 150 may use,
at least in part, item allocations for the target dealer 420,
target feature level inventory mix rates 1104 for the target dealer
420, dealer projected inventory 1006 for the target dealer 420, and
feature level dealer projected inventory 1012 for the target dealer
420 to perform feature allocation optimization. In addition, the
inventory management server 150 may further use production
constraints and product definition constraints determined during
the setup process 2500. Given this information, the feature
allocation optimization may be performed as a mixed integer
quadratic programming problem whose output is an allocation of
features to all target dealers 420 in a sales region that should
lead to a balanced inventory, while ensuring that the vehicle
allocation, production definitions, and production constraint are
all satisfied. In other examples, rather than mixed integer
quadratic programming, alternative approximate formulations that
are faster and yield similar results may be used. Exemplary
alternate formulations may initially be real-valued quadratic
programming problems, followed by integerization of the solution,
which can be structured as a mixed integer linear programming
problem. Feature allocation optimization is discussed in more
detail with regard to FIG. 30.
[0318] In step 2650, which may be executed in parallel with steps
2620, 2630, and 2640, turn rate calculators are generated. For
example, the inventory management server 150, or the inventory
management server 150 in combination with supercomputer 170 for use
of parallelizing the calculations may create a set of turn rate
calculators. Creation of the turn rate calculators is discussed
above and also with regard to FIG. 31.
[0319] Once steps 2640 and 2650 are executed, step 2660 may be
executed. In step 2660, turn rate calculators are evaluated for a
target dealer 420. For example, the inventory management server
150, or the inventory management server 150 in combination with
supercomputer 170 may use the determined feature allocations and
the created turn rate calculators to determine average turn rates
for each possible configuration that fits within the optimized
feature allocations. Further, a variance for the estimated turn
rates may also be computed which is used to adjust average turn
rates according to their variance. Evaluation of the turn rate
calculators is discussed in more detail with regard to FIG. 32.
[0320] Next in step 2670, dealer specific order selections are
made. For example, these dealer specific order selections may
include which configurations of features should be ordered, and how
many of each configuration that should be ordered. Generation of
dealer specific order selections is discussed in more detail with
regard to FIG. 33.
[0321] Next in step 2680, individualized dealer reports 2102 are
generated. For example, the inventory management server 150 may
create a dealer report 2102 based on the dealer specific order
selections generated for a target dealer 420 in step 2670. Dealer
reports 2102, including recommended order configurations, may be
made available in either human-readable and/or machine-readable
format. For example, a dealer report 2102 may include a list of the
possible configurations 1802 that are recommended to be ordered,
along with quantities of each configuration that is
recommended.
[0322] These dealer reports 2102 may be made accessible to a dealer
computer 110 of the target dealer 420. In some cases, the dealer
reports 2102 may be e-mailed to the dealer computer 110, placed on
a web site accessible by the dealer computer 110, or sent through
another electronic or physical mechanisms to the address of the
target dealer 420, etc.
[0323] In some instances the target dealer 420 may enter orders
into the ordering server 120 based on the dealer report 2102.
Additionally or alternately, the recommended order configurations
may be entered into the ordering server 120 by the inventory
management server 150, and may be reviewed by the target dealer
420.
[0324] Next, process 2600 ends.
[0325] C. Data Preprocessing
[0326] FIG. 27 illustrates an exemplary process 2700 for data
preprocessing. FIG. 27 describes details of data preprocessing
referenced above with respect to step 2610 of process 2600
discussed in FIG. 26.
[0327] Process 2700 begins in a step 2702, in which a request for
data is received. For example, data server 130 may receive a
request for data from the inventory management server 150. The
request for data may include requests for dealer data, vehicle
allocations, production constraints, and sales and inventory
history. One or more of these items of data may be requested by the
inventory management server 150, and the requests may be handled by
the data server 130 sequentially or in parallel. Accordingly, one
or more of steps 2704, 2710, 2716, and 2718 may be executed
following step 2702.
[0328] In step 2704, data for the target dealer 420 is requested.
This data includes, for example, dealer name 202 and dealer
location 204, dealer market region 206, etc. For example, data for
the target dealer 420 may be requested from data store 140 by data
server 130, based on a request for dealer data received from
inventory management server 150. In some instances, data for the
target dealer 420 is requested by the inventory management server
150 each time the periodic process 2600 is run, so that the most
current information for target dealers 420 is available. Once this
information is retrieved, the inventory management server 150 may
determine which target dealers 420 are currently active dealers,
and which target dealers 420 have become inactive, due to temporary
or permanent closure.
[0329] Next, in step 2706, the retrieved dealer data may be used to
determine the dealer market areas 410 for each target dealer 420
determined to be active.
[0330] Next, in step 2708, mapping weights are determined for each
competing dealer 430 in the target dealer market area 410. As
discussed above, these weights may be based on the distance of the
competing dealer 430 from the target dealer 420 and a dealer market
weighting function 310 that determines the weighting for a
competing dealer 430 according to the distance. Next, step 2724 is
executed.
[0331] Step 2710 is executed after step 2702 determined whether an
item allocation estimation is needed. If not, then step 2714 is
executed next. Otherwise, step 2712 is executed next. Total item
allocations are the number of units a dealer is contractually
obligated to order for a given item within a given time period,
such as a month. In some cases, item allocations may simply be
retrieved from data store 140 by data server 130 and returned to
the inventory management server 150. In these cases, an item
allocations determination is not needed, and item allocations are
simply retrieved in step 2714.
[0332] However, in cases where an actual item allocation is
unavailable, an estimate of item allocations for a dealer may be
determined. For example, item allocations may not have been agreed
upon during a negotiation process between manufacturer and dealer,
in which case a proposed value or a prior period value may be used.
Thus, step 2712, item allocations are estimated. For example, the
inventory management server 150 or the data server 130 may estimate
item allocations, for example, based on item allocations from prior
weeks. To take another example, the allocations may be determined
for the period of a month, but the period for recommending orders
may be performed weekly. Accordingly, the monthly allocations are
thus converted into weekly allocations. Following step 2712, step
2714 is executed.
[0333] In step 2714, allocations for the current period are
determined from the actual or estimated allocations. Next, step
2724 is executed.
[0334] As discussed above, step 2716 is executed next after step
2702 if production constraints are requested. In step 2716, the
data server 130 retrieves production constraints. For example, if
feature demand is higher than supply, production constraints must
be imposed. As another example, a business decision may be is made
to force or limit a mix rate or volume for a feature. Next, step
2724 is executed.
[0335] As discussed above, step 2718 is executed next after step
2702 if sales and inventory history are requested. Data store 140
may maintain one record for each item. Within each record, a
variety of item attributes are stored, including all item features,
and status data such as the production date, sales date and other
related sales information. As discussed above, data store 140 is
used by the data server 130 to provide information on vehicle
content, a variety of relevant event dates, including item trade
dates, a variety of other attributes such as whether an item was a
stock or retail order type, and information about ordering and
selling dealers. This data may require preprocessing to construct
relevant period inventory and sales time series, and to assemble
item-level data sets for developing neural network inventory turn
rate calculators.
[0336] Accordingly, in step 2718, in order to construct a periodic
time series and item-level data sets, data retrieved from the data
store 140 via data server 130 is translated into definitions of
feature families and superfamilies, as well as constituent
features. The constituent features may be further encoded as a
sequence of independent binary variables to denote the presence or
absence of features, as also discussed above.
[0337] Next, in step 2720, the retrieved data is transformed to
ensure that events occur in a logical date-ordered sequence. For
example, a vehicle should not be indicated as arriving at a dealer
before the item has been manufactured, or as being sold before
being manufactured or arriving.
[0338] Next, in step 2722, dealer trades of items are tracked for
items in inventory. For example, item records may include a field
indicating a prior stocking dealer. If this field includes a
non-null value, then the item may be determined as having been
traded. This step is important to allow for tracking of which
dealer has what items in inventory. For example, an item may be
sold by a trading dealer. Where an item is traded, the item sale
should reflect the dealer who sold the item, not the dealer from
which the item was previously traded. Ensuring that item trades are
properly accounted for allows for maintaining accuracy in the
computation of inventory mix rates, among other things. Next, step
2724 is executed.
[0339] In step 2724, the data requested in step 2702 is returned to
the inventory management server 150.
[0340] Process 2700 ends following step 2724.
[0341] D. Inventory Projection Process
[0342] FIG. 28 illustrates an exemplary process for inventory
projection. FIG. 28 includes additional details of the step 2620 of
process 2600 discussed in FIG. 26.
[0343] In step 2802, the inventory management server 150 receives,
for an item, feature level actual dealer sales 314 and feature
level actual dealer inventory 710 for a plurality of features,
i.e., for a plurality of combinations of one or more features that
may be associated with the item. For example, the sales and
inventory information may be retrieved based on a request for data
from the inventory management server 150 to the data server
130.
[0344] Next, in step 2804, the inventory management server 150
determines weighted sales and inventory. For example, the inventory
management server 150 may determine weighted feature level dealer
sales 316 and/or weighted feature level dealer inventory 712 for a
plurality of features from the feature level actual dealer
inventory 710 as discussed above.
[0345] Next, in step 2806, the inventory management server 150
determines weighted dealer and market turn rates. For example, as
discussed above, the inventory management server 150 may determine
weighted dealer turn rates 802, weighted feature level dealer turn
rates 804, weighted market turn rates 806, and/or weighted feature
level market turn rates 808 for a plurality of features.
[0346] Next, in step 2808, the inventory management server 150
determines projected sales. For example, as discussed above, the
inventory management server 150 may determine dealer projected
sales 1002 and/or feature level dealer projected sales 1008 for a
plurality of features.
[0347] Next, in step 2810, the inventory management server 150
determines projected inventory. For example, as discussed above,
the inventory management server 150 may determine dealer projected
inventory 1006 and/or feature level dealer projected inventory
1012.
[0348] Next, in step 2812, the inventory management server 150
determines whether feature level dealer projected inventory 1012
for any features are negative. If so, then step 2814 is executed
next. Otherwise step 2824 is executed next.
[0349] In step 2814, the inventory management server 150 determines
the total inflated sales for the feature family 214 including the
negative feature level dealer projected inventory 1012. Inflated
sales are projected sales that are in excess of inventory available
for sale. For example, the inventory management server 150 may
determine inflated sales for a feature family as the number of
units projected to have been sold that are in excess of available
inventory over all features in a feature family 214.
[0350] Next, in step 2816, the inventory management server 150
determines the total sales for non-inflated sibling features. For
example, the inventory management server 150 may determine total
sales for other features in the feature family 214 that have sales
less than feature level dealer projected inventory 1012 for the
features.
[0351] Next, in step 2818, the inventory management server 150
resets feature level dealer projected sales 1008 to available
feature level dealer projected inventory 1012 for features with
inflated sales. For example, feature level dealer projected sales
1008 for the feature with inflated sales may be reset to feature
level dealer projected inventory 1012 for that feature.
[0352] Next in step 2820, the inventory management server 150
computes and applies a sales adjustment to each feature in the
feature family 214 that does not have inflated sales. For example,
the inventory management server 150 may move projected sales for
the feature with inflated sales into projected sales for other
features in the feature family 214 that do not have inflated
sales.
[0353] Next, in step 2822, the inventory management server 150
determines if any other features have inflated sales. If yes, then
step 2818 is executed next. Otherwise step 2824 is executed.
[0354] In step 2824, the inventory management server 150 returns
the feature level dealer projected inventory 1012 for further
processing. For example, the inventory management server 150 may
send the feature level dealer projected inventory 1012 to the data
server 130 to be stored in the data store 140.
[0355] Next, the process 2800 ends.
[0356] Steps 2812 through 2822 redistribute negative inventory,
because, in reality, a dealer cannot have negative items in
inventory. However, it is possible that in some examples the
calculated feature level dealer projected inventory 1012 may be
returned as feature level dealer projected inventory 1012 without
regard to the process for adjusting features with inflated sales
(i.e., step 2824 may be executed following step 2810, bypassing
steps 2812 through 2822). This is possible because, in principle,
the system can predict negative inventories for items including
various features, where these negative inventories may simply be
taken to be an indication that demand is even greater than the
inventory that is available. Accordingly, the system may take into
account negative projected inventory by simply setting an
appropriate target feature level inventory mix rate 1104 for the
appropriate item feature.
[0357] E. Establishing Target Inventory Mix Rates
[0358] FIG. 29 illustrates an exemplary process for establishing
target inventory mix rates. FIG. 29 includes additional details
concerning 2630 of process 2600 illustrated in FIG. 26.
[0359] In step 2902, the inventory management server 150 retrieves
dealer and market turn rates and weighted sales mix rates. For
example, the inventory management server 150 may retrieve one or
more of weighted dealer turn rates 802, weighted feature level
dealer turn rates 804, weighted market turn rates 806, weighted
feature level market turn rates 808, weighted feature level dealer
sales mix rates 902, and weighted feature level market sales mix
rates 904 from the data store 140 via data server 130.
[0360] Next, in step 2904, the inventory management server 150
determines relative total weighted dealer turn rates 1102 per
dealer. As discussed above, a relative total weighted dealer turn
rate 1102 is a measure of whether a target dealer 420 is over or
underperforming compared to competing dealers 430 in a market area
410.
[0361] Next, in step 2906, the inventory management server 150
determines whether the relative total weighted dealer turn rate
1102 is positive. A positive result indicates that the target
dealer 420 is performing better than the average for the dealer
market area 410, while a negative value indicates that the target
dealer 420 is under-performing relative to the dealer market area
410. If the result is positive or zero, step 2908 is executed next.
Otherwise, step 2910 is executed next.
[0362] In step 2908, the inventory management server 150 sets the
target feature level inventory mix rates 1104 for the dealer equal
to the corresponding weighted feature level dealer sales mix rate
902. Step 2912 is executed next.
[0363] In step 2910, the inventory management server 150 sets the
target feature level inventory mix rates 1104 for the dealer
according to a blend of the corresponding weighted feature level
dealer sales mix rate 902 and the corresponding weighted feature
level market sales mix rates 904. For example, the blending of
weighted feature level dealer sales mix rates 902 and weighted
feature level market sales mix rate 904 may be based on relative
total weighted dealer turn rate 1102, such that a dealer that is
only slightly underperforming may have a target feature level
inventory mix rate 1104 consisting mostly of weighted feature level
dealer sales mix rates 902. However, for a dealer that is at the
other extreme (e.g., no sales in the past 16 weeks), target feature
level inventory mix rate 1104 would be determined entirely or
almost entirely based on the weighted feature level market sales
mix rate 904. As with step 2908, step 2912 is executed next.
[0364] In step 2912, the inventory management server 150 optionally
overrides the target feature level inventory mix rates 1104. For
example, in some instances a user of the system may cause the
inventory management server 150 to override or otherwise adjust
target feature level inventory mix rates 1104 to take into account
a temporary incentive offered on items that may have affected sales
of particular items and thus skewed the data used to generate the
target feature level inventory mix rates 1104. For example an
incentive lowering the price on vehicles with moonroofs may have
temporarily increased the sales of vehicles with moonroofs compared
to those without. However, once the incentive is removed, the
increased sales may not be expected to continue. Thus, a target
feature level inventory mix rate 1104 for vehicles with moonroofs
may be lowered by a target feature level inventory mix rate 1104
override.
[0365] When performing target inventory mix rate overrides, it is
generally preferable to apply the override such that it can be
applied to each dealer's target feature mix rates in a way that
preserves the heterogeneity of the mix rates across dealers. For
example, if a feature is selling in a region at a 30% mix, and a
known price increase on that feature is anticipated to decrease the
sales mix rate to 20%, a dealer that had been selling vehicles with
an 85% mix of that feature might expect to drop to an 80% mix while
a dealer that had been selling at a 15% mix of that feature might
predict a drop to 8%. Further, the target inventory mix rates for
all other features within the feature family may be adjusted so
that the sum of mix rates over all features within a feature family
remains equal to one. One way to accomplish this is to adjust the
remaining mix rates in proportion to the distance between the
current mix rates and zero, or to adjust the remaining mix rates in
proportion to the distance between one and the current mix rates.
Thus, the further a beginning mix rate is from a target value of
zero or one, the larger its relative change, subject to the
condition that the final mix rates must sum to unity.
[0366] Next, in step 2914, the inventory management server 150
returns the target feature level inventory mix rates 1104 for
further processing. For example, the inventory management server
150 may send the target feature level inventory mix rates 1104 to
the data server 130 to be stored in the data store 140. Next, the
process 2900 ends.
[0367] F. Feature Allocation Process
[0368] FIG. 30 illustrates an exemplary process for feature
allocation. FIG. 30 includes additional details of the perform
feature allocation optimization step 2640 of process 2600
illustrated in FIG. 26.
[0369] In step 3002, the inventory management server 150 retrieves
projected inventory, expected arrivals, and inventory mix rate
targets. For example, the inventory management server 150 may
request for the data server 130 to retrieve from the data store 140
one or more of initial total dealer inventory 1202, actual dealer
inventory 702, dealer expected arrivals 1004, dealer projected
inventory 1006, feature level dealer expected arrivals 1010, target
feature level inventory mix rates 1104, and initial feature level
total dealer inventory 1206.
[0370] Next, in step 3004, the inventory management server 150
determines the initial feature level total dealer inventory 1206,
discussed above.
[0371] Next, in step 3008, the inventory management server 150
determines expected feature level total dealer inventory 1208,
discussed above.
[0372] Next in step 3010, the inventory management server 150
determines expected feature level dealer inventory mix rates 1210,
discussed above.
[0373] Next, in step 3012, the inventory management server 150
determines the difference between the target feature level
inventory mix rates 1104 and the expected feature level dealer
inventory mix rates 1210. For example, the inventory management
server 150 may subtract the target feature level inventory mix
rates 1104 from the expected feature level dealer inventory mix
rates 1210.
[0374] Next, in step 3014, the inventory management server 150
determines whether the difference is zero or negative. If yes, then
step 3018 is executed next. Otherwise, step 3016 is executed
next.
[0375] In step 3016, the inventory management server 150 optionally
adjusts the difference between the target feature level inventory
mix rates 1104 and the expected feature level dealer inventory mix
rates 1210. For example, the inventory management server 150 may
adjust the target feature level inventory mix rate 1104 to be
closer to the expected feature level dealer inventory mix rate 1210
to encourage variety during feature allocation.
[0376] In step 3018, the inventory management server 150, or a
single processor or node of supercomputer 170, determines feature
allocations 1212 as discussed above, feature allocations 1212
include quantities of each feature that are recommended to be
ordered, but do not include the other features that may be included
in an item configuration. The feature allocations 1212 may be
optimized using the feature allocation objective function, also
taking into account the other constraints, so as to achieve the
desired feature allocations 1212 while simultaneously adhering to
any imposed constraints (e.g., product definition constraints,
inequality constraints, etc.). It should be noted that the feature
allocations are based on product definitions. Therefore, while the
individual features may not directly interact within the objective
function, and are basically isolated and independent with
essentially no cross coupling, there is still coupling between the
features in the optimization due to the product definition
constraints. For example, feature allocations 1212 would not
allocate two heated seats features, and one leather seat feature,
if heated seats require for seats to be leather.
[0377] A function for feature allocation 1212 may be structured as
a minimization of the squared difference between the target feature
level inventory mix rates 1104 and actual inventory mix rates at a
future time for all item features that can be ordered by a target
dealer 420. Feature allocation may be performed by inventory
management server 150, or by a dedicated computing device suited to
complex computing tasks such as supercomputer 170.
[0378] Next, in step 3020, the inventory management server 150
returns the dealer specific feature allocations. For example, the
feature allocation 1212 may be sent by the inventory management
server 150 to the data server 130 to be stored in the data store
140 for further use.
[0379] Next, the process 3000 ends.
[0380] G. Creating Inventory Turn Rate Calculators
[0381] FIG. 31 illustrates an exemplary process for creating neural
network inventory turn rate calculators. FIG. 31 includes
additional details of step 2650 of process 2600 illustrated in FIG.
26.
[0382] In step 3102, the inventory management server 150 selects
sales and inventory data. The inventory management server 150 may
retrieve records relating to the configuration of items that have
sold or are in inventory, and also contextual information for the
items to be input to neural network models executed by
supercomputer 170, such as discussed above. For example, the
inventory management server 150 may retrieve from the data store
140 using data server 130 data including historical item
orders/configurations 208, item transactions 210, dealer locations
204, feature families 214, actual market inventory 706, actual
dealer inventory 702, weighted feature level market turn rates 808,
weighted feature level market sales mix rates 904, feature bundling
220 and incentives 228.
[0383] Next, in step 3104, the inventory management server 150
creates a matrix of sales and inventory data. For example, as
discussed above, inventory management server 150 may take item
configuration data 208 in combination with weighted feature level
market inventory mix rates 908 to construct a representation of
configurations of individual items. The inventory management server
150 may create a data record for each item being encoded, where the
data record includes a list of features associated with an item by
placing the value one in a field associated with a feature if the
feature exists with respect to the item, and placing the value zero
in the field associated with the feature if the feature does not
exist with respect to the item.
[0384] Next, in step 3106, the inventory management server 150
encodes the matrix as a mix rate modulated item configuration data
1402 matrix. For example, the inventory management server 150 may
subtract a market level inventory mix rate for the feature from the
one or the zero encoded in the field corresponding to that
feature.
[0385] Next, in step 3108, the inventory management server 150
reduces the dimensionality of the mix rate modulated item
configuration data 1402 matrix. For example, the inventory
management server 150 may perform principal components analysis
(PCA) on the matrix, as discussed above.
[0386] Next, in step 3110, the inventory management server 150
combines context variables and status information with the mix rate
modulated item configuration data 1402 matrix. For example, each
data record may further include context variables 1602 relating to
the item associated with the data record, such as dealer latitude
1604, dealer longitude 1606, dealer item market inventory 1608,
retail/stock order type indicator 1610, number of weeks in
inventory 1612, dealer fraction of item market inventory 1614, and
market turn rate 1616. Additionally, each data record may further
include a sold status 1702, which may be appended to the rows in
the matrix to indicate whether or not the item corresponding to the
data row was sold.
[0387] Next, in step 3112, the inventory management server 150
and/or supercomputer 170 train the neural network models using the
mix rate modulated item configuration data 1402 matrix including
context variables 1602 and sold status 1702 information. For
example, each neural network model may be trained to determine a
dealer configuration turn rate 1902 for an item configuration in
the matrix. Each turn rate calculator 1302 may include one or more
such trained models, and may be used to determine an expected
inventory turn rate for an item configuration.
[0388] Next, in step 3110, the trained calculated turn rate
calculators 1302 may be returned for later use. For example, the
inventory management server 150 may receive the calculated turn
rate calculators, and may send the calculated turn rate calculators
to the data server 130 for storage in the data store 140.
[0389] Following step 3110, the process 3100 ends.
[0390] H. Evaluating Turn Rate Calculators
[0391] FIG. 32 illustrates an exemplary process for evaluating
neural network inventory turn rate calculators 1302. FIG. 32
includes additional details of step 2660 of process 2600
illustrated in FIG. 26.
[0392] In step 3202, the inventory management server 150 receives
context variables 1602, weighted feature level market inventory mix
rates 908 and feature allocations 1212 for a target dealer 420.
Each target dealer 420 should be considered in isolation for the
neural network, because each target dealer 420 will have
potentially unique characteristics, such as context variables
1602.
[0393] Next, in step 3204, the inventory management server 150
creates a possible configurations matrix 1802. For example, the
inventory management server 150 may create a matrix including all
realizable configurations that may be built. Then, any
configuration that is precluded from being considered due to zero
allocation of features based on the feature allocations 1212 may be
removed to create a possible configurations matrix 1802. Creation
of the possible configurations matrix 1802 may also take into
account other constraints, such as production constraints 222 and
material availability constraints 226.
[0394] Next, in step 3206, the inventory management server 150
creates a mix rate modulated possible configurations matrix 1804
from the possible configurations matrix 1802. For example, the
inventory management server 150 may determine a percentage of items
in inventory for which the feature exists for each feature listed
in the possible configurations matrix 1802. Then, for each field
associated with a feature in the data record, the supercomputer 170
may subtract the percentage of items in inventory for which the
feature is expected to exist from the one or the zero encoded in
the field, thereby generating the mix rate modulated possible
configurations matrix 1804.
[0395] Next, in step 3208, the inventory management server 150
transforms the mix rate modulated possible configurations matrix
1804. For example, the mix rate modulated possible configurations
matrix 1804 may be transformed according to the rotation matrix
1506 previously computed during the PCA of the mix rate modulated
item configuration data 1402. Specifically, the rotation matrix
1506 from the PCA performed in the neural network training step may
be applied to the matrix of mix rate modulated possible
configurations matrix 1804 to transform the matrix 1804 into a
transformed mix rate modulated possible configurations matrix 1810.
The resulting transformed mix rate modulated possible
configurations matrix 1810 may thus be transformed into a reduced
space.
[0396] Next, in step 3210, the inventory management server 150
combines context variables 1602 with the mix rate modulated item
configurations matrix 1804. Exemplary context variables 1602 may
include, but are by no means limited to, dealer latitude 1604,
dealer longitude 1606, retail/stock indicator 1610, number of weeks
in inventory 1612, dealer fraction of item market inventory 1614,
and market turn rate 1616.
[0397] Next, in step 3212, the inventory management server 150
evaluates the mix rate modulated item configurations matrix 1804
using the turn rate calculators 1302. For example, the inventory
management server 150 may evaluate the mix rate modulated item
configurations matrix 1804 against inventory turn rate calculators
1302 created through use of the neural network. Accordingly, the
inventory management server 150 may run many different inventory
turn rate calculators 1302, for example 100 models, on the matrix
to determine multiple dealer configuration turn rates 1902 that
indicate the expected turn rate for each configuration in the
matrix according to each model. In some instances, these
calculations of multiple dealer configuration turn rates 1902 may
be performed in parallel through use of supercomputer 170 for
various item types and sales regions.
[0398] Next, in step 3214, the inventory management server 150
determines dealer configuration turn rates 1902 and dealer
configuration turn rate variances 1904 based on the multiple dealer
configuration turn rates 1902. For example, the inventory
management server 150 may determine an average dealer configuration
turn rate 1902 based on an average of a set of independent models
run for each configuration in the matrix 1804. For example, in one
implementation, the set of independent models includes one hundred
different models. The neural network may further produce dealer
configuration turn rate variances 1904 for each dealer
configuration turn rate 1902 that indicate a confidence factor for
the associated dealer configuration turn rates 1902.
[0399] Next, in step 3216, the inventory management server 150
determines risk-adjusted dealer configuration turn rates 1906 based
on the configuration turn rates 1902 and dealer configuration turn
rate variances 1904. For example, the supercomputer 170 may
determine risk-adjusted dealer configuration turn rates 1906 by
subtracting dealer configuration turn rate variances 1904
multiplied by a constant from dealer configuration turn rates
1902.
[0400] Next, in step 3218, the inventory management server 150
returns risk-adjusted dealer configuration turn rates 1906. For
example, the inventory management server 150 may receive the
risk-adjusted dealer configuration turn rates 1906 from the
supercomputer 170. The inventory management server 150 may then
send the risk-adjusted dealer configuration turn rates 1906 to the
data server 130 to store in the data store 140. Next, the process
3200 ends.
[0401] I. Generating Recommended Orders
[0402] FIG. 33 illustrates an exemplary process for generating
recommended orders for a target dealer 420. FIG. 33 includes
additional details of step 2680 of process 2600 illustrated in FIG.
26.
[0403] In step 3302, the inventory management server 150 receives
risk-adjusted dealer configuration turn rates 1906 for possible
configurations of the item of interest. That is, the possible
configurations may correspond to the list of possible
configurations 1802 determined above with regard to turn rate
calculator evaluation discussed above with regard to process 3200
in FIG. 32.
[0404] Next, in step 3304, the inventory management server 150
identifies feature allocations 1212 and dealer allocations 212
relating to the target dealer 420. For example, the inventory
management server 150 may retrieve from the data store 140, e.g.,
using data server 130, the feature allocations 1212 and dealer
allocations 212 corresponding to the target dealer 420.
[0405] Next, in step 3306, the inventory management server 150
performs an optimization that takes into account the feature
allocations 1212 and any diversity penalties 2002. For example,
specific dealer orders recommendations may be generated, including
quantities, such that the sum of risk-adjusted dealer configuration
turn rates 1906 of the recommended configurations is maximized. A
penalty 2002 for selecting the same configuration multiple times
may further be imposed on the optimization to introduce greater
diversity in the inventory of a target dealer 420.
[0406] Next, in step 3308, the inventory management server 150
generates a list of recommended order configurations. For example,
the inventory management server 150 may determine a corresponding
numeric count of the number of items of each configuration of the
possible configurations 1802 to recommend.
[0407] Next the process 3300 ends.
V. Conclusion
[0408] With regard to the processes, systems, methods, heuristics,
etc. described herein, it should be understood that, although the
steps of such processes, etc. have been described as occurring
according to a certain ordered sequence, such processes could be
practiced with the described steps performed in an order other than
the order described herein. It further should be understood that
certain steps could be performed simultaneously, that other steps
could be added, or that certain steps described herein could be
omitted. In other words, the descriptions of processes herein are
provided for the purpose of illustrating certain embodiments, and
should in no way be construed so as to limit the claimed
invention.
[0409] Accordingly, it is to be understood that the above
description is intended to be illustrative and not restrictive.
Many embodiments and applications other than the examples provided
would be apparent upon reading the above description. The scope of
the invention should be determined, not with reference to the above
description, but should instead be determined with reference to the
appended claims, along with the full scope of equivalents to which
such claims are entitled. It is anticipated and intended that
future developments will occur in the technologies discussed
herein, and that the disclosed systems and methods will be
incorporated into such future embodiments. In sum, it should be
understood that the invention is capable of modification and
variation.
[0410] All terms used in the claims are intended to be given their
broadest reasonable constructions and their ordinary meanings as
understood by those knowledgeable in the technologies described
herein unless an explicit indication to the contrary in made
herein. In particular, use of the singular articles such as "a,"
"the," "said," etc. should be read to recite one or more of the
indicated elements unless a claim recites an explicit limitation to
the contrary.
* * * * *