U.S. patent application number 16/985448 was filed with the patent office on 2021-04-29 for systems and methods for generating graphical user interfaces for adaptive delivery scheduling.
This patent application is currently assigned to Coupang Corp.. The applicant listed for this patent is Coupang Corp.. Invention is credited to Yoo Suk KIM, Hyun Sik Eugene MINH.
Application Number | 20210125140 16/985448 |
Document ID | / |
Family ID | 1000005001646 |
Filed Date | 2021-04-29 |
![](/patent/app/20210125140/US20210125140A1-20210429\US20210125140A1-2021042)
United States Patent
Application |
20210125140 |
Kind Code |
A1 |
KIM; Yoo Suk ; et
al. |
April 29, 2021 |
SYSTEMS AND METHODS FOR GENERATING GRAPHICAL USER INTERFACES FOR
ADAPTIVE DELIVERY SCHEDULING
Abstract
A computerized system for delivery scheduling. The system may
include a processor and a non transitory storage medium comprising
instructions. When executed by the at least one processor, the
instructions may cause the at least one processor to perform steps.
The steps may include receiving (from a remote system) an
electronic request to order a product, determining information
associated with the remote system and a fulfillment center
associated with the information and the product, generating an
electronic message, and forwarding (to the fulfillment center) the
electronic message and instructions to generate a graphical user
interface displaying request with the product and the delivery wave
estimate.
Inventors: |
KIM; Yoo Suk; (Seoul,
KR) ; MINH; Hyun Sik Eugene; (Seoul, KR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Coupang Corp. |
Seoul |
|
KR |
|
|
Assignee: |
Coupang Corp.
Seoul
KR
|
Family ID: |
1000005001646 |
Appl. No.: |
16/985448 |
Filed: |
August 5, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
16666373 |
Oct 28, 2019 |
10769588 |
|
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16985448 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04N 21/4532 20130101;
G06Q 10/087 20130101; G06F 16/9535 20190101; G06Q 30/02 20130101;
H04N 21/812 20130101; H04N 21/44222 20130101; H04N 21/4622
20130101 |
International
Class: |
G06Q 10/08 20060101
G06Q010/08; H04N 21/45 20060101 H04N021/45; G06F 16/9535 20060101
G06F016/9535; H04N 21/462 20060101 H04N021/462; G06Q 30/02 20060101
G06Q030/02; H04N 21/442 20060101 H04N021/442; H04N 21/81 20060101
H04N021/81 |
Claims
1.-20. (canceled)
21. A computer implemented system for automated communication with
fulfillment centers, the system comprising: at least one
non-transitory computer-readable medium configured to store
instructions; and at least one processor configured to execute the
instructions to perform operations comprising: generating a
predictive model based on a training data set comprising a
plurality of historic orders associated with requesting clients and
assigned fulfillment centers; receiving an order from a client
device, the order comprising a client location and product
information; determining a delivery process for the order by
applying the predictive model to the client location and the
product information, the determined delivery process being selected
from one of two delivery processes; assigning a fulfillment center
based on the delivery process and an estimated arrival from the
fulfillment center to a camp zone; and transmitting an electronic
message to the fulfillment center, the electronic message
comprising: the determined fulfillment center, a product, a time of
day, and the delivery process.
22. The computer implemented system of claim 21, wherein the two
delivery processes comprise a shift delivery process and a wave
delivery process.
23. The computer implemented system of claim 22, wherein: the shift
delivery process arranges order deliveries based on specific areas;
and the wave delivery process arranges order deliveries based on
delivery times.
24. The computer implemented system of claim 22, wherein the
electronic message further comprise instructions for printing a
label specifying one of the shift delivery process or the wave
delivery process.
25. The computer implemented system of claim 21, wherein the
operations further comprise: determining temporary workers are
available; and updating the delivery process by reapplying the
predictive model to the client location, the product information,
and a location of temporary workers.
26. The computer implemented system of claim 21, wherein the
operations further comprise: generating a graphical user interface
comprising an option banner specifying the delivery process and a
modification icon to manually adapt the delivery process; and
forwarding the generated graphical user interface to one or more of
a plurality of mobile devices, the plurality of mobile devices
being associated with couriers.
27. The computer implemented system of claim 21, wherein the
operations further comprise: determining whether orders have been
assigned to different delivery processes generating a delivery
conflict; and modifying delivery processes by applying
deconflicting rules, the deconflicting rules comprising at least
one of: modifying last mile delivery, or modifying agreed upon
timeslot in direct customer-courier contact.
28. The computer implemented system of claim 21, wherein
determining the delivery process comprises: determining whether the
order comprises a dawn delivery promise; and in response to
determining the order comprises the dawn delivery promise:
assigning the order to an expedited delivery; and generating label
printing instructions including dawn delivery specific
instructions.
29. The computer implemented system of claim 28, wherein the label
printing instructions are configured in Line Printer Daemon
protocol for networked printers.
30. The computer implemented system of claim 21, wherein generating
the predictive model comprises: dividing the plurality of historic
orders in a training dataset and a validation dataset, the training
dataset having more orders than the validation dataset; generating
the predictive model based on the training dataset configuring the
predictive model to associate orders and fulfillment centers; and
validating the predictive model using the validation dataset.
31. A computer implemented method for automated communication with
fulfillment centers , the method comprising: generating a
predictive model based on a training data set comprising a
plurality of historic orders associated with requesting clients and
assigned fulfillment centers; receiving an order from a client
device, the order comprising a client location and product
information; determining a delivery process for the order by
applying the predictive model to the client location and the
product information, the determined delivery process being selected
from one of two delivery processes; assigning a fulfillment center
based on the delivery process and an estimated arrival from the
fulfillment center to a camp zone; and transmitting an electronic
message to the fulfillment center, the electronic message
comprising: the determined fulfillment center, a product, a time of
day, and the delivery process.
32. The computer implemented method of claim 31, wherein the two
delivery processes comprise a shift delivery process and a wave
delivery process.
33. The computer implemented method of claim 32, wherein: the shift
delivery process arranges order deliveries based on specific areas;
and the wave delivery process arranges order deliveries based on
delivery times.
34. The computer implemented method of claim 32, wherein the
electronic message further comprise instructions for printing a
label specifying one of the shift delivery process or the wave
delivery process.
35. The computer implemented method of claim 31, further
comprising: determining temporary workers are available; and
updating the delivery process by reapplying the predictive model to
the client location, the product information, and a location of
temporary workers.
36. The computer implemented method of claim 31, further
comprising: generating a graphical user interfaces comprising an
option banner specifying the delivery process and a modification
icon to manually adapt the delivery process; and forwarding the
generated graphical user interface to one or more of a plurality of
mobile devices, the plurality of mobile devices being associated
with couriers.
37. The computer implemented method of claim 31, further
comprising: determining whether orders have been assigned to
different delivery processes generating a delivery conflict; and
modifying delivery processes by applying deconflicting rules, the
deconflicting rules comprising at least one of: modifying last mile
delivery, or modifying agreed upon timeslot in direct
customer-courier contact.
38. The computer implemented method of claim 31, wherein
determining the delivery process comprises: determining whether the
order comprises a dawn delivery promise; and in response to
determining the order comprises the dawn delivery promise:
assigning the order to an expedited delivery; and generating label
printing instructions including dawn delivery specific
instructions, the label printing instructions being configured in
Line Printer Daemon protocol for networked printers.
39. The computer implemented method of claim 31, wherein generating
the predictive model comprises: dividing the plurality of historic
orders in a training dataset and a validation dataset, the training
dataset having more orders than the validation dataset; generating
the predictive model based on the training dataset configuring the
predictive model to associate orders and fulfillment centers; and
validating the predictive model using the validation dataset.
40. A system comprising: one or more processors; and one or more
memory devices coupled to the one or more processors, the one or
more memory devices storing instructions that configure the one or
more processors to: generate a predictive model based on a training
data set comprising a plurality of historic orders associated with
requesting clients and assigned fulfillment centers; receive an
order from a client device, the order comprising a client location
and product information; determine a delivery process for the order
by applying the predictive model to the client location and the
product information, the determined delivery process being selected
from one of a shift delivery process and a wave delivery process;
assign a fulfillment center based on the delivery process and an
estimated arrival from the fulfillment center to a camp zone;
transmit an electronic message to the fulfillment center, the
electronic message comprising: the determined fulfillment center, a
product, a time of day, the delivery process, and label printing
instructions specifying one of the shift delivery process or the
wave delivery process, the label printing instructions being
configured in Line Printer Daemon protocol for networked printers;
generate a graphical user interface comprising an option banner
specifying the delivery process and a modification icon to manually
adapt the delivery process; and forward the generated graphical
user interface to one or more of a plurality of mobile devices, the
plurality of mobile devices being associated with couriers.
Description
TECHNICAL FIELD
[0001] The present disclosure generally relates to computerized
systems and methods for computerized delivery scheduling. In
particular, embodiments of the present disclosure relate to
inventive and unconventional systems for adaptive delivery
scheduling that categorize deliveries, adapt delivery groups, and
transmit instructions to client devices via graphical user
interfaces.
BACKGROUND
[0002] Fulfillment of online orders involves an intricate network
of electronic systems. Particularly nowadays that customers require
next-day deliveries, automated returns, and minimal shipping costs,
fulfillment of online orders require using a complex network of
electronic systems that support order preparation and delivery.
Multiple electronic systems in a network need to communicate with
each other in real time to determine fulfillment processes,
including for example shipping routes and/or distribution
centers.
[0003] Standard electronic systems that manage distribution
operations are not being able to handle the increasing volume and
particularity demands. Standard systems do not adapt well with new
requirements of expanded item-level handling, processing of smaller
orders, and greater frequency of orders. For example, current
electronic systems normally operate by batching orders that are
going to a location. A retailer's electronic system may collect
orders for a time period, group them, and then provide batched
instructions to a shipping electronic system. These batching
operations, however, may not achieve the expedited delivery times
required for certain orders.
[0004] Moreover, because multiple parties are normally involved in
fulfilling an online order, the current network of electronic
systems electronic systems may have multiple elements that need to
communicate frequently and in real-time. Electronic systems of
online retailers frequently communicate in real-time with multiple
distribution and fulfillment systems and may employ several hubs or
camp zones to be able to quickly fulfill orders. This complexity of
the electronic system network, however, is not easily scalable and
prevents adjustments required for new customer demands. For
example, current electronic systems are not well-suited to handle
large numbers of orders that have distinct customer privileges
because they are unable to efficiently assign delivery channels
from premium users, rushed deliveries, or lowest-cost deliveries.
Further, current systems are rigid and are not easily adaptable to
face dynamic demand changes or customer requirements. Standard
electronic systems are rigid and have established operations that
are difficult to adapt throughout the delivery process. These
electronic systems cannot easily adapt to new delivery chains that
include, for example, temporary or on-demand workers and
contractors. Thus, they do not efficiently handle the delivery
requests or adapt delivery processes to meet customer demands.
[0005] Furthermore, current electronic systems for managing
deliveries have outdated communication platforms that that fail to
provide dynamic instructions required for adaptable systems.
Current electronic systems rely on outdated technology that force
users to use multiple platforms throughout the distribution
chain.
[0006] Therefore, there is a need for improved methods and systems
for adaptive delivery scheduling systems. The disclosed systems and
methods for generating graphical user interfaces for adaptive
delivery scheduling address one or more of the problems set forth
above and/or other problems in the prior art.
SUMMARY
[0007] One aspect of the present disclosure is directed to a
computerized system for delivery scheduling. The system may include
at least one processor and at least one non transitory storage
medium comprising instructions that, when executed by the at least
one processor, cause the at least one processor to perform steps.
The steps may include receiving (from a remote system) an
electronic request to order a product, and determining information
associated with the remote system determining a fulfillment center
associated with the information and the product. The steps may also
include generating (based on the information associated with the
remote system) an electronic message including the determined
fulfillment center, the product, a time of day, and a delivery wave
estimate. The steps may also include forwarding (to the fulfillment
center) the electronic message and instructions to generate a
graphical user interface displaying the product and the delivery
wave estimate.
[0008] Another aspect of the present disclosure is directed a
non-transitory computer-readable medium, storing instructions that,
when executed by a processor, perform operations for delivery
scheduling. The operations may include receiving (from a remote
system) an electronic request to order a product, and determining
information associated with the remote system determining a
fulfillment center associated with the information and the product.
The operations may also include generating (based on the
information associated with the remote system) an electronic
message including the determined fulfillment center, the product, a
time of day, and a delivery wave estimate. The operations may also
include forwarding (to the fulfillment center) the electronic
message and instructions to generate a graphical user interface
displaying the product and the delivery wave estimate.
[0009] Yet another aspect of the present disclosure is directed to
a computer-implemented method for delivery scheduling. The method
may include operations of receiving (from a remote system) an
electronic request to order a first product and a second product,
determining information associated with the remote system,
determining a first fulfillment center associated with the
information and the first product and a second fulfillment center
associated with the second product and the second fulfillment
center. The operations may also include generating a first delivery
wave for the first product and a second delivery wave for the
second product; generating (based on the information associated
with the remote system) an electronic message comprising the first
and second fulfillment centers, the first and second products, a
time of day, and the first and second delivery wave estimates.
Further, the operations may include determining that the second
delivery wave estimate is different from the first delivery wave;
modifying at least one of the first or second delivery wave
estimates to match the other delivery wave estimate. The operations
may also include forwarding, to the fulfillment center, the
electronic message and instructions to generate a graphical user
interface displaying a list with the first and second products
color-coded with corresponding delivery wave estimate; and sending
an instruction to the fulfillment center to print shipping labels
listing the first or second delivery wave estimates.
[0010] Other systems, methods, and computer-readable media are also
discussed herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] 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.
[0012] 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.
[0013] 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.
[0014] 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.
[0015] 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.
[0016] FIG. 2 is a diagrammatic illustration of an exemplary
fulfillment center configured to utilize disclosed computerized
systems, consistent with the disclosed embodiments.
[0017] FIG. 3 is a schematic block diagram of an exemplary system,
consistent with disclosed embodiments.
[0018] FIG. 4 is a block diagram of an exemplary client device,
consistent with disclosed embodiments.
[0019] FIG. 5 is a block diagram of an exemplary database,
consistent with disclosed embodiments.
[0020] FIG. 6 is a flow chart of an exemplary product request
handling process, consistent with disclosed embodiments.
[0021] FIG. 7 is a flow chart of an exemplary delivery
classification process, consistent with disclosed embodiments.
[0022] FIG. 8 is a flow chart of an exemplary delivery optimization
process, consistent with disclosed embodiments.
[0023] FIG. 9 is an exemplary flow chart illustrating a predictive
model training process, consistent with disclosed embodiments.
[0024] FIG. 10 is a front view of an exemplary graphical user
interface in an administrator device, consistent with disclosed
embodiments.
[0025] FIG. 11 is an exemplary shipping label, consistent with
disclosed embodiments.
[0026] FIG. 12A is a front view of a first exemplary graphical user
interface in a mobile device, consistent with disclosed
embodiments.
[0027] FIG. 12B is a front view of a second exemplary graphical
user interface in a mobile device, consistent with disclosed
embodiments.
[0028] FIG. 12C is a front view of a third exemplary graphical user
interface in a mobile device, consistent with disclosed
embodiments.
[0029] FIG. 12D is a front view a fourth exemplary graphical user
interface in a mobile device, consistent with disclosed
embodiments.
[0030] FIG. 12E is a front view of a fifth exemplary graphical user
interface in a mobile device, consistent with disclosed
embodiments.
[0031] FIG. 12F is a front view of a sixth exemplary graphical user
interface in a mobile device, consistent with disclosed
embodiments.
[0032] FIG. 12G is a front view of a seventh exemplary graphical
user interface in a mobile device, consistent with disclosed
embodiments.
[0033] FIG. 12H is a front view of an eighth exemplary graphical
user interface in a mobile device, consistent with disclosed
embodiments.
DETAILED DESCRIPTION
[0034] 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. 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.
[0035] Embodiments of the present disclosure are directed to
systems and methods configured for generating graphical user
interfaces for adaptive delivery scheduling. The disclosed systems
and methods enable automated and optimized order handling by, for
example, determining fulfilment centers that are in best position
to meet order requirements. The disclosed systems and methods also
provide users of the system with graphical user interfaces and
interactive tools to efficiently manage curriers and prioritize
work. For example, the disclosed systems and methods improve the
technical field of automated categorization of deliveries by
providing supply chain managers greater visibility into forecasted
orders to evaluate demand. In such embodiments, the disclosed
systems and methods may employ machine-learning techniques to
identify trends and predict fulfilment center of camp demand to
make assignments.
[0036] Further, the disclosed systems and methods improve
scheduling systems by enabling automatically adapting delivery
processes. Disclosed delivery systems may operate in different
delivery processes or paradigms. For example, the system may
operate using a "waves process," a "shift process," or a
combination. The waves process may arrange deliveries in waves of
deliveries at different times. For example, wave deliveries may
include a first wave of packages around a specific area (e.g., a
route comprising sub-routes) several times a day. In contrast, a
shift process may arrange deliveries to different areas, delivering
first to a portion of a specific area (typically 50%), followed by
a later delivery to the remaining portion of the specific area. The
disclosed systems and methods may be configurable to reconfigure
routes and worker schedules based on optimization parameters for
the delivery process. For example, the disclosed systems and
methods may analyze previous orders and performance of fulfilment
centers or camps, to determine forecasted demand of each camp or
fulfillment center. Then, the disclosed systems may use
machine-learning predictive models to evaluate which delivery
process has the most effective outcome and update all elements of
the system accordingly. For example, if the system determines that
delivering a group of orders is more efficient using a two-shift
process, the disclosed systems and methods may update fulfilment
centers, shipping authorities, and workers, to operate under the
two-shift process. In such embodiments predictive analytics to
configure the delivery process improves the systems for delivery
scheduling with a particular computerized method to identify
optimized delivery schedules.
[0037] Moreover, the disclosed systems and methods improve the
flexibility of the delivery process by enabling dynamic changes of
delivery processes. For example, if throughout one shift the system
identifies that more temporary workers are available than expected,
the system may re-run forecast models to update the delivery
process and reconfigure the system, as needed. The disclosed
systems also provide graphical user interfaces to provide feedback,
modify groups, or change delivery processes.
[0038] The disclosed systems and methods may also improve the field
of automated delivery scheduling by transforming order information
into a sequence of instructions for delivery. For example, the
disclosed systems and methods may process order requests
automatically until generating a printed label with information for
delivery. The label may include information of required delivery
times, destinations, and priority information.
[0039] Furthermore, the disclosed systems and methods solves
technical issues of poor display of information in mobile devices
because the disclosed systems may generate user interfaces that
effectively manage screen space. Particularly for portable devices
used by couriers, in which the screen space is limited, the
disclosed systems and methods may generate dynamic GUIs that
specifically display elements that are relevant for the courier
while removing irrelevant elements. For example, the disclosed
methods may include dynamically modifying GUIs based on adaptable
delivery modes, showing couriers notifications, changes, groups of
deliveries or required prioritization. The disclosed systems and
methods also include GUI configurations that may facilitate
establishing delivery routes, creating groups, or updating the
system.
[0040] Reference will now be made in detail to the disclosed
embodiments, examples of which are illustrated in the accompanying
drawings.
[0041] FIG. 1A shows a schematic block diagram of system 100
illustrating an exemplary embodiment of a system comprising
computerized systems for communications enabling shipping,
transportation, and logistics operations. 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, 1076, 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, workforce 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.
[0042] 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.
[0043] 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.
[0044] 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.
[0045] 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 POD, 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.)
[0046] 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).
[0047] 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.
[0048] 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 POD. External front end system 103 may deliver
the SDP to the requesting user device (e.g., via a network).
[0049] 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.
[0050] 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.
[0051] 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.
[0052] The user device may enter information on the order page and
click or otherwise interact with a user interface 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.
[0053] In some embodiments, external front end system 103 may be
further configured to enable sellers to transmit and receive
information relating to orders.
[0054] 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 SAT system 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.
[0055] 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.
[0056] 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.
[0057] In some embodiments, certain users may use one kind of
mobile device (e.g., permanent workers may use a specialized PDA
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).
[0058] 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.
[0059] 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.
[0060] 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.
[0061] 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
users (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 workforce 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.
[0062] 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.).
[0063] 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 POD 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.
[0064] 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.
[0065] 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, arid vice versa.
[0066] 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, 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.
[0067] Workforce 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).
[0068] 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).
[0069] 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.
[0070] 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).
[0071] 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.
[0072] 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.
[0073] 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.
[0074] 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.
[0075] 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 may 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.
[0076] 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.
[0077] 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).
[0078] 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.
[0079] 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.
[0080] 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 more
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.
[0081] 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.
[0082] 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.
[0083] 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.
[0084] 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.
[0085] FIG. 3 is a block diagram of an exemplary system 300,
consistent with disclosed embodiments. In system 300, a scheduling
system (e.g., 320) may process data streams in real-time to, for
example, allocate deliveries, generate delivery routes, and
communicate with workers. System 300 may include one or more
scheduling systems 320, online resources 340, client devices 350,
third party systems 360, client systems 390, and database 380. In
some embodiments, as shown in FIG. 3, components of system 300 may
be connected to a network 370. However, in other embodiments
components of system 300 may be connected directly with each other,
without network 370. For example, database 380 may be directly
coupled to scheduling system 320.
[0086] In some embodiments, scheduling system 320 may be
implemented with one or more of the components of system 100 (FIG.
1A). For example, scheduling system 320 may include front-end
system 105, FO system 113, SCM system 117, and WMS 119 (FIG. 1A).
In other embodiments, scheduling system 320 may be implemented with
one or more independent servers configured to perform operations
for delivery scheduling.
[0087] Online resources 340 may include one or more servers or
storage services provided by an entity such as a provider of
website hosting, networking, cloud, or backup services. In some
embodiments, online resources 340 may be associated with hosting
services or servers that store web pages for authentication
services, Domain Name System (DNS), or landing pages. In other
embodiments, online resources 340 may be associated with a cloud
computing service. In yet other embodiments, online resources 340
may be associated with a messaging service, such as, for example,
Apple Push Notification Service, Azure Mobile Services, or Google
Cloud Messaging. In such embodiments, online resources 340 may
handle the delivery of messages and notifications related to
functions of the disclosed embodiments, such as handling digital
rights management.
[0088] Client devices 350 may include one or more computing devices
configured to perform one or more operations consistent with
disclosed embodiments. For example, client devices 350 may include
a desktop computer, a laptop, a server, a mobile device (e.g.,
tablet, smart phone, etc.), a set-top box, a gaming device, a
wearable computing device, or other type of computing device. In
some embodiments client devices 350 may be part of system 100 (FIG.
1A). In other embodiments, however, client devices 350 may be
independent from system 100. Client devices 350 may include one or
more processors configured to execute software instructions stored
in memory, such as memory included in client devices 350, to
perform operations to implement the functions described below.
Client devices 350 may be configured for wired and/or wireless
communications and may include software that when executed by a
processor performs internet-related communication (e.g., TCP/IP)
and content display processes. For instance, client devices 350 may
execute browser software that generates and displays interfaces
including content on a display device included in, or connected to,
client devices 350. Client devices 350 may execute applications
that allow client devices 350 to communicate with components over
network 370 and display content in interfaces via display devices
included in client devices 350.
[0089] In some embodiments, as further disclosed in connection to
FIG. 4, client devices 350 may run applications specifically
configured to interact with scheduling system 320. Moreover, client
devices 350 may store one or more accounts. For example, client
devices 350 may store information about a worker account, including
worker identification, password, location, and/or delivery
preferences.
[0090] The disclosed embodiments are not limited to any particular
configuration of client devices 350. For instance, a client device
350 may be a mobile device that stores and executes mobile
applications to perform operations that provide functions offered
by scheduling system 320 and/or online resources 340. In certain
embodiments, client devices 350 may be configured to execute
software instructions relating to location services, such as GPS
locations. For example, client devices 350 may be configured to
determine a geographic location and provide location data and time
stamp data corresponding to the location data. Client devices 350
are further described in connection with FIG. 4.
[0091] Database 380 may include one or more computing devices
configured with appropriate software to perform operations
consistent with providing scheduling system 320 data for
calculating delivery routes or performing transactions with client
devices 350. Database 380 may include, for example, Oracle.TM.
databases, Sybase.TM. databases, or other relational databases or
non-relational databases, such as Hadoop.TM. sequence files,
HBase.TM., or Cassandra.TM.. Database 380 may include computing
components (e.g., database management system, database server,
etc.) configured to receive and process requests for data stored in
memory devices of the database(s) and to provide data from the
database(s).
[0092] While database 380 are shown separately, in some embodiments
database 380 may be included in, or otherwise related to scheduling
systems 320 or online resources 340.
[0093] Database 380 may be configured to collect and/or maintain
the data associated with user accounts and user preferences. For
example, database 380 may store information about user profiles for
users of scheduling system 120. Database 380 may collect the data
from a variety of sources, including, for instance, online
resources 340. Further, database 380 may include information of the
user delivery preferences. For example, database 380 may include
information of whether the user is subscribed to dawn deliveries.
Database 380 are further described below in connection with FIG.
5.
[0094] Third party systems 360 may include one or more servers or
storage services provided by an entity related to scheduling system
320, such as a provider of services or a fulfillment center. Third
party systems 360 may be connected to system 300 via network 370,
but in other embodiments third party systems 360 may include direct
connections with some elements of system 300. Further, third party
systems 360 may be configured to provide and/or request information
from scheduling system 320, or other elements of system 300. In
some embodiments, while third party systems 360 may also be coupled
to network 370, they may not be clients of scheduling system 320.
Instead, third party systems 360 may include systems that include
information of users or clients of scheduling system 320. For
example, third party systems 360 may include servers of delivery
contractors such as FedEx.RTM., which may be used by scheduling
system 320 when requiring additional resources to meet target
schedules.
[0095] Client systems 390 may include one or more servers or
storage services in communication with scheduling system 320 via
network 370. In some embodiments, client systems 390 may send order
requests to scheduling system 320, which may process in real-time
the order requests using disclosed systems and methods. For
example, client systems 390 may transmit data streams with order
requests, which may include promised delivery, item info, and
address of delivery, among other parameters. In such embodiments,
the orders transmitted from client systems 390 may further include
user information, location, transaction amount, IP address, and/or
currency. Further, client systems 390 may operate Windows.RTM.,
macOS.RTM., or Linux.RTM. operating systems.
[0096] Network 370 may be any type of network configured to provide
communications between components of system 300. For example,
network 370 may be any type of network (including infrastructure)
that provides communications, exchanges information, and/or
facilitates the exchange of information, such as the Internet, a
Local Area Network, near field communication (NFC), optical code
scanner, or other suitable connection(s) that enables the sending
and receiving of information between the components of system 300.
In other embodiments, one or more components of system 300 may
communicate directly through a dedicated communication link(s). In
yet other embodiments, network 370 may include multiple networks,
organizing for example a network or networks.
[0097] It is to be understood that the configuration and boundaries
of the functional building blocks of system 300 have been defined
herein for the convenience of the description. Alternative
boundaries can be defined so long as the specified functions and
relationships thereof are appropriately performed. Alternatives
(including equivalents, extensions, variations, deviations, etc.,
of those described herein) will be apparent. Such alternatives fall
within the scope of the disclosed embodiments.
[0098] Referring now to FIG. 4, there is shown a block diagram of
an exemplary client device 350 (FIG. 3), consistent with disclosed
embodiments. In some embodiments, client devices 350 may include
transportation system mobile devices 107A, 1078, and 107C, or
management system devices 119A-119C (FIG. 1A).
[0099] In one embodiment, client devices 350 may include one or
more processors 402, one or more input/output (I/O) devices 404,
and one or more memories 410. In some embodiments, client devices
350 may take the form of mobile computing devices such as
smartphones or tablets, general purpose computers, or any
combination of these components. Alternatively, client devices 350
(or systems including client devices 350) may be configured as a
particular apparatus, embedded system, dedicated circuit, and the
like based on the storage, execution, and/or implementation of the
software instructions that perform one or more operations
consistent with the disclosed embodiments. According to some
embodiments, client devices 350 may include web browsers or similar
computing devices that access web site consistent with disclosed
embodiments.
[0100] Processor 402 may include one or more known processing
devices, such as mobile device microprocessors manufactured by
Intel.TM., NVIDIA.TM., or various processors from other
manufacturers. The disclosed embodiments are not limited to any
specific type of processor configured in client devices 350.
[0101] Memory 410 may include one or more storage devices
configured to store instructions used by processor 402 to perform
functions related to disclosed embodiments. For example, memory 410
may be configured with one or more software instructions, such as
programs 412 that may perform operations when executed by processor
402. The disclosed embodiments are not limited to separate programs
or computers configured to perform dedicated tasks. For example,
memory 410 may include a single program 412 that performs the
functions of the client devices 350, or program 412 may include
multiple programs. Memory 410 may also store data 416 that may be
used during scheduling system 320 operation to assign deliveries to
the client device.
[0102] In certain embodiments, memory 410 may store instructions
for accessing scheduling system 320. For example, memory 410 may
include an application that communicates with scheduling system 320
via TCP/IP. Moreover, other software components may be configured
to request information from scheduling system 320 or determine the
location of client devices 350. For instance, these software
instructions, when executed by processor(s) 402 may process
information to display the status of a transaction.
[0103] I/O devices 404 may include one or more devices configured
to allow data to be received and/or transmitted by client devices
350 and to allow client devices 350 to communicate with other
machines and devices, such as other components of system 300. For
example, I/O devices 404 may include a screen for confirming
delivery of a parcel or providing information to the user. I/O
devices 404 may also include components for NFC communication. I/O
devices 404 may also include one or more digital and/or analog
devices that allow a user to interact with client devices 350 such
as a touch-sensitive area, buttons, or microphones. I/O devices 404
may also include one or more accelerometers to detect the
orientation and inertia of client devices 350. I/O devices 404 may
also include other components known in the art for interacting with
scheduling system 320.
[0104] In some embodiments, client devices 350 may also include a
camera 420 that capture images and may be used for verification of
delivery completion. Additionally, or alternatively, client devices
350 may include a fingerprint sensor 430 that allows users to
unlock client devices 350 and/or perform authentications. Both
camera 420 and fingerprint sensor 430 may be operated by processor
402 and use encryption security to make it impossible for users to
externally access fingerprint or camera information.
[0105] The components of client devices 350 may be implemented in
hardware, software, or a combination of both hardware and software,
as will be apparent to those skilled in the art,
[0106] Referring now to FIG. 5, there is shown a block diagram of
an exemplary database 380 (FIG. 1), consistent with disclosed
embodiments. In some embodiments, database 380 may be included in
elements of system 100. For example, database 380 may be part of
the FO system 113 or the WMS 119 (FIG. 1A).
[0107] Database 380 may include a communication device 502, one or
more database processors 504, and database memory 510 including one
or more database programs 512 and data 514. Database 380 may
include NoSQL databases such as HBase, MongoDB.TM. or
Cassandra.TM.. Alternatively, database 380 may include relational
databases such as Oracle, MySQL and Microsoft SQL Server.
[0108] In some embodiments, database 380 may take the form of
servers, general purpose computers, mainframe computers, or any
combination of these components. In some embodiments, database 380
are included within other elements of system 300, such as
scheduling system 320. Other implementations consistent with
disclosed embodiments are possible as well.
[0109] In some embodiments, database 380 may include both
non-relational and embedded databases. For example, database 380
may include a non-relational database, such as an Hbase, and an
embedded database, such as a RocksDB (e.g., a key-value store
database).
[0110] Communication device 502 may be configured to communicate
with one or more components of system 300 or system 100, such as
online resources 340, scheduling system 320, or SCM system 117. In
particular, communication device 502 may be configured to provide
scheduling system 320 order information, user preferences and
privileges, and/or historic trends for developing predictive
models.
[0111] The components of database 380 may be implemented in
hardware, software, or a combination of both hardware and software.
For example, although one or more components of database 380 may be
implemented as computer processing instruction modules, all or a
portion of the functionality of database 380 may be implemented
instead in dedicated electronics hardware.
[0112] Database memory 510 may include programs 512, which may
include instructions to support handling of orders from client
systems 390 and interactions between, for example, client devices
350 and scheduling system 320. Further programs 512 may include
instructions to store information in real-time as it is processed
by scheduling system 320.
[0113] Data 514 may also be data associated with websites, such as
online resources 340, or user accounts from client devices 350.
Data 514 may include, for example, information relating to users
and their credentials to obtain content. Data 314 may also include
content files and accumulation variables to evaluate historic
trends associating fulfillment centers and orders.
[0114] FIG. 6 is a flow chart of an exemplary product request
handling process 600, consistent with disclosed embodiments. In
some embodiments, elements of system 300 may perform process 600.
For example, as disclosed in the steps description below,
scheduling system 320 may perform process 600. This is just an
illustrative example of process 600, however, and in other
embodiments system 100, or parts of system 100, may perform process
600. For example, Shipment Authority Technology System 101, FO
System 113, and FC Auth 123 (FIG. 1A) may perform one or more of
the steps in process 600.
[0115] In step 602, scheduling system 320 may receive an electronic
request. The electronic request may be received from a remote
location. For example, the electronic request may be received from
a remote client system 390 (FIG. 3). Moreover, the electronic
request may include product information (such as item ID and price)
and delivery information (such as promised delivery date and/or
specific instructions).
[0116] In step 604, scheduling system 320 may determine information
of the remote system that sent the request. For example, when
client system 390 sends the requests to scheduling system 320,
scheduling system may identify information of client systems 390
based on the order information. For example, scheduling system 320
may identify postal code, customer information, preferences,
region, address, and/or privileges based on the order information
in step 604. Further, scheduling system 320 may identify a
username, a postal code, a physical location, or user preferences
in step 604.
[0117] In step 606, scheduling system 320 may determine one or more
fulfilment centers that may complete and dispatch the order. The
fulfilment center may be determined based on proximity to a postal
code (identified for example in step 604), availability of
products, and/or forecasted capacity for the target delivery date.
In step 606, scheduling system 320 may also determine a region
associated with the electronic request that may be used to identify
target fulfillment centers. For example, scheduling system 320 may
identify a fulfillment center that is within a radius of the postal
code associated with the request received in step 602.
[0118] In some embodiments, as further described in connection to
FIG. 9, determining the fulfilment center in step 606 may include
using a predictive model or tool that is based on historic trends
or previous orders. For example, to determine the fulfilment
center, scheduling system 320 may perform operations of storing in
database 380 (FIG. 3) a plurality of previous electronic requests
and associated fulfillment centers and dividing the previous
electronic requests in a training dataset and a validation dataset,
the training dataset having more requests than the validation
dataset. Scheduling system 320 may also perform operations of
generating a predictive model based on the training data set
associating request information and fulfillment centers, validating
the predictive model using the validation dataset, and determining
the fulfillment center by applying the predictive model to the
electronic request.
[0119] In step 608, scheduling system 320 may determine a delivery
process for the products. For example, as previously discussed,
scheduling system 320 may operate using two-wave or two-shift
processes. In step 608 scheduling system 320 may analyze forecasted
data for the fulfillment centers identified in step 606 to
determine which of the delivery processes best accommodate
requirements for the order received in step 602. Moreover, in step
608 scheduling system 320 may classify the order depending on the
delivery process. For example, if scheduling system 320 determines
that the optimized delivery process is two-waves, in step 608
scheduling system 320 may also determine that the order should be
categorized in the first wave, as further described in connection
to FIG. 7. Alternatively, if scheduling system determines that the
optimized delivery is two-shift, in step 608 scheduling system 320
may determine the order should be delivered in the first or second
shifts. Moreover, in some embodiments determining delivery waves or
shifts may be based on a cutoff time delivery for the order. For
example, a cutoff time of 12:00 will require assignment of delivery
in wave 1 (defined between 0:00-18:00).
[0120] In some embodiments, determining the delivery process for
waves/shifts includes modifying the estimated delivery process
based on a day and time period when the product will be delivered
to camp zone 215 (FIG. 2). For example, a delivery wave estimate
may be determined in step 608 based on promised time and date for a
parcel. Further, the delivery wave estimate may be updated with FC
200 changes or updates. For example, a delivery wave estimate may
be wave 1 for one type of FC 200 but be shift 2 for a second type
of FC 200.
[0121] In step 610, scheduling system 320 may determine whether
products in the order have been assigned to different waves or
shifts. For example, if an order includes two products and the
first product was assigned to wave 1, defined as delivery from
0:00-18:00, and the second product was assigned to wave 2, defined
as delivery from 18:00-24:00, scheduling system 320 may determine
that the two products have been assigned to different waves in step
610. Alternatively, the two products may have been assigned to
different shifts. For instance, product 1 may have been assigned to
a first shift, defined as a west area (covering 50% of the FC 200
region), while the second product may have been assigned to a
second shift, defined as an east area (covering the remaining 50%
of the FC 200 region). In such cases scheduling system 320 may also
determine that the product have different shifts. Alternatively, or
additionally, the two products may have been assigned to different
delivery processes. For example, the first product may have been
assigned to wave delivery (i.e., segregated by times) but the
second product may have been assigned to shift delivery (i.e.,
segregated by area). In such cases, scheduling system 320 may also
determine the products are assigned to different waves or
shifts.
[0122] If in step 610 scheduling system 320 determines there are
products that have different delivery waves or shifts (step 610:
yes), process 600 may continue to step 612, in which scheduling
system 320 may modify delivery waves or shifts to match efficiency
conditions. When scheduling system 320 determines there is a
conflict in the waves or shifts assigned to products, scheduling
system 320 may apply deconflicting rules to modify the product
assignments. The deconflicting rules may include changes in the
last mile, such as changes in location, time, route and/or
priority. Further, the deconflicting rules may include modifying
order promise (updating at a delivery time, while using timeslots),
modify the location of camp zone 215 or FC 200 (reassigning
efficient delivery schedules), or modifying agreed upon timeslot in
direct customer-courier contact. Finally, deconflicting rules may
include separating shipments when, for example, deconflicting rules
are uncapable of achieving promised delivery date in a single
shipment.
[0123] If, however, scheduling system 320 determines products do
not have different delivery waves or shifts (step 610: no), process
600 may skip step 612 and continue directly to step 614. In step
614, scheduling system 320 may generate an electronic message with
information for delivery, or the multiple deliveries if they are
needed. The electronic message may include information of the
determined fulfillment centers, products to be shipped, a time of
day, and a delivery wave or shift estimate. The electronic message
may be configured as a TCP/IP message being directed to one or more
elements in system 100 and/or client devices 350. In other
embodiments, the electronic message of step 614 may configured for
printing labels at camps or fulfilment centers. For example, the
electronic message may already include printing information because
a seamless integration between systems relieves manager of manual
entry of order information when producing shipping labels. The
electronic message in step 614 may be configured to allow automatic
printing of shipping labels. For example, the electronic message in
step 614 may be directly configured in Line Printer Daemon
protocol/Line Printer Remote protocol (or LPD, LPR) for networked
printers. Alternatively, or additionally, the electronic message in
step 614 may be configured in Internet Printing Protocol (IPP), or
(Common UNIX Printing System) CUPS. Thus, the electronic message
may be configured to print shipping labels as further discussed in
connection with FIG. 11.
[0124] In step 616, scheduling system 320 may forward or send the
electronic message of step 614 to a fulfilment center or camp that
has been identified for completing the order. In some embodiments,
the electronic message may be transmitted through a network to
administrator servers that then relay the information to the
corresponding managers or workers. Alternatively, or additionally,
the electronic message may be transmitted directly to mangers or
devices. For example, electronic messages may be directly
transmitted to printers to automatically print labels for
parcels.
[0125] In step 618, scheduling system 320 may generate one or more
graphical user interfaces (GUIs) for displaying products and/or
their associated waves. The GUIs may be configured for displaying
in an administrator screen. For example, the GUIs may be configured
to be displayed in screens of LMS 125 (FIG. 1A). Alternatively,
GUIs may be configured to be displayed in screens of client devices
350, which may be operated by delivery workers to get notifications
and/or task assignments. Exemplary GUIs generated in step 618 are
further described in connection to FIGS. 10 and 12A-12H.
[0126] In step 620, scheduling system 320 may generate and send
instructions to the fulfillment center to print shipping labels.
For example, in embodiments where the electronic message is not
directed immediately for the printer but is a TCP/IP message,
scheduling system 320 may generate printing instructions for
printing shipping labels. The instructions for printing shipping
labels may include color-coded labels for different waves or
shifts. Furthermore, the instructions may include priority notices,
such as "dawn delivery" or "premium delivery."
[0127] FIG. 7 is a flow chart of an exemplary delivery
classification process 700, consistent with disclosed embodiments.
In some embodiments, elements of system 300 may perform process
700. For example, as described below, scheduling system 320 may
perform process 700. In other embodiments, however, process 700 and
in other embodiments system 100, or parts of system 100, may
perform process 700. For example, Shipment Authority Technology
System 101, FO System 113, and FC Auth 123 (FIG. 1A) may perform
one or more of the steps in process 700.
[0128] In step 702, scheduling system 320 may receive an order or
request. For example, scheduling system 320 may receive an order
from client system 390 or a request from third party systems 360.
In some embodiments, the order may have similar characteristics to
the order received in step 602, including customer information,
product information, and delivery promises.
[0129] In step 704, scheduling system 320 may determine a delivery
location, an associated delivery promise, and an order time based
on the received order or request. For example, in step 704
scheduling system 320 may determine a postal code, customer info,
and costumer preferences based on the received order
information.
[0130] In step 706, scheduling system 320 may determine whether
there is a delivery promise associated with the order. For example,
some orders may include a delivery promise of "dawn delivery." In
contrast, other orders or request may not be associated with any
promise. In step 706, scheduling system 320 may decode information
in the order or request to determine if there is a delivery
promise. If scheduling system 320 determines there is a delivery
promise (step 706: yes), scheduling system 320 may continue to step
708.
[0131] In step 708, scheduling system 320 may determine if there is
a promise for "dawn delivery" or if the promise is for an
alternative delivery time. If the promise is for dawn delivery
(step 708: yes), scheduling system 320 may continue to step 710 and
expedite order for delivery. Expediting the delivery may include
generating a GUI for the administrator notifying that a dawn
delivery is incoming. Further expediting the order for dawn
delivery may include generate notification GUI's for client devices
350 and specific printing instructions. If the promise is not for
dawn delivery (step 708: no), scheduling system 320 may continue to
step 712 and assign a wave or shift matching the promise. For
example, if the promise is for delivery at 20:00, scheduling system
320 may assign the order to a wave 2, defined as 18:00-24:00.
[0132] If in step 706 scheduling system 320 determines that there
is no delivery promise associated with the order (step 706: no),
scheduling system 320 may continue to step 714 and determine an
estimated arrival of the order from the fulfilment center to the
camp. For example, after identifying a fulfilment center,
scheduling system 320 may estimate arrival to delivery camp based
on historic trends, distance, and/or delivery rotations.
[0133] In step 716, scheduling system 320 determines whether the
order can arrive to the delivery camp before the first wave or
shift. If scheduling system 320 determines the order may arrive
before (step 716: yes), scheduling system 320 may continue to step
718 and assign the order to the first wave or shift. However, if
scheduling system 320 determines that the order cannot arrive
before the first (step 716: no), scheduling system 320 may assign
the order to a second wave or shift in step 720.
[0134] As shown in FIG. 7, different steps of process 700 may
converge in step 722, in which scheduling system 320 may generate
GUIs for indicating order, delivery wave, shift wave, and/or
addresses. That is, regardless of the specific wave/shift or
priority assigned to the order, process 700 may generate GUIs for
administrator and/or workers based on the order categorization,
[0135] In step 724, scheduling system 320 may generate instructions
to print a shipping label including the delivery wave/shift. As
further discussed in connection to FIG. 11, the shipping label may
include indications for delivery process, such as delivery wave or
shift number.
[0136] In step 726, scheduling system 320 may generate instructions
for displaying delivery grouping options in client devices 350. For
example, as further discussed in connection to FIG. 12G, scheduling
system 320 may generate instructions for grouping in notifications
in client devices 350 based on the assignments for orders during
process 700.
[0137] FIG. 8 is a flow chart of an exemplary delivery optimization
process, consistent with disclosed embodiments. In some
embodiments, elements of system 300 may perform process 800. For
example, as described below, scheduling system 320 may perform
process 800. Process 800 may alternatively be performed by system
100, or parts of system 100. For example, Shipment Authority
Technology System 101, FO System 113, and FC Auth 123 (FIG. 1A) may
perform one or more of the steps in process 800.
[0138] In step 802, scheduling system 320 may receive an electronic
request with product, location, and delivery promise information.
The electronic request may be similar to the request that is
received in step 602 (FIG. 6).
[0139] In step 804, scheduling system 320 may apply a default
delivery process of wave scheduling. Wave scheduling may have
better coverage of the area and facilitates logistics to meet
guaranteed dates. Thus, in step 804, scheduling system 320 may
default to wave scheduling process, to initially guarantee meeting
delivery promises.
[0140] In step 806, scheduling system 320 may forecast demand for
multiple camps and/or fulfilment centers based on predictive
models. As further disclosed in connection to FIG. 9, scheduling
system 320 may forecast demand using previous data to train and
validate predictive models. For example, scheduling system 320 may
generate predictive models based on random forests to estimate
demand in each fulfilment center based on historic data of its
performance.
[0141] In step 808, scheduling system 320 may identify an optimized
delivery process based on the forecast, promise, location, and
product information, In step 808 scheduling system 320 may perform
a computation of delivery routes based on the default delivery
process. For example, scheduling system 320 may generate nodes for
customer locations, compute the travel time between each pair of
customers and camp zone 215 (or FC 200), and assign chosen
locations to the nearest available address. In some embodiments,
distances between each pair of destinations may be computed with
the Manhattan distance. Scheduling system 320 may then compute
delivery routes and constrain delivery routes based on worker
hours, driver capacity, and traffic. Then, each one of the routes
is optimized by distance, number of workers, and number of required
visits to camps.
[0142] In some embodiments, scheduling system 320 may identify an
optimized delivery process using combinatorial optimization.
Scheduling system 320 may compute cost-efficient delivery routes by
utilizing heuristics for each vehicle routing. For instance,
scheduling system 320 may use the Clark-Wright Savings algorithm,
or similar methods, to identify optimized delivery processes. In
such embodiments, optimized delivery processes may be identified
based on minimal travel time. Alternatively, or additionally,
scheduling system 320 customers to spatial clusters and algorithms
to enhance delivery efficiencies. In such embodiments, clusters may
be computed for each delivery day in such a way that they do not
exceed a maximum number of customers per route.
[0143] Once an optimized delivery process is identified, scheduling
system 320 may continue to step 810 and determine if the identified
delivery process is the same as the default delivery process. For
example, if the optimization of step 808 indicates that an
optimized delivery process is wave-delivery, then scheduling system
320 would determine that the optimized delivery process matches the
default delivery process (step 810: yes). However, if the
optimization of step 808 indicates that an optimized delivery
process is shift-delivery, then scheduling system 320 would
determine that the optimized delivery process does not match the
default delivery process (step 810: no).
[0144] If scheduling system 320 determines the delivery process
does not match (step 810: no), scheduling system 320 may continue
to step 812 and change the delivery process to the optimized
delivery, For example, in step 812 scheduling system 320 may switch
the delivery process for a group of orders from wave delivery to
shift delivery based on the forecast and optimization of steps 806
and 808.
[0145] In step 814, scheduling system 320 may update elements of
system 100 and/or system 300 according to the optimized delivery
process. For example, scheduling system 320 may update FO system
113, shipment authority technology system 101, SCM system 117, WMS
119, and LMS 125 (FIG. 1A).
[0146] In step 816, scheduling system 320 may generate scheduling
notification GUIs for administration or workers. For example,
updates to the delivery process may also include transmitting
notifications to client devices 350 and/or updating GUI's in
administration screens, as further discussed in connection to FIG.
9. Further, changes in delivery processes based on the optimization
may be reflected in the printing instructions.
[0147] With process 800, scheduling system 320 may improve the
technical field of automated delivery scheduling because it creates
an adaptive system that can choose between different delivery
processes based on optimization variables. While current systems
require system managers to manually select between different
delivery processes (e.g., between wave or shift delivery), process
800 allows using demand forecasts to dynamically adapt the system
based on optimization conditions. Indeed, the forecasting and
optimization described in steps 806-808, enable frequent and
concurrent selection of delivery processes. Thus, process 800 shows
a particular computerized method for operating the parcel delivery
system with improved technical advantages.
[0148] FIG. 9 is an exemplary flow chart illustrating a predictive
model training process 900, in accordance with disclosed
embodiments. In some embodiments, elements of system 300 may
perform process 900. For example, as described below, scheduling
system 320 may perform process 900. This is just an illustrative
example of process 900, however, and in other embodiments system
100, or parts of system 100, may perform process 600. For example,
Shipment Authority Technology System 101, FO System 113, and FC
Auth 123 (FIG. 1A) may perform one or more of the steps in process
900.
[0149] In step 902, scheduling system 320 may receive a request for
prediction models for demand in fulfillment centers. In some
embodiments, the request may specify a target FC 200 or camp zone
215 (FIG. 2). The request may include information about client
devices 350 or former order information. Further the request may
come from client devices 350.
[0150] In step 904, scheduling system 320 may generate a modeling
data set. Scheduling system 320 may generate the modeling data set
using information from database 380, online resources 340, and/or
client devices 350. For example, scheduling system 320 may
retrieve, from database 380, historic trends of orders and their
corresponding fulfillment centers. The previous order data may also
include performance metrics, including distance between deliveries,
failed promised dates, and parcel priorities.
[0151] In step 906, scheduling system 320 may create modeling data
subsets by dividing modeling data sets generated in step 904. For
example, scheduling system 320 may divide the modeling data set in
a training data subset and a validation data subset. In some
embodiments, the training and validation data sets may be randomly
created by aleatory selection of elements from the modeling data
set for each subset. In other embodiments, however, scheduling
system 320 may divide the data with a predetermined rule. In some
embodiments, elements in the modeling data subsets may be unique to
each subset to create independent training data and validation
subsets. Alternatively, modeling data subsets may share elements
and overlap. In other embodiments, scheduling system 320 may divide
the modeling data set using division rules. The modeling data set
division rules may indicate the number of divisions and/or ratios
between different groups. For example, the modeling data set may be
divided using an 80/20 split for testing and validation data.
[0152] Based on the modeling data set partitioning, scheduling
system 320 may select a classifier in step 907. Scheduling system
320 may also process the modeling data set of step 906 to determine
coefficients (step 908) and hyper parameters (step 910) for a
prediction model. The prediction models may be specific for FC 200
demand and may be parametric, non-parametric, or semi-parametric.
For instance, in some embodiments, scheduling system 320 may create
a plurality of decision trees as prediction models to identify a
probability of fraud. In other embodiments, scheduling system 320
may generate neural networks, Group Method of Data Handling (GMDH)
algorithms, Naive Bayes classifiers, and/or Multivariate Adaptive
Regression Splines. Alternatively, or additionally, scheduling
system 320 may generate models based on linear regressions, random
forests, and/or logistic regressions. For example, scheduling
system 320 may develop a random forest model to predict demand in
both FC 200 and camp zone 215. Having parameters like day of the
week, time, order type, and destination address, models generated
in steps 906-910 may create a tool for forecasting demand in
fulfillment centers or camps, and the ability to meet delivery
goals.
[0153] In step 914, scheduling system 320 may evaluate if the model
is completed or if it has reached a stopping criteria. For example,
when scheduling system 320 generates decision trees, in step 914
scheduling system 320 may evaluate if a stopping criteria is
fulfilled for the end nodes. In some embodiments, stopping criteria
may be intrinsic to the model or defined by hyper parameters.
[0154] If the stop criteria in not fulfilled, scheduling system 320
may continue to step 916 and select a new variables or parameters
to determine new classifiers. For example, to variables of delivery
date and time, scheduling system 320 may include variables of
delivery preferences or costumer privileges. Alternatively, when
the stop criteria is fulfilled, scheduling system 320 may continue
to step 918, in which scheduling system 320 may calculate the
accuracy of the model using a portion of the training data set.
[0155] In step 920, scheduling system 320 may evaluate whether the
accuracy for the model is above an accuracy threshold. In some
embodiments, the accuracy threshold for the model may be
automatically adjusted based on optimization objectives set for the
prediction models. If the accuracy for the model is not above the
threshold (step 920: no) the model may be discarded in step 926. If
the calculated accuracy is above the threshold (step 920: yes),
scheduling system 320 may assign a weighted coefficient to the
model in step 922 and include the model to the set of models in
step 924. The weighted coefficient may be associated with the
calculated accuracy. For example, the weighted coefficient may be
proportional to the accuracy.
[0156] Process 900 may be repeated a plurality of times to generate
a plurality of models. In some embodiments, scheduling system 320
may repeat the process until a minimum of models is generated.
[0157] FIG. 10 is a front view of an exemplary graphical user
interface (GUI) 1000 in an administrator device, consistent with
disclosed embodiments. GUI 1000 may be displayed on devices of
management system 119 (e.g., devices 119A-119C) and may be updated
as orders are being received. GUI 1000 may be displayed also in
other devices of system 100, such as devices in internal front-end
system 105 (FIG. 1A).
[0158] GUI 1000 includes a plurality of rows 1002 (a)-(n). Each one
of the rows 1002 may identify a parcel to be delivered. For
example, each one of the rows 1002 may be associated with orders
received in, for example, step 602 (FIG. 6).
[0159] As shown in FIG. 10, GUI 1000 may include a plurality of
columns detailing different fields of information for each one of
rows 1002. GUI 1000 may include a tracking number column 1004, a
shipper name column 1006, a customer ID column 1008, a date column
1010, and a delivery process column 1012. In some embodiments, the
delivery process column 1012 may specify which wave or shift the
order has been assigned with for example process 900 (FIG. 9).
Delivery process column 1012 may also indicate whether the order is
scheduled for dawn delivery.
[0160] Moreover, GUI 1000 may include an action column 1014 that
allows a system manager to execute an action for the order in the
corresponding row 1002. The action may include print shipping
label, delete, modify, and or/reassign.
[0161] FIG. 11 is an exemplary shipping label 1100, consistent with
disclosed embodiments. Shipping label 1100 may be generated in step
620 (FIG. 6) or step 724 (FIG. 7).
[0162] Shipping label 1100 may include a delivery process note
1102. Delivery process note 1102 may specify a wave or shift
delivery, or a dawn or overnight delivery. Shipping label 1100 may
also include a bar code 1104, destination address 1106, and return
address 1108. As previously discussed in connection to FIG. 6, in
some embodiments shipping label 1100 may be automatically generated
and printed based on classifications or categorizations.
[0163] FIG. 12A is a front view of a first exemplary graphical user
interface (GUI) 1201 in a mobile device, consistent with disclosed
embodiments. GUI 1201 shows a schematic of a home menu 1202 for a
delivery service application.
[0164] FIG. 12B is a front view of a second exemplary graphical
user interface (GUI) 1203 in a mobile device, consistent with
disclosed embodiments. GUI 1203 shows a list of deliveries
1204(a)-1204(n). For each one of deliveries 1204, GUI 1203 displays
a plurality of buttons for accepting, rejecting, and completing
actions, among others.
[0165] FIG. 12C is a front view of a third exemplary graphical user
interface (GUI) 1205 in a mobile device, consistent with disclosed
embodiments. GUI 1205 shows a grouping option banner 1206. Based on
the delivery process (i.e., wave delivery or shift delivery),
banner 1206 gives users the option to manually adapt the delivery
in different groupings. For example, users of client devices 350
may update the system directly in their mobile phones when they
have suggestions to adapt the delivery process. In some situations,
a worker may have more updated knowledge of delivery capabilities.
Road accidents or other unforeseeable situations may impede
delivery with the optimized schedules automatically performed by,
for example, scheduling system 320. GUI 1205 allows users to
provide feedback and propose different groupings for delivery.
[0166] FIG. 12D is a front view a fourth exemplary graphical user
interface (GUI) 1207 in a mobile device, consistent with disclosed
embodiments. To address technical issues of having multiple items
all showed in the limited screen space of a mobile phone, GUI 1207
shows a filter 1208. Filter 1208 may be used reduce the number of
deliveries 1204 displayed in the mobile phone. Filter 1208 may
operate by selecting order based on delivery process, delivery
wave, and/or delivery area.
[0167] FIG. 12E is a front view of a fifth exemplary graphical user
interface (GUI) 1209 in a mobile device, consistent with disclosed
embodiments. GUI 1209 shows a new delivery banner 1210 to add new
deliveries that are being displayed in the screen. Moreover, banner
1210 may allow users to request additional deliveries or inform
pending deliveries cannot be completed.
[0168] FIG. 12F is a front view of a sixth exemplary graphical user
interface (GUI) 1211 in a mobile device, consistent with disclosed
embodiments. GUI 1211 shows options to group deliveries 1212. As
shown in FIG. 12F, GUI 1211 associates each one of the deliveries
1212 with a grouping icon.
[0169] FIG. 12G is a front view of a seventh exemplary graphical
user interface (GUI) 1213 in a mobile device, consistent with
disclosed embodiments. After selecting deliveries to be groups in
GUI 1211, GUI 1213 may be displayed to allow workers to select the
specific wave/shift, or other delivery process for the group of
selected deliveries 1212. As shown in FIG. 12G, GUI 1213 may
include a selection menu 1214 in which the user can indicate which
group should the selected deliveries be assigned to.
[0170] FIG. 12H is a front view of an eighth exemplary graphical
user interface (GUI) 1215 in a mobile device, consistent with
disclosed embodiments. GUI 1215 shows a new group creation menu
1216. Users may add new ways or shifts to the delivery process
using GUI 1215.
[0171] Another aspect of the disclosure is directed to a
non-transitory computer-readable medium storing instructions that,
when executed, cause one or more processors to perform the methods,
as discussed above. The computer-readable medium may include
volatile or ran-volatile, magnetic, semiconductor, tape, optical,
removable, non-removable, or other types of computer-readable
medium or computer-readable storage devices. For example, the
computer-readable medium may be the storage unit or the memory
module having the computer instructions stored thereon, as
disclosed. In some embodiments, the computer-readable medium may be
a disc or a flash drive having the computer instructions stored
thereon.
[0172] It will be apparent to those skilled in the art that various
modifications and variations can be made to the disclosed system
and related methods. Other embodiments will be apparent to those
skilled in the art from consideration of the specification and
practice of the disclosed system and related methods. It is
intended that the specification and examples be considered as
exemplary only, with a true scope being indicated by the following
claims and their equivalents.
[0173] 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.
[0174] 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.
[0175] 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.
[0176] Thus, the foregoing description has been presented for
purposes of illustration only. It is not exhaustive and is not
limiting 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.
[0177] 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, which examples are to be
construed as non-exclusive. Further, the steps of the disclosed
methods may be modified in any manner, including by reordering
steps and/or inserting or deleting steps.
* * * * *