U.S. patent application number 13/357405 was filed with the patent office on 2013-03-28 for system and method for logistics optimization constrained by inventory requirements.
The applicant listed for this patent is Anthony DeFrances, Boyett Judson Hennington, IV, Steven LaVoie, John William Michalski, Shrinivas Sale, Michael Robb Swihart, Varunraj Valsaraj. Invention is credited to Anthony DeFrances, Boyett Judson Hennington, IV, Steven LaVoie, John William Michalski, Shrinivas Sale, Michael Robb Swihart, Varunraj Valsaraj.
Application Number | 20130080206 13/357405 |
Document ID | / |
Family ID | 46581138 |
Filed Date | 2013-03-28 |
United States Patent
Application |
20130080206 |
Kind Code |
A1 |
LaVoie; Steven ; et
al. |
March 28, 2013 |
SYSTEM AND METHOD FOR LOGISTICS OPTIMIZATION CONSTRAINED BY
INVENTORY REQUIREMENTS
Abstract
A system and method is provided that optimizes order and routing
patterns that considers inventory constraints, including days on
hand and shelf life. The results of the optimization of order and
routing patterns includes an impact on inventory carried and
frequency of inventory turns, to enable Purchasing personnel to
gauge the viability of the solutions with respect to capacity,
carrying costs, and continuity of supply.
Inventors: |
LaVoie; Steven; (LaGrange,
IL) ; Sale; Shrinivas; (Dunlap, IL) ; Swihart;
Michael Robb; (Wheaton, IL) ; Valsaraj; Varunraj;
(Chicago, IL) ; Hennington, IV; Boyett Judson;
(Chicago, IL) ; DeFrances; Anthony; (Barrington,
IL) ; Michalski; John William; (Evanston,
IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
LaVoie; Steven
Sale; Shrinivas
Swihart; Michael Robb
Valsaraj; Varunraj
Hennington, IV; Boyett Judson
DeFrances; Anthony
Michalski; John William |
LaGrange
Dunlap
Wheaton
Chicago
Chicago
Barrington
Evanston |
IL
IL
IL
IL
IL
IL
IL |
US
US
US
US
US
US
US |
|
|
Family ID: |
46581138 |
Appl. No.: |
13/357405 |
Filed: |
January 24, 2012 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61435563 |
Jan 24, 2011 |
|
|
|
61587999 |
Jan 18, 2012 |
|
|
|
Current U.S.
Class: |
705/7.25 |
Current CPC
Class: |
G06Q 10/06315 20130101;
G06Q 10/08355 20130101; G06Q 10/04 20130101; G06Q 10/06312
20130101; G06Q 10/0838 20130101; G06Q 10/083 20130101 |
Class at
Publication: |
705/7.25 |
International
Class: |
G06Q 10/06 20120101
G06Q010/06 |
Claims
1. A system for logistics optimization constrained by inventory
requirements.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application claims the benefit of U.S.
Provisional Application No. 61/435,563, filed Jan. 24, 2011,
entitled "System and Method For Transportation Management" and also
claims the benefit of U.S. Provisional Application No. 61/587,999,
filed Jan. 18, 2012, entitled "System and Method For Transportation
Management," both of which are hereby incorporated by reference in
their entirety.
BACKGROUND OF THE INVENTION
[0002] The present invention generally relates to a system and
method for logistics. More particularly, the present invention
relates to a system and method for improving logistics cost,
trailer utilization, number of truck used, or miles driven.
[0003] Logistics involves the transportation of goods from a source
to a destination. Typically, the source is a seller of goods such
as a manufacturer and the destination is a buyer of goods such as a
retailer. Moving goods between the source and destination at the
lowest possible cost has long been a goal of logistics and numerous
prior art systems and methods have been developed in an attempt to
do so.
BRIEF SUMMARY OF THE INVENTION
[0004] One or more embodiments of the present invention provide a
logistics system optimizes order and routing patterns that
considers inventory constraints, including days on hand and shelf
life. The results of the optimization of order and routing patterns
includes an impact on inventory carried and frequency of inventory
turns, to enable Purchasing personnel to gauge the viability of the
solutions with respect to capacity, carrying costs, and continuity
of supply.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] FIG. 1 illustrates a system for logistics optimization using
lane order pattern flexing according to an embodiment of the
present invention.
[0006] FIG. 2 illustrates further detail of the optimization
process performed by the modeling processor of FIG. 1.
[0007] FIG. 3 illustrates how the present system for logistics
optimization using lane order pattern flexing may provide a savings
increase of 20-30%.
[0008] FIG. 4 illustrates the example of FIG. 3 at an inventory
rather than a lane level.
[0009] FIG. 5 illustrates the addition of the present system for
logistics optimization 100 into the logistics process.
[0010] FIG. 6 illustrates a screen shot of an Inbound Transport
Management (ITM) system according to an embodiment of the present
invention.
[0011] FIG. 7 illustrates a screenshot of the ITM lane import
criteria screen.
[0012] FIG. 8 illustrates a screenshot of the constraints
preferably entered for the implementation of the combo model of
FIG. 2.
[0013] FIG. 9 illustrates a screenshot of an ITM scenario analysis
screen providing a view of optimization solutions with the ability
to lock, exclude, and mark solutions for publication.
[0014] FIG. 10 illustrates a screenshot of a lane profiles tool for
visualization and what-if analysis of lane optimization and order
flexing results.
[0015] FIG. 11 illustrates a screenshot of a lane analysis tool
used to examine shipment, purchase order, inventory, and sales
information summarized to a lane level to support order and route
pattern determination.
[0016] FIG. 12 illustrates a screenshot of lane order profiles
which include purchasing guidelines for communication to purchasing
systems or processes.
[0017] FIG. 13 illustrates a screenshot of the compliance
detail.
[0018] FIG. 14 illustrates a screenshot of the gross margin
dashboard.
[0019] FIG. 15 illustrates a business information flow according to
the present Inbound Transportation Management (ITM) system.
[0020] FIG. 16 illustrates the combo model stack generation process
according to an embodiment of the present invention.
DETAILED DESCRIPTION OF THE INVENTION
[0021] FIG. 1 illustrates a system for logistics optimization 100
using lane order pattern flexing according to an embodiment of the
present invention. The system for logistics optimization 100
includes an Inbound Transportation Management (ITM) primary
database 110, an ITM data preparation processor 120, an ITM data
importer 130, a constraint set-up processor 140, a scenario set-up
processor 150, a modeling processor 160, a model results viewing
application 170, and a results publisher 180.
[0022] In operation, ITM data is retrieved from the ITM database
110 by the ITM data preparation processor 120. Preferably, the data
is retrieved or refreshed nightly, but may be retrieved or
refreshed in other intervals such as weekly, hourly, monthly, or
continuously. The ITM data preferably includes purchase order (PO)
information with regard to each purchase order passing through the
ITM system, but may instead operate with a subset of all of the
available purchase orders. In addition to the PO information, the
ITM data preferably includes item information for all of the items
shipped in each of the POs. The ITM data may be retrieved from a
remote site such as a data center, for example. The system for
logistics optimization 100 may be co-located or remotely located
from the data center.
[0023] Next, at the ITM data preparation processor 120, the item
data is aggregated at the PO level. For example, individual items
that were historically purchased during the last 60 days are
combined into a single or multiple POs representing a re-ordering
of the same items during the next 60 day period. Additionally, data
from the PO level may be taken and aggregated into a frequency and
load size for the lane. This may be viewed as a theoretical set of
POs. For example, a frequency of four and a load size of 40,000 lbs
may be viewed as four POs in the next time period at 40,000 lbs
each.
[0024] In addition to historical information, other data may be
employed. For example, forecast information may be employed such as
for highly seasonal products, for example. Additionally, third
party information such as lanes that are currently serviced by a
third party, either inbound or backhaul, may be used. Additionally,
instead of PO history, distribution center use or sale of items, or
other inventory information may be used.
[0025] Additionally, the PO data is aggregated at the lane level. A
lane preferably includes a unique combination of 5 elements,
partner identification, vendor number, original location,
destination location, and temperature protection (TP). Partner
identification is an identification of the company receiving the
goods. Vendor number is an identification of the vendor selling the
goods. Original location is an indication of where the goods are
first picked-up or ship from. Destination location is an indication
of where the goods are eventually intended to arrive. Temperature
protection is an indication of whether the goods much remain
refrigerated, frozen, or if no temperature protection is needed.
Although in the above example 5 elements are used when setting a
lane, a greater or lesser number of elements may be employed.
[0026] As mentioned above, the PO data is aggregated at the lane
level. That is, for each lane, all POs associated with a specific
lane during the previous 60 days are electronically associated an
identification of the specific lane. Additionally, the data can be
for lanes that the user's company is not currently moving, so that
the tool can be used to optimize them in order to try to bring in
more freight under management.
[0027] Next, the associated data is passed to the ITM data importer
130. The ITM data importer 130 filters the data and enters the data
in an optimization module as further described below. For example,
when filtering the data, the ITM data importer 130 presents several
choices or options to a user. The user may then filter the network
as desired. For example, a user may only want a subset of the
distribution centers in a network, such a subset may represent a
geographic area such as the east coast, for example. Further, a
user may be interested in filtering the data in this way for any of
several reasons, for example the user may have a new customer in
the east coast area and be looking to determine the impact on
logistics in the area.
[0028] Additionally, the user may filter by temperature
requirement, such as refrigerated vs. non refrigerated, or by the
other options shown in the screen shot.
[0029] Additionally, the ITM data importer summarizes the imported
data by displaying the imported data for a user, for example, for
displaying the data by lane. Additionally, the ITM data importer
performs a quality check to identify missing information in the
imported data. That is, for the imported attributes, the ITM data
importer identifies the lanes with missing information and presents
them to the user. For example, for the attribute of "weight", the
ITM data importer may determine the total number of lanes with no
weight, and the total revenue and other aspects associated with
those lanes, and then present those lanes to the user. Similarly,
the ITM data importer may give total number of lanes missing cubic
feet (cube) or pallet.
[0030] For example, the ITM data importer may identify the total
number of lanes with any quality issue (missing any of weight,
cube, or pallet) and provide a link to the lane so that the user
can review the lane and attempt to determine what is wrong or enter
in the missing data. Alternatively, the user is provided with the
option to exclude lanes with incomplete data from the analysis.
[0031] Finally, the ITM data importer preferably displays a
graphical view of the data including plot vendor maps, data center
map, and lane map for the imported data.
[0032] Alternatively, filtering of data may be done in the normal
manner of data base based on the criteria that the user enters,
that is, a user might specify lanes going to a specific
distribution center, or exclude lanes with a weight below a certain
amount.
[0033] Alternatively, with regard to the quality check, the quality
check may do separate summaries of lanes that are missing weight
info, missing cube info, missing ship from info, etc. and then
permit the user to find the individual lanes that fall into each
category. For example, the quality check may report that there are
23 lanes missing weight with a total revenue of $53,121 per month,
and 45 lanes missing cube with a total rev of $43,634 per month,
etc. The 23 lanes missing weight may also include lanes that are in
the 45 missing cube.
[0034] Next, the imported data is passed to the constraint set-up
processor 140. At the constraint set-up processor, global
constraints are established for the modeling process as further
described below. However, such global constraints may later be
overwritten at the scenario level and/or the model level.
[0035] Next, the scenario set-up processor 150 is used to specify
models that constitute a scenario, as further described below. The
scenario set-up processor 150 may also specify data and set
constraints used for optimization as further described below.
[0036] Next, at the modeling processor 160, all of the models for a
specific scenario are run, as further described below. The modeling
processor 160 includes an optimization engine that provides
optimized solutions in accordance with the specified scenario and
constraints. The solutions from the optimization engine are
obtained. Further, an optimization log is provided so that the
optimized solutions may be stored in the optimization log.
[0037] Next, the results of the modeling processor 160 are reviewed
at the model results viewing application 170. At the model results
viewing processor 160, several options are available. First, a
specific solution or option may be locked or excluded based on the
review. The solutions may also be excluded from future models so
that a user need not re-reject the solution. Additionally, when
locked, the solution is forced to come up again during the next
solution determination. For example, even if the modeling processor
160 determines that a certain solution with a certain number of
specific lanes is one of the desirable solutions, a reviewer may
choose to discard the solution or discard specific lanes for
reasons not related to profitability. As another example, a lane or
solution may be chosen that has a specific truck in a specific city
on a specific day or on weekends so that a driver may visit
family.
[0038] Additionally, the results viewing application 170 allows the
reviewer to publish a solution or mark a solution for publication.
That is, a particular chosen solution may be shared with other
people in the company, such as purchasing employees, to make sure
that the proposed solution provides for the needs of the purchasing
employees as well--for example, with regard to inventory turns and
desired stockpiles of inventory. The results viewing application
170 also makes a profile. The profile may be passed between
employees to obtain consensus as to the solution.
[0039] Also, after any change to a parameter of the model, such as
locking or deleting a solution or lane, the model may be re-run to
display a new set of solutions which may then be further analyzed
and potentially modified by the user as desired.
[0040] Once a desired solution is determined and agreed to by all
responsible employees, for example, both logistics and purchasing
employees, the results publisher 180 publishes the results to the
ITM database 110 to create a solution profile in the ITM database
110. Alternatively, the solution profile may have already been
published and solution profile may now be made active. By
publishing the results to the ITM database 110, the actual
logistics instructions for the company are changed. For example,
the company's goods will now be shipped to the company based on the
shipping/logistic instructions that are now published to the ITM
database 110 rather than previous instructions. Note however, that
the published results sent to the ITM database 110 may but need not
change all of the previous logistics instructions.
[0041] In addition, the results publisher keeps a summary and/or
copy of the result published to allow for later analysis and
potential modification.
[0042] FIG. 2 illustrates further detail of the optimization
process performed by the modeling processor 160 of FIG. 1. In FIG.
2, several individual models 210-208 are used to form a stack of
lane solutions 210 which are then passed to a Mixed Integer Program
(MIP) Optimizer 220 to determine the optimal solution set. Then, a
final consistent optimal solution set 230 is output.
[0043] More specifically, the individual models 201-208 include a
one way Truck Load (TL) model 201, a one way Less Than Truckload
(LTL) model 202, a one way Inter Modal (IM) model 203, a combo
model 204, a buy and fill moel 205, a loop/continuous move model
206, a cross dock model 207, and a backhaul model 208 each of which
is further detailed below.
[0044] First, the one way model Truck Load (TL) model 201 creates
individual solutions for each lane in a list and order frequency is
flexed/altered to maximize revenue per truck. In a preferred
embodiment, the following formula is employed to find the optimum
frequency:
OptFreq=max(min(total monthly wt/max wt per truck, total monthly
cube/max cube per truck, total monthly pallets/max cube per
pallet), MinFreq)
MinFreq=min(historical freq, 1/max time between orders)
max time between orders=preset value based on inventory
requirements
[0045] Where monthly weight is the total weight on the lane over
all items. Max weight is based on type of truck. Monthly cube is
total cubic feet in volume required for the month over all items.
Monthly pallets is the total pallets required over all items for a
month. Trucks are generally measured in three dimensions--weight,
cube, and pallet--each with its own max capacity. A truck may run
out of capacity due to any one of these depending on what is being
hauled.
[0046] Alternatively, the model may be controlled to ignore one or
two of weight, cube, or pallets, but preferably all three are used
where available in order to better confirm a lane solution.
[0047] The max time between orders is a constraint set by the user,
for example, to make sure that minimum inventory targets are
maintained. MinFreq is the minimum frequency of orders and is the
lesser of the historical frequency or orders or the inverse of the
max time between orders as set by the user.
[0048] The next model is the one way LTL model 202. In the one way
LTL model 202, individual solutions are created for each lane and
placed in a list. The individual solutions use a
less-than-truckload mode of transportation. The order frequency is
preferably not flexed or altered from the current information.
Additionally, weight, cube, and pallets per load are based on the
recent historical purchase orders. Additionally, lanes are
restricted based on LTL operational constraints set by the user.
Some constraints for LTL include: max weight, max cube, and max
pallets. If the weight, cube or pallets are larger than the max,
then the load is TL (truckload) and typically cannot be moved LTL
because LTL carrier would typically refuse to take it.
[0049] The next model is the one way intermodal model 203. In the
one way intermodal model, 203, individual solutions are created for
each lane that will use an inter-modal method of transportation,
such as both trucking and rail, for example. Other modes include
shipping, parcel and backhaul (BH).
[0050] With regard to backhaul, a company that owns its own fleet
may have trucks that are normally routed to a remote location to
make a drop off or delivery, but are then forced to return empty to
the distribution center from the drop off point. However, these
trucks may be used to make an inbound pick-up and delivery at very
low cost since they had to travel in proximity anyway to return to
the DC.
[0051] That is, instead of the truck returning to the distribution
center or manufacturer empty, the truck may be used as a carrier
back. The inclusion of possible backhaul lanes in the model may be
accomplished by its own mode, called backhaul mode, or it may be
implemented in other tools. For example, the combo model may
combine lanes and used backhaul lanes as an option. In one or more
embodiments of the description of models above, the
trucks/equipment are owned by the distributor so as to take
advantage of the backhaul opportunities.
[0052] Additionally, with regard to the "parcel" mode, the parcel
mode takes into account the shipping cost of moving parcel-size and
weight items using a common carrier such as UPS or the US or
international mail.
[0053] In the one way intermodal model 203, order frequency is
flexed using the same formula as in the one way truckload model
201. Additionally, lanes are restricted based on intermodal
operational constraints set by the user, for example, weight, cube,
and pallet, as well as also temp, length of lane, and origin and
destination.
[0054] The next model is the combo model 204. In the combo model
204, individual solutions are created where each solution includes
two or more lanes. More specifically, the lanes of a solution may
be identified based on pick-up proximity, drop-off proximity,
temperature protection, revenue generated, or restrictions based on
one or more of: 1) out of route miles, 2) number of picks, 3)
number of drops, or 4) number of stops. All lanes in the solution
are preferably set to have the same OptFreq (that is, all lanes are
picked up together each time. However, in alternatives this may be
varied. The formula for determining OptFreq is preferably the same
formula employed by the one way truckload model 201.
[0055] The next model is the buy and fill model 205. The buy and
fill model 205 creates individual solutions where each solution
includes 2 lanes: the buy lane and the filler lane. More
specifically, the lanes of a solution may be identified based on
pick-up proximity, drop-off proximity, temperature protection,
revenue generated, or restrictions based on one or more of: 1) out
of route miles, 2) number of picks, 3) number of drops, or 4)
number of stops. The buy and fill model 205 is typically only used
for some orders and the filler lane is not always transported with
the buy lane. Preferably the filler lane is flexed in order to
sufficiently fill a truck. However, the buy lane is typically not
flexed and the frequency is set to the historical frequency. For
example, the filler lane may be four purchase orders per month,
each taking up 90% of a truck while the buy lane may be one load
per month taking up 10% of a truck. Once per month the two lanes
may be shipped together, but 3 times per month the filler may be
shipped all alone.
[0056] The next model is the loop/continuous move model 206. In the
loop/continuous move model 206, individual solutions are created
where each solution includes two or more lanes. More specifically,
the lanes of a solution may be identified based on pick-up
proximity, drop-off proximity, temperature protection, revenue
generated, or restrictions based on one or more of: 1) out of route
miles, and 2) number of stops. Frequency of delivery is flexed. The
loop/continuous move model 206 differs from the combo model 204 in
that loads are transported in sequence in the loop/continuous move
model 206 rather than at the same time in the combo model 204.
[0057] The next model is the cross dock model 207. The cross dock
model 207 creates individual solutions where each solution includes
multiple lanes with consolidation and/or deconsolidation points
such as provided by a cross dock. In the cross dock model 207 order
frequency is flexed. Additionally, many lanes can be covered in a
single solution. That is, one cross dock solution may be the
optimal way to move the flow for several lanes. This is different
from one ways where one one way solution only involves one lanes.
One cross dock solution will typically always involve multiple
lanes.
[0058] The final model is the backhaul model 208. As discussed
above with regard to the intermodal model 203, a company that owns
its own fleet may have trucks that are normally routed to a remote
location to make a drop off or delivery, but are then forced to
return empty to the distribution center from the drop off point.
However, these trucks may be used to make an inbound pick-up and
delivery at very low cost since they had to travel in proximity
anyway to return to the DC.
[0059] That is, instead of the truck returning to the distribution
center or manufacturer empty, the truck may be used as a carrier
back. The inclusion of possible backhaul lanes in the model may be
accomplished by its own model, called backhaul model, or it may be
implemented in other tools. In one or more embodiments of the
description of models above, the trucks/equipment are owned by the
distributor so as to take advantage of the backhaul
opportunities.
[0060] One aspect of the present invention is the recognition that
there is a considerable difference between inbound logistics and
outbound logistics. For example, one or more embodiments of the
present invention provide an achievable strategy for inbound
logistics organizations to elevate freight savings by 20-30%,
through a collaborative, technology-enabled approach to logistics
and purchasing planning. More than a new set of tactics, the
approach implements a paradigm shift, away from a model that tends
to mimic an outbound logistics program, and towards one that
extracts full value from the advantages of inbound freight
control.
[0061] One important difference is, unlike the outbound side,
Inbound Freight Management has a revenue component, originating in
the freight allowances on products provided by the shipper. If the
logistics team can source carriers at a rate lower than the
allowances, Inbound Logistics can become a profit center, earning
income on lanes taken over from shippers. Because of this, in the
inbound world, load profitability and total landed cost (in
addition to service level) are important metrics requiring
management. Freight cost reduction, the traditional barometer of
logistics performance, tells only half the story. Moreover, the
story must be told at an item level. Logistics income is impacted
by the viability of SKU-level freight allowances in reflecting true
manufacturer freight costs, and also by the mix of items on the
truck. Item-level visibility is a valuable asset while managing
inbound freight and pursuing lowest total landed cost.
[0062] Another important difference is that Inbound freight
programs can largely be selective in the lanes they convert to
their management. Increased profitability may be as much a question
of what lanes an organization manages--or choose to cease
managing--as how well they manage them. Effective monitoring of
lane profitability enables Inbound Logistics departments to build
the network they want, rather than manage the network they are
given. In practice, organizations struggle in matching up daily
load planning to the network planning exercise that persuaded them
to take over management of a freight lane. Proper synchronicity
between these processes is important to deliver predictability of
results in Inbound Freight.
[0063] Another difference is that Inbound Freight planners work in
the same company as the buyers placing the orders--and consequently
can vary or flex the orders in terms of amount and frequency so as
to maximize logistics efficiency. This is an opportunity for
collaboration between Purchasing and Logistics, to provide ordering
guidelines that create routing efficiencies. Equipment utilization
is the largest single driver of freight cost per case, and the
largest single driver of equipment utilization is the buying
pattern: how much is ordered, when it is ordered, and with what
frequency. Outbound shippers will attempt to influence purchasing
behaviors through order volume price breaks, and in some instances
vendor managed inventory programs. However, Inbound Logistics has
the far greater opportunity for true, broad-based collaboration
with Purchasing.
[0064] The differences between inbound logistics and the typical
logistics program outlined above are very significant. Inbound and
Outbound logistics are, truly, entirely different business
functions. Unfortunately, technology providers have largely ignored
the differences between them. Transportation Management System
(TMS) solutions purchased for inbound freight management are
precisely the same systems purchased for outbound freight, and
implemented nearly identically. Little or no consideration has been
given to load profitability or per case analysis, and item-level
visibility is rare.
[0065] No prior art systems address the selective nature of the
freight under management, the need to build synchronicity between
network planning and load planning, and none expose or manage the
opportunities to collaborate with Purchasing. Put simply, in
commercial transportation management systems, the world is seen
through the lens of a manufacturer shipping outbound product. This
is the arena in which the products have been developed and tested,
and it represents the largest market segment their sales forces
pursue. As a result, inbound logistics personnel are forced to fit
within the mold of outbound transportation management processes, or
struggle to change or augment those capabilities to meet their
objectives.
[0066] When it comes to collaboration between Purchasing and
Logistics, without the right tools, most supply chain organizations
find limits to what they can achieve. Absent a well-defined and
technology-enabled platform for partnership, these highly
inter-dependent functions remain at arm's length, communicating
without collaborating, bound to different and often conflicting
incentives.
[0067] When it comes to locating and quantifying the potential
savings by integrating inbound logistics with purchasing, one of
the differentiating aspects of inbound freight matters above all
others: control of the freight resides in the same organization as
control of the order. The potential power of this is easy to
understand, in theory. After all, if logistics personnel placed the
orders, every truck would be 100% utilized, every time (or better
yet, running on rail). Back in the real world of changing customer
demand, short product shelf life, inventory carrying costs, and
storage capacity constraints, a separate purchasing and inventory
control function is required.
[0068] However, there is a middle ground where a deeper logistics
savings consideration can become a greater part of purchasing
operations. Few would disagree that if purchase orders are aligned
to more consistently fill trucks to capacity and minimize miles
driven, logistics costs will improve. However, purchasing and
replenishment systems that include freight cost consideration do so
at only the most rudimentary levels, if at all.
[0069] If a supply chain leader asks the question: "What is the
absolute minimum total landed cost that can be achieved by the
combined order-to-delivery process, without putting customers at
risk?" most Purchasing and Logistics teams do not have the ability
to answer. Instead, current systems rely on the following three
assumptions: Assumption #1: Purchasing needs no further guidance.
Our Buyers already try to order in full truckloads wherever they
can; Assumption #2: Logistics' requests for order pattern changes
will generally be infeasible, as they do not consider customer
demand; and Assumption #3: Since logistics savings are based on
freight consolidation, every attempt to save in freight costs will
come at the expense of inventory levels. These assumptions come to
rule the relationship between Purchasing and Logistics. As is often
the case with deeply embedded assumptions, they are
self-fulfilling: they quash any momentum to fully collaborate in
driving savings, thereby limiting logistics to offer only the most
rudimentary and uninformed purchasing guidelines, which only appear
to further prove out the assumptions. The guidelines, born in the
manual spreadsheet manipulations of a logistics engineer, tend only
to increase order sizes and reduce inventory turns (putting them at
immediately odds with Purchasing performance metrics), and often
ask for alignment of orders in ways that will risk stocking out of
a product. In practice, a few vendors may be found that both sides
agree can be regularly scheduled to deliver simultaneously, but
even these requests from logistics are frequently ignored in favor
of daily decision-making on the part of the buyer. As embedded as
it is, this is a cycle of behavior that can only be broken with a
clear measure of the value of breaking it.
[0070] FIG. 3 illustrates how the present system for logistics
optimization 100 using lane order pattern flexing may provide a
savings increase of 20-30%. FIG. 3 includes a current delivery
pattern 310, a current shipment truck fill 320 and a current order
summary 330. FIG. 3 also illustrates a new delivery pattern 350, a
new shipment truck fill 360, and a new order summary 370, as well
as a logistic results summary 380 and a route map 390.
Additionally, the items carried by the trucks are differentiated
into product A and B based on their shading. Further, although only
two items and a single route are shown, FIG. 3 is meant to be a
simplified example of the present system for logistics optimization
100.
[0071] Turning to FIG. 3, the current delivery pattern 310
illustrates that the company receives in a 20-day period two
deliveries of product A and four deliveries of product B. The
frequency and days of the weeks of each delivery are shown. As
shown, none of the six total deliveries take place on the same
day.
[0072] Turning to the current shipment truck fill 320, is it shown
that the two deliveries of product A take place using a truck that
is 90% filled, while the deliveries of product B take place using a
truck that is anywhere from 45% to 75% filled. Such a situation may
occur often in the real world where product B's usage over the
month or the demand for product B over the month is
non-uniform.
[0073] The current order summary 330 reveals that the current
logistics process to deliver items A and B uses 6 trucks which are
on average 67% filled and 3600 total miles are driven per month.
The cost for these trucks to make the deliveries is $7700 per month
in this example--although this number may vary depending, for
example, on route, temperature protection, and truck size.
[0074] In other words, the left side of FIG. 3 represents a sample
current state: freight running on two lanes on a monthly basis,
both dropping off at the same facility. One product is ordered in
near full truckload quantities, twice a month. The other is ordered
in smaller quantities, required at least four times a month.
Assuming that no other shipments exist that could fill out the
trucks on the second lane, both Purchasing and Logistics would
typically claim comfort with the current state. The buyer is
filling equipment where they can, and only ordering smaller
quantities where they must.
[0075] On the Logistics side, the prior art TMS route optimization
software leaves the full truckloads alone (no TMS system on the
market ever seeks to break a truckload shipment), and sees no way
to improve upon the second lane. Logistics engineers may ask
Purchasing to place larger orders on Lane 2 for product B, only to
be told that inventory turns cannot be increased without risk of
stocking out.
[0076] Turning to the new delivery pattern 350, it shows a new
delivery pattern in which there are only four deliveries during a
20 day period and each delivery includes a delivery of both Product
A and Product B. Turning to the new shipment truck fill 360, it is
seen that each of the new shipments is composed of about 40% of
Product A and about 60% of Product B.
[0077] Thus, the two approximately full truckload shipments of
Product A have been broken into four shipments of partial truckload
and the remainder of each shipment is filled with Product B. As
shown in the new order summary 370, the new plan only involves four
trucks rather than 6, and each of the trucks is about 99% filled.
Further, the monthly cost is about $6150 and the miles traveled is
about 2900.
[0078] The improvement of the new delivery pattern over the old
delivery pattern is summarized in the logistics results summary
380. More specifically, two fewer trucks are used, the trailers of
the trucks that are used are much more fully utilized--up to about
99% from 67%, there is a $1500 per month savings (20.1%) and 700
fewer miles are traveled in all.
[0079] FIG. 4 illustrates the example of FIG. 3 at an inventory
rather than a lane level. FIG. 4 shows the current replenishment
pattern 410, current order 420, and current order summary 430, as
well as a new replenishment pattern 450, new order 460, new order
summary 470, purchasing results 480 and route map 490.
[0080] As shown in FIG. 4, the current order replenishment plan 410
shows the current order 420 is delivered on six different days and
that the delivery amount of Product B varies. As shown in the
current order summary 430, the current order provides six total
inventory turns and provides an average of 21 days on hand of
Product A.
[0081] Turning now to the new order, as shown in the new
replenishment plan 450, deliveries are down to four days and both
Product and Product B are delivered together. Further, a smaller
amount of Product A is delivered in each of the four shipments and
the deliveries of Product B are set to an average number, as shown
in the new order 460. As shown in the new order summary, the new
order 460 represents eight total inventory turns and reduces
Product A to 17.25 days on hand. Finally, as shown in the
purchasing results, the new order 460 has increased the overall
turns by 33%, reduced the inventory of Product A by 18, and made
the order pattern of Product B more predictable.
[0082] In other words, by scaling down the truckload orders to free
up enough space to absorb the shipments on the second lane, a new
picture emerges: four full multi-stop truckloads a month. This
concept goes against prior art TMS systems which would not break up
the shipments of Item A because they are approximately a full
truckload. The results of the example of FIG. 3 include: 20%
reduction in freight cost, 60% increase in overall inventory turns,
33% reduction in deliveries hitting the dock, and 19% reduction in
miles driven.
[0083] These results are very beneficial, and not just in the
savings they deliver. Importantly, they protect and even improve
upon key purchasing metrics as well. Add to this the operations
benefit of reduced dock congestion, and a significant carbon
footprint improvement, and this example begins to speak loudly for
a new way of thinking about logistics ability to impact supply
chain objectives. The example shatters the assumption that
logistics savings only comes at the expense of inventory risk. In
fact, all three assumptions in the prior section are challenged in
this one example, for one very counterintuitive reason: scaling
down orders can improve logistics efficiency.
[0084] The present logistics optimization system considers the full
range of possible adjustments to order size, frequency, and timing
to exponentially increase the possibilities to mine for freight
consolidation. Unlike the old method of route optimization alone
that waits for matching shipments, combined optimization of
ordering and routing essentially lets the user match shipments as
desired.
[0085] The present logistics optimization system may expose and
assess the universe of permutations of ordering and routing. When
the present optimization algorithms are employed to uncover these
"win-win" scenarios, the results can be surprising in scale.
Assessments of inbound freight networks large and small have shown
that solutions such as the example above are so prevalent in a
network that the network-wide savings of 20-30% is accompanied by
an average total inventory reduction of 1.5%. This inventory
reduction is a net number, inclusive of solutions that scale orders
up (within reasonable constraints, such as maximums of 3-4 weeks
inventory) or scale orders down. This means that the impact of
scaled down orders is outstripping the impact of scaling them up.
While these results can vary from one inbound network to the next,
most organizations can minimally expect to keep inventory levels
flat while still achieving significant savings.
[0086] The logistics changes found by the present logistics system
may be implemented without significant process or systems upheaval.
In a preferred system, buyers still place the orders, using
existing systems and logistics planners still plan the routes,
using existing TMS capabilities. Collaboration preferably does not
require any change to the fundamental responsibilities or personnel
makeup of these teams. It also does not require a disruption in the
flow of orders from purchasing to transportation systems. Instead,
implementation is building new connective tissue between purchasing
and logistics processes, based on up-front planning and a closed
feedback loop for compliance monitoring and corrective action. The
connective tissue is found in specific new activities and
technologies at three junctures in the order-to delivery sequence:
prior to order, prior to tender, and post-delivery.
[0087] FIG. 5 illustrates the addition of the present system for
logistics optimization 100 into the logistics process 500. As shown
in FIG. 5, the logistics process 500 includes sales 510, purchasing
520, and logistics 530. Sales 510 includes the function of creating
a forecast 515 of inventory or products needed. Purchasing 520
includes the function of creating an order 525 to obtain the
desired inventory or products. Logistics 530 includes the functions
of tendering the load 532 and delivering the load 535.
Additionally, one or more aspects of the present invention may
interact with the logistics process 500 at one or more of prior to
order 550, prior to tender 560, and post-delivery 570.
[0088] With regard to the interaction of the present logistics
system 100 with the logistics process prior to order 550, the most
expedient way to adjust order patterns and set routing guidelines
is to do so with a planning-based approach, pro-actively, before
the orders are placed. This periodic planning process is performed
on the side of the existing buying and freight execution sequence.
It is certainly possible to implement a more invasive and exacting
process, generating replenishment orders systematically that
consider forecast, inventory, and logistics impact. However, if the
intention is to capture the bulk of these savings with the minimum
of systems and process turnover (as is likely), a planning-based
approach is advisable.
[0089] In a planning-based approach, a technology solution is
leveraged, likely by a Logistics Engineering person or team, to
periodically examine demand requirements, based on recent order
history, updated with any seasonal or other demand forecasting
information. This process might be run once a week, once a month,
once a quarter--the frequency depends on network volatility, and
how tightly the organization wants to manage the ordering
guidelines to support the highest profitability. The logistics
optimization system accepts order history, forecast information,
and carrier rate information, and uses optimization technology as
described above to identify the most profitable ordering and
routing scenarios available for each freight lane.
[0090] Constraints may be applied at a global, supplier, and item
level to mark the boundaries of feasibility. Some constraints
likely to be required include (but are not limited to) equipment
type, limitations on products that cannot be consolidated, pallet
space, and on the order pattern side, shelf-life restrictions, and
the degree to which order frequency can be adjusted.
[0091] The output of this process is not orders or loads. It is a
set of guidelines on how to purchase and route product:
recommendations on order size, frequency, and timing, to set up
ideal consolidation solutions. The optimization technology accounts
for the opportunities available to your network, by leveraging
multiple models as described above. This may further include
backhaul opportunities and fleet utilization, continuous moves, and
cross-dock or pooling scenarios.
[0092] A process is then implemented to review, approve, and
"publish" these guidelines. This involves software-supported
workflow to track agreement from both Logistics and Purchasing, and
signoff on the savings and inventory impact for each solution. Once
published, the guidelines are fed to purchasing, for adoption
during the replenishment process. Most robust purchasing systems
may accept the types of parameters required, but some buying
organizations may be more comfortable using them in a more manual
fashion. In addition, the profitability expectations of each
solution are stored, as targets to be measured against later in the
process.
[0093] The present proactive planning process is typically not
resource-intensive for each implementation. The first time it is
run, the entire network is under review, and the list of solutions
to assess quite long. From that point forward, the full network is
preferably included in the optimization process, but only the
resulting solutions that are new or changed need enter into the
review and approval process. This is typically a manageable list,
on the order of 3 to 5% of total freight lanes on a monthly basis,
even in large-scale networks.
[0094] In fact, the overall resource impact of this approach can be
very favorable. Today, many organizations leverage optimization
technology within their selected TMS solution to select routing for
freight just prior to load tender. The simpler solutions that
emerge from this process can largely be tendered with little
oversight. However, freight planners often find that they need to
review all suggested consolidations that emerge from these tools,
to ensure feasibility. Despite the promise of automation, too many
business exceptions exist to permit this sort of hands-off freight
routing. In contrast, an up-front planning approach seeks to smooth
out and standardize purchase orders, such that route determination
more often follows a plan that has already been vetted. In an
environment of collaboration between Purchasing and Logistics,
daily exception management at the point of freight execution is
significantly reduced, in favor of a more efficient, proactive
planning regimen. Before moving on, it should be mentioned that the
planning function can and should be leveraged to examine freight
that is not yet under management, where a freight allowance is
known (or a true freight cost has been broken out). Completely
separate from the 20-30% savings improvement stated earlier is the
added revenue achievable by finding new lanes that fit with the
buyer's network. In many instances these are lanes previously
ignored as unprofitable, when order pattern changes were not
considered.
[0095] With regard to the interaction of the present logistics
system 100 with the logistics process prior to tender 560, it is
recognized that lasting success in any collaboration activity
requires more than just a joint planning function. A closed
feedback loop is desirable to monitor compliance to plan, and
support timely corrective action between both teams. Since this
solution involves building better order patterns up front, it is
possible within this model to recapture load profitability before
it is even lost (i.e. shipped).
[0096] This may be done by leveraging exception management
technology to highlight non-compliant purchase orders as soon as
they are created, and facilitate communication between load
planners and buyers to revise the order before it is built into a
shipment and tendered. There is no need for this process to
interrupt the automated flow of orders to a TMS system, as long as
the compliance alerts are acted upon before the tender occurs. This
may often be accomplished through simple process timing (checking
compliance alerts prior to running the load creation process in the
TMS).
[0097] Not all instances of non-compliance may require action. Some
may arise from unanticipated inventory needs. Some may be close
enough to target thresholds that a decision can be made to allow
the order through. Some may simply highlight that a plan needs to
be changed for future orders to reflect new realities. To
facilitate this decision process, it may be important that the
exact reason for non-compliance and the profitability impact
(dollar variance from target) is available with the compliance
exception alert. It is also desirable to log reason codes whenever
a non-compliant order is allowed through, to facilitate summary
reporting of process effectiveness.
[0098] This "soft checkpoint" (soft, meaning that orders are not
automatically adjusted to be compliant), along with the periodic
re-assessment of plans discussed earlier, enables order patterns to
be changed in a way that is still responsive to a dynamic supply
network. As valid exceptions occur, they are allowed through, but
measured, and if representative of the new operating rules, used to
trigger updates to the plans.
[0099] With regard to the interaction of the present logistics
system 100 with the logistics process post delivery 570, the final
step in the closed-loop process is trend reporting at a lane level,
and root cause analysis on the margin of delivered loads. A host of
factors may reduce load profitability from the targets set during
planning, including freight allowance changes, order size
fluctuations, and product mix on the revenue side, and secondary
carrier usage, fuel rate changes, and one of a host of possible
unplanned accessorial charges in the load cost.
[0100] In depth visibility and drilldown root-cause analysis into
these drivers is desirable for any inbound freight management team
(even those not taking this approach in full), as well as a tracked
workflow process to ensure that steps are taken to prevent or
offset margin decay over the life of a freight lane. It is noted
that commercial TMS solutions largely neglect freight margin
analysis. A few may carry PO-level revenue through, but cannot
measure the impact of item mix and lack the ability to drive to
SKU-level analysis. Without the capability to perform detailed root
cause analysis into both revenue and cost movement, inbound freight
management teams may struggle to maintain a rigorous focus on
sustaining savings.
[0101] FIG. 6 illustrates a screen shot 600 of an Inbound Transport
Management (ITM) system according to an embodiment of the present
invention. As shown in FIG. 6, the screenshot 600 includes project
information 610, such as a name and description, the dates created,
modified, and published, and any status.
[0102] The screenshot 600 also shows a set up section 620 including
data for several lanes. Each lane preferably includes information
about the type of data, the data set name, the date it was loaded,
the data it may have been modified, and the number of records. A
list of at least some of the project constraints is also shown at
622.
[0103] The screenshot 600 also shows an optimize section 630
including several scenarios for consideration for implementation.
Each scenario is preferably associated with an ID, a name, a run
history, the number of solutions, the monthly savings, and the
schedule. Additionally, a summary of scenario results is shown at
632.
[0104] The screenshot 600 also shows a publish section 640
including a listing of scenarios that have been published. Each
published scenario is preferably associated with a date, name,
person publishing, savings, and solutions.
[0105] FIG. 7 illustrates a screenshot 700 of the ITM lane import
criteria screen. As mentioned above with regard to FIG. 1, recent
historical lane data is imported into the ITM system. As shown in
FIG. 7, the recent historical data includes lane and vendor
numbers, vendor name, ship-from city, freight allowance, weight,
and cube, monthly frequency, and several other factors.
[0106] Additionally, FIG. 7 illustrates the "grade" column. The
grade column represents a grade that is manually by a reviewer to
indicate lanes that are more profitable than another, for example,
for review and discussion of taking over such lanes. Alternatively,
the grade may be assigned based on profitability and risk of
execution of the lane or solution.
[0107] FIG. 8 illustrates a screenshot 800 of the constraints
preferably entered for the implementation of the combo model 204 of
FIG. 2. As shown in the screen shot 800, the constraints preferably
include load size constraints, lane constraints, financial
constraints, cost settings, flexing constraints, and solution
constraints. Similar constraints may be entered for each of the
models 201-207 of FIG. 2.
[0108] FIG. 9 illustrates a screenshot 900 of an ITM scenario
analysis screen providing a view of optimization solutions with the
ability to lock, exclude, and mark solutions for publication. The
screenshot 900 includes set up information 910 including lane data
and scenario constraints and overrides. The screenshot 900 also
includes optimize information 920 identifying each model employed,
a description of the model, any constraints employed, the cost
savings, and the model results. The screen shot 900 also includes
scenario solution detail information 930 including status and
information about the solutions.
[0109] FIG. 10 illustrates a screenshot 1000 of a lane profiles
tool for visualization and what-if analysis of lane optimization
and order flexing results. From the screenshot of FIG. 10, the user
may view and modify drivers financial, operations, and inventory
impact of one-way, consolidation (combo), cross-dock, backhaul
solutions. The user may also view related solutions and access lane
and item information on recent sales, purchase, and inventory. The
user may also manage workflow in activating solution.
[0110] Further, as shown in the screenshot 1000 of FIG. 10, it
includes a lane identification 1005 and a financial and operations
summary 1010 that includes new order frequency, size, and estimated
revenue, cost, margin, and inventory impact. The screenshot 1000
also shows a list of lanes included in the solution profile 1020
with summary statistics related to purchase patterns and freight
financials. The screenshot 1000 also shows a list 1030 of other
profiled that include lanes in this profile.
[0111] FIG. 11 illustrates a screenshot 1100 of a lane analysis
tool used to examine shipment, purchase order, inventory, and sales
information summarized to a lane level to support order and route
pattern determination. Additionally, the screenshot 1100 shows a
lane-level summarization 1110 of order/shipment information and
product sales, by min, max, average, etc. across recent history.
Further, the screenshot 1100 shows recent purchase order activity
1120.
[0112] FIG. 12 illustrates a screenshot of lane order profiles
which include purchasing guidelines for communication to purchasing
systems or processes. The purchasing guidelines may include order
size, timing, and frequency rules for one or more lanes in a
routing solution. The screenshot includes solution (load) level
rules 1210, and lane (PO) level rules for Pickup #1 1220 and Pickup
#2 1230.
[0113] FIG. 13 illustrates a screenshot 1300 of the compliance
detail. The compliance detail displays expected and actual purchase
order size, timing, frequency, revenue, cost, and highlights
elements of non-compliance with order rules, to enable logistics or
purchasing personnel to assess the financial impact and consider
correcting orders prior to load tender.
[0114] FIG. 14 illustrates a screenshot 1400 of the gross margin
dashboard. The gross margin dashboard enables root cause analysis
of margin shortfalls against target, showing performance against
target, over time, by revenue and cost component/driver. It also
enables view of only negative impact components, to identify
improvement opportunities whether or not the load met margin
targets at an overall level. Further, the gross margin dashboard
allows drill down into lane and shipment level to examine root
cause and allows the user to filter by "lane issue", a workflow
mechanism for tracking resolution of issues found.
[0115] As shown in FIG. 14, the gross margin dashboard 1400 allows
a user to click on an entry such as the Jul 11 "Lower Load Qty"
entry. A summary screen 1410 detailing those loads having lower
load quantity is then displayed. Additionally, the summary screen
1410 allows a user to click on a specific load to display a detail
screen 1430 displaying details for that particular load.
[0116] FIG. 15 illustrates a business information flow 1500
according to the present Inbound Transportation Management (ITM)
system. The business flow 1500 includes an Information Management
System (IMS) Platform 1510 including an ITM Profitability Optimizer
1512 and an ITM Profitability manager 1514. The business flow 1500
also includes a Transportation Management System (TMS) 1520 and the
Purchasing department 1530.
[0117] As described above, order history 1550 is passed to the ITM
Profitability Optimizer 1512 which generates a solution of
optimized logistics representing a set of lane profiles 1552 and
passes the lane profiles to the ITM Profitability Manager 1514.
Additionally, the ITM Profitability Optimizer 1512 passes order
rules 1554 back to the Purchasing department.
[0118] The purchasing department 1530 then places orders 1560 with
the ITM Profitability Manager 1514. The ITM Profitability Manager
1514 identifies exceptions to the lane profile in the purchase
orders 1560 and passes an identification of exceptions 1562 back to
the purchasing department for review and potential modification to
conform to the optimized lane profile.
[0119] Once the compliance exceptions have been resolved, the ITM
Profitability Manager 1514 relays purchase orders and routing
instructions 1570 to the TMS 1520. The ITM Profitability Manager
1514 also receives data with regard to the actual shipments 1572
back from the TMS 1520. The received data may be used to
recalculate a new optimal lane pattern or to perform a root cause
analysis on variance against profitability targets and initiate
corrective action.
[0120] FIG. 16 illustrates the combo model stack generation process
according to an embodiment of the present invention. The combo
model 204 was referenced in FIG. 2. In general, as further
described below, the combo model stack generation process first
filters the lanes, then creates a proximity list of pairs of lanes,
then creates a list of base 2 lane combos, then filters to solution
the list for 2 way combos, then creates a list of base 3 lane
combos, then filters to solution the list for 3 way combos, and
repeats the process up until the N-way combos where N is
pre-selected by a user.
[0121] Turning to FIG. 16, first, at step 1605 the list of lanes
with lane data is compiled. Next at step 1610, the total list of
lanes is filtered to selects a list of usable lanes. Lanes may be
unusable for several reasons, such as a shipment date outside the
current stack date.
[0122] Next, at step 1615, a pairwise proximity list is formed.
Proximity is preferably defined by the distance between picks and
drops. The distance is preferably calculated by using the havesine
formula:
a=sin.sup.2(.DELTA.lat/2)+cos(lat.sub.1)*cos(lat.sub.2)*sin.sup.2(.DELTA-
.long/2)
c=2*a tan 2( a, (1-a))
d=R*c where R is earth's radius
[0123] The pairwise proximity list is then used to form a base list
of 2 way solutions at step 1620. Solutions may be filtered out at
step 1625 if the proximity exceeds a value set by the user or for
other reasons described below. The remaining solutions are added to
the stack 1630.
[0124] Additionally, the model may filter out a solution based on
one or more of the following: out of route miles, value of revenue
on lane, value on lane vs cost on lane, quantity on trucks, margin,
or other criteria. The model may also provide exact filtering based
on whether lanes are reviewed in order by length, by revenue, or by
quantity on truck.
[0125] Alternatively, solutions may be filtered out based on zip
code regions. For example, the distance between a zip code in CA
and a zip code from NJ is available from a lookup table or other
database, and may be directly compared to the desired
proximity.
[0126] With regard to the three-way base list, the three way base
list is created by adding lanes to the two way list a step 1635 and
then filtering the lanes for the desired solutions at step 1640.
These lanes may be selected based on the proximity list and is
preferably in proximity for both current lanes in the list.
Similarly, a four way base list may be constructed by adding an
additional lane to the three way base list using the proximity list
at step 1645 and proximity for the previous lanes on the list and
then filtering the list at step 1650. This process may be repeated
up to an N-way base list where N may be set by the user. The stack
1630 of all available solutions may then be reviewed and ordered by
a criteria such as margin to obtain the desired solution.
[0127] With regard to calculating margin, the solutions described
above preferably include a calculation of margin as well. Margin is
viewed as revenue minus cost. In this case, the cost estimate is
based on dollar per mile cost for traveling from the pick up to the
drop off point supplied in original data times the total miles for
the shortest route.
[0128] Further, when determining the combo, the transported
products are treated as first in last out so that the last product
loaded on the truck is the first product out. However, in an
alternative embodiment, it is allowed to remove this constraint.
Additionally, in the combo model, there is preferably always a lane
in the combo where the pick up for the lane is the first pick up
for the route and the drop off for the lane is the last drop off
for the route.
[0129] Additionally, in the model, the route is determined
optimally by examining combinations with reductions due to the
final lane in route being constrained to be shorter than initial
lane in route. If not, then one can simply reverse the entire pick
sequence and get a shorter total route, which implies that the
route was not optimal. This cuts the number of potential
combinations in half. For example if lane A>lane B>lane C in
length then only need to check ABC, ACB, and BAC.
[0130] Additionally, a combo may be included in the list of bases
but not in the list of solutions because the margin is too low for
the solution. Additionally, a combo might be excluded from the list
of bases because it has such a low margin that no possible future
lanes will bring the value up to the required threshold margin.
[0131] With regard to calculating frequency, the following formulas
may be employed:
OptFreq=max(min(total monthly weight/max weight per truck, total
monthly cube/max cube per truck, total monthly pallets/max cube per
pallet), MinFreq)
Total monthly weight=sum of weight over all items over all
lanes
Total monthly cube=sum of cube over all items over all lanes
Total monthly pallet=sum of pallets over all items over all
lanes
MinFreq=min(max(historical freq by lane), 1/max time between
orders)
max time between orders=preset value based on inventory
requirements, can be a function of temp
max(historical freq by lane)=largest freq on any individual
lane
[0132] Additionally, frequency may be restricted if a lane is
marked as "do not flex". In such a situation, the frequency of the
solution is not allowed to drop below the current frequency of the
lane.
[0133] For example, if Lane A is marked as "do not flex", and the
frequency for lane A=freqA then MinFreq=max(min(max(historical freq
by lane for lanes that can be flexed), 1/max time between orders),
max(historical freq by lane for lanes that cannot be flexed)).
[0134] In the Inbound Transportation Management (ITM) system
described above, the ITM system includes unique aspects in terms of
inventory features within the Lane Level Logistics Routing
Optimization models. More specifically, the order flexing within
the Lane Level Logistics Routing Optimization models may be
constrained based upon inventory restrictions based on days on hand
and shelf life.
[0135] Additionally, the ITM system also includes reporting of the
impact on inventory for Current Avg Cycle Stock, Optimized Avg
Cycle Stock, and Inventory Change for the purposes of performing
what-if analysis. For example, when choosing between a first
solution and a second solution, the user may see the inventory
change for each solution prior to deciding on the solution.
[0136] One or more embodiments of the present invention discussed
above provide optimization of order and routing patterns that
considers inventory constraints, including days on hand and shelf
life. These constraints prevent or limit proposal of solutions that
adversely impact inventory levels and continuity of supply, and
exceed inventory storage capacities.
[0137] Results of optimization of order and routing patterns
include impact on inventory carried and frequency of inventory
turns, to enable Purchasing personnel to gauge the viability of the
solutions with respect to capacity, carrying costs, and continuity
of supply. In displaying results, Current Average Cycle Stock,
Optimized Average Cycle Stock, and Inventory Change are provided,
and incorporated into what-if analysis to maintain visibility into
Purchasing impact as users validate the optimized solutions.
[0138] While particular elements, embodiments, and applications of
the present invention have been shown and described, it is
understood that the invention is not limited thereto because
modifications may be made by those skilled in the art, particularly
in light of the foregoing teaching. It is therefore contemplated by
the appended claims to cover such modifications and incorporate
those features which come within the spirit and scope of the
invention.
* * * * *