U.S. patent application number 11/114364 was filed with the patent office on 2005-12-01 for product offering management and tracking system.
Invention is credited to Benda, Peter, Clarke, Brendan, Davison, Gary, Defrances, Tony, LaVoie, Steven, Osborn, William, Rocha, Peter D., Savarese, James.
Application Number | 20050267791 11/114364 |
Document ID | / |
Family ID | 25020679 |
Filed Date | 2005-12-01 |
United States Patent
Application |
20050267791 |
Kind Code |
A1 |
LaVoie, Steven ; et
al. |
December 1, 2005 |
Product offering management and tracking system
Abstract
Disclosed is a system for product tracking and management of
merchandise and a method of accomplishing the same. The disclosed
system can also be used to forecast and adjust projected
allocations of merchandise based upon the product tracking
information collected and managed. There is also provided a method
for substantially optimizing logistics for loading vehicles and
transporting goods which is capable of being utilized with the
disclosed tracking and management system.
Inventors: |
LaVoie, Steven; (LaGrange,
IL) ; Savarese, James; (Chicago, IL) ;
Defrances, Tony; (Barrington, IL) ; Clarke,
Brendan; (Atlanta, GA) ; Benda, Peter; (Glen
Allen, VA) ; Osborn, William; (Frisco, TX) ;
Davison, Gary; (Kingsville, MD) ; Rocha, Peter
D.; (Alexandria, VA) |
Correspondence
Address: |
MCANDREWS HELD & MALLOY, LTD
500 WEST MADISON STREET
SUITE 3400
CHICAGO
IL
60661
|
Family ID: |
25020679 |
Appl. No.: |
11/114364 |
Filed: |
April 26, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11114364 |
Apr 26, 2005 |
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09751144 |
Dec 29, 2000 |
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6937992 |
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Current U.S.
Class: |
705/7.31 |
Current CPC
Class: |
G06Q 10/0875 20130101;
G06Q 10/08355 20130101; G06Q 10/063 20130101; G06Q 10/08 20130101;
G06Q 30/0202 20130101; G06Q 10/06316 20130101; G06Q 10/087
20130101; G06Q 10/083 20130101; G06Q 10/0835 20130101; G06Q 10/04
20130101; G06Q 10/06375 20130101; G06Q 10/0833 20130101 |
Class at
Publication: |
705/007 |
International
Class: |
G06F 017/60 |
Claims
What is claimed is:
1. A method of managing one or more product offerings, the method
comprising the steps of: defining one or more parameters for the
one or more product offerings; defining one or more receivers of
the one or more product offerings; defining one or more identified
products of the one or more product offerings; defining a forecast
of the one or more identified products projected to be allocated to
the one or more receivers; defining one or more product offering
commitments of the one or more receivers; shipping the one or more
identified products to the one or more receivers from one or more
distributors based at least in part upon the forecast; tracking
sales or dispensation of the one or more identified products to
generate sales or dispensation data of the identified products;
identifying one or more imbalances between the forecast and the
sales or dispensation data; and adjusting subsequent shipments of
the one or more identified products based at least in part upon the
one or more imbalances.
2. The method of claim 1, further comprising the step of archiving
the sales or dispensation data.
3. The method of claim 1, wherein the parameters of the one or more
product offerings comprise one or more identified product
components of the one or more identified products, a start date of
the one or more product offerings, an end date of the one or more
product offers; one or more promotional materials, one or more
advertising materials, and one or more logistics materials.
4. The method of claim 3, wherein the one or more components of the
one or more identified products are identified by one or more
identification codes.
5. The method of claim 4, wherein the one or more identification
codes is a member selected from the group consisting essentially of
an EAN, an SKU, an EPC, a GS1 GDSN, or a UPC.
6. The method of claim 1, wherein the sales or dispensation data
comprises identification codes of the one or more identified
products sold.
7. The method of claim 1, wherein the tracking step is performed on
a real-time tracking basis.
8. The method of claim 1, wherein the steps are implemented by
computer hardware, computer software, or a combination of computer
hardware and computer software.
9. The method of claim 1, wherein the shipping step further
comprises the steps of: determining the one or more identified
products required to be maintained in inventory by the one or more
receivers in response to data received from the one or more
receivers from one or more shippers; and substantially optimizing
the shipment of the one or more identified products by determining
one or more substantially maximum loads of one or more transport
vehicles at least in part by calculating an amount of the one or
more identified products for shipment from the one or more shippers
by one or more transport vehicles from the one or more shippers to
the one or more receivers that reduces the logistics costs and
maintains the inventory within the amount of one or more identified
products required to be maintained according to an algorithm
employing one or more metrics and data.
10. The method of claim 9, wherein the one or more metrics comprise
the level of the inventory.
11. The method of claim 9, wherein the one or more metrics comprise
the time of the shipment of the one or more identified
products.
12. The method of claim 9, wherein the one or more metrics comprise
at least one of the following: capacity utilization per vehicle
mile; total transportation cost metric; transportation cost as a
percentage of product value shipped metric; shipping revenue
metric; total logistics cost metric; and shipping revenue less
freight cost metric.
13. The method of claim 9, wherein the one or more transport
vehicles have one or more capacities and wherein the one or more
metrics comprise bin-packaging characteristics of the one or more
vehicles, including one or more of the amount of pallet layers,
pallets, pallet foot prints, and cases of the one or more
identified products within the one or more capacities of the one or
more vehicles.
14. The method of claim 9, wherein the calculating further
comprises the step of providing a trade allowance or a profit share
to the one or more receivers.
15. The method of claim 1, wherein the defining a forecast step
further comprises the step of tracking the sales or dispensation of
one or more individual components of the identified product to
generate sales or dispensation data of the one or more individual
components.
16. The method of claim 15, wherein the steps are implemented by
computer hardware, computer software, or a combination of computer
hardware and computer software.
17. The method of claim 1, wherein the method further comprises the
steps of: adjusting the forecast based at least in part upon the
one or more product offering commitments of the one or more
receivers; and shipping the identified product to the one or more
receivers from the one or more distributors based upon the adjusted
forecast.
18. The method of claim 17, wherein the steps are implemented by
computer hardware, computer software, or a combination of computer
hardware and computer software.
19. A method of managing a product offering, the method comprising
the steps of: defining a product offering having an identified
product; defining one or more receivers of the product offering;
defining a forecast of the identified product projected to be
allocated to the one or more receivers; defining one or more
product offering commitments of the one or more receivers;
adjusting the forecast based at least in part upon the one or more
product offering commitments of the one or more receivers; shipping
the identified product to the one or more receivers from one or
more distributors based upon the adjusted forecast; tracking sales
or dispensation of the identified product to generate sales or
dispensation data; identifying one or more imbalances between the
adjusted forecast and the sales or dispensation data; and adjusting
subsequent shipments of the identified product based at least in
part upon the one or more imbalances.
20. The method of claim 19, wherein the tracking step is performed
on a real-time basis.
21. The method of claim 19, wherein the steps are implemented by
computer hardware, computer software, or a combination of computer
hardware and computer software.
22. The method of claim 19, wherein the shipping step further
comprises the steps of: determining the one or more identified
products to be maintained in inventory by the one or more receivers
in response to data received from the one or more receivers from
one or more shippers; and substantially optimizing the shipment of
the one or more identified products by determining one or more
substantially maximum loads of one or more transport vehicles at
least in part by calculating an amount of the one or more
identified products for shipment from the one or more shippers by
one or more transport vehicles from the one or more shippers to the
one or more receivers that reduces the logistics costs and
maintains the inventory within the amount of one or more identified
products required to be maintained according to an algorithm
employing one or more metrics and data.
23. A computer program embodied on a tangible medium for managing a
product offering comprising: a first set of instructions to define
a product offering; a second set of instructions to define one or
more receivers of the product offering; a third set of instructions
to define one or more parameters of an identified product of the
product offering; a fourth set of instructions to define a forecast
of the identified product projected to be allocated to the one or
more receivers; a fifth set of instructions to define one or more
product offering commitments of the one or more receivers; a sixth
set of instructions to substantially optimize shipping of the
identified product to the one or more receivers from one or more
distributors based at least in part upon the forecast; a seventh
set of instructions to track sales or dispensation of the
identified product to generate sales or dispensation data; an
eighth set of instructions to identify one or more imbalances
between the forecast and the sales or dispensation data; and a
ninth set of instructions to adjust subsequent shipments of the
identified product based at least in part upon the one or more
imbalances.
24. The computer program embodied on a tangible medium for managing
a product offering on-line of claim 23, further comprising: a tenth
set of instructions to adjust the forecast based at least in part
upon the one or more product offering commitments of the one or
more receivers; an eleventh set of instructions to ship the
identified product to the one or more receivers from the one or
more distributors based at least in part upon the adjusted
forecast; and a twelfth set of instructions to identify the one or
more imbalances between the adjusted forecast and the sales or
dispensation data.
25. An on-line method of providing and replenishing one or more
products of a product offering to one or more receivers by
shipments from one or more distributors, comprising the steps of:
defining a product offering; defining one or more receivers of the
product offering; defining one or more parameters of an identified
product of the product offering; defining a forecast of the
identified product projected to be allocated to the one or more
receivers; defining one or more product offering commitments of the
one or more receivers; shipping the identified product to the one
or more receivers from one or more distributors based at least in
part upon the forecast; tracking sales or dispensation of the
identified product to generate sales or dispensation data;
identifying one or more imbalances between the forecast and the
sales or dispensation data; and adjusting subsequent shipments of
the identified product based at least in part upon the one or more
imbalances.
26. The method of claim 25, wherein the steps are implemented by
computer hardware, computer software, or a combination of computer
hardware and computer software.
27. The on-line method of providing and replenishing one or more
products of a product offering of claim 25, further comprising the
steps of: adjusting the forecast based at least in part upon the
one or more product offering commitments of the one or more
receivers; shipping the identified product to the one or more
receivers from the one or more distributors based upon the adjusted
forecast; and identifying the one or more imbalances between the
adjusted forecast and the sales or dispensation data.
28. The method of claim 27, wherein the shipping step further
comprises the steps of: determining the one or more identified
products to be maintained in inventory by the one or more receivers
in response to data received from the one or more receivers from
one or more shippers; and substantially optimizing the shipment of
the one or more identified products by determining one or more
substantially maximum loads of one or more transport vehicles at
least in part by calculating an amount of the one or more
identified products for shipment from the one or more shippers by
one or more transport vehicles from the one or more shippers to the
one or more receivers that reduces the logistics costs and
maintains the inventory within the amount of one or more identified
products required to be maintained according to an algorithm
employing one or more metrics and data.
29. The method of claim of claim 28, wherein the steps are
implemented by computer hardware, computer software, or a
combination of computer hardware and computer software.
30. A signal-bearing medium having encoded machine-readable
instructions for managing a product offering comprising: a first
set of instructions to define a product offering; a second set of
instructions to define one or more receivers of the product
offering; a third set of instructions to define one or more
parameters of an identified product of the product offering; a
fourth set of instructions to define a forecast of the identified
product projected to be allocated to the one or more receivers; a
fifth set of instructions to define one or more product offering
commitments of the one or more receivers; a sixth set of
instructions to substantially optimize shipping of the identified
product to the one or more receivers from one or more distributors
based at least in part upon the forecast; a seventh set of
instructions to track sales of the identified product to generate
sales or dispensation data; an eighth set of instructions to
identify one or more imbalances between the forecast and the sales
or dispensation data; and a ninth set of instructions to adjust
subsequent shipments of the identified product based at least in
part upon the one or more imbalances.
31. A signal-bearing medium having encoded machine-readable
instructions for managing a product offering of claim 30, further
comprising: a tenth set of instructions to adjust the forecast
based at least in part upon the one or more product offering
commitments of the one or more receivers; an eleventh set of
instructions to ship the identified product to the one or more
receivers from the one or more distributors based at least in part
upon the adjusted forecast; and a twelfth set of instructions to
identify the one or more imbalances between the adjusted forecast
and the sales or dispensation data.
Description
RELATED APPLICATIONS
[0001] This application is a Continuation-in-Part application of
currently pending U.S. patent application Ser. No. filed under
attorney docket No. 13149US02 on Apr. 18, 2005 entitled "Transport
Vehicle Capacity Maximization Logistics System and Method of Same,"
which is a Continuation-in-Part of currently pending U.S. patent
application Ser. No. 09/751,144 filed on Dec. 29, 2000.
[0002] All patent applications noted above are incorporated by
reference in their entirety to provide for continuity of
disclosure.
FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT
[0003] Not Applicable
MICROFICHE/COPYRIGHT REFERENCE
[0004] Not Applicable
TECHNICAL FIELD OF THE INVENTION
[0005] The presently described technology relates to a product
management and tracking system.
BACKGROUND OF THE INVENTION
[0006] Many national chains in the foodservice industry use the
concept of limited-time offers (LTOs) and promotions to stimulate
their business and drive revenue through the year. Limited-time
offers are special product offerings available for specific time
periods, often 4-6 weeks around key events of year. Often, these
LTOs are comprised of product items which distributors do not
normally carry and manufacturers do not normally produce. As such,
the chain organization must (1) find new manufacturers to produce
the unique product items for the promotion period; (2) forecast
what the independent demand will be for the product offerings and
therefore dependent demand for component items; (3) load the
distribution centers with the appropriate amount of product at the
right time to satisfy demand at the least total cost and (4) track
actual movements for each product item against the forecast
real-time during the promotional period so that product imbalances
can be identified and resolved quickly. Today, the foodservice
industry, for example, does not generally manage this process
effectively.
[0007] Typically, the prior art LTO process operates as
follows:
[0008] (1) The marketing director in a chain organization defines
the scope of an LTO. The scope may include many elements,
including: timing, product options, bill of materials for each
product option, SKU ("Stock Keeping Unit") for each component of
each menu option, and the store/unit participation. The information
is then recorded into a document and mailed out to all franchise
owners, manufacturers and distributors participating in the
promotion.
[0009] (2) The chain then creates an Excel worksheet on demand
assumptions for the promotion (estimated lift, average sales per
store, bill of material ratios for each of the component SKUs,
price points for each target product configuration). Based on
demand assumptions, target serving requirements for each product
option and each of the component SKUs are calculated.
[0010] (3) LTO demand calculations are emailed to franchise owners
for review and adjustment. Store operators/franchise partners make
modifications to the product offering and component item forecasts
manually for their store group. Modified forecasts are then emailed
back to the chain organization. However, sending modified forecasts
back to the chain organization may be too cumbersome for some
chains to even execute and franchise input in the process is
therefore skipped entirely.
[0011] (4) The chain manually rolls-up the operator forecasts by
distributor center and sends out individual emails, with individual
reporting attachments to each entity on the amount needed for
production and distribution.
[0012] (5) When a promotion is launched, the chain on a weekly
basis will track store orders for given component SKUs against
forecast. Excel sheets are often faxed back and forth between the
chain and its distributors to capture this information. This
process is a manual one and incredibly time-consuming. Therefore,
the step may be too cumbersome for some chains to even execute and
SKU tracking is skipped all together.
[0013] (6) A tracking spreadsheet is compiled and then analyzed
manually for demand-supply imbalances. If an imbalance exists, that
imbalance is researched. This practice is done throughout the
launch of an LTO. It is often reactive, taking place well after a
demand imbalance had occurred.
[0014] There are a number of drawbacks with the prior art LTO
process. The end-to-end process is largely manual and fragmented.
There are many "step" owners and "touch-points" in this process
(i.e., the chain, the franchise organizations, the purchasing
cooperative, the distributors and the manufacturers), which are not
integrated. Currently, the process is cumbersome to execute;
communication between all parties is poor; and the information is
slow to disseminate.
[0015] Another drawback is that the forecasting process is not
predictive. Often, it does not capture the knowledge from the
marketplace operators who have the best gauge on what will sell.
Lack of collaboration and communication systems in the LTO planning
process makes it difficult to institute an effective planning
process.
[0016] Another disadvantage is that the ability to gain timely
visibility to actual identified product movements against the
forecast does not happen, which impedes a chain's ability to
respond quickly to demand imbalances. Anticipating product
imbalances faster and before they happen is desired but often not
possible. Product obsolescence builds up where demand is lagging
resulting in excess inventory and loss, and product stock-outs
occur when demand is leading, resulting in lost sales that would
have occurred if products had been timely shipped to meet
demand.
[0017] Therefore, there is a need in the art for an LTO product
offering management process that integrates a tracking system with
on-line forecasting and commitment-capture tools. One advantage of
such a management process is the integration of the many "step"
owners and "touch-points" in the management process. Another
advantage is to institute an effective planning process that
captures the knowledge from the marketplace operators and thus
provides predictive forecasting. Yet another advantage is to allow
for quick responses to demand imbalances by providing timely
visibility to actual identified product movements as compared
against the forecast. For example, these advantages allow
manufacturers to have visibility of how an LTO is progressing in
the marketplace so that they can adjust their production scheduling
to respond to demand that is either above or below forecasted
demand. Additionally, the destinations of originally forecasted
shipments can be altered so that already manufactured product is
distributed in the most efficient manner. These advantages allow
for the avoidance of lost sales by allowing for more product to be
provided to areas that are exceeding the initial sales forecast.
The advantages also allow for the avoidance of wasted product due
to overstocking in underselling areas.
BRIEF SUMMARY OF THE INVENTION
[0018] The presently described technology is useful for managing a
product offering, such as an LTO, which may, in one embodiment,
utilize a method having the steps of: defining one or more
parameters for the one or more product offerings; defining one or
more receivers of the one or more product offerings; defining one
or more identified products of the one or more product offerings;
defining a forecast of the one or more identified products
projected to be allocated to the one or more receivers; defining
one or more product offering commitments of the one or more
receivers; shipping the one or more identified products to the one
or more receivers from one or more distributors based at least in
part upon the forecast; tracking sales or dispensation of the one
or more identified products to generate sales or dispensation data
of the identified products; identifying one or more imbalances
between the forecast and the sales or dispensation data; and
adjusting subsequent shipments of the one or more identified
products based at least in part upon the one or more
imbalances.
[0019] In another embodiment, the present described technology may
utilize a method having the steps of: defining a product offering
having an identified product; defining one or more receivers of the
product offering; defining a forecast of an identified product
projected to be allocated to the one or more receivers; defining
one or more product offering commitments of the one or more
receivers; adjusting the forecast based at least in part upon the
one or more product offering commitments of the one or more
receivers; shipping the identified product to the one or more
receivers from one or more distributors based upon the adjusted
forecast; tracking sales or dispensation of the identified product
to generate sales or dispensation data; identifying one or more
imbalances between the adjusted forecast and the sales or
dispensation data; and adjusting subsequent shipments of the
identified product based at least in part upon the one or more
imbalances.
[0020] In a further embodiment of the presently described
technology, shipping of the identified products in the product
offering may be optimized by utilizing a method having the steps
of: determining the one or more identified products required to be
maintained in inventory by the one or more receivers in response to
data received from the one or more receivers from one or more
shippers; and substantially optimizing the shipment of the one or
more identified products by determining one or more substantially
maximum loads of one or more transport vehicles at least in part by
calculating an amount of the one or more identified products for
shipment from the one or more shippers by one or more transport
vehicles from the one or more shippers to the one or more receivers
that reduces the logistics costs and maintains the inventory within
the amount of one or more identified products required to be
maintained according to an algorithm employing one or more metrics
and data.
BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS
[0021] FIG. 1 is a block diagram of the current order-shipping
model.
[0022] FIG. 2 is a block diagram of the current
manufacturer-distributor model.
[0023] FIG. 3 is a block diagram of a multiple manufacturer
multiple distributor model.
[0024] FIG. 4 is a block diagram of a vendor managed inventory
model.
[0025] FIG. 5 is one embodiment of the presently described
technology.
[0026] FIG. 6 is another embodiment of the presently described
technology.
[0027] FIG. 7 is a block diagram of a remote vendor managed
inventory model.
[0028] FIG. 8 is another embodiment of the presently described
technology.
[0029] FIG. 9 is another embodiment of the presently described
technology.
[0030] FIG. 10 is a message flow diagram of the presently described
technology.
[0031] FIG. 11 is a server block diagram of the system of the
presently described technology.
[0032] FIG. 12 demonstrates another feature of the presently
described technology.
[0033] FIG. 13 is a block diagram of the presently described
product offering management and tracking system.
[0034] FIG. 13a is another embodiment of the presently described
product offering management and tracking system utilizing optional
forecast adjustment.
[0035] FIG. 14 is another embodiment of the presently described
technology illustrating an optimized shipping process integrated
with the presently described product offering management and
tracking system.
DETAILED DESCRIPTION OF THE INVENTION
[0036] As used herein, the term "product offering" refers to
special product and/or menu options, unique product items, special
promotional items and the like that are offered for sale or other
dispensation (such as a give-away). Typically, although not always,
such product offerings are made available through a limited time
offer (LTO). The product offering refers not only to the special or
unique item itself, but also to the components that make up the
item, components that would be associated with sales of the item
(e.g., packaging), promotional and/or marketing materials for
promoting and/or marketing sales of the item, the timing and length
of the LTO, the store/unit participation, and demand assumptions,
including estimated lift, average sales per store and bill of
materials ratios for each component. These are also referred to
herein as parameters for the product offering. It will be
understood by those skilled in the art that the parameters for the
product offering will depend, at least in part, upon the nature of
the product offering and the item or items included in the product
offering, and that other parameters not specifically mentioned
herein may be associated with a particular product offering.
[0037] As also used herein, the term "identified products" refers
to the item or items included in the product offering, such as the
unique product items, special product and/or menu options and
special promotional items, that are offered for sale or
dispensation by a store/unit. The identified products also includes
the components or materials that make up each product. For example,
the identified product may be a special sandwich that is being
offered for a limited time by a restaurant chain, and would also
include each individual ingredient that goes into making the
sandwich (e.g., bread, fillings, toppings, seasonings, etc.). It
will be understood by those of skill in the art that the identified
product may be a single component or multicomponent product.
[0038] Also as used herein, the terms "identified product
component(s)" and "product component(s)" refer to the individual
component(s) that make up each identified product. Each product
component may be identified by an identification or product code.
Typically, the identification is an SKU code, i.e., "Stock Keeping
Unit." An SKU is an identification number assigned to a unique item
or a unique type of item by the retailer. The SKU may be an
internal number to that retailer, or may be tied to an item's UPC
(Universal Product Code), EAN (the EAN-UCC identification number),
EPC (Electronic Product Code) in relation to RFID (Radio Frequency
Identification) systems and the like, and GS1 GDSN (GS1 Global Data
Synchronization Network), an alternative to the EAN system. The
actual identification code is not critical as long as it permits
the product component to be tracked through a chain of
distribution.
[0039] Referring initially to FIG. 13, there is shown an embodiment
of the method of managing one or more product offerings as
described herein. The first step, according to the method, is to
create a management profile for the product offering. A necessary
component of creating a management profile for a product offering
is to define the parameters of the product offering. These
parameters typically include identifying the product or products
that are going to be promoted or offered, the start date for the
period that the identified products are going to be promoted, the
end date for the promotion period, identifying the components that
make up the identified product, determining whether there are
optional components for the identified products, and determining
store/unit participation. The marketing director for a chain in the
foodservice industry, for example, typically makes the decisions
necessary to define the product offering.
[0040] One advantage of the present technology is that it allows
the parameters of the product offering to be entered into a
computer. A computer program allows the marketing director to
develop menu and/or product components and include identification
indicia, such as SKU's, for each component of the offered product
and/or menu item. As will be described in further detail below, use
of a computer program to set up all of the parameters of a product
offering allows the entire LTO process to be automated and lays the
foundation for real-time tracking of movement of the LTO
product(s).
[0041] As further shown in FIG. 13, the next step of the product
offering management process is to define one or more receivers of
the product offering. As used herein, receivers include end use
retailers/stores, but may also include entities higher up in the
distribution chain, such as franchise owners. The step of defining
the receivers of the product offering involves determining what
stores/units are going to participate in the product offering.
Participation may be geographically based, such as by a particular
area, region, state or country. Alternatively, participation may be
based upon franchise type.
[0042] The next step in the process as shown in FIG. 13 is to
define one or more identified products of the product offering.
This step involves determining what products are going to be
included in the product offering, as well as determining each
component of each product. The step also includes determining
whether there are optional products that will be included and, if
so, what those optional products are.
[0043] Information about the parameters of the product offering,
the receivers of the product offering, and the identified product
or products of the product offering is entered into the computer.
The computer program is designed to allow a forecast of the product
sales or dispensation projected to be made at each store/unit based
upon the information entered. The forecast determines which
stores/units will receive the identified products, and the
quantities of each component to be allocated based upon the demand
assumptions. The computer program automatically e-mails
notification of the product offering to the receivers, including
forecasts of the identified product or products to be allocated to
each store/unit.
[0044] Upon receipt of the notification and forecast of the product
offering, each receiver reviews the forecast and can make
modifications to the offered product or products, the component
items, and the quantities set forth in the forecast. These
modifications enable receivers to input their commitment level for
their store group. One advantage of receiver input is that
receivers can make adjustments based upon intimate knowledge of
their own local markets. The adjustments then define the commitment
level of the receiver and can be emailed back to the marketing
director or management of the chain organization.
[0045] In an alternative embodiment, the forecasts are on-line,
internet-based forecasts that allow each receiver to enter into the
system through the internet and to make adjustments to the
forecasts for each of their stores/units. The ability to make
on-line adjustments to the forecasts allows the advantage of
receiver input to be fully realized. Additionally, such on-line
forecasting also allows information to be inputted and potentially
tracked on a real-time basis, which further streamlines and
enhances the product offering management system of the presently
described technology.
[0046] The product offering commitments from each of the receivers
are entered into the system which then allocates the components of
the identified products (or, in the case of single component
products, the identified products themselves) to the receivers. The
commitments are also sent to other constituents in the product
offering supply chain including, but not limited to, manufacturers,
suppliers and distributors, to insure that each constituent is
notified of the amounts of product components needed for production
and distribution. The identified products are then shipped from the
distributors to the receivers based at least in part on the
forecast of the identified products and the commitments received
from the receivers.
[0047] In an alternative embodiment of the invention, as
illustrated in FIG. 13a, the forecast of the identified products
projected to be allocated to the receivers is adjusted based upon
the product offering commitments received from one or more of the
receivers. Adjusting the forecast based upon product commitments
enables better initial product allocation and distribution due to
improved overall predictability of the forecast, rather than
limiting the management system to correcting imbalances that become
evident after product shipment and sales/dispensation have
begun
[0048] Because all SKU's or identification indicia of the product
components are entered into the computer, sales or dispensation of
the identified product can be easily tracked throughout the product
offering period. Information about identified product sales or
dispensation is entered into the system. Preferably this sales or
dispensation data is updated daily, but it may also be updated
bi-weekly, weekly or some other convenient time period that would
permit sales or dispensation data to be tracked on a regular basis.
Further as noted above, if such information is inputted
contemporaneously and/or simultaneously when a product or product
component is scanned electronically based upon its identification
indicia (e.g., SKU, EAN, or GS1-GDSN), then the sales or
dispensation data may be tracked on a contemporaneous and/or
simultaneous basis utilizing the presently described
technology.
[0049] The generated sales or dispensation data is then compared to
the forecast of identified products to determine whether sales or
dispensation of the identified product are meeting the sales or
dispensation projected in the forecast. The comparison allows
demand imbalances to be detected and identified very quickly.
Demand imbalances occur where actual product sales or product
dispensations are either less than those forecasted, or greater
than those forecasted.
[0050] One key feature of the present product offering management
and tracking system is the ability to respond quickly to identified
imbalances by adjusting subsequent shipments of the identified
product. For example, where demand is lagging and there is excess
identified product on hand at a particular receiver location,
subsequent shipments to that receiver may be delayed, cancelled
altogether, or rerouted to locations where demand exceeds the sales
or dispensations forecasted. On the other hand, where demand
exceeds the sales or dispensations forecasted for a particular
receiver location, additional product may be shipped from the
distributor to that receiver to insure that the receiver has an
adequate supply of identified product to meet demand. By adjusting
subsequent shipments of the product, the present system insures
that the right product gets to the right place at the right time.
Such rapid adjustments are advantageous for a chain because they
reduce obsolescence due to poor performing product offerings
thereby reducing excess inventory, and they avoid lost sales by
allowing rapid replenishment of inventory at high performing
locations.
[0051] In an alternative embodiment, imbalances can be identified
by predefining and programming demand variance thresholds into the
system. If such variance thresholds are exceeded, then the
imbalances are automatically identified and highlighted, and
adjustments can be made to the shipping schedule.
[0052] The present product offering management and tracking process
also allows the results of the product offering to be archived.
Creating an on-line archive detailing the performance of each
product offering allows for better and more accurate forecasts for
future product offerings.
[0053] Since technology permits product management and tracking to
be computerized, the presently described technology may partially
reside in a computerized form. For example, the presently described
technology may include a computer program embodied on a tangible
medium, such as a disk drive, CD ROM, network, floppy disk, zip
drive, or server, to automate all steps of the process and enable
real-time tracking of product movement. The computer program may
include a first set of instructions to define a product offering; a
second set of instructions to define one or more receivers of the
product offering; a third set of instructions to define one or more
identified products; a fourth set of instructions to define a
forecast of the identified product projected to be allocated to the
receiver; a fifth set of instructions to define one or more product
offering commitments; a sixth set of instructions to substantially
optimize shipping of the identified product to one or more
receivers; a seventh set of instructions to track sales or
dispensations of the identified product to generate sales or
dispensation data; an eighth set of instructions to identify one or
more imbalances between the forecast and the sales or dispensation
data; and a ninth set of instructions to adjust subsequent
shipments of the identified product based at least in part upon the
imbalances.
[0054] For the alternative embodiment illustrated in FIG. 13a, the
computer program may further include a tenth set of instructions to
adjust the forecast based upon the one or more product offering
commitments; an eleventh set of instructions to ship the identified
product to one or more receivers based at least in part upon the
adjusted forecast; and a twelfth set of instructions to identify
one or more imbalances between the adjusted forecast and the sales
or dispensation data.
[0055] It is appreciated by those skilled in the art that the
process shown herein may selectively be implemented in hardware,
software, or a combination of hardware and software. An embodiment
of the process steps employs at least one machine-readable
signal-bearing medium. Examples of machine-readable signal-bearing
mediums include computer-readable mediums such as a magnetic
storage medium (i.e., hard drives, floppy disks), or optical
storage such as compact disk (CD) or digital video disk (DVD), a
biological storage medium, or an atomic storage medium, a discrete
logic circuit(s) having logic gates for implementing logic
functions upon data signals, an application specific integrated
circuit having appropriate logic gates, a programmable gate
array(s) (PGA), a field programmable gate array (FPGA), a random
access memory device (RAM), read only memory device (ROM),
electronic programmable random access memory (EPROM), or
equivalent. Note that the computer-readable medium could even be
paper (e.g., tape or punch cards) or another suitable medium, upon
which the computer instruction is printed, as the program can be
electronically captured, via for instance optical scanning of the
paper or other medium, then compiled, interpreted or otherwise
processed in a suitable manner if necessary, and then stored in a
computer memory.
[0056] Additionally, machine-readable signal bearing medium
includes computer-readable signal-bearing mediums.
Computer-readable signal-bearing media have a modulated carrier
signal transmitted over one or more wire-based, wireless or fiber
optic networks or within a system. For example, one or more
wire-based, wireless or fiber optic network, such as the telephone
network, a local area network, the Internet, or a wireless network
having a component of a computer-readable signal residing or
passing through the network. The computer-readable signal is a
representation of one or more machine instructions written in or
implemented with any number of programming languages.
[0057] Furthermore, the multiple process steps implemented with a
programming language, which comprises an ordered listing of
executable instructions for implementing logical functions, can be
embodied in any machine-readable signal bearing medium for use by
or in connection with an instruction execution system, apparatus,
or device, such as a computer-based system, controller-containing
system having a processor, microprocessor, digital signal
processor, discrete logic circuit functioning as a controller, or
other system that can fetch the instructions from the instruction
execution system, apparatus, or device and execute the
instructions.
[0058] Thus, it can be seen that the product offering management
and tracking system presently described overcomes many of the
drawbacks of the prior art processes. All steps of the present
process can be automated, permitting integration of the people,
activities and data into one collaborative process. Automation of
the process permits on-line forecasting and allocation to
individual store units, as well as on-line commitment input from
receivers. The ability of receivers to make on-line modifications
and provide input into the product offering forecasts offers a
significant improvement over the prior art processes where such
receiver input is often cumbersome and omitted altogether. The
present process allows menu forecasts of offered products and their
components to be developed on-line, including SKU or other
identification of the components. The on-line SKU identification
permits real-time tracking of actual product component movement and
allows product component movement to be automatically compared to
forecasted movement on a daily or other regular periodic basis. The
ability to track product component movement and compare it to
forecasted movements allows imbalances to automatically be
identified. The present system also allows product shipments to be
adjusted to resolve the imbalances, resulting in a reduction of
product obsolescence due to poor performing product offerings and
an increase in sales or dispensations at high performing locations
because of the ability to quickly replenish depleted inventory. In
addition, the present system allows product offering performance
and descriptive data to be archived, resulting in better and more
complete product offering forecasts in the future.
[0059] In another embodiment of the product offering management and
tracking process as described herein, additional efficiencies and
optimization may be realized if the step of shipping the identified
product or products is accomplished by utilizing a method of
shipping product that maximizes vehicle capacity, as described
hereinafter. This embodiment is illustrated in FIG. 14.
[0060] As used herein, the term shipper is used to denote a company
that ships products or goods to another, such as a receiver.
However, for the purpose of clarity only, and without being limited
thereto, some embodiments may describe shippers as manufacturers
and receivers as distributors. In some embodiments, receivers may
be customers also. It is understood that the presently described
technology is not so limited between manufacturers and
distributors. As also explained herein, shippers are different
legal entities, or companies that operate separately.
[0061] As used herein, the term "vehicle" is used to denote any
modality of shipping or anything capable of carrying goods. It can
include, but is not limited to, ships, barges, vans, trailers,
cars, trucks, trains, airplanes, containers, pallets, cubes,
etc.
[0062] Also as used herein, the term "product" can mean either the
same product(s), a different product(s), or a newly created
product(s). In other words, just because product X is initially
ordered, does not mean that any further optimized product must be
product X, as it could be products Y or Z, etc. The term "product"
is also interchangeable with the terms, "merchandise," "good(s)" or
"item(s)."
[0063] For all embodiments, it should be noted that "capacity",
also used synonymously as "load", can be measured as volume
capacity (such as cubic capacity, or height, weight, or length) or
by weight capacity (such as poundage), or by pallet footprint, or
by number of cubes, cartons, containers, boxes, or the like. Also,
it should also be noted that capacity is also a function of the
size of the vehicle. For example, in the trucking industry, moving
from single unit trucks to truck-trailer combinations or
semi-trailer combinations can increase capacity. Furthermore,
multi-trailer combinations such as double or triple trailer
combinations can affect capacity. Trailer size may range, but may
include the standard 28 foot, 28.5 foot, or 48 foot trailers.
Similarly, cargo hold size, and the number of containers placed
above deck may affect capacity in boats/ships or other transport
vehicles. Similarly, train capacity is a function of the number of
cars, boxcars, liquid container cars, etc. In addition, the number
of pallets (usually, but not exclusively, 44 pallets per standard
truck) is another possible constraint.
[0064] FIGS. 1 through 4 show embodiments of the prior art. In FIG.
1, a manufacturer 10 such as manufacturer M1 receives orders from a
receiver, such as customer 12 and then ships the merchandise to the
customer 12. The customer 12 can place many orders with other
manufacturers 10n, such as manufacturer M2 or M3. In this regard,
the logistical issues involve multiple shipments from a plurality
of manufacturers 10n to a single customer 12. If the customer 12
orders too little merchandise, then the manufacturer 10 will ship a
partial vehicle load to the customer 12. From the customer's
vantage, orders must be placed with each individual manufacturer
and the customer 12 receives shipments from a plurality of
manufacturers. This becomes an administrative problem for the
customer. It is a shipping problem also for the manufacturer since
it may have to ship small volumes of merchandise to many customers.
The large number of small shipments can clog loading docks.
Finally, it is well established that the cost per pound is
inversely related to the load of the vehicle. This implies that the
separation of order control (performed by the customer) and payment
of freight cost (performed by the seller) can lead to outcomes that
would be more costly than if both parties were better
coordinated.
[0065] FIG. 2 demonstrates another embodiment of the prior art and
is a further refinement of the embodiment described further in FIG.
1. Shown is a distributor 14 that interfaces between manufacturers
and the customer. In this example, the distributor 14 receives
orders from the customer 12 and ships merchandise to the customer
12 directly. The distributor 14 may ship many types of merchandise
to the customer 12. For example, the customer 12 may order from the
distributor some level or amount of merchandise, goods, items, or
products from M1, M2, and M3. As the inventory of these products is
reduced, the distributor replenishes its stock by placing orders
with the respective manufacturers M1, M2, and/or M3. In this
regard, the distributor acts as an intermediary in which the
customer 12 need only interface with a distributor 14 for most or
all of its needs. Even if it deals only with one customer, the
distributor can add economic value by providing low cost storage to
the customer.
[0066] FIG. 3 demonstrates yet another embodiment of a larger scale
and can be illustrative of the current industry. Shown is the
situation in which many customers C1 and C2 interface with many
distributors D1 and D2. These distributors may interface with a
plurality of manufacturers 10 such as M1 through M4. Since not all
distributors carry every manufacturer's merchandise, the customer
12 may have to interface with many distributors. In this regard,
again a distributor may ship partial vehicle loads to the customer
and the distributor may receive partial vehicle loads from the
various manufacturers. Similarly, the distributor may ship partial
loads to the customer. One well-understood benefit of this model is
that, since each distributor services multiple customers, the total
amount of stored goods required will be less than if the goods were
stored at each separate customer. Again, this model represents the
industry.
[0067] FIG. 4 is a Vendor Managed Inventory (VMI) model. As
described herein, the VMI model permits open ordering in which the
manufacturer monitors the distributor's inventory and replenishes
it as needed. This is in sharp contrast with the current paradigm
in which the distributor places orders with the manufacturer and
maintains control over the ordering process. In FIG. 4, the VMI
system 16 monitors the inventory level at the distributor 14. When
inventory levels drop, the VMI system 16, usually resident at the
manufacturer's situs, sends purchase orders to the manufacturer's
shipment center to ship merchandise to the distributor 16 for
subsequent shipment to the customer 12. Because the manufacturer
takes responsibility for ordering and transportation costs, it is
able to send the order to the distributor without the distributor
actually requesting each product, good, item, or merchandise.
[0068] FIG. 5 demonstrates a simple embodiment of the presently
described technology. Shown is the manufacturer 10 interfacing with
a central facility or a cross-dock 18, which interfaces with the
customers 12. The central facility may be adapted to receive and
process inventory information of distributors or customers and then
correlate this information to shipments from the manufacturer to
the customer. One non-exclusive purpose of the central facility or
cross-dock is to maximize transport vehicle capacity. The actual
transport vehicle is largely inconsequential so long as the
capacity of the vehicle can be determined. For example, it is well
known that the standard truck has a capacity of about 44,000 pounds
(around 2,000 cubic feet) and/or can carry about 44 pallets of
merchandise. Similarly, the standard train car has a predetermined
capacity. For example, a 50 foot boxcar has about 6,235 cubic feet
and a weight capacity of about 213,000 pounds. A 60 foot boxcar has
about 7,500 cubic feet and about 207,000 pounds of weight
capacity.
[0069] Thus, in its simplest form, maximization of vehicle capacity
compares the maximum vehicle capacity measured against the capacity
requirements associated with the merchandise initially ordered. The
subtraction of these measurements yields the amount of unused
capacity. Thus, new merchandise may be added sufficient to fill up
and/or substantially optimize this unused capacity. This creates
maximum or substantially maximum vehicle capacity. As used herein
with respect to the presently described technology, the term
"maximum" shall mean any amount or capacity (e.g., in terms of
volume, weight, or other applicable parameter) at a substantial
level, including but not limited to the substantially greatest
quantity or amount feasible or practical. In any embodiment,
though, the presently described technology can be modified to
manage multi-pickup and multi-drop-off shipments, as well as
shipments that travel between cross-docks. Per the presently
described technology, filling a vehicle can be done iteratively
(while the vehicle is being loaded), or can be filled in advance by
manipulating the order sequence of order generation and/or vehicle
optimization, before the goods are finally ordered. As used herein
with respect to the presently described technology, the terms
"optimization", "optimize", and "optimizing" shall mean at a
substantially optimal level in terms of a level, an amount, a
volume, a weight, or any other applicable parameter.
[0070] The filling/loading of the vehicle may concentrate on the
filling/loading of a single vehicle, or on providing a globally
optimized solution that fills all vehicles going between various
destinations. By shifting the load between multiple vehicles, a
result can be attained that will be more optimal than first
optimizing at the individual vehicle level.
[0071] In another embodiment, once the vehicle capacity of a
vehicle destined to a particular destination is determined, for
example, customer C1, an optimization model can be engaged. In this
regard, knowing (e.g., in advance) that a partial truckload is
destined from a shipper such as a manufacturer to a receiver such
as a customer C1, the central facility can use this information to
place additional orders with the manufacturer to increase the
amount of merchandise on that shipment. The vehicle is sent to the
central facility or the cross-dock (if the two are not at the same
location) where the merchandise can be unloaded. Thus, a full
truckload or substantially full truckload departs from the
manufacturer M1. Similarly, merchandise may be sent from
manufacturer M2 and M3, etc., to the cross-dock too, thus having
full or substantially full trucks arrive at the cross-dock. At the
cross-dock, the merchandise are reorganized and/or commingled such
that similarly destined merchandise are placed on the same vehicle
and sent to the ultimate customer(s), such as customer C1. Thus,
the presently described technology permits trucks to travel
full/loaded or substantially full/loaded from the manufacturer(s)
to the cross-dock, and from cross-dock to customer(s).
[0072] By the way of example, the manufacturers may be large
foodservice industry manufacturers, such as M1, M2, and M3, where
M1 sells boxes of ketchup to a series of restaurants, M2 may sell
boxes of plastic utensils, and M3 sells napkins. Customer C1 may be
a restaurant chain that requires ketchup, utensils, and napkins. In
this regard, customer C1 could receive shipments from each
manufacturer directly as in FIG. 1. However, the presently
described technology substantially maximizes truckload capacity
such that a substantially full truckload of ketchup boxes leaves
M1, a substantially full truckload of utensil boxes leaves M2, and
a substantially full truckload of napkins leaves M3. By collecting
and reorganizing the merchandise at the cross-dock, a shipment
comprising ketchup, utensils, and napkins is sent to customer C1.
However, recognizing that the outbound vehicle also has a truck
capacity, if the capacity is not maximized, then the central
facility will substantially optimize to add extra merchandise, such
as more ketchup, utensils, or napkins onto the truck to
substantially achieve maximum capacity. Since full or substantially
full truckloads are sent from the manufacturer to the customer,
significant savings are achieved and few less-than-truckload
("LTL") shipments are dispatched.
[0073] By way of further example, if the truckload capacity
comprises 100 boxes, and the Customer C1 destined initial shipment
comprises 60% ketchup, 30% utensils, and 10% napkins, the extra
merchandise added to obtain the 100 box capacity can be prorated
among the percentages. For example, if after the initial load
capacity is calculated it is found that another 10 boxes can be
added to achieve maximum or substantially maximum truckload
capacity, then this amount of boxes can be added to achieve the
maximum or substantially maximum load. The extra 10 boxes can be
prorated among ketchup, napkins, and utensils. Although shown as
manufacturers in FIG. 5, this model can also work with
distributors. The additional merchandise need not be prorated
though, as the additional merchandise can be the result of a
bin-packing optimization model that accounts for the three
dimensional aspect of the vehicle (pallet layers, pallets, volume,
cases, and weight) as well as the differences in the marginal
value-added that come from shipping each additional increment of a
given product.
[0074] To maximize efficiency, the presently described technology
may be configured to monitor the demand of the receivers or buyers,
the levels of "safety stock" needed to prevent stock-outs, the
amount of stock on hand, any promotional stock needed, stock needed
for seasonal demand, forecasts of stock demand, stock in transit,
priorities of stock needed, etc. Prioritization may occur when the
merchandise are needed at different times, such as if the
merchandise are perishables, if high revenue merchandise are
needed, high profit merchandise is needed, to prevent stock-outs,
promotional seasonal, etc. Similarly, the system may be configured
to provide reports, such as printouts of the various demands,
schedules, etc.
[0075] In another embodiment, the presently described technology
may determine substantial optimization in a predetermined manner
prior to shipping. It is capable of coordinating the shipments from
shipper(s) to receiver(s) even before the first shipment actually
leaves. In this regard, the presently described technology
generates orders for its customers versus generating orders in
response to the customer's request. The presently described
technology may arrange for and substantially optimizes the
transportation and order flow simultaneously, thus pre-scheduling
most, if not all, of the shipping components. Since title to the
goods remains either with the shipper or receiver, the company
operating the presently described technology need not take title to
the goods.
[0076] FIG. 6 demonstrates another embodiment of the presently
described technology in which receivers, such as distributors are
involved. In this model, a plurality of distributors 14 transport
merchandise to a plurality of customers 12. The central facility,
which may include the cross-dock 18 may coordinate inventory and
orders at the distributor. Again, it should be noted that the
cross-dock need not be collocated with the central facility. In
this model, a VMI-like system may be used in conjunction with the
central facility. Accordingly, as the central facility monitors the
distributor's inventory, the central facility prepares to order the
merchandise on behalf of the distributor. The central facility,
such as cross-dock 18, monitors the merchandise to be shipped to
the distributor. The central facility also has enough information
to determine on its own if an outgoing truck is full or not. If the
truck to be dispatched is not full, the central facility will send
an order for more merchandise to be added to the level that will
fill or substantially fill the truck. Similarly, the central
facility will monitor shipments originating at the other
manufacturers such as M2 and M3. In essence, the optimization model
creates an order plan for full or substantially full shipments from
the manufacturers before it is shipped or before the order is
finalized. The coordination with other shipments in the supply
chain with the central facility monitoring system is also
available.
[0077] In any embodiment, the external packaging, external labels,
SKU codes, pallet tags, UPC codes, etc., may classify the
merchandise. Merchandise lacking any indicia may be tagged in any
manner to identify the merchandise. "SKU" stands for a Stock
Keeping Unit, which is an identification number assigned to a
unique item or a unique type of item by the retailer. The SKU may
be an internal number to that retailer or may be tied to an item's
UPC (Universal Product Code), EAN (the EAN-UCC identification
number), EPC (Electronic Product Code in relation to RFID (Radio
Frequency Identification) systems and the like), and GS1 GDSN (GS1
Global Data Synchronization Network, an alternative to the EAN
system). Accordingly, the commingling of merchandise is maximized
when the merchandise are adequately identified. Naturally in some
circumstances, not all merchandise arriving at the cross-dock are
destined for the same place. Accordingly, it may be necessary to
determine the destinations of each item and further label or track
its destination. Thus, marking products with unique destination
indicia can facilitate the process of determining destinations of
merchandise.
[0078] In one embodiment of the presently described technology, a
shipment from, for example, M1 can go directly to the distributor
D1. Similarly, shipments from M2 can go directly to D1 also.
Similarly destined merchandise, such as merchandise going to the
same customer C1, can be coordinated such that merchandise from a
variety of manufacturers are on the same truck. If the truck is not
full/loaded, then the central facility will monitor the capacity
and order more merchandise to be loaded onto the truck until it is
full/loaded or substantially full/loaded. Thus, a full/loaded or
substantially full/loaded truck will arrive at the customer C1. As
described more fully herein, the optimization model may consider
the option of putting or not putting the truck through the
cross-dock.
[0079] In another embodiment, the merchandise from the manufacturer
may arrive at a cross-dock 18 and its merchandise may commingle
with merchandise from other manufacturers. The cross-dock permits
loading of similarly destined merchandise for shipment to the same
distributor or to the same customer. It should be noted that the
system does not just monitor truckload capacity. Rather, it
arranges for truckload capacity sufficient to transport the
required product.
[0080] Thus, in one exemplary model, the cross-dock or central
facility may perform some or all of the following steps of
receiving forecasts of customer demand for a product: monitoring
truckload capacity requirements, arranging orders in such a way
that more merchandise is filled or loaded into the truck,
commingling the merchandise with other party's merchandise, loading
similarly destined merchandise onto the same truck, adding more
merchandise if the truck is not full/loaded, and then sending this
truck along to a destination, such as another distributor or a
customer. The optimization model can take into account the relative
schedules of shipments in advance to coordinate arrivals at the
cross-dock and outgoing shipments from the cross-dock.
[0081] In yet another embodiment of the presently described
technology, it is not necessary to commingle merchandise arriving
at a cross-dock of various manufacturer's merchandise at the same
time. For example, using the models of FIG. 5 and FIG. 6, a full or
substantially full truckload of merchandise may arrive at the
cross-dock 18 or distributor 14. These newly arrived merchandise
may be commingled with merchandise that have been earlier
inventoried at the cross-dock or distributor. Merchandise of a
similar destination are then placed on the outgoing truck. Any
empty capacity can then be filled up with older or lower priority
merchandise from the cross-dock or distributor.
[0082] In yet a further embodiment, the presently described
technology further envisages the coordination of pick-ups and
drop-offs of shipments among customers (e.g., C1, C2, etc.),
manufacturers (e.g., M1, M2, etc.), and/or distributors (D1, D2,
etc.), for example, through a central facility and/or cross-dock.
Such coordinated picking up and dropping off of shipments allows
each customer, manufacturer, and/or distributor (i.e., collectively
"members" utilizing the presently described technology) to schedule
such shipment activities in a manner that is mutually beneficial.
For example, a member can schedule a truck that has taken product
to one receiver to then pick up product from somewhere near that
receiver's location and deliver that product to a second receiver
location somewhere near the original shipping location (e.g., the
original departure point of the truck). Thus, where the truck would
originally depart with shipment for one "member" and return to its
original departure location empty, the truck now also picks-up and
drops-off shipments to other "members" (i.e., C's, M's, or D's)
utilizing the presently described technology as well. Such a
coordinated option is not available in systems that do not allow
for or offer coordination between its same or different
"members".
[0083] One simple implementation of optimization technology to the
current invention can be viewed as a variant on the well-understood
maximum flow method developed by Ford and Fulkerson. This approach
makes some simplifying assumptions. Only one set of cost
constraints applies (e.g., product density per unit shipped is
sufficiently high to ensure that weight will always be the
constraint). Additionally, the goods shipped is assumed to be
either continuous or sufficiently discrete to permit high
granularity of shipments. In addition, each type of product is
available from only one geographic source. Finally, all shipments
under this simple model are assumed to pass through a single
cross-dock.
[0084] To apply this technique to the problem, each combination of
source, destination, and product type (e.g., SKU) is assigned a
value associated with a performance metric, a single cost
constraint (e.g., weight), the ratio of performance metric to cost
constraint, a minimum amount to ship, and a maximum amount to ship.
In addition, the algorithm uses a matrix or list of nodes,
including sources of goods, destinations of goods, and cross-docks,
as illustrated in FIG. 5 and FIG. 6.
[0085] Under this approach, the computer running the program
traverses the list of source-destination-SKU combinations to
determine the minimum shipment requirements for each
source-destination-SKU combination. The program also creates and
generates a list of sources and destinations that tracks the amount
of shipping required to move goods between each source and each
destination via the cross-dock. The result of this step is a matrix
that lists each combination of source and destination, and the
total amount of shipping capacity required to transport the
required minimum shipment of goods from its respective source to
its respective destination.
[0086] Furthermore, the computer with memory running the program
also traverses the source-destination-SKU list to determine the
amount of shipping required to ship the amount of goods that must
be shipped. Since this implementation assumes only a single
cross-dock, vehicle capacity must be assigned to the trip from the
source to the cross-dock and from the cross-dock to the
destination. Whenever insufficient vehicle capacity exists to carry
all mandatory orders on a given route into or out of the
cross-dock, another vehicle is assigned to that route. Assigning
goods to a vehicle and assigning a vehicle to a route changes the
amount of excess capacity available to carry discretionary goods on
that route.
[0087] Eventually the computer with memory running the program
processes the mandatory orders for all source-destination-SKU
combinations. This operation results in a set of unused vehicle
capacities from each source that has shipped mandatory orders into
a cross-dock, and from the cross-dock to each destination that will
receive mandatory orders of goods that have passed through
cross-dock.
[0088] Once the total shipping capacity required to move the
required number of goods between any source and destination is
determined, the computer with memory operating the program then
sorts the list of source-destination-SKU combinations by the ratio
of the performance metric to the cost constraint. This process
yields a list that provides the order in which the program should
evaluate adding discretionary goods to the order plan and to the
shipping capacity that travels between a given source and
destination.
[0089] The computer with memory then traverses the sorted list of
source-destination-SKU combinations. For each
source-destination-SKU combination, it determines if additional
discretionary orders are possible, if spare capacity exists going
from the source to the cross-dock, and from the cross-dock to the
destination. It also calculates the minimum of the amount of
discretionary orders available, shipping capacity into the
cross-dock, and capacity out of the cross-dock. This number is the
maximum or substantially maximum amount of discretionary orders
that can be placed, given the number of vehicles assigned to each
route (e.g., maximum or substantially maximum and feasible order
size).
[0090] At this point, the computer with memory running the program
adds an order in the amount of the maximum feasible order size to
the order plan, and reduces the available capacity going from the
source to the cross-dock and from the cross-dock to the destination
by the combined cost constraint represented by the amount of the
maximum feasible order size.
[0091] This procedure is repeated for each successive member of the
sorted source-destination-SKU list until the list is traversed or
there is no more available capacity/substantial capacity. The
computer then generates a source-destination-SKU list that denotes
the amount of each good ordered from each source by each
destination. It also generates a list or shipping plan denoting how
many items are being shipped from each source through the
cross-dock to each destination, and on what vehicle they will be
transported.
[0092] This relatively simple method can be supplemented by
allowing for the possibility that shipments can travel directly
from the source to the destination without passing through the
cross-dock, or that a given path between a source and destination
can include either multiple sources of product (multiple pickup) or
multiple destinations (multiple drop-off).
[0093] A more complete approach of the presently described
technology uses integer linear programming to solve a multistage
transshipment problem. In this case, the system is again modeled as
a network of sources, destinations, and cross-docks. In this case,
the algorithm maximizes the difference between positive (e.g.,
revenue) and negative (e.g., cost) performance metrics, subject to
the usual constraints found in a trans-shipment problem, including
vehicle capacity (e.g., height, weight, width, length, volume),
non-negativity of shipment quantities, zero product left at a
cross-dock, etc.
[0094] An additional extension of the presently described
technology would include the ability to commingle products
traveling between different legal entities with those of the same
entity. Thus, for example, the presently described technology may
note that product is required at a facility in Houston, and that
there is a large supply of product at a facility in Dallas owned by
the same distributor. In this case, the presently described
technology may be able to determine that the substantially optimal
solution to the problem would involve adding product from the
Dallas facility to a vehicle traveling from Chicago to Houston via
Dallas.
[0095] The Ford-Fulkerson models are described in the following
articles, the disclosures of which are expressly incorporated by
reference herein: L. R. Ford, Jr. and D. R. Fulkerson, Maximal Flow
Through a Network, Canadian Journal of Mathematics, 8:399-404
(1956); L. R. Ford, Jr. and D. R. Fulkerson, A Simple Algorithm for
Finding Maximal Network Flows and an Application to the Hitchcock
Problem, Canadian Journal of Mathematics, 9:210-218 (1957); and L.
R. Ford, Jr. and D. R. Fulkerson, Flows in Networks, Princeton
University Press, Princeton, N.J. (1962). Other models include
branch and bound algorithms.
[0096] Technology also may be derived from other simulation
oriented software such as "war games" or chess software that play
out various permutations, combinations, or solutions, predicts the
best "move" and executes it.
[0097] Another implementation of the presently described technology
optimizes shipments of standardized pallets for each given SKU on
standardized vehicles. This approach further assumes that a
profit-maximizing firm receives revenue from manufacturers to
deliver product from a source S to a destination D over a fully
connected network of nodes N, which may be sources, destinations,
or transshipment points. In this approach, the firm selects routes
R for pallets and r for vehicles, both of which consist of an
ordered finite list of nodes. Routes R or r may also include no
elements, which denotes that the pallet is not shipped, or that the
vehicle is not employed.
[0098] For this approach, the optimization problem can be
represented as a variant of transshipment problem in which the two
sets of control variables are the number of pallets of product type
SKU traveling in vehicle V on route R from source S to destination
D, x.sub.SKU,V,R,S,D, and the route of each vehicle V,r.sub.V. 1
Max x SKU , V , R , S , D r V SKU S D Income ( SKU , S , D ) x SKU
, V , R , S , D - V VehicleCost ( r V , V ) - n SKU PerNodeCost ( x
SKU , V 1 , R , S , D , i , n , x SKU , V 2 , R , S , D , n , j ) x
SKU , V 1 , R , S , D , i , n
[0099] The above objective function for the firm consists of three
different elements. The first is the revenue function for shipping
a pallet of type SKU to a destination D, times the number of
pallets of product type SKU shipped from source S to destination D.
This formulation of the revenue function permits the possibility of
the firm receiving different levels of revenue from the
manufacturer depending where the firm picks up the product from the
manufacturer.
[0100] The first cost component is the cost of running all vehicles
V along all routes r.sub.V. The second cost component represents
the total cost of all pallets of type SKU traversing a node n. In
this expression, the expression x.sub.SKU,V,R,S,D,i,n represents
the number of pallets of product type SKU moving on vehicle V
following route R from source S to destination D that travel
between nodes i and n. Note that the formulation of this function
permits the pallets to arrive at node n on one vehicle and leave it
on another. Thus, the per node cost can be used to account for
cross-docking fees as the pallet, moving on route R on vehicle
V.sub.1, arrives at node n from node i, and is transferred to
vehicle V.sub.2 moving to node j. In this formulation, the
PerNodeCost is expressed on a per pallet basis, and can vary as a
function of the product type. Note that, although V.sub.1 and
V.sub.2 are separate variables, they can both refer to the same
vehicle. Note also that this system can be used to account for
pickup or delivery costs by setting i to S or j to D,
respectively.
[0101] This system is also subject to a set of constraints. Among
them are constraints on the number of pallets that can be shipped
on a given vehicle: 2 SKU x SKU , V , R , S , D , i , n
MaxPalletsPerVehicle ( V )
[0102] where MaxPalletsPerVehicle is 44 for a typical trailer, but
can vary, depending on the type of vehicle used as described
herein. This constraint applies whenever the pallets move on a
vehicle.
[0103] Similarly, the weight constraint must be met: 3 SKU
WeightPerPallet ( SKU ) x SKU , V 1 , R , S , D , i , n
MaxWeightPerVehicle ( V )
[0104] where MaxWeightPerVehicle would be about 44,000 lbs. for a
typical trailer. Again, this parameter is a function of vehicle
type as described herein.
[0105] In this simplified case, since a pallet size is
standardized, it is assumed that the volume constraint is accounted
for by the pallet count constraint.
[0106] A non-negativity constraint must also be met for
shipments:
x.sub.SKU,V,R,S,D,i,n.gtoreq.0
[0107] This constraint applies for all SKU, V, R, S, D, i, and
n.
[0108] Finally, there is the flow constraint on each node, where
the net flow of product through a node must exceed some minimum
value, and must not exceed some maximum: 4 V ( x SKU , V , R , S ,
D , i , n - x SKU , V , R , S , D , n , j ) MaxNetNodeFlow ( SKU ,
n ) V ( x SKU , V , R , S , D , i , n - x SKU , V , R , S , D , n ,
j ) MinNetNodeFlow ( SKU , n )
[0109] where MaxNetNodeFlow and MinNetNodeFlow are the maximum and
minimum value for the number of pallets that enter the node, less
the number that leave. For a source, these numbers are typically
negative. For a destination, these numbers are expected to be
positive. For a transshipment point, these numbers typically zero.
The above constraint applies to all nodes, whether they are
sources, destinations, or cross-docks. The only difference between
these three different types of nodes is the value of the parameters
MaxNetNodeFlow and MinNetNodeFlow, which are functions of the node
and the SKU.
[0110] If the objective function and the constraints can be
formulated as linear functions, a linear program can be formulated
based on this problem and solved.
[0111] FIG. 7 demonstrates one prior art system for VMI management.
This system is based on the IBM Continuous Replenishment Process
(CRP) VMI system. Essentially, one part of the IBM VMI system
records the inventory of the distributor at the day's close. This
part then transmits the information to the main VMI server. The
server prioritizes optimal or substantially optimal shipment
levels. This information is then transmitted to the distributor's
purchasing department and the manufacturer's VMI system operator
for approval. The manufacturer's VMI then receives a purchase order
from the VMI server and acknowledges receipt of the purchase order.
The VMI server also sends an acknowledgement to the distributor
that the manufacturer has accepted the VMI purchase order.
Meanwhile, the manufacturer's VMI system operator then cuts a sales
order at the manufacturer site and processes a shipment. An order
acknowledgement and an advance shipping notice is sent from the VMI
server to the distributor notifying it about the order, contents,
estimated time of arrival, price, etc. The merchandise is then
shipped from the manufacturer to the distributor. As can be seen,
this is a typical VMI system in which because of the "open books"
format of the distributor, the manufacturer can regulate the
inventory levels at the distributor.
[0112] FIG. 8 demonstrates an embodiment of the presently described
technology integrating the IBM VMI system. The presently described
technology may also include the allocation resource protocol set
forth in U.S. Pat. No. 5,216,593 (issued 1 Jun. 1993); or the
optimized logistics planner disclosed in U.S. Pat. No. 5,450,317
(issued 12 Sep. 1995); or the integrated monitoring system
disclosed in U.S. Pat. No. 5,983,198 (issued 9 Nov. 1999); the
disclosures of which are expressly incorporated by reference
herein. As before, the VMI system records the inventory levels at
the distributor. This information is sent to the VMI server, which
correlates optimal shipment levels outbound from the cross-dock for
each manufacturer and prioritizes merchandise. An independently
managed inventory system provider (IMI) system of the presently
described technology reads the VMI information, such as the
optimized shipping schedules at the distributor site. Based on the
vehicle capacity, the IMI system of the presently described
technology generates another set of purchase orders. This new set
of orders may, but need not be, taken to the distributor's
purchasing manager for approval. This new set of orders may reflect
the cost savings for substantially optimizing the truckload. The
approved order is sent to the manufacturer and the cross-dock. The
central facility substantially optimizes the shipment from the
manufacturer into the cross-dock by arranging for pick up, etc. In
the meanwhile, the approved order arrives at the manufacturer for
approval, processing, and subsequent shipment from the manufacturer
to the cross-dock. Merchandise arrive at the cross-dock and are
substantially optimized with other merchandise going to the same
distributor. Ultimately, the merchandise of a variety of
manufacturers arrive at the distributor. Thus, as shown, the IMI
can be an independent third party company, that is, a company not
related to the distributor or manufacturer.
[0113] FIG. 9 demonstrates an embodiment in which the distributor
is eliminated. In this embodiment, by refining the calculations,
near full truckload capacity can be achieved without using a
distributor. In this example, the IMI system may be part of the
customer's facility in which the cross-dock IMI system monitors the
inventory level at the customer. The cross-dock IMI assembles and
correlates the inventory levels across all the customers. Thus, the
cross-dock IMI derives a truckload capacity and the requirements of
each customer. This information is substantially optimized and sent
to the various manufacturers. Once it is determined what vehicle
the manufacturer will use for transport, the IMI system will
substantially optimize the capacity utilization of the vehicle by
adding more merchandise to the truck. Meanwhile, this process
continues across all the vehicles receiving goods from all the
manufacturers. In this regard, this creates substantially maximum
shipping capacity from the manufacturers to the cross-dock. The
merchandise are then unloaded and reassembled into similar
destinations. Since the IMI has already substantially optimized
what merchandise are needed by the customers, the cross-dock system
will collect similarly destined merchandise and substantially
maximize truckload capacity to the customer. Vehicle size such as
truck size can be adjusted by using smaller trucks or larger ones
as needed.
[0114] Since technology permits logistics to be computerized, the
presently described technology may partially reside in a
computerized form. For example, the presently described technology
may include a computer program embodied on a tangible medium, such
as a disk drive, CD ROM, network, floppy disk, zip drive, or
server, to optimize shipment of merchandise on a vehicle by
filling/loading or substantially filling/loading the vehicle. The
computer program may include a first set of instructions to
determine a vehicle load capacity; a second set of instructions to
determine a shipment requirement or discretionary order; a third
set of instructions to generate a comparison by comparing the
vehicle load capacity with the shipment requirement; and a fourth
set of instructions to load more merchandise on the vehicle if the
comparison indicates that the vehicle is not yet full/loaded or
substantially full/loaded. These instructions may also code for
monitoring the inventory levels at the distributor, manufacturer,
customer, or cross-dock.
[0115] The presently described technology may also reside in a
signal. The signal may further include other signals that: (a)
signal the inventory level at the customer, manufacturer,
distributor, or cross-dock; (b) identify maximum vehicle load
capacity; (c) facilitate replenishment of the vehicle if the
vehicle is not yet full; (d) facilitate correlations at the
cross-dock; (e) provide feedback to the manufacturer, distributor,
customer, or cross-dock; (f) provide a purchase order generation
and confirmation system; or (g) otherwise permit vehicle capacity
to be maximized.
[0116] It is appreciated by those skilled in the art that the
process shown herein may selectively be implemented in hardware,
software, or a combination of hardware and software. An embodiment
of the process steps employs at least one machine-readable
signal-bearing medium. Examples of machine-readable signal-bearing
mediums include computer-readable mediums such as a magnetic
storage medium (i.e., hard drives, floppy disks), or optical
storage such as compact disk (CD) or digital video disk (DVD), a
biological storage medium, or an atomic storage medium, a discrete
logic circuit(s) having logic gates for implementing logic
functions upon data signals, an application specific integrated
circuit having appropriate logic gates, a programmable gate
array(s) (PGA), a field programmable gate array (FPGA), a random
access memory device (RAM), read only memory device (ROM),
electronic programmable random access memory (EPROM), or
equivalent. Note that the computer-readable medium could even be
paper (e.g., tape or punch cards) or another suitable medium, upon
which the computer instruction is printed, as the program can be
electronically captured, via for instance optical scanning of the
paper or other medium, then compiled, interpreted or otherwise
processed in a suitable manner if necessary, and then stored in a
computer memory.
[0117] Additionally, machine-readable signal bearing medium
includes computer-readable signal-bearing mediums.
Computer-readable signal-bearing media have a modulated carrier
signal transmitted over one or more wire-based, wireless or fiber
optic networks or within a system. For example, one or more
wire-based, wireless or fiber optic network, such as the telephone
network, a local area network, the Internet, or a wireless network
having a component of a computer-readable signal residing or
passing through the network. The computer-readable signal is a
representation of one or more machine instructions written in or
implemented with any number of programming languages.
[0118] Furthermore, the multiple process steps implemented with a
programming language, which comprises an ordered listing of
executable instructions for implementing logical functions, can be
embodied in any machine-readable signal bearing medium for use by
or in connection with an instruction execution system, apparatus,
or device, such as a computer-based system, controller-containing
system having a processor, microprocessor, digital signal
processor, discrete logic circuit functioning as a controller, or
other system that can fetch the instructions from the instruction
execution system, apparatus, or device and execute the
instructions.
[0119] In FIG. 10, a message flow diagram 1000 for the optimizing
transport vehicle load capacity process is shown. A server 1002,
such as a VMI server, sends and receives messages from a
distributor 1004 and a manufacturer 1006. A distributor 1004 sends
a periodic inventory message 1008 to the server 1002. The periodic
inventory message 1008 is preferably sent every business day, but
in alternate embodiments may be sent hourly, daily, bi-weekly,
weekly, monthly, or some upon some other triggering event (e.g.,
changes in inventory level). The periodic inventory message is
formatted so information contained in the message corresponds to
the removed or sold distributor inventory. The server 1002 receives
the periodic inventory message 1008 and processes it using
information about the inventory needs stored in a database. The
database contains information about the type and amount of
inventory normally maintained by the distributor 1004.
[0120] The server 1002 also has access to vehicle load sizes that
are also stored in the database. The server 1002 determines the
optimal shipment to meet the inventory needs of the distributor
1004 and sends an optimal shipment order message 1010 to the
manufacturer 1006. The server 1002 then receives an order
acknowledgement 1012 from the manufacturer 1006 signifying that the
order has been received. The server 1002 sends an order
acknowledgement message 1014 to the distributor 1004 in response to
reception of the order acknowledgement message 1012 from the
manufacturer 1006. A sales order 1016 is also sent from the
manufacturer 1006 to the distributor 1004.
[0121] In FIG. 11, a server 1100 that performs the optimizing
transport vehicle load capacity process is shown. The server 1100
is made up of a number of components including a controller 1102
connected to a data bus 1104. The data bus is connected to a
communication port 1106, a RF communication port 1108, an internal
storage medium 1110, an input/output port 1112, a memory 1114, and
a printer port 1116. The RF communication port 1108 is connected to
the data bus 1104 and an antenna 1118 for reception of RF signals
1120. The communication port 1106 is connected to the data bus 1104
and a public switched telephone network (PSTN) 1122. The printer
port 1116 is connected to the data bus 1104 and the output device
1124 (printer, video display, LCD display, or any other device
capable of generating an output viewable by a human). The
input/output port 1112 is connected to the data bus 1104 and an
external storage device 1126. The memory 1114 contains database
tables 1128, report formats 1130 and machine readable code
1132.
[0122] As shown in FIG. 10, a distributor 1004 sends a periodic
inventory message 1008 via the PSTN 1122 (see FIG. 11) to the
server 1100. The server 1100 receives the periodic inventory
message 1008 (see FIG. 10), at the communication port 1106 (see
FIG. 11). The controller 1102 accesses the periodic inventory
message 1008 (see FIG. 10), over the data bus 1104. The controller
1102 accesses the database tables 1128 to determine what inventory
the distributor 1004 (see FIG. 10) requires. The controller 1102
executing the machine-readable code 1132, such as "C++" code,
identifies one or more vehicle(s) and vehicle load size contained
in the database tables 1128. The controller then generates an
optimal shipment order 1010 (see FIG. 10). The optimal shipment
order can then be printed out to an output device 1124 (see FIG.
11) by the printer port 1116 and sent to the manufacturer 1006 (see
FIG. 10) by the communication port 1106 (see FIG. 11) via the PSTN
1122. The format of the printed out substantially optimal shipment
order 1010 (see FIG. 10) is determined by the report format 1130
contained in the memory 1114 of the server 1100. In alternate
embodiments, a different type of communication network other than a
PSTN 1122 may be accessed, such as a packet-switch network,
wireless network, hybrid-fiber network, LAN, WAN, or a combination
of networks.
[0123] The controller 1102 generates the optimal shipment order
message to substantially maximize the capacity utilization of one
or more vehicle(s) from the manufacturer 1006 to the distributor
1004. After the optimal shipment message 1010 is sent to the
manufacturer 1006, the server 1002 receives an order
acknowledgement message 1012 from the manufacturer 1006 at the
communication port 1106 via the PSTN 1122. The controller 1102
formats an order acknowledgement message 1014 (see FIG. 10) for the
distributor 1004 upon receipt of the order acknowledgement message
1012 from the manufacturer 1006. Additionally, the manufacturer
1006 may send a sales order 1016 directly to the distributor 1004.
The server 1002 may also cut a purchase order to the carrier via
the communication port 1106.
[0124] As with any embodiment, the system may also include vehicles
equipped with satellite tracking systems, such as a Global
Positioning System. For example, the system may include a QTRACS
system manufactured by Qualcomm, Inc. to monitor vehicle position.
In this regard, coordination at the cross-dock may be facilitated
knowing that inbound trucks are coming, or otherwise provide
dynamic shipping information. In addition, the tracking permits
rapid communication with the customer to inform them that a truck
is expected soon or that the truck is remaining on schedule. As
with any embodiment herein, all communications between units or
components, may be via cellular, telephone lines, satellite,
wireless, etc. In other embodiments, the GPS technology may be
utilized with the pallets, boxes, cartons, or the like themselves.
In particular, GPS may be used with high value items so that
tracking these items is facilitated. In other embodiments, using
transponders, such as RF transponders, the pallets or goods
themselves could be tracked to see what goods are on what truck. If
GPS is used with the truck, then it becomes rudimentary to know
what goods (e.g. what pallets) are where at all times.
[0125] The server 1100 is able to receive global positioning
service (GPS) data about vehicle positions from an RF communication
port 1108. The controller 1102 correlates the data about the
vehicle positions in order to identify a vehicle to carry the
shipment. The vehicle selection and inventory requirements are both
used by the controller 1102 to identify the optimal shipment order.
The controller 1102 also receives vehicle position data from the RF
port 1108, and uses it to determine estimates on arrival times to a
cross-dock, correlates these arrival times, and modifies shipping
schedules to substantially optimize logistics costs. In an
alternate embodiment, the GPS data is received at the server 1100
via the communication port 1106.
[0126] As with any embodiment described herein, the merchandise may
be prioritized based on any immediate, medium term, or long term
needs. Accordingly for example, immediately needed merchandise at
the cross-dock can be substantially optimized with medium term
needed merchandise. Similarly, the optimization function may be
performed concurrently with order placement or before. The
optimization may be based on a single vehicle, or by obtaining a
globally and substantially optimized value across a plurality of
vehicles. Similarly, as with any embodiment, there may be single or
a plurality of manufacturers , distributors, customers, or
cross-docks. The system can accommodate multiple pick-ups and
drop-offs on vehicle trips between the shipper and receiver.
[0127] Similarly, the various entities involved may be
geographically closely located, or quite some distance apart. In
one embodiment though, having the cross-dock in relatively the same
geocenter will facilitate implementation of the system. In
addition, as with any embodiment herein, the system may be divided
up so that various components are not in the same location. For
example, order processing can be geographically remote from any
other entity, such as the cross-dock or the manufacturers. On the
other hand, system implementation may occur in generally the same
location or at the same facility, such as if most of the IMI system
is at the distributor facility. In addition , it should be
recognized that the legal entity receiving the goods could be a
different entity than the one that actually receives the goods. For
example, Company X headquartered in California may be the legal
entity "receiving" the goods, but the actual shipping location to
receive the goods could be in Illinois. It should also be
appreciated that the presently described technology may include
many cross-docks, either all or some located in the same geocenter;
and/or all cross-docks in different geocenters. It should also be
appreciated that the presently described technology may schedule
shipments that may require products to pass through multiple
cross-docks.
[0128] In yet another embodiment, the presently described
technology may be adapted to provide shipping to remote locations
not currently accessible by road. For example, most shipping to the
Hawaiian Islands is via boat. However, the presently described
technology may be adapted to coordinate and substantially optimize
shipments of goods from across the country (or the world) into the
shipping port, for subsequent shipment to Hawaii.
[0129] FIG. 12 also demonstrates another feature of the presently
described technology. The optimization step may further include the
step of exercising discretionary control over the products to be
shipped. In this regard, higher priority goods may be shipped and
lower priority goods not shipped for later shipment. Thus, the
presently described technology contemplates the step of
prioritizing the products to be shipped. The presently described
technology also includes the ability to optimize shipments for
horizontal integration across different legal entities. The
presently described technology also includes the ability to
vertically integrate where multiple shipments across time are now
consolidated into one shipment. Thus, the presently described
technology includes the step of substantially optimizing the
product shipment temporally among at least one other shipment.
[0130] Thus, many features of the presently described technology
are realized singularly or in combination, such as, but not limited
to, the prioritization step further including the step of
determining at least one of the following steps:
[0131] (a) calculating a mix of additional products to be added to
at least part of the shipment when a total amount of product
shipped is greater than a minimum amount of product initially
ordered;
[0132] (b) calculating a mix of additional product to be added to
at least part of the shipment when the maximum vehicle load is not
exceeded;
[0133] (c) scheduling the shipment from the plurality of shippers
to arrive at a cross-dock before shipping the product to the at
least one receiver; and
[0134] (d) substantially optimizing the optimization metric.
[0135] Accordingly, the presently described technology also
includes the step of manipulating the shipment at a cross-dock in
the manners described herein. This may include the use of
destination indicia and may further include ensuring that products
entering the cross-dock have a predefined destination beyond the
cross-dock. As mentioned herein though, the cross-dock is not
critical to the operation of the presently described technology.
For example, optimization may occur without the physical
cross-dock. Two trucks operating within the presently described
technology system may meet somewhere, such as a truck stop or rest
stop. In one example, the first truck unhitches its trailer and
re-hitches it to the second truck. In this manner, the presently
described technology contemplates that optimization of these
trailers may be in order to substantially maximize that one truck
carrying two trailers arrives at a receiver. In another example,
the contents of the first truck may be packed into the second truck
so that the second truck capacity is substantially maximized,
without the use of formal cross-dock.
[0136] Therefore, one embodiment of the presently described
technology comprises a method of substantially optimizing a
shipment of at least one product from a plurality of shippers to at
least one receiver, the plurality of shippers comprising different
legal entities; or a method of substantially optimizing shipments
from a plurality of shippers to a plurality of receivers; or a
method of substantially optimizing shipments from at least one
shipper to at least one receiver, the presently described
technology comprising the steps of determining a maximum or
substantially maximum load of at least one transport vehicle from
the shippers; and substantially optimizing the maximum or
substantially maximum load of the least one transport vehicle.
[0137] As with any embodiment, optimization may include one or more
factors, such as the step of determining at least one of a
substantially maximum mass, maximum length, maximum height, maximum
width, maximum volume, and pallet footprint of the at least one
transport vehicle. Optimization may further include the step of
establishing at least one optimization metric, which may include
but is not limited to, a metric establishing step which further
includes the step of establishing at least one of the following
metrics: a capacity utilization per vehicle mile, total
transportation cost metric; transportation cost as percentage of
product value shipped metric; total logistics costs; shipping
revenue metric; and shipping revenue less freight cost metric.
[0138] As an inducement to participate, the presently described
technology also contemplates the providing of a trade allowance to
the receiver, for example, from the IMI to the receiver. The trade
allowance may include, but is not limited to, a rebate. Other
inducements such as percent off, coupons, rebates, premium
give-away, or other such commonly known features are expressly
contemplated.
[0139] The presently described technology also allows for a profit
sharing program, which is a further benefit to manufacturers,
distributors, and in particular, customers. For example, for any
given route run (the series of pick-ups and drop-offs a truck goes
through before returning to its original starting location) where
there are at least two customers, distributors, or manufacturers
involved (in any combination), a gross margin percentage may be
calculated from taking the total revenue generated by the route and
subtracting the total costs of the route. In doing so, one is able
to calculate the percentage remaining of the total revenue
generated from the operation of a particular route. For each member
(i.e., C, M, or D), that percentage remaining is then multiplied by
that member's gross revenue from the particular route run to
determine the amount of profit sharing.
[0140] More specifically, by way of one illustrative example,
assume a customer C1 had $1,000 dollar revenue generated from the
run/route, customer C2 had $2,000 dollar revenue generated from the
route run, and the gross margin percentage was 30%. Utilizing the
profit sharing program of the presently described technology,
customer Cl would receive 30% of $1000 and customer C2 would
receive30% of $2000 as their respective profit sharing for the
particular run/route.
[0141] It should be understood that the foregoing relates only to a
limited number of embodiments that have been provided for
illustration purposes only. It is intended that the scope of
invention is defined by the appended claims and that modifications
to the embodiment above may be made that do not depart from the
scope of the claims.
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