U.S. patent application number 16/888281 was filed with the patent office on 2021-06-10 for computer-implemented systems and methods for intelligent prediction of out of stock items and proactive reordering.
The applicant listed for this patent is COUPANG CORP.. Invention is credited to Liwen HE, Pil Su KIM, Min Woo LEE, Wei WEI, Zonghan WU, Ping YIN, Yong ZANG.
Application Number | 20210174298 16/888281 |
Document ID | / |
Family ID | 1000004860456 |
Filed Date | 2021-06-10 |
United States Patent
Application |
20210174298 |
Kind Code |
A1 |
YIN; Ping ; et al. |
June 10, 2021 |
COMPUTER-IMPLEMENTED SYSTEMS AND METHODS FOR INTELLIGENT PREDICTION
OF OUT OF STOCK ITEMS AND PROACTIVE REORDERING
Abstract
Methods and systems for determining a cause of out of stock
condition by running a decision tree against historical information
associated with the out of stock item and contacting a supplier of
the out of stock item to request items based on the determined
cause. The system receives information associated with an out of
stock item from a system storing information associated with items
in a fulfillment center, the information collected over an extended
period, determines a cause of the out of stock condition by running
a decision tree against the received information, the decision tree
includes a plurality of conditions, and predicts an out of stock
condition of the item based on the determined cause. Based on the
prediction, the system contacts a supplier of the out of stock item
to request more items.
Inventors: |
YIN; Ping; (Shanghai,
CN) ; WU; Zonghan; (Shanghai, CN) ; LEE; Min
Woo; (Suwon, KR) ; KIM; Pil Su; (Suwon,
KR) ; ZANG; Yong; (Shanghai, CN) ; WEI;
Wei; (Shanghai, CN) ; HE; Liwen; (Shanghai,
CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
COUPANG CORP. |
Seoul |
|
KR |
|
|
Family ID: |
1000004860456 |
Appl. No.: |
16/888281 |
Filed: |
May 29, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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16705572 |
Dec 6, 2019 |
10713622 |
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16888281 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q 10/087 20130101;
G06N 20/00 20190101; G06Q 30/0635 20130101; G06Q 10/06 20130101;
G06Q 20/203 20130101; G06Q 10/0875 20130101 |
International
Class: |
G06Q 10/08 20060101
G06Q010/08; G06N 20/00 20060101 G06N020/00; G06Q 30/06 20060101
G06Q030/06 |
Claims
1-20. (canceled)
21. A system, comprising: one or more memory devices storing
instructions; one or more processors configured to execute the
instructions to perform operations comprising: receiving
information associated with an out of stock item from a system
storing information associated with items in a fulfillment center,
the information collected over an extended period; determining a
cause of the out of stock condition by running a decision tree
against the received information, the decision tree including one
or more conditions selected from a low forecast for purchasing the
out of stock item, an unexecuted purchase order, an unreceived
purchase order by a supplier, a failure from a supplier to deliver
an ordered amount of the out of stock item, a defect with the out
of stock item, and a canceled purchase order; predicting an out of
stock condition of the item based on the determined cause;
contacting, based on the prediction, a supplier of the out of stock
item to order one or more items of the out of stock item; ordering
the one or more items of the out of stock item from the supplier;
and monitoring the status of the order.
22. The system of claim 21, wherein determining a cause of the out
of stock condition by running a decision tree comprises: measuring
a purity for each of the one or more conditions; choosing from the
one or more conditions the condition with the highest purity that
splits a decision tree into branches; storing the chosen condition
in a data structure;
23. The system of claim 22, wherein determining a cause of the out
of stock condition by running a decision tree further comprises:
repeating measuring a purity for each remaining condition and
choosing a condition with the highest purity to form a sub-divided
branch based on the chosen condition until all conditions split the
decision tree into branches; and determining a cause of out of
stock condition by finding a condition forming the longest path
from a root of the decision tree.
24. The system of claim 22, wherein the operations further comprise
limiting a number of conditions for measuring a purity.
25. The system of claim 22, wherein the purity provides a certainty
about whether an item is out of stock or not after a condition
splits a decision tree.
26. The system of claim 22, wherein the purity is symmetric.
27. The system of claim 21, wherein the one or more processors are
further configured to execute the instructions to perform
operations comprising: preventing the determined cause of the out
of stock condition from affecting ordering the one or more items of
the out of stock item.
28. The system of claim 21, wherein the information associated with
an out of stock item includes at least one cause for the out of
stock condition on a day.
29. The system of claim 1, wherein monitoring the status of the
order comprises contacting a supplier to check on status of
purchase orders associated with the out of stock item.
30. A method for determining a cause of out of stock condition,
comprising: receiving information associated with an out of stock
item from a system storing information associated with items in a
fulfillment center, the information collected over an extended
period; determining a cause of the out of stock condition by
running a decision tree against the received information, the
decision tree includes one or more conditions selected from a low
forecast for purchasing the out of stock item, an unexecuted
purchase order, an unreceived purchase order by a supplier, a
failure from a supplier to deliver an ordered amount of the out of
stock item, a defect with the out of stock item, and a canceled
purchase order; predicting an out of stock condition of the item
based on the determined cause; ordering, based on the prediction,
one or more of the out of stock item from a supplier; and
monitoring the status of the order.
31. The method of claim 30, wherein determining a cause of the out
of stock condition by running a decision tree comprises: measuring
a purity for each of the one or more conditions; and choosing from
the one or more conditions the condition with the highest purity
that splits a decision tree into branches.
32. The method of claim 31, wherein determining a cause of the out
of stock condition by running a decision tree further comprises:
repeating measuring a purity for each remaining condition and
choosing a condition with the highest purity to form a sub-divided
branch based on the chosen condition until all conditions split the
decision tree into branches; and determining a cause of out of
stock condition by finding a condition forming the longest path
from a root of the decision tree.
33. The method of claim 31, wherein the operations further comprise
limiting a number of conditions for measuring a purity.
34. The method of claim 31, wherein the purity provides a certainty
about whether an item is out of stock or not after a condition
splits a decision tree.
35. The method of claim 31, wherein the purity is symmetric.
36. The method of claim 30, further comprising: preventing the
determined cause of the out of stock condition from affecting
ordering the one or more items of the out of stock item.
37. The method of claim 30, wherein the information associated with
an out of stock item include at least one cause for the out of
stock condition on a day.
38. The method of claim 30, wherein the operations further comprise
contacting a supplier to check on status of purchase orders
associated with the out of stock item.
39. A system, comprising: one or more memory devices storing
instructions; one or more processors configured to execute the
instructions to perform operations comprising: receiving
information associated with an out of stock item from a system
storing information associated with items in a fulfillment center,
the information collected over an extended period; determining a
cause of the out of stock condition by running a decision tree
including a limited number of conditions against the received
information, wherein determining a cause of the out of stock
condition by running a decision tree comprises: measuring a purity
for each condition, the purity providing a certainty about whether
an item is out of stock or not after a condition splits a decision
tree; and choosing a condition with the highest purity that splits
a decision tree into branches; predicting an out of stock condition
of the item based on the determined cause; ordering, based on the
prediction, one or more of the out of stock item from a supplier;
monitoring the status of the order; and preventing the determined
cause of the out of stock condition from affecting ordering the one
or more items of the out of stock item.
40. The system of claim 39, wherein determining a cause of the out
of stock condition by running a decision tree further comprises:
repeating measuring a purity for each of remaining condition and
choosing a condition with the highest purity to form a sub-divided
branch based on the chosen condition until all conditions split the
decision tree into branches; and determining a cause of out of
stock condition by finding a condition forming the longest path
from a root of the decision tree.
Description
TECHNICAL FIELD
[0001] The present disclosure generally relates to computerized
systems and methods for predicting out of stock items. Embodiments
of the present disclosure relate to inventive and unconventional
systems for predicting out of stock items by running a decision
tree on information associated with items stored in a fulfillment
center to determine a cause of the out of stock condition.
BACKGROUND
[0002] Fulfillment centers (FCs) encounter more than millions of
products daily as they operate to fulfill consumer orders as soon
as the orders are placed and enable shipping carriers to pick up
shipments. Operations for managing inventory inside FCs may include
ordering products and stocking the ordered products so the products
can be shipped quickly as soon as the FCs receive the consumer
orders. Although currently existing FCs and systems for inventory
management in FCs are configured to forecast demands for products,
a common issue arises when a FC runs out of stock by purchasing
fewer products than an amount of consumer orders because of flawed
predictions on product demand. For example, a consumer visits a
website associated a merchant associated with an FC to purchase a
desired product, but the consumer discovers that the desired
product is out of stock. This leads to lost sales and poor customer
satisfaction, and a review from the dissatisfied consumer may
discourage potential sales from other buyers.
[0003] To mitigate such problems, conventional inventory management
systems improve a prediction on demands of products by determining
out of stock reasons. For example, the systems record one or more
occurrences relating to an out of stock condition to determine a
reason for the out of stock condition. While these systems attempt
to determine out of stock reasons in an efficient manner, the
process is manual and inconsistent.
[0004] Therefore, there is a need for improved methods and systems
for predicting an out of stock item by determining a cause of out
of stock condition.
SUMMARY
[0005] One aspect of the present disclosure is directed to a system
including a memory storing instructions and at least one processor
programmed to execute the instructions to perform a method for
predicting out of stock items by running a decision tree against
historical information associated with the out of stock items and
contacting a supplier of the out of stock item to request more
items based on the prediction. The method includes receiving
information associated with an out of stock item from a system
storing information associated with items in a fulfillment center,
the information collected over an extended period, determining a
cause of the out of stock condition by running a decision tree
against the received information, the decision tree includes a
plurality of conditions, and predicting an out of stock condition
of the item based on the determined cause. The method further
includes contacting a supplier of the out of stock item to request
more items based on the prediction.
[0006] Another aspect of the present disclosure is directed to a
method for predicting out of stock items by running a decision tree
against historical information associated with the out of stock
items and contacting a supplier of the out of stock item to request
more items based on the prediction. The method includes receiving
information associated with an out of stock item from a system
storing information associated with items in a fulfillment center,
the information collected over an extended period, determining a
cause of the out of stock condition by running a decision tree
against the received information, the decision tree includes a
plurality of conditions, and predicting an out of stock condition
of the item based on the determined cause. The method further
includes contacting a supplier of the out of stock item to request
more items based on the prediction.
[0007] Yet another aspect of the present disclosure is directed to
a system including a memory storing instructions and at least one
processor programmed to execute the instructions to perform a
method for predicting out of stock items by running a decision tree
against historical information associated with the out of stock
items and contacting a supplier of the out of stock item to request
more items based on the prediction. The method includes receiving
information associated with an out of stock item from a system
storing information associated with items in a fulfillment center,
the information collected over an extended period, and a limited
number of conditions. Based on the received information and the
limited number of conditions, the system determines a cause of the
out of stock condition by running a decision tree against the
received information, the decision tree includes the limited number
of conditions and predicts an out of stock condition of the item
based on the determined cause. The system may contact a supplier of
the out of stock item to request more items based on the
prediction.
[0008] Other systems, methods, and computer-readable media are also
discussed herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] FIG. 1A is a schematic block diagram illustrating an
exemplary embodiment of a network comprising computerized systems
for communications enabling shipping, transportation, and logistics
operations, consistent with the disclosed embodiments.
[0010] FIG. 1B depicts a sample Search Result Page (SRP) that
includes one or more search results satisfying a search request
along with interactive user interface elements, consistent with the
disclosed embodiments.
[0011] FIG. 1C depicts a sample Single Display Page (SDP) that
includes a product and information about the product along with
interactive user interface elements, consistent with the disclosed
embodiments.
[0012] FIG. 1D depicts a sample Cart page that includes items in a
virtual shopping cart along with interactive user interface
elements, consistent with the disclosed embodiments.
[0013] FIG. 1E depicts a sample Order page that includes items from
the virtual shopping cart along with information regarding purchase
and shipping, along with interactive user interface elements,
consistent with the disclosed embodiments.
[0014] FIG. 2 is a diagrammatic illustration of an exemplary
fulfillment center configured to utilize disclosed computerized
systems, consistent with the disclosed embodiments.
[0015] FIG. 3A shows an exemplary method for predicting an out of
stock condition of item by running a decision tree on supply chain
management system, consistent with the disclosed embodiments.
[0016] FIG. 3B shows an exemplary method for determining a cause of
out of stock condition by running a decision tree, consistent with
the disclosed embodiments.
[0017] FIG. 4A shows an exemplary table comprising data associated
with out of stock condition of item.
[0018] FIGS. 4B and 4C show exemplary methods for measuring a
purity for each condition and choosing a condition with the highest
purity.
[0019] FIG. 4D shows an exemplary decision tree comprising a
plurality of conditions.
[0020] FIG. 5A shows an exemplary method for analyzing a reason for
out of stock by running an out of stock root cause calculation
algorithm or a decision tree construction algorithm on Supply Chain
Management system, consistent with the disclosed embodiments.
[0021] FIG. 5B shows an exemplary method for analyzing a reason for
out of stock by running an out of stock root cause calculation
algorithm, consistent with the disclosed embodiments.
[0022] FIG. 5C shows an exemplary method for analyzing a reason for
out of stock by running a decision tree construction algorithm on
Supply Chain Management system, consistent with the disclosed
embodiments.
[0023] FIG. 6A-D show exemplary tables for analyzing a reason for
out of stock by running an out of stock root cause calculation
algorithm.
[0024] FIG. 7 shows an exemplary decision tree hierarchy list.
DETAILED DESCRIPTION
[0025] The following detailed description refers to the
accompanying drawings. Wherever possible, the same reference
numbers are used in the drawings and the following description to
refer to the same or similar parts. While several illustrative
embodiments are described herein, modifications, adaptations and
other implementations are possible. For example, substitutions,
additions, or modifications may be made to the components and steps
illustrated in the drawings, and the illustrative methods described
herein may be modified by substituting, reordering, removing, or
adding steps to the disclosed methods, or by performing
non-dependent steps in parallel with each other. Accordingly, the
following detailed description is not limited to the disclosed
embodiments and examples. Instead, the proper scope of the
invention is defined by the appended claims.
[0026] Embodiments of the present disclosure are directed to
computer-implemented systems and methods configured for predicting
an out of stock condition of item by running a decision tree. The
disclosed embodiments provide innovative technical features that
allow proactive ordering of an item in a fulfilment center by
predicting an out of stock condition of the item, wherein the
prediction is attained by running a decision tree against
information related to the item. For example, the disclosed
embodiments enable determination of a cause of out of stock
condition by running a decision tree against information received
from a system storing information associated with items in a
fulfillment center, enable prediction on an out of stock condition
of the item based on the determined cause, and enable efficient
transmission of request to a supplier of the out of stock item to
request more items based on the prediction.
[0027] Referring to FIG. 1A, a schematic block diagram 100
illustrating an exemplary embodiment of a system comprising
computerized systems for communications enabling shipping,
transportation, and logistics operations is shown. As illustrated
in FIG. 1A, system 100 may include a variety of systems, each of
which may be connected to one another via one or more networks. The
systems may also be connected to one another via a direct
connection, for example, using a cable. The depicted systems
include a shipment authority technology (SAT) system 101, an
external front end system 103, an internal front end system 105, a
transportation system 107, mobile devices 107A, 107B, and 107C,
seller portal 109, shipment and order tracking (SOT) system 111,
fulfillment optimization (FO) system 113, fulfillment messaging
gateway (FMG) 115, supply chain management (SCM) system 117,
warehouse management system 119, mobile devices 119A, 119B, and
119C (depicted as being inside of fulfillment center (FC) 200),
3.sup.rd party fulfillment systems 121A, 121B, and 121C,
fulfillment center authorization system (FC Auth) 123, and labor
management system (LMS) 125.
[0028] SAT system 101, in some embodiments, may be implemented as a
computer system that monitors order status and delivery status. For
example, SAT system 101 may determine whether an order is past its
Promised Delivery Date (PDD) and may take appropriate action,
including initiating a new order, reshipping the items in the
non-delivered order, canceling the non-delivered order, initiating
contact with the ordering customer, or the like. SAT system 101 may
also monitor other data, including output (such as a number of
packages shipped during a particular time period) and input (such
as the number of empty cardboard boxes received for use in
shipping). SAT system 101 may also act as a gateway between
different devices in system 100, enabling communication (e.g.,
using store-and-forward or other techniques) between devices such
as external front end system 103 and FO system 113.
[0029] External front end system 103, in some embodiments, may be
implemented as a computer system that enables external users to
interact with one or more systems in system 100. For example, in
embodiments where system 100 enables the presentation of systems to
enable users to place an order for an item, external front end
system 103 may be implemented as a web server that receives search
requests, presents item pages, and solicits payment information.
For example, external front end system 103 may be implemented as a
computer or computers running software such as the Apache HTTP
Server, Microsoft Internet Information Services (IIS), NGINX, or
the like. In other embodiments, external front end system 103 may
run custom web server software designed to receive and process
requests from external devices (e.g., mobile device 102A or
computer 102B), acquire information from databases and other data
stores based on those requests, and provide responses to the
received requests based on acquired information.
[0030] In some embodiments, external front end system 103 may
include one or more of a web caching system, a database, a search
system, or a payment system. In one aspect, external front end
system 103 may comprise one or more of these systems, while in
another aspect, external front end system 103 may comprise
interfaces (e.g., server-to-server, database-to-database, or other
network connections) connected to one or more of these systems.
[0031] An illustrative set of steps, illustrated by FIGS. 1B, 1C,
1D, and 1E, will help to describe some operations of external front
end system 103. External front end system 103 may receive
information from systems or devices in system 100 for presentation
and/or display. For example, external front end system 103 may host
or provide one or more web pages, including a Search Result Page
(SRP) (e.g., FIG. 1B), a Single Detail Page (SDP) (e.g., FIG. 1C),
a Cart page (e.g., FIG. 1D), or an Order page (e.g., FIG. 1E). A
user device (e.g., using mobile device 102A or computer 102B) may
navigate to external front end system 103 and request a search by
entering information into a search box. External front end system
103 may request information from one or more systems in system 100.
For example, external front end system 103 may request information
from FO System 113 that satisfies the search request. External
front end system 103 may also request and receive (from FO System
113) a Promised Delivery Date or "PDD" for each product included in
the search results. The PDD, in some embodiments, may represent an
estimate of when a package containing the product will arrive at
the user's desired location or a date by which the product is
promised to be delivered at the user's desired location if ordered
within a particular period of time, for example, by the end of the
day (11:59 PM). (PDD is discussed further below with respect to FO
System 113.)
[0032] External front end system 103 may prepare an SRP (e.g., FIG.
1B) based on the information. The SRP may include information that
satisfies the search request. For example, this may include
pictures of products that satisfy the search request. The SRP may
also include respective prices for each product, or information
relating to enhanced delivery options for each product, PDD,
weight, size, offers, discounts, or the like. External front end
system 103 may send the SRP to the requesting user device (e.g.,
via a network).
[0033] A user device may then select a product from the SRP, e.g.,
by clicking or tapping a user interface, or using another input
device, to select a product represented on the SRP, The user device
may formulate a request for information on the selected product and
send it to external front end system 103. In response, external
front end system 103 may request information related to the
selected product. For example, the information may include
additional information beyond that presented for a product on the
respective SRP. This could include, for example, shelf life,
country of origin, weight, size, number of items in package,
handling instructions, or other information about the product. The
information could also include recommendations for similar products
(based on, for example, big data and/or machine learning analysis
of customers who bought this product and at least one other
product), answers to frequently asked questions, reviews from
customers, manufacturer information, pictures, or the like.
[0034] External front end system 103 may prepare an SDP (Single
Detail Page) (e.g., FIG. 1C) based on the received product
information. The SDP may also include other interactive elements
such as a "Buy Now" button, a "Add to Cart" button, a quantity
field a picture of the item, or the like. The SDP may further
include a list of sellers that offer the product. The list may be
ordered based on the price each seller offers such that the seller
that offers to sell the product at the lowest price may be listed
at the top. The list may also be ordered based on the seller
ranking such that the highest ranked seller may be listed at the
top. The seller ranking may be formulated based on multiple
factors, including, for example, the seller's past track record of
meeting a promised PDD. External front end system 103 may deliver
the SDP to the requesting user device (e.g., via a network).
[0035] The requesting user device may receive the SDP which lists
the product information. Upon receiving the SDP, the user device
may then interact with the SDP. For example, a user of the
requesting user device may click or otherwise interact with a
"Place in Cart" button on the SDP. This adds the product to a
shopping cart associated with the user. The user device may
transmit this request to add the product to the shopping cart to
external front end system 103.
[0036] External front end system 103 may generate a Cart page
(e.g., FIG. 1D). The Cart page, in some embodiments, lists the
products that the user has added to a virtual "shopping cart." A
user device may request the Cart page by clicking on or otherwise
interacting with an icon on the SRP, SDP, or other pages. The Cart
page may, in some embodiments, list all products that the user has
added to the shopping cart, as well as information about the
products in the cart such as a quantity of each product, a price
for each product per item, a price for each product based on an
associated quantity, information regarding PDD, a delivery method,
a shipping cost, user interface elements for modifying the products
in the shopping cart (e.g., deletion or modification of a
quantity), options for ordering other product or setting up
periodic delivery of products, options for setting up interest
payments, user interface elements for proceeding to purchase, or
the like. A user at a user device may click on or otherwise
interact with a user interface element (e.g., a button that reads
"Buy Now") to initiate the purchase of the product in the shopping
cart. Upon doing so, the user device may transmit this request to
initiate the purchase to external front end system 103.
[0037] External front end system 103 may generate an Order page
(e.g., FIG. 1E) in response to receiving the request to initiate a
purchase. The Order page, in some embodiments, re-lists the items
from the shopping cart and requests input of payment and shipping
information. For example, the Order page may include a section
requesting information about the purchaser of the items in the
shopping cart (e.g., name, address, e-mail address, phone number),
information about the recipient (e.g., name, address, phone number,
delivery information), shipping information (e.g., speed/method of
delivery and/or pickup), payment information (e.g., credit card,
bank transfer, check, stored credit), user interface elements to
request a cash receipt (e.g., for tax purposes), or the like.
External front end system 103 may send the Order page to the user
device.
[0038] The user device may enter information on the Order page and
click or otherwise interact with a user interlace element that
sends the information to external front end system 103. From there,
external front end system 103 may send the information to different
systems in system 100 to enable the creation and processing of a
new order with the products in the shopping cart.
[0039] In some embodiments, external front end system 103 may be
further configured to enable sellers to transmit and receive
information relating to orders.
[0040] Internal front end system 105, in some embodiments, may be
implemented as a computer system that enables internal users (e.g.,
employees of an organization that owns, operates, or leases system
100) to interact with one or more systems in system 100. For
example, in embodiments where network 101 enables the presentation
of systems to enable users to place an order for an item, internal
front end system 105 may be implemented as a web server that
enables internal users to view diagnostic and statistical
information about orders, modify item information, or review
statistics relating to orders. For example, internal front end
system 105 may be implemented as a computer or computers running
software such as the Apache HTTP Server, Microsoft Internet
Information Services (IIS), NGINX, or the like. In other
embodiments, internal front end system 105 may run custom web
server software designed to receive and process requests from
systems or devices depicted in system 100 (as well as other devices
not depicted), acquire information from databases and other data
stores based on those requests, and provide responses to the
received requests based on acquired information.
[0041] In some embodiments, internal front end system 105 may
include one or more of a web caching system, a database, a search
system, a payment system, an analytics system, an order monitoring
system, or the like. In one aspect, internal front end system 105
may comprise one or more of these systems, while in another aspect,
internal front end system 105 may comprise interfaces (e.g.,
server-to-server, database-to-database, or other network
connections) connected to one or more of these systems.
[0042] Transportation system 107, in some embodiments, may be
implemented as a computer system that enables communication between
systems or devices in system 100 and mobile devices 107A-107C.
Transportation system 107, in some embodiments, may receive
information from one or more mobile devices 107A-107C (e.g., mobile
phones, smart phones, PDAs, or the like). For example, in some
embodiments, mobile devices 107A-107C may comprise devices operated
by delivery workers. The delivery workers, who may be permanent,
temporary, or shift employees, may utilize mobile devices 107A-107C
to effect delivery of packages containing the products ordered by
users. For example, to deliver a package, the delivery worker may
receive a notification on a mobile device indicating which package
to deliver and where to deliver it. Upon arriving at the delivery
location, the delivery worker may locate the package (e.g., in the
back of a truck or in a crate of packages), scan or otherwise
capture data associated with an identifier on the package (e.g., a
barcode, an image, a text string, an RFID tag, or the like) using
the mobile device, and deliver the package (e.g., by leaving it at
a front door, leaving it with a security guard, handing it to the
recipient, or the like). In some embodiments, the delivery worker
may capture photo(s) of the package and/or may obtain a signature
using the mobile device. The mobile device may send information to
transportation system 107 including information about the delivery,
including, for example, time, date, GPS location, photo(s), an
identifier associated with the delivery worker, an identifier
associated with the mobile device, or the like. Transportation
system 107 may store this information in a database (not pictured)
for access by other systems in system 100. Transportation system
107 may, in some embodiments, use this information to prepare and
send tracking data to other systems indicating the location of a
particular package.
[0043] In some embodiments, certain users may use one kind of
mobile device (e.g., permanent workers may use a specialized FDA
with custom hardware such as a barcode scanner, stylus, and other
devices) while other users may use other kinds of mobile devices
(e.g., temporary or shift workers may utilize off-the-shelf mobile
phones and/or smartphones).
[0044] In some embodiments, transportation system 107 may associate
a user with each device. For example, transportation system 107 may
store an association between a user (represented by, e.g., a user
identifier, an employee identifier, or a phone number) and a mobile
device (represented by, e.g., an International Mobile Equipment
Identity (IMEI), an International Mobile Subscription Identifier
(IMSI), a phone number, a Universal Unique Identifier (UUID), or a
Globally Unique Identifier (GUID)). Transportation system 107 may
use this association in conjunction with data received on
deliveries to analyze data stored in the database in order to
determine, among other things, a location of the worker, an
efficiency of the worker, or a speed of the worker.
[0045] Seller portal 109, in some embodiments, may be implemented
as a computer system that enables sellers or other external
entities to electronically communicate with one or more systems in
system 100. For example, a seller may utilize a computer system
(not pictured) to upload or provide product information, order
information, contact information, or the like, for products that
the seller wishes to sell through system 100 using seller portal
109.
[0046] Shipment and order tracking system 111, in some embodiments,
may be implemented as a computer system that receives, stores, and
forwards information regarding the location of packages containing
products ordered by customers (e.g., by a user using devices
102A-102B). In some embodiments, shipment and order tracking system
111 may request or store information from web servers (not
pictured) operated by shipping companies that deliver packages
containing products ordered by customers.
[0047] In some embodiments, shipment and order tracking system 111
may request and store information from systems depicted in system
100. For example, shipment and order tracking system 111 may
request information from transportation system 107. As discussed
above, transportation system 107 may receive information from one
or more mobile devices 107A-107C (e.g., mobile phones, smart
phones. PDAs, or the like) that are associated with one or more of
a user (e.g., a delivery worker) or a vehicle (e.g., a delivery
truck). In some embodiments, shipment and order tracking system 111
may also request information from warehouse management system (WMS)
119 to determine the location of individual products inside of a
fulfillment center (e.g., fulfillment center 200). Shipment and
order tracking system 111 may request data from one or more of
transportation system 107 or WMS 119, process it, and present it to
a device (e.g., user devices 102A and 102B) upon request.
[0048] Fulfillment optimization (FO) system 113, in some
embodiments, may be implemented as a computer system that stores
information for customer orders from other systems (e.g., external
front end system 103 and/or shipment and order tracking system
111). FO system 113 may also store information describing where
particular items are held or stored. For example, certain items may
be stored only in one fulfillment center, while certain other items
may be stored in multiple fulfillment centers. In still other
embodiments, certain fulfilment centers may be designed to store
only a particular set of items (e.g., fresh produce or frozen
products). FO system 113 stores this information as well as
associated information (e.g., quantity, size, date of receipt,
expiration date, etc.).
[0049] FO system 113 may also calculate a corresponding PDD
(promised delivery date) for each product. The PDD, in some
embodiments, may be based on one or more factors. For example, FO
system 113 may calculate a PDD for a product based on a past demand
for a product (e.g., how many times that product was ordered during
a period of time), an expected demand for a product (e.g., how many
customers are forecast to order the product during an upcoming
period of time), a network-wide past demand indicating how many
products were ordered during a period of time, a network-wide
expected demand indicating how many products are expected to be
ordered during an upcoming period of time, one or more counts of
the product stored in each fulfillment center 200, which
fulfillment center stores each product, expected or current orders
for that product, or the like.
[0050] In some embodiments, FO system 113 may determine a PDD for
each product on a periodic basis (e.g., hourly) and store it in a
database for retrieval or sending to other systems (e.g., external
front end system 103, SAT system 101, shipment and order tracking
system 111). In other embodiments, FO system 113 may receive
electronic requests from one or more systems (e.g., external front
end system 103, SAT system 101, shipment and order tracking system
111) and calculate the PDD on demand.
[0051] Fulfilment messaging gateway (FMG) 115, in some embodiments,
may be implemented as a computer system that receives a request or
response in one format or protocol from one or more systems in
system 100, such as FO system 113, converts it to another format or
protocol, and forward it in the converted format or protocol to
other systems, such as WMS 119 or 3.sup.rd party fulfillment
systems 121A, 121B, or 121C, and vice versa.
[0052] Supply chain management (SCM) system 117, in some
embodiments, may be implemented as a computer system that performs
forecasting functions. For example, SCM system 117 may forecast a
level of demand for a particular product based on, for example,
based on a past demand for products, an expected demand for a
product, a network-wide past demand, a network-wide expected
demand, a count products stored in each fulfillment center 200,
expected or current orders for each product, or the like. In
response to this forecasted level and the amount of each product
across all fulfillment centers, SCM system 117 may generate one or
more purchase orders to purchase and stock a sufficient quantity to
satisfy the forecasted demand for a particular product.
[0053] Warehouse management system (WMS) 119, in some embodiments,
may be implemented as a computer system that monitors workflow. For
example, WMS 119 may receive event data from individual devices
(e.g., devices 107A-107C or 119A-119C) indicating discrete events.
For example, WMS 119 may receive event data indicating the use of
one of these devices to scan a package. As discussed below with
respect to fulfillment center 200 and FIG. 2, during the
fulfillment process, a package identifier (e.g., a barcode or RFID
tag data) may be scanned or read by machines at particular stages
(e.g., automated or handheld barcode scanners, RFID readers,
high-speed cameras, devices such as tablet 119A, mobile device/PDA
119B, computer 119C, or the like). WMS 119 may store each event
indicating a scan or a read of a package identifier in a
corresponding database (not pictured) along with the package
identifier, a time, date, location, user identifier, or other
information, and may provide this information to other systems
(e.g., shipment and order tracking system 111).
[0054] WMS 119, in some embodiments, may store information
associating one or more devices (e.g., devices 107A-107C or
119A-119C) with one or more users associated with system 100. For
example, in some situations, a user (such as a part- or full-time
employee) may be associated with a mobile device in that the user
owns the mobile device (e.g., the mobile device is a smartphone).
In other situations, a user may be associated with a mobile device
in that the user is temporarily in custody of the mobile device
(e.g., the user checked the mobile device out at the start of the
day, will use it during the day, and will return it at the end of
the day).
[0055] WMS 119, in some embodiments, may maintain a work log for
each user associated with system 100. For example, WMS 119 may
store information associated with each employee, including any
assigned processes (e.g., unloading trucks, picking items from a
pick zone, rebin wall work, packing items), a user identifier, a
location (e.g., a floor or zone in a fulfillment center 200), a
number of units moved through the system by the employee (e.g.,
number of items picked, number of items packed), an identifier
associated with a device (e.g., devices 119A-119C), or the like. In
some embodiments, WMS 119 may receive check-in and check-out
information from a timekeeping system, such as a timekeeping system
operated on a device 119A-119C.
[0056] 3.sup.rd party fulfillment (3PL) systems 121A-121C, in some
embodiments, represent computer systems associated with third-party
providers of logistics and products. For example, while some
products are stored in fulfillment center 200 (as discussed below
with respect to FIG. 2), other products may be stored off-site, may
be produced on demand, or may be otherwise unavailable for storage
in fulfillment center 200. 3PL systems 121A-121C may be configured
to receive orders from FO system 113 (e.g., through FMG 115) and
may provide products and/or services (e.g., delivery or
installation) to customers directly. In some embodiments, one or
more of 3PL systems 121A-121C may be part of system 100, while in
other embodiments, one or more of 3PL systems 121A-121C may be
outside of system 100 (e.g., owned or operated by a third-party
provider).
[0057] Fulfillment Center Auth system (FC Auth) 123, in some
embodiments, may be implemented as a computer system with a variety
of functions. For example, in some embodiments, FC Auth 123 may act
as a single-sign on (SSO) service for one or more other systems in
system 100. For example, FC Auth 123 may enable a user to log in
via internal front end system 105, determine that the user has
similar privileges to access resources at shipment and order
tracking system 111, and enable the user to access those privileges
without requiring a second log in process. FC Auth 123, in other
embodiments, may enable users (e.g., employees) to associate
themselves with a particular task. For example, some employees may
not have an electronic device (such as devices 119A-119C) and may
instead move from task to task, and zone to zone, within a
fulfillment center 200, during the course of a day. FC Auth 123 may
be configured to enable those employees to indicate what task they
are performing and what zone they are in at different times of
day.
[0058] Labor management system (LMS) 125, in some embodiments, may
be implemented as a computer system that stores attendance and
overtime information for employees (including full-time and
part-time employees). For example, LMS 125 may receive information
from FC Auth 123, WMA 119, devices 119A-119C, transportation system
107, and/or devices 107A-107C.
[0059] The particular configuration depicted in FIG. 1A is an
example only. For example, while FIG. 1A depicts FC Auth system 123
connected to FO system 113, not all embodiments require this
particular configuration. Indeed, in some embodiments, the systems
in system 100 may be connected to one another through one or more
public or private networks, including the Internet, an Intranet, a
WAN (Wide-Area Network), a MAN (Metropolitan-Area Network), a
wireless network compliant with the IEEE 802.11a/b/g/n Standards, a
leased line, or the like. In some embodiments, one or more of the
systems in system 100 may be implemented as one or more virtual
servers implemented at a data center, server farm, or the like.
[0060] FIG. 2 depicts a fulfillment center 200. Fulfillment center
200 is an example of a physical location that stores items for
shipping to customers when ordered. Fulfillment center (FC) 200 may
be divided into multiple zones, each of which are depicted in FIG.
2. These "zones," in some embodiments, may be thought of as virtual
divisions between different stages of a process of receiving items,
storing the items, retrieving the items, and shipping the items. So
while the "zones" are depicted in FIG. 2, other divisions of zones
are possible, and the zones in FIG. 2 may be omitted, duplicated,
or modified in some embodiments.
[0061] Inbound zone 203 represents an area of FC 200 where items
are received from sellers who wish to sell products using system
100 from FIG. 1A. For example, a seller ay deliver items 202A and
202B using truck 201. Item 202A may represent a single item large
enough to occupy its own shipping pallet, while item 202B may
represent a set of items that are stacked together on the same
pallet to save space.
[0062] A worker will receive the items in inbound zone 203 and may
optionally check the items for damage and correctness using a
computer system (not pictured). For example, the worker may use a
computer system to compare the quantity of items 202A and 202B to
an ordered quantity of items. If the quantity does not match, that
worker may refuse one or more of items 202A or 202B. If the
quantity does match, the worker may move those items (using, e.g.,
a dolly, a handtruck, a forklift, or manually) to buffer zone 205.
Buffer zone 205 may be a temporary storage area for items that are
not currently needed in the picking zone, for example, because
there is a high enough quantity of that item in the picking zone to
satisfy forecasted demand. In some embodiments, forklifts 206
operate to move items around buffer zone 205 and between inbound
zone 203 and drop zone 207. If there is a need for items 202A or
202B in the picking zone (e.g., because of forecasted demand), a
forklift may move items 202A or 202B to drop zone 207.
[0063] Drop zone 207 may be an area of FC 200 that stores items
before they are moved to picking zone 209. A worker assigned to the
picking task (a "picker") may approach items 202A and 202B in the
picking zone, scan a barcode for the picking zone, and scan
barcodes associated with items 202A and 202B using a mobile device
(e.g., device 119B). The picker may then take the item to picking
zone 209 (e.g., by placing it on a cart or carrying it).
[0064] Picking zone 209 may be an area of FC 200 where items 208
are stored on storage units 210. In some embodiments, storage units
210 may comprise one or more of physical shelving, bookshelves,
boxes, totes, refrigerators, freezers, cold stores, or the like. In
some embodiments, picking zone 209 may be organized into multiple
floors. In some embodiments, workers or machines may move items
into picking zone 209 in multiple ways, including, for example, a
forklift, an elevator, a conveyor belt, a cart, a handtruck, a
dolly, an automated robot or device, or manually. For example, a
picker may place items 202A and 202B on a handtruck or cart in drop
zone 207 and walk items 202A and 202B to picking zone 209.
[0065] A picker may receive an instruction to place (or "stow") the
items in particular spots in picking zone 209, such as a particular
space on a storage unit 210. For example, a picker may scan item
202A using a mobile device (e.g., device 119B). The device may
indicate where the picker should stow item 202A, for example, using
a system that indicate an aisle, shelf, and location. The device
may then prompt the picker to scan a barcode at that location
before stowing item 202A in that location. The device may send
(e.g., via a wireless network) data to a computer system such as
WMS 119 in FIG. 1A indicating that item 202A has been stowed at the
location by the user using device 119B.
[0066] Once a user places an order, a picker may receive an
instruction on device 119B to retrieve one or more items 208 from
storage unit 210. The picker may retrieve item 208, scan a barcode
on item 208, and place it on transport mechanism 214. While
transport mechanism 214 is represented as a slide, in some
embodiments, transport mechanism may be implemented as one or ore
of a conveyor belt, an elevator, a cart, a forklift, a handtruck, a
dolly, a cart, or the like. Item 208 may then arrive at packing
zone 211.
[0067] Packing zone 211 may be an area of FC 200 where items are
received from picking zone 209 and packed into boxes or bags for
eventual shipping to customers. In packing zone 211, a worker
assigned to receiving items (a "rebin worker") will receive item
208 from picking zone 209 and determine what order it corresponds
to. For example, the rebin worker may use a device, such as
computer 119C, to scan a barcode on item 208. Computer 119C may
indicate visually which order item 208 is associated with. This may
include, for example, a space or "cell" on a wall 216 that
corresponds to an order. Once the order is complete (e.g., because
the cell contains all items for the order), the rebin worker may
indicate to a packing worker (or "packer") that the order is
complete. The packer may retrieve the items from the cell and place
them in a box or bag for shipping. The packer may then send the box
or bag to a hub zone 213, e.g., via forklift, cart, dolly,
handtruck, conveyor belt, manually, or otherwise.
[0068] Hub zone 213 may be an area of FC 200 that receives all
boxes or bags ("packages"' from packing zone 211. Workers and/or
machines in hub zone 213 may retrieve package 218 and determine
which portion of a delivery area each package is intended to go to,
and route the package to an appropriate camp zone 215. For example,
if the delivery area has two smaller sub-areas, packages will go to
one of two camp zones 215. In some embodiments, a worker or machine
may scan a package (e.g., using one of devices 119A-119C) to
determine its eventual destination. Routing the package to camp
zone 215 may comprise, for example, determining a portion of a
geographical area that the package is destined for (e.g., based on
a postal code) and determining a camp zone 215 associated with the
portion of the geographical area.
[0069] Camp zone 215, in some embodiments, may comprise one or more
buildings, one or more physical spaces, or one or more areas, where
packages are received from hub zone 213 for sorting into routes
and/or sub-routes. In some embodiments, camp zone 215 is physically
separate from FC 200 while in other embodiments camp zone 215 may
form a part of FC 200.
[0070] Workers and/or machines in camp zone 215 may determine which
route and/or sub-route a package 220 should be associated with, for
example, based on a comparison of the destination to an existing
route and/or sub-route, a calculation of workload for each route
and/or sub-route, the time of day, a shipping method, the cost to
ship the package 220, a PDD associated with the items in package
220, or the like. In some embodiments, a worker or machine may scan
a package (e.g., using one of devices 119A-119C) to determine its
eventual destination. Once package 220 is assigned to a particular
route and/or sub-route, a worker and/or machine may move package
220 to be shipped. In exemplary FIG. 2, camp zone 215 includes a
truck 222, a car 226, and delivery workers 224A and 224B. In some
embodiments, truck 222 may be driven by delivery worker 224A, where
delivery worker 224A is a full-time employee that delivers packages
for FC 200 and truck 222 is owned, leased, or operated by the same
company that owns, leases, or operates FC 200. In some embodiments,
car 226 may be driven by delivery worker 224B, where delivery
worker 224B is a "flex" or occasional worker that is delivering on
an as-needed basis (e.g., seasonally). Car 226 may be owned,
leased, or operated by delivery worker 224B.
[0071] According to an aspect of the present disclosure, a
computer-implemented system for predicting an out of stock
condition may comprise one or more memory devices storing
instructions, and one or more processors configured to execute the
instructions to perform operations. The out of stock condition can
be predicted or analyzed by running an out of stock (OOS) Root
Cause Calculation Algorithm, decision tree construction algorithm,
or a decision tree against historical information associated with
the out of stock item. In some embodiments, the disclosed
functionality and systems may be implemented as part of SCM system
117. The preferred embodiment comprises implementing the disclosed
functionality and systems on SCM system 117, but one of ordinary
skill will understand that other implementations are possible.
[0072] Stock availability can be determined by one or more
contributors associated with supply chain ordering and inventory
replenishing. For example, contributors may be an error or mistake
from commercial decisions, a defect from suppliers, a defect from
ordering items, and a defect from fulfillment centers. The
contributors may include one or more root causes for out of stock
condition. For example, an error or mistake from commercial
decisions may include root causes such as obsolete confirming and
strategic decision; a defect from suppliers may include none
delivery and short delivery; a defect from ordering items may
include low recommended order quantity and sales spike; and a
defect from fulfillment centers may include a delay in receiving
items and a delay in stowing items. The root causes may be arranged
in a decision tree by their priorities. The priorities may be
determined by a static rule. For example, if a static rule for
determining priorities of root causes prioritizes internal issues
over external issues, then a root cause associated with a defect
from fulfillment centers is prioritized over a root cause
associated a defect from suppliers.
[0073] FIG. 3A shows an exemplary method 300 for predicting an out
of stock condition of item by running a decision tree on SCM system
117. The method or a portion thereof may be performed by SCM system
117. For example, the system may include one or more processors and
a memory storing instructions that, when executed by the one or
more processors, cause the system to perform the steps shown in
FIG. 3A.
[0074] In step 301, SCM 117 may receive information associated with
an out of stock item from FO system 113. As described above with
respect to FIG. 1A, FO system 113 may store information related to
items stored in fulfillment center 200. The stored information may
also include one or more conditions causing an out of stock
condition. For example, conditions causing an out of stock
condition may include, but are not limited to, a low prediction on
forecasting a demand for an item, an unexecuted purchase order for
an item, that a purchase order was placed but not yet received by a
fulfillment center, a supplier of out of stock item failed to
deliver the ordered amount, a defect on an out of stock item, and a
cancelation of purchase order associated with an out of stock item.
When a quantity of an item stored in fulfillment center reaches
zero (Out of Stock), FO system 113 may transmit information
associated with the item to SCM system 117 for determining a cause
of the out of stock condition. The transmitted information was
collected over an extended period. For example, FO system 113 may
transmit an exemplary table 400 in FIG. 4A (discussed further
below) to SCM system 117. The exemplary table 400 may include
information related to one or more conditions causing an out of
stock condition and whether an item was out of stock on a given
day. The information presented in the exemplary table 400 was
collected over ten days. The exemplary table 400 presents only
three conditions and data collected over ten days but one of
ordinary skill will understand that other configurations are
possible.
[0075] In step 302, SCM system 117 may determine a cause of the out
of stock condition by running a decision tree against the received
information, wherein the decision tree includes a plurality of
conditions. Step 302 is further described with respect to step 311
in FIG. 3B. In step 311 (FIG. 3B), SCM system 117 may limit a
number of conditions. The number of conditions may refer to the
length of the longest path from a root to a leaf in a decision
tree. For example, as shown in FIG. 4D (discussed further below),
decision tree 450 includes three conditions representing the length
of the longest path from a root (Low Forecast?) to a leaf (Out of
Stock or Not Out of Stock under Unexecuted Purchase Order).
Limiting a number of conditions may assist in reducing overfitting
of decision tree, wherein the overfitting results from creating
over-complex decision trees that do not generalize data well.
[0076] In step 312, SCM system 117 may measure a purity for each
condition. The purity may provide a certainty about whether an item
goes out of stock or not after a condition splits a decision tree.
For example, as shown in FIG. 4A, item was out of stock for six
days over past ten days. In FIG. 4B, a purity for each of
conditions 410, 411, and 412 is measured. For example, condition
410 has a pure set (3 Yes-Out of Stock on days D3, D7 and D10/0
No-Not Out of Stock) because an item as always out of stock when
the item was forecasted low. The pure set provides a complete
certainty on an occurrence of out of stock condition. The purity
must be symmetric. For example, a condition comprising 4 Out of
Stock/0 Not Out of Stock is as "pure" as 0 Out of Stock/4 Not Out
of Stock. Unlike condition 410, conditions 411 and 412 do not
result a pure set thus in step 313, SCM system 117 may choose
condition 410, the condition with the highest purity. The chosen
condition (e.g., condition 410) may split a decision tree. SCM 117
may store the chosen condition further splitting the decision tree
in a data structure.
[0077] In step 314, SCM system 117 may determine whether all
conditions split the decision tree into branches after measuring a
purity for each of remaining conditions and choosing a condition
with the highest purity. If all conditions do not split the
decision trees into branches, SCM system 117, in step 312, may
measure a purity for each of remaining conditions. For example, as
shown in FIG. 4C, SCM 117 may measure purities for conditions 411
and 412 after choosing highest purity condition 410 in FIG. 4B. By
way of further example, as shown in FIG. 4C, an item was out of
stock for three days and not out of stock for four days when the
item was not forecasted low. Among days when the item as not
forecasted low (Days D1, D2, D4, D5, D6, D8, and D9), condition 412
has a pure set (0 Yes-Out of Stock/2 No-Not Out of Stock on days D2
and D6) because the item was always not out of stock when purchase
orders associated the item were not canceled. Thus, SCM system 117
may choose condition 412 as a condition splitting the tree after
condition 410 and store the condition 412 in the data structure
storing the decision tree and chosen conditions splitting the
decision tree. As shown in FIG. 4D, condition 412 splits the
decision tree 450 branching out of condition 410. Condition 411, as
an only remaining condition may split the decision tree after
condition 412 in the exemplary decision tree 450 in FIG. 4D.
[0078] If all conditions split the decision tree (e.g., decision
tree 450 in FIG. 4D) into branches, SCM system 117, in step 315,
may determine a cause of out of stock condition by finding a
condition forming the longest path from a root of the decision
tree. The condition forming the longest path from a root of the
decision tree may represent a cause of out of stock condition. For
example, as shown in FIG. 4D, condition 411 (purchase orders
associated with the out of stock item was unexecuted) forms the
longest path from a root (condition 410) of the decision tree 450
and SCM 117 may determine that unexecuted purchase orders
associated with the out of stock item is a cause of out of stock
condition for the item.
[0079] After step 315 in FIG. 3B, the process moves to step 303 in
FIG. 3A. In step 303, SCM 117 may predict an out of stock condition
of the item based on the determined cause.
[0080] In step 304, SCM 117 may contact a supplier of the out of
stock item to request more items based on the prediction. For
example, if an item was predicted to be out of stock, SCM 117 may
contact a supplier of the out of stock item to request more items.
SCM 117 may also check status of purchase orders to prevent
unexecuting the purchase orders if the determined cause is
unexecuted purchase orders of the item.
[0081] FIG. 5A shows an exemplary method 500 for analyzing a reason
for out of stock by running an out of stock root cause calculation
algorithm or a decision tree construction algorithm on SCM system
117. The method or a portion thereof may be performed by SCM system
117. For example, the system may include one or more processors and
a memory storing instructions that, when executed by the one or ore
processors, cause the system to perform the steps shown in FIG.
5A.
[0082] In step 501, SCM 117 may determine an out of stock scope.
SCM 117 may receive information associated with an out of stock
item from FO system 113. As described above with respect to FIG.
1A, FO system 113 may store information related to items stored in
fulfillment center 200. The stored information may provide a list
of items (SKUs) that are out of stock when the quantity of the
items stored in fulfillment center reaches zero (Out of Stock).
[0083] In step 502, SCM 117 may determine attributes associated
with out of stock items determined in step 501 from a data source.
The data source may provide SKU-level data from purchase orders,
receiving and stowing time, master data, sales, order cycles, etc.
The data source may refer to FO system 113 which stores information
associated with items stored in fulfilment centers.
[0084] In step 503, SCM 117 may determine root cause conditions for
each determined out of stock item from step 501. For example, SCM
117 may determine a first determined item went out of stock from a
mistake from commercial decisions such as obsolete confirming and
strategic decision; a second determined item went out of stock from
the supplier's defect such as none delivery and short delivery; a
third determined item went out of stock from a defect in ordering
items such as low recommended order quantity and sales spike; and a
fourth determined item wen out of stock from a fulfillment center's
defect such as a delay in receiving items and a delay in stowing
items. SCM 117 may decide which category an out of stock item can
be assigned based on determined attributes from step 502.
[0085] In step 504, SCM 117 may execute an out of stock root cause
calculation algorithm to analyze the reason for out of stock. Step
504 is further described with respect to step 511 in FIG. 5B.
[0086] In step 511 (FIG. 5B), SCM 117 may determine a single out of
stock root cause level hierarchy. The single out of stock root
cause is determined by locating a determined out of stock item
(from step 501), in the determined data source from step 502. For
example, as shown in FIG. 6A, list 601 provides a list of out of
stock items determined in step 501 and data source 602 provides
attributes associated with items such as a purchase order status
code. As shown in box 603, SCM 117 may determine a single out of
stock root cause level hierarchy by locating item 10002 (depicted
as SKUSEQ 10002 in FIG. 6A) in data source 602 and assigning
corresponding PO_STATUS_CODE (new sku) as the single out of stock
root cause. In another example, SCM 117 may assign level1_level1,
level1_level12, or level2_level21 as a single out of stock root
cause. Moreover, it is appreciated that SCM 117 may assign
different single out of stock root cause based on the determined
data source associated with the determined out of stock item.
[0087] In step 512, SCM 117 may join all reason levels of out of
stock root causes for each out of stock item. Each of the reason
levels is determined in step 511. For example, as shown in FIG. 6B,
determined out of stock root causes for each determined out of
stock item (depicted in 611) is joined as depicted in 612. By way
of further example, out of stock reason levels level 1 and level 11
for item 10001 are joined to provide reasons for the out of stock
condition of item 10001. The joined reason levels are arranged in
table in respect to its priority. For example, as shown in
exemplary table 612, reason levels are arranged in respect to its
priorities, wherein "NEW_SKU" is associated with the highest
priority and "level2_level21" is associated with the least
priority.
[0088] In step 513, SCM 117 may determine a main out of stock root
cause and an out of stock reason for each level. As shown in
hierarchy node 621 in FIG. 6C, SCM 117 may assign a first not null
reason in hierarchy node as the main reason for out of stock for
each item. As depicted in hierarchy node 621 in FIG. 6C,
level1_level11 is assigned as the main reason for out of stock for
item 10001. Table 622 provides determined main out of stock causes
for out of stock items. SCM 117 may further resolve out of stock
reasons based on the main reason. For example, as shown in table
623 in FIG. 6C, SCM 117 may determine that a first level of reason
for out of stock is level 1 and a second level of reason for out of
stock is level 11 for item 10001 based on its main reason
level1_level11. By way of further example, level 1 may refer to a
defect from suppliers and level 11 may refer to a non-delivery from
the suppliers. Moreover, it is appreciated that reasons may
comprise conditions suitable to cause out of stock conditions.
[0089] In step 514, SCM 117 may add additional columns representing
another root cause for each out of stock item. For example, SCM 117
may add information such as SKU description, SKU Bands, etc. As
shown in FIG. 6C, SCM 117 may add SKU names 623 for each out of
stock item as additional information
[0090] In another embodiment, SCM 117 may, in step 505 (of FIG.
5A), execute a decision tree construction algorithm to analyze the
reason for out of stock. Step 505 is further described with respect
to step 521 in FIG. 5C. In step 521, SCM 117 may filter out a list
of all none-root out of stock conditions. The determined attributes
from step 502 may comprise information describing whether a
condition is a root or none-root. For example, if a determined root
condition from step 503 comprises information that the determined
root condition is root, then the root condition is root while other
root conditions are none-root conditions.
[0091] In step 522, SCM 117 may filter a list of all leaf from the
filtered list of none-root out of stock conditions. The determined
attributes from step 502 may comprise information describing
whether a condition is parent of other conditions. For example, if
a determined root condition from step 503 comprises information
that the determined root condition is not parent of other
conditions, then the root condition is a leaf.
[0092] In step 523, SCM 117 may build a list of hierarchy. SCM 117
may determine a parent condition of each leaf condition filtered in
step 522. The parent condition is included in the determined
attributes from step 502 for each leaf condition. When the parent
condition is determined, SCM 117 may add the determined parent
condition to corresponding leaf condition as the new leaf
condition.
[0093] In step 524, SCM 117 may determine whether all hierarchy's
last nodes are root. If all hierarchy's last nodes are not root,
SCM 117 may, in step 525, locate and add a parent of the last
decision tree node for each hierarchy. If all hierarchy's last
nodes are root, SCM 117 may, in step 526 reverse the hierarchy
list.
[0094] FIG. 7 shows an exemplary decision tree hierarchy list. The
exemplary decision tree hierarchy list depicts three tree
hierarchies: a first tree hierarchy 701, a second tree hierarchy,
and a third tree hierarchy. First tree hierarchy 701 includes
Global Sourcing leaves (including leaf 702). Second tree hierarchy
includes an FC Defect leaf and a Stow Delay leaf. Third tree
hierarchy may include a Supplier Defect leaf 703, a None Delivery
leaf 704, and a Zero Confirmed leaf 705.
[0095] While the present disclosure has been shown and described
with reference to particular embodiments thereof, it will be
understood that the present disclosure can be practiced, without
modification, in other environments. The foregoing description has
been presented for purposes of illustration. It is not exhaustive
and is not limited to the precise forms or embodiments disclosed.
Modifications and adaptations will be apparent to those skilled in
the art from consideration of the specification and practice of the
disclosed embodiments. Additionally, although aspects of the
disclosed embodiments are described as being stored in memory, one
skilled in the art will appreciate that these aspects can also be
stored on other types of computer readable media, such as secondary
storage devices, for example, hard disks or CD ROM, or other forms
of RAM or ROM, USB media, DVD, Blu-ray, or other optical drive
media.
[0096] Computer programs based on the written description and
disclosed methods are within the skill of an experienced developer.
Various programs or program modules can be created using any of the
techniques known to one skilled in the art or can be designed in
connection with existing software. For example, program sections or
program modules can be designed in or by means of .Net Framework,
.Net Compact Framework (and related languages, such as Visual
Basic, C, etc.), Java, C++, Objective-C, HTML, HTML/AJAX
combinations, XML, or HTML with included Java applets.
[0097] Moreover, while illustrative embodiments have been described
herein, the scope of any and all embodiments having equivalent
elements, modifications, omissions, combinations (e.g., of aspects
across various embodiments), adaptations and/or alterations as
would be appreciated by those skilled in the art based on the
present disclosure. The limitations in the claims are to be
interpreted broadly based on the language employed in the claims
and not limited to examples described in the present specification
or during the prosecution of the application. The examples are to
be construed as non-exclusive. Furthermore, the steps of the
disclosed methods may be modified in any manner, including by
reordering steps and/or inserting or deleting steps. It is
intended, therefore, that the specification and examples be
considered as illustrative only, with a true scope and spirit being
indicated by the following claims and their full scope of
equivalents.
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