U.S. patent application number 14/750981 was filed with the patent office on 2015-10-15 for mobile device based inventory management and sales trends analysis in a retail environment.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to PRISCILLA BARREIRA AVEGLIANO, SERGIO BORGER, CARLOS HENRIQUE CARDONHA, DIEGO SANCHEZ GALLO, RICARDO GUIMARAES HERRMANN, CESAR KAWABATA, ANDREA BRITTO MATTOS, DANIEL ALVES DA SILVA.
Application Number | 20150294333 14/750981 |
Document ID | / |
Family ID | 53798419 |
Filed Date | 2015-10-15 |
United States Patent
Application |
20150294333 |
Kind Code |
A1 |
AVEGLIANO; PRISCILLA BARREIRA ;
et al. |
October 15, 2015 |
MOBILE DEVICE BASED INVENTORY MANAGEMENT AND SALES TRENDS ANALYSIS
IN A RETAIL ENVIRONMENT
Abstract
A method for calculating sales trend of a product at a store
shelf based on crowdsourcing, includes receiving, by a retail store
server, availability data of a product measured on a shelf in the
retail store from a portable device, where the availability data is
in the form of a picture acquired of the product on the shelf,
identifying products on the shelf using tags attached to the
shelves, calculating sales velocity and sales trends of the product
from the identified products, and transmitting the sales velocity
and sales trend of the product to one or more third parties'
systems in a supply chain of said retail store. Products and their
locations on retail store shelves have been cataloged in a product
database.
Inventors: |
AVEGLIANO; PRISCILLA BARREIRA;
(SAO PAULO, BR) ; BORGER; SERGIO; (SAO PAULO,
BR) ; CARDONHA; CARLOS HENRIQUE; (SAO PAULO, BR)
; GALLO; DIEGO SANCHEZ; (SAO PAULO, BR) ;
HERRMANN; RICARDO GUIMARAES; (SAO PAULO, BR) ;
KAWABATA; CESAR; (SAO PAULO, BR) ; MATTOS; ANDREA
BRITTO; (SAO PAULO, BR) ; SILVA; DANIEL ALVES DA;
(SAO PAULO, BR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
ARMONK |
NY |
US |
|
|
Family ID: |
53798419 |
Appl. No.: |
14/750981 |
Filed: |
June 25, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14622360 |
Feb 13, 2015 |
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14750981 |
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61939782 |
Feb 14, 2014 |
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Current U.S.
Class: |
705/7.31 |
Current CPC
Class: |
A47F 5/0043 20130101;
G06Q 10/06315 20130101; G06Q 10/087 20130101; G06Q 30/0202
20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06Q 10/08 20060101 G06Q010/08; G06Q 10/06 20060101
G06Q010/06; A47F 5/00 20060101 A47F005/00 |
Claims
1. A method for calculating sales trend of a product at a store
shelf based on crowdsourcing, comprising the steps of: receiving,
by a retail store server, availability data of a product measured
on a shelf in the retail store from a portable device, wherein the
availability data is in the form of a picture acquired of the
product on the shelf; identifying products on the shelf using tags
attached to the shelves; calculating sales velocity and sales
trends of the product from the identified products; and
transmitting the sales velocity and sales trend of the product to
one or more third parties' systems in a supply chain of said retail
store, wherein products and their locations on retail store shelves
have been cataloged in a product database.
2. The method of claim 1, further comprising triggering a warning
message if the retail store shelf is empty.
3. The method of claim 1, wherein identifying products on the shelf
using tags comprises the steps of: identifying the tags on the
shelves using tag template information and information regarding a
distribution of tags on the retail store shelves that is read from
a tag database; mapping the identified tags to a shelf and its
corresponding assortment of items; identifying products in the
picture by reading product templates related to the identified tags
from the product database; and transmitting a list of identified
products and their quantities to the server.
4. The method of claim 3, wherein identifying the tags is performed
by a first image processing application, and identifying products
in the picture is performed by a second image processing
application.
5. The method of claim 3, further comprising filtering the results
of identified products based on product dimensions, to eliminate
false positives.
6. The method of claim 1, wherein calculating sales velocity and
sales trends of the product comprises the steps of: receiving for
each product p a timestamp t and a number of items n of product p
on the retail store shelf, where the product information is
organized as pairs p(t, n); ordering product pairs p(t, n)
according to the timestamp t to generate an ordered sequence;
calculating a least square line fit for the sequence to estimate a
sales' velocity; and comparing changes in a slope of the fitted
line to determine a change in the sale's velocity over time.
7. A method for minimizing a products out-of-the-shelf time based
on data collected by mobile applications, comprising the steps of:
receiving, by a retail store server, data acquired by a mobile
application regarding a number of missing product items on a shelf
in the retail store; calculating the product's turnover; receiving,
by the retail store server, a request for an optimized
replenishment route; generating an optimized replenishment route
from the products' turnover calculation that minimizes
out-of-the-shelf occurrences; and transmitting the optimized
replenishment route to the mobile application for display to a
user.
8. The method of claim 7, wherein the product's turnover is
calculated by a first sales trend calculation application.
9. The method of claim 7, further comprising updating demand
forecasts of the reported missing product items in a product
localization and turnover database.
10. The method of claim 7, wherein the optimized replenishment
route is calculated by a second sales trend calculation
application.
11. The method of claim 7, wherein the data of number of missing
product items on a shelf acquired by the mobile application is in
the form of a picture, wherein products and their locations on
retail store shelves have been cataloged in a product database, and
wherein the method further comprises identifying products on the
shelf using tags attached to the shelves.
12. The method of claim 11, wherein identifying products on the
shelf using tags comprises the steps of: identifying the tags on
the shelves using tag template information and information
regarding a distribution of tags on the retail store shelves that
is read from a tag database; mapping the identified tags to a shelf
and its corresponding assortment of items; identifying products in
the picture by reading product templates related to the identified
tags from the product database; and transmitting a list of
identified products and their quantities to the server.
13. The method of claim 12, wherein identifying the tags is
performed by a first image processing application, and identifying
products in the picture is performed by a second image processing
application.
14. The method of claim 12, further comprising filtering the
results of identified products based on product dimensions, to
eliminate false positives.
Description
CROSS REFERENCE TO RELATED UNITED STATES APPLICATIONS
[0001] This case is a continuation of, and claims priority from,
U.S. patent application Ser. No. 14/622,360 of Avegliano, et al.,
filed on Feb. 13, 2015 in the U.S. Patent and Trademark Office,
which in turn claims priority from U.S. Provisional Application No.
61/939,782 of Avegliano, et al., filed Feb. 14, 2014, the contents
of both of which are herein incorporated by reference in their
entireties.
BACKGROUND
[0002] 1. Technical Field
[0003] Embodiments of the present disclosure are directed to
mobile-based technologies for the retail industry.
[0004] 2. Discussion of the Related Art
[0005] Retailers, consumer packaged goods (CPGs) companies, and
manufacturers face many challenges, such as frequent out-of-shelf
and out-of-stock conditions, and poor demand forecasts, that
generate inefficient production, excessive inventory or
out-of-stocks conditions, that clearly leads to lost sales. The
process of sales estimation is crucial to a supply chain. A poor
demand forecast may frequently cause a bullwhip effect to occur,
which can result in inefficient production, excessive inventory or
out-of-stock condition, that can lead to lost sales. It is known
that visibility of customer demand is essential for a better supply
chain performance.
[0006] Manufacturers, consumer goods companies and producers (MCP)
typically have little to no real time visibility on consumption and
consumption speed information of their products at distribution
points and the retailer shelf. Among others, the consequences
include 8% out-of-shelf situations that represent about a 3.9%
sales loss. For MCP enterprises this may also include possible
brand loyalty loss as well as issues with product planning,
merchandizing, advertizing and new product introduction. On the to
other hand, today's consumers have a wide variety of mobile
information tools that allow them to verify price and availability
of products in the majority of online stores, making shelf
information important to future MCP enterprises.
[0007] Demand estimation is another important challenge in retail
industry. Even though the task is of unquestionable economical
relevance, current forecast models cannot be considered
satisfactory for most practical scenarios. It is interesting to
notice, though, that demand fluctuations caused by out-of-shelf
events have been overlooked so far. Incidents of this type happen
not only when products are out-of-stock, but also when retailers
employ poor shelf replenishment practices and do not have their
products properly organized in the store. Out-of-shelf events
clearly lead to immediate sales losses, but their side effects are
much more profound: since there is no record of unmet demand, a
silent deformation of the demand curve is produced. As a result,
forecast models are inevitably inaccurate, as they are based on
misleading data, resulting in more out-of-shelf events; hence, a
vicious cycle arises.
[0008] Out-of-the-shelf conditions are also a delicate situation,
since they represent an impact on sales. When faced with an
out-of-the-shelf situation, a consumer might take one of these
actions: (1) buy a similar product from other brand; (2) leave the
store and look for the desired product on another store, or (3) do
not buy the desired product at all. From the store's perspective,
option (1) might not have a negative impact because a sale was
made, but from a CPG's perspective, option (1) might be considered
the most dangerous option since the consumer might try the
competitor's product and prefer it. On the other hand, options (2)
and (3) represent a sale loss to the store and to CPG,
respectively.
[0009] Consequently, out-of-the-shelf conditions should be
minimized as much as possible. This situation can occur if the
product is out-of-stock or simply if the shelf replenishment was
not made satisfactorily.
[0010] Shelf monitoring in supermarkets is necessary in order to
avoid situations such as items going out of stock. Monitoring
products in supermarket is an intensive work typically performed by
the employees.
[0011] In a vendor-managed inventory (VMI) business model, the
vendor supplier is responsible for maintaining an agreed level of
products in stock. In some countries, such as in Brazil, this
responsibility is extended to the task of shelf-replenishment:
there are employees of the CPG inside the store transporting items
from the backroom stock to the shelves. In this context, the
determination of how many products should be replaced at each
period of the day and with what frequency is determined by the
CPG's employees who do not have access to point-of-sale (POS)
information or have permission to make structural changes in the
store's facility.
[0012] One of the main reasons why even the most sophisticated
analytics and optimization tools have not been able to properly
address the problem is the unsatisfactory (and actually almost
inexistent) amount of information related to these events and to
the status of shelves over time.
[0013] There are some solutions that are aimed at monitoring the
level of backroom stocks and the number of items disposed in the
shelves, but they are primarily based on information of sales
collected from POS or on solutions that demand infrastructure's
modifications, such as the use of an RFID, which demands the
installation of a RFID reader, or with presence sensors on the
shelves, and image processing techniques that load product
templates and try to determine if each product is displayed in the
input picture.
[0014] Specialized sensors may have high cost and be challenging to
obtain and install. On the other hand, image processing is a low
cost solution that can be implemented using pictures taken from any
kind of mobile device. However, previous methods either require a
planogram (a planogram is an organization of the assortment on a
shelf) of the current shelf, or try to match all products templates
from the store against the picture.
[0015] Therefore, an additional solution using image processing can
allow a fully automatic procedure in which the user does not have
to provide a planogram, but can obtain an identification with high
accuracy and speed by taking a picture of a shelf rack, matching
only templates of products from the assortment of the current
shelf. This list of products can retrieved automatically from
tagged shelves. Shelf monitoring and product replacement can also
be made more efficient using mobile devices.
SUMMARY
[0016] According to an embodiment of the disclosure, there is
provided a method for calculating sales trend of a product at a
store shelf based on crowdsourcing, including receiving, by a
retail store server, availability data of a product measured on a
shelf in the retail store from a portable device, wherein the
availability data is in the form of a picture acquired of the
product on the shelf, identifying products on the shelf using tags
attached to the shelves, calculating sales velocity and sales
trends of the product from the identified products, and
transmitting the sales velocity and sales trend of the product to
one or more third parties' systems in a supply chain of said retail
store, where products and their locations on retail store shelves
have been cataloged in a product database.
[0017] According to a further embodiment of the disclosure, the
method includes triggering a warning message if the retail store
shelf is empty.
[0018] According to a further embodiment of the disclosure,
identifying products on the shelf using tags includes identifying
the tags on the shelves using tag template information and
information regarding a distribution of tags on the retail store
shelves that is read from a tag database, mapping the identified
tags to a shelf and its corresponding assortment of items,
identifying products in the picture by reading product templates
related to the identified tags from the product database, and
transmitting a list of identified products and their quantities to
the server.
[0019] According to a further embodiment of the disclosure,
identifying the tags is performed by a first image processing
application, and identifying products in the picture is performed
by a second image processing application.
[0020] According to a further embodiment of the disclosure, the
method includes filtering the results of identified products based
on product dimensions, to eliminate false to positives.
[0021] According to a further embodiment of the disclosure, where
calculating sales velocity and sales trends of the product includes
receiving for each product p a timestamp t and a number of items n
of product p on the retail store shelf, where the product
information is organized as pairs p(t, n), ordering product pairs
p(t, n) according to the timestamp t to generate an ordered
sequence, calculating a least square line fit for the sequence to
estimate a sales' velocity, and comparing changes in a slope of the
fitted line fits to determine a change in the sale's velocity over
time.
[0022] According to a another embodiment of the disclosure, there
is provided a method for minimizing a products out-of-the-shelf
time based on data collected by mobile applications, including
receiving, by a retail store server, data acquired by a mobile
application regarding a number of missing product items on a shelf
in the retail store, calculating the product's turnover, receiving,
by the retail store server, a request for an optimized
replenishment route, generating an optimized replenishment route
from the products' turnover calculation that minimizes
out-of-the-shelf occurrences, and transmitting the optimized
replenishment route to the mobile application for display to a
user.
[0023] According to a further embodiment of the disclosure, the
product's turnover is calculated by a first sales trend calculation
application.
[0024] According to a further embodiment of the disclosure, the
method includes updating demand forecasts of the reported missing
product items in a product localization and turnover database.
[0025] According to a further embodiment of the disclosure, the
optimized replenishment route is calculated by a second sales trend
calculation application.
[0026] According to a further embodiment of the disclosure, the
data of a number of missing product items on a shelf acquired by
the mobile application is in the form of a picture, where products
and their locations on retail store shelves have been cataloged in
a product database. The method includes identifying products on the
shelf using tags attached to the shelves.
[0027] According to a further embodiment of the disclosure,
identifying products on the shelf using tags includes identifying
the tags on the shelves using tag template information and
information regarding a distribution of tags on the retail store
shelves that is read from a tag database, mapping the identified
tags to a shelf and its corresponding assortment of items,
identifying products in the picture by reading product templates
related to the identified tags from the product database, and
transmitting a list of identified products and their quantities to
the server.
[0028] According to a further embodiment of the disclosure,
identifying the tags is performed by a first image processing
application, and identifying products in the picture is performed
by a second image processing application.
[0029] According to a further embodiment of the disclosure, the
method includes the results of identified products based on product
dimensions, to eliminate false positives.
[0030] According to a another embodiment of the disclosure, there
is provided a non-transitory program storage device readable by a
computer, tangibly embodying a program of instructions executed by
the computer to perform the method steps for managing inventory and
sales trends in a retail environment using mobile devices. The
method includes receiving, by a retail store server, product
availability data acquired by a mobile application regarding a
number of missing product items on a shelf in the retail store,
wherein availability data is in the form of a picture acquired of
the product on the shelf, identifying products on the shelf using
tags attached to the shelves, calculating turnover, sales velocity
and sales trends of the identified products, transmitting the sales
velocity and sales trend of the product to one or more third
parties' systems in a supply chain of said retail store, receiving,
by the retail store server, a request for an optimized
replenishment route, generating an optimized replenishment route
from the products' turnover calculation that minimizes
out-of-the-shelf occurrences, and transmitting the optimized
replenishment route to the mobile application for display to a
user, where products and their locations on retail store shelves
have been cataloged in a product database.
[0031] According to a further embodiment of the disclosure, the
method includes triggering a warning message if the retail store
shelf is empty.
[0032] According to a further embodiment of the disclosure,
identifying products on the shelf using tags includes identifying
the tags on the shelves using tag template information and
information regarding a distribution of tags on the retail store
shelves that is read from a tag database, mapping the identified
tags to a shelf and its corresponding assortment of items,
identifying products in the picture by reading product templates
related to the identified tags from the product database, and
transmitting a list of identified products and their quantities to
the portable device.
[0033] According to a further embodiment of the disclosure,
calculating sales velocity and sales trends of the product includes
receiving for each product p a timestamp t and a number of items n
of product p on the retail store shelf, where the product
information is organized as pairs p(t, n), ordering product pairs
p(t, n) according to the timestamp t to generate an ordered
sequence, calculating a least square line fit for the sequence to
estimate a sales' velocity, and comparing changes in a slope of the
line fits to determine a change in the sale's velocity over
time.
[0034] According to a another embodiment of the disclosure, there
is provided a system to calculate sales trend of a product at a
store shelf based on crowdsourcing, including at least one portable
application configured to be executed on a plurality of portable
computing devices, a plurality of tags attached to each store shelf
rack, and a retail store server connected to the portable
application on each of the plurality of portable computing devices
over a wireless local network, where availability data of a product
is measured on each store shelf rack by the at least one portable
application using the tags attached to each store shelf rack, and
the retail store server is configured to determine sales trends of
the product from the availability data received from the at least
one portable application over the wireless local area network.
BRIEF DESCRIPTION OF THE DRAWINGS
[0035] FIG. 1 is a schematic block diagram of a system according to
an embodiment of the disclosure.
[0036] FIG. 2A is a flowchart of a method for calculating sales
trend at the shelf based on a crowdsourcing portable application,
according to an embodiment of the disclosure.
[0037] FIG. 2B is a flowchart of an exemplary method for
calculating the sales trend of a product's demand, according to an
embodiment of the disclosure.
[0038] FIG. 3 is a flowchart of a method for identifying products
on tagged shelves, according to an embodiment of the
disclosure.
[0039] FIG. 4 shows an example of a shelf rack containing four
types of products and four tags, according to an embodiment of the
disclosure.
[0040] FIG. 5 shows the list of products for the shelf with
corresponding picture and dimensions according to an embodiment of
the disclosure.
[0041] FIG. 6 shows a list of products associated with the current
shelf retrieved from the product database, along with each
product's associated width and height, according to an embodiment
of the disclosure.
[0042] FIG. 7 illustrates the matching of the template to every
item from the list in the image using the height and width
information, according to an embodiment of the disclosure.
[0043] FIG. 8 is a flowchart of an exemplary, non-limiting method
for minimizing out-of-the-shelf products based on data collected by
mobile applications, according to an embodiment of the
disclosure.
[0044] FIG. 9 is a block diagram of an exemplary computer system
for implementing a method for managing inventory and sales trends
in a retail environment using mobile devices according to an
embodiment of the disclosure.
DETAILED DESCRIPTION
[0045] Exemplary embodiments of the disclosure as described herein
generally include systems and methods for managing inventory and
analyzing sales trends in a retail environment using mobile
devices. Accordingly, while embodiments of the disclosure are
susceptible to various modifications and alternative forms,
specific embodiments thereof are shown by way of example in the
drawings and will herein be described in detail. It should be
understood, however, that there is no intent to limit embodiments
of the disclosure to the particular exemplary embodiments
disclosed, but on the contrary, embodiments of the disclosure cover
all modifications, equivalents, and alternatives falling within the
spirit and scope of the disclosure.
[0046] Embodiments of the present disclosure can provide a crowd
sourced-based solution to monitor sales velocity in retail stores.
An exemplary crowdsourced based data collection solution according
to an embodiment of the disclosure can measure product availability
on the shelf based on portable devices, not RFIDs or sensors, and
can send sales trend analyses, such as sales velocity and sales
acceleration, to manufacturers, that can prevent the bullwhip
effect. Two kinds of mobile applications can be used: one, designed
for stockists, can be used to collect data regarding the number of
products in the shelf and turnover of products in a period of time;
and another for consumers can be used to report that a product is
missing on the shelf. The collected data can be analyzed and
information regarding sales velocity and sales trends can be
extracted and transmitted to the whole supply chain. Exemplary
embodiments of the disclosure can be used anywhere without
infrastructure intervention and are capable of registering
unattended demand.
[0047] Further exemplary embodiments of the present disclosure can
provide a decision-support platform for supply-chain activities of
MCP enterprises that includes a mobile application object
recognition algorithm to augment the content being produced by
merchants, and of a series of analytics modules, based on image
processing, multi-agent simulation, optimization, statistics, and
visual analytics, that use statistical analysis to estimate sales
velocity and predict out-of-shelf conditions to generate tailored
recommendations. A visualization component presents information
about sales velocity, predictive estimation of probable
out-of-shelf conditions, and recommendations to prevent their
occurrence. To leverage the content being captured by workers using
the mobile application, embodiments of the disclosure support
image-processing algorithms in tasks such as identifying objects as
well as their absence on retailers' shelves. Embodiments of the
disclosure can minimize the time of out-of-the-shelf situations by
generating an optimized route and schedule of items' replenishment,
based on information of items' turnover collected by a mobile
application.
[0048] A mobile application, deployed on customers and merchant
devices to periodically monitor product sales velocity, can reduce
the high communication latency between the distribution point and
retail stores under out-of-shelf condition and suppliers. A
platform according to an embodiment of the disclosure can consider
the impact of out-of-shelf events on demand estimation. Current
analytics and optimization solutions cannot address this task
properly due to the lack of real-time information about events and
activities related to shelves, as data coming from points-of-sale
provide a partial picture of the whole process.
[0049] According to embodiments of the disclosure, multi-agent
simulations were used in the investigation of out-of-shelf events
on demand forecasts and to support test replenishment and ordering
techniques that could be potentially used by retailers. Results
show that out-of-shelf events indeed have an important impact in
demand forecast.
[0050] Optimization and visualization have the potential to further
improve the quality of a platform according to an embodiment of the
disclosure. For example, techniques dedicated to the Vehicle
Routing Problem can employ knowledge about sales velocity to
generate replenishment for merchants. Similarly, scenarios with
several retailers and several producers may have distribution plans
properly modeled as Multi-commodity Flow problems.
[0051] Further embodiments of the disclosure can receive a
user-supplied picture of a supermarket shelf rack that contains
tags, and can identify the tagged shelves and each product
displayed in the picture. A solution according to an embodiment of
the disclosure is fully automatic, does not require a planogram,
and can identify with high accuracy and speed, matching only
templates of products from the assortment of the current shelf.
This list of products can be retrieved automatically from tagged
shelves.
[0052] A method according to an embodiment of the disclosure can
receive an image of a tagged shelf rack and can identify the
products displayed in the picture, using image processing. The tags
are identified, retrieving the shelf assortment of items and this
information is used to match only templates from the products of
the current shelf. Also, knowing the distance between the tags in
the picture and in the actual shelf, can help the product
identification method.
[0053] A method according to an embodiment of the disclosure, being
a low cost solution, does not require specialized sensors. A method
according to an embodiment of the disclosure, by retrieving its
list of products from the tags, does not require that a user
manually identify the current shelf. Also, a method according to an
embodiment of the disclosure does not have to match all products
templates from the store. Finally, the scale information that can
be computed from known distances between the tags can increase the
identification accuracy and provide additional information, such as
estimating the shelf row in which a product is identified.
[0054] The insight provided by the optimization analytics will
allow MCP enterprises to better plan product introduction,
manufacturing and distribution based on the information collected
by merchants and consumers about the product availability and cost
at point of distribution or retail shelf. Further the image
processing information added allows an MCP enterprise to "see" the
status of product placement, organization thus allowing MCP to
create and control consumer oriented merchandizing strategies at
the shelf.
[0055] Timely (near real time) information about product
availability and consumption speeds and trends has always been a
desire of MCP enterprises. Embodiments of the disclosure can
address supply chain visibility and optimization for MCP
enterprises using merchants and crowdsourced information at the
point of distribution or retail shelf by using mobile social
networking technologies to collect information to forecast
out-of-stock situations at the shelf for consumers without
depending on point of sale information. Further, the addition of
image processing capabilities allows for opportunities for an MCP
enterprise to create and control consumer oriented merchandizing
strategies at the shelf towards optimizing customer experience.
[0056] A system according to an embodiment of the disclosure can
calculate sales trend at the shelf based on a crowdsourcing
portable application, identify products on tagged shelves based on
image processing, and minimize out-of-the-shelf products based on
data collected by mobile applications in a vendor inventory
management model. FIG. 1 is a schematic block diagram of a system
according to an embodiment of the disclosure. A system according to
an embodiment of the disclosure includes a plurality of mobile
devices 10 provided to stockists, a network 11, a retail store
server 12, one or more applications 13 installed on each mobile
device, and one or more databases 15, 16. In the drawing figure,
only one mobile device 10 with one application 13, and one mobile
wireless network signal 11 are labeled, for clarity. The network 11
may be local wireless network such as a WiFi network, a 3G network,
a 4G network, etc. A system according to an embodiment of the
disclosure may also include a set of tags that will be attached to
each store shelf rack. The mobile devices may include devices
possessed by customers. Applications that may be installed on the
mobile device include a camera application to acquire pictures, an
application to acquire data of products' turnover in a store, and
an application to request an optimal replenishment route. In some
embodiments, the applications to acquire product turnover data may
be the camera application. The retail store server 12 may be
further provided with an image processing module 17 and a sales
trend calculation module 18. The image processing module 17 may
include a first app that can identify tags, and a second app that
can identify products. The sales trend calculation module 18 may
include a first app that can calculate a product's turnover, and a
second app that can determine an optimized product replenishment
route. The databases may include a first database 15 that stores
product and tag distribution templates containing the product name,
picture and dimensions, and a second database 16 that stores
information about product localization and turnover. The system may
further include an application that can trigger a warning message
if an empty shelf is reported. A system according to an embodiment
of the disclosure may also be connected to systems in the supply
chain of the store over a computer network such as the
Internet.
[0057] FIG. 2A is a flowchart of an exemplary method according to
an embodiment of the disclosure for calculating sales trend at the
shelf based on a crowdsourcing portable application. A method
according to an embodiment of the disclosure assumes that an
application is installed on stockists' and customers' portable
devices. Referring now to the figure, such a method includes the
retail store server receiving at step 21 information regarding the
number of products on the shelf from the portable devices and
calculating at step 22 sales velocity and sales trends of the
product. The retail store server can transmit sales velocity and
sales trend information to third parties' systems involved in the
supply chain at step 23. If the information received from the
portable devices indicates an empty shelf, a warning message can be
triggered at step 24.
[0058] FIG. 2B is a flowchart of an exemplary method according to
an embodiment of the disclosure for calculating the sales trend of
a product's demand based on its turnover in the retail store and on
the reported sales loss communicated by a customer that did not
find the desired product. This calculation would be performed by
the sales trend calculation module 18 on the retail store server 12
of FIG. 1. Referring now to the figure, such a method includes, at
step 25, receiving for each product p a timestamp t and a number of
items n of product p on the shelf, where the product information is
organized as pairs p(t, n). At step 26, the product pairs are
ordered according to the timestamps to generate an ordered
sequence. Then, a least squares fitting line is calculated for the
sequence at step 27 to estimate the sales' velocity. At step 28,
the changes in the slope of the fitting line are compared with past
fitting lines to see if sale's velocity is changing over time, and,
if yes, how it is changing.
[0059] As an exemplary, non-limiting scenario of calculating sales
trend at the shelf based on a crowdsourcing portable application,
consider the following. A stockist courses through a store and
registers in the mobile application the initial number of products
on the shelves and the number of missing products. The product
registration can be accomplished by, for example, processing a
photograph taken by the stockiest. The system installed on the
retail store server, based on the collected data, updates
information regarding sales velocity and sales trends and sends,
via the Internet, the new calculated values to other parties
involved in the supply chain. The stockist replenishes the shelf
and again registers the number of replenished products through the
mobile application. Then, after a few hours, a consumer reports via
the mobile application that the product is missing in the shelf.
The server system then updates the information regarding sales
velocity and sales trends of the product and sends, via Internet,
the new calculated values to other parties involved in the supply
chain. In addition, a warning signal for replenishing the shelf can
be triggered. The stockist can then replenish the shelf and
register the number of replenished products through the mobile
application.
[0060] A system according to an embodiment of the disclosure for
identifying products on tagged shelves includes a set of tags that
will be attached to each shelf rack. The tags can be a low cost
paper models, each with a distinctive pattern. A predetermined
number of tags are placed in a shelf rack and the tags themselves
and distances between the tags are recorded. The tags may be
distributed so that whenever a picture of a shelf is taken, at
least k tags are displayed in the picture. An exemplary,
non-limiting value of k is 3. An exemplary, non-limiting system
also includes the first database 15 of product templates that
include, inter alia, the product name, picture and dimensions, and
tag templates and their distribution on the store shelves. Thus, a
set of items can be mapped to a shelf, and each set of tags is
mapped to a shelf, to define which products are displayed in a rack
of shelves. An exemplary, non-limiting system further includes a
camera application on the mobile devices that can take pictures of
the shelves and transmit them to the server. These pictures may
record the tags and their separation distances. An exemplary,
non-limiting server includes a first image processing application
to identify the tags and a second image processing application to
identify the products.
[0061] A method according to an embodiment of the disclosure for
identifying products on tagged shelves is based on image processing
and uses the database of products templates that include each
product's picture and dimensions, the database of tag templates and
their distribution on the shelves, and the tags themselves placed
at each shelf rack of a retail store. Each set of tags, whose
location is known, can be mapped to a shelf and its corresponding
assortment of items. A picture is acquired by the mobile device of
one shelf in which at least k tags are visible. The tags can be
recognized by the first image processing application, so that the
current shelf can be identified and its assortment retrieved, as
well as estimating the picture scale and correcting perspective
distortion. With this information, it can be determined what
products templates should be identified in the image. Products
belonging to other shelves that are in incorrect positions are not
identified. According to an embodiment of the disclosure, the
second image processing application can be used to match templates
in the input picture using dimension information to increase
recognition accuracy and to filter false positives.
[0062] FIG. 3 is a flowchart of a method according to an embodiment
of the disclosure for identifying products on tagged shelves.
Referring now to the figure, at step 31, a picture is acquired by
the mobile device of one shelf in which at least k tags are
visible, and is transmitted to the remote server. The remote server
calls the first image processing application at step 32 to identify
the tags, and the identified tags and their coordinates, along with
the picture, are passed to the second image processing application.
The first image processing application uses the tag template and
distribution information read from the tag database in identifying
the tags. At step 33, the second image processing application uses
the identified tags and their coordinates, along with the picture,
to map the tags to a shelf and its corresponding assortment of
items. The second image processing application reads product
templates related to the identified tags from the product database
to identify the products in the picture, at step 34. The second
image processing application filters the results of identified
products based on the product dimensions, to eliminate false
positives at step 35, and transmits a list of identified products
and its quantities to the user at step 36.
[0063] The following is an exemplary, non-limiting scenario of
identifying products on tagged shelves. FIG. 4 shows an example of
a shelf rack S1, containing four types of products A, B, C and D,
and four tags T1.1, T1.2, T1.3 and T1.4. The distance of each tag
to the others is measured and stored. Note that each tag T1.1,
T1.2, T1.3 and T1.4 has a unique pattern, which enables the tag to
be mapped to its location. FIG. 5 shows the list of products A, B,
C, and D for the shelf S1, with corresponding picture and
dimensions. The dimensions include a width and height of each
product A, B, C, and D. When a user takes a picture of an arbitrary
shelf, the first image-processing application identifies the
current shelf from the tags, and then retrieves its list of
products associated with the current shelf from the product
database, along with each product's associated width and height, as
illustrated in FIG. 6. After loading the templates for the current
shelf, the second image-processing application attempts to match
the template to every item from the list in the image using the
height and width information, as shown in FIG. 7. Each detected
product and its coordinates are then stored and sent to the
user.
[0064] In a VIM model, a method to minimize out-of-the-shelf
products based on data collected by mobile applications uses a
database that stores a catalogue of each product and its location
in the store. FIG. 8 is a flowchart of an exemplary, non-limiting
method for minimizing a product's out-of-the-shelf time based on
data collected by mobile applications. Referring to the figure, a
method starts when the CPG employee provides information regarding
the description, quantity, and location of missing items on each
shelf of the store to the mobile application. The information can
be entered manually by the employee or it can be extracted
automatically from a picture taken of the shelf by the employee. At
step 81, the product turnover application of the mobile application
transmits the data collected regarding the number of missing items
on the shelf to a remote server. The data may be in the form of a
picture. After receiving the data, the retail store server calls
the first sales trend calculation application to calculate the
product's turnover, at step 82. The first sales trend calculation
application updates demand forecasts of the reported missing items
in the second database 16 at step 83. At step 84, after walking
through the whole store and reposting all the missing items on the
shelves, the CPG employee uses the optimal replenishment route
request application to transmit a request for an optimized
replenishment route to the to the retail store server. The retail
store server receives the user's request through the network and
calls the second sales trend calculation application to generate
the optimized route, at step 85. The second sales trend calculation
application generates an estimated optimized route to minimize
out-of-the-shelf occurrences from the items' turnover calculation
received from the first sales trend calculation application. At
step 86, the optimized replenishment route is sent to the optimal
replenishment route request application of the mobile application,
which can display the replenishment order and items' quantity to be
replenished to the user.
[0065] An exemplary, non-limiting example of minimizing
out-of-the-shelf products based on data collected by mobile
applications is as follows. A CPG employee makes a first patrol of
the day and informs the mobile application that there are 20
packets of toiled paper missing, 14 packets of diapers missing and
30 napkin's packets missing. Since toilet paper historically has a
greater turnover when compared to the other products, and since it
has a higher priority (the profit margin is higher), the
replenishment route calculation application prioritizes
replenishment of toilet paper. However, the number of estimated
diapers packets on the shelf is below a lower limit, as determined
by the number of fronts in the shelf. On the other hand, the
shopping chart can transport at most 15 packets of toilet paper and
no diapers. A route optimization algorithm according to an
embodiment of the disclosure, in turn, can determine that the
employee makes two journeys: one with 12 packets of toilet paper
and 3 diapers, assuring, this way, the number of fronts, and a
second journey, with 8 toilet paper packets, 9 diapers and 30
napkins.
[0066] As will be appreciated by one skilled in the art, aspects of
the present disclosure may be embodied as a system, method or
computer program product. Accordingly, aspects of the present
disclosure may take the form of an entirely hardware embodiment, an
entirely software embodiment (including firmware, resident
software, micro-code, etc.) or an embodiment combining software and
hardware aspects that may all generally be referred to herein as a
"circuit," "module" or "system." Furthermore, aspects of the
present disclosure may take the form of a computer program product
embodied in one or more computer readable medium(s) having computer
readable program code embodied thereon.
[0067] Any combination of one or more computer readable medium(s)
may be utilized. The computer readable medium may be a computer
readable signal medium or a computer readable storage medium. A
computer readable storage medium may be, for example, but not
limited to, an electronic, magnetic, optical, electromagnetic,
infrared, or semiconductor system, apparatus, or device, or any
suitable combination of the foregoing. More specific examples (a
non-exhaustive list) of the computer readable storage medium would
include the following: an electrical connection having one or more
wires, a portable computer diskette, a hard disk, a random access
memory (RAM), a read-only memory (ROM), an erasable programmable
read-only memory (EPROM or Flash memory), an optical fiber, a
portable compact disc read-only memory (CD-ROM), an optical storage
device, a magnetic storage device, or any suitable combination of
the foregoing. In the context of this document, a computer readable
storage medium may be any tangible medium that can contain, or
store a program for use by or in connection with an instruction
execution system, apparatus, or device.
[0068] A computer readable signal medium may include a propagated
data signal with computer readable program code embodied therein,
for example, in baseband or as part of a carrier wave. Such a
propagated signal may take any of a variety of forms, including,
but not limited to, electro-magnetic, optical, or any suitable
combination thereof. A computer readable signal medium may be any
computer readable medium that is not a computer readable storage
medium and that can communicate, propagate, or transport a program
for use by or in connection with an instruction execution system,
apparatus, or device.
[0069] Program code embodied on a computer readable medium may be
transmitted using any appropriate medium, including but not limited
to wireless, wireline, optical fiber cable, RF, etc., or any
suitable combination of the foregoing.
[0070] Computer program code for carrying out operations for
aspects of the present disclosure may be written in any combination
of one or more programming languages, including an object oriented
programming language such as Java, Smalltalk, C++ or the like and
conventional procedural programming languages, such as the "C"
programming language or similar programming languages. The program
code may execute entirely on the user's computer, partly on the
user's computer, as a stand-alone software package, partly on the
user's computer and partly on a remote computer or entirely on the
remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider).
[0071] Aspects of the present disclosure has been described above
with reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems) and computer program products
according to embodiments of the disclosure. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer program
instructions. These computer program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or
blocks.
[0072] These computer program instructions may also be stored in a
computer readable medium that can direct a computer, other
programmable data processing apparatus, or other devices to
function in a particular manner, such that the instructions stored
in the computer readable medium produce an article of manufacture
including instructions which implement the function/act specified
in the flowchart and/or block diagram block or blocks.
[0073] The computer program instructions may also be loaded onto a
computer, other programmable data processing apparatus, or other
devices to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other devices to
produce a computer implemented process such that the instructions
which execute on the computer or other programmable apparatus
provide processes for implementing the functions/acts specified in
the flowchart and/or block diagram block or blocks.
[0074] FIG. 9 is a block diagram of an exemplary computer system
for implementing a method for managing inventory and sales trends
in a retail environment using mobile devices according to an
embodiment of the disclosure. Referring now to FIG. 9, a computer
system 91 for implementing the present disclosure can comprise,
inter alia, a central processing unit (CPU) 92, a memory 93 and an
input/output (I/O) interface 94. The computer system 91 is
generally coupled through the I/O interface 94 to a display 95 and
various input devices 96 such as a mouse and a keyboard. The
support circuits can include circuits such as cache, power
supplies, clock circuits, and a communication bus.
[0075] The memory 93 can include random access memory (RAM), read
only memory (ROM), disk drive, tape drive, etc., or a combinations
thereof. The present disclosure can be implemented as a routine 97
that is stored in memory 93 and executed by the CPU 92 to process
the signal from the signal source 98. As such, the computer system
91 is a general purpose computer system that becomes a specific
purpose computer system when executing the routine 97 of the
present disclosure.
[0076] The computer system 91 also includes an operating system and
micro instruction code. The various processes and functions
described herein can either be part of the micro instruction code
or part of the application program (or combination thereof) which
is executed via the operating system. In addition, various other
peripheral devices can be connected to the computer platform such
as an additional data storage device and a printing device.
[0077] The flowchart and block diagrams in the figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods and computer program products
according to various embodiments of the present disclosure. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of code, which comprises one or more
executable instructions for implementing the specified logical
function(s). It should also be noted that, in some alternative
implementations, the functions noted in the block may occur out of
the order noted in the figures. For example, two blocks shown in
succession may, in fact, be executed substantially concurrently, or
the blocks may sometimes be executed in the reverse order,
depending upon the functionality involved. It will also be noted
that each block of the block diagrams and/or flowchart
illustration, and combinations of blocks in the block diagrams
and/or flowchart illustration, can be implemented by special
purpose hardware-based systems that perform the specified functions
or acts, or combinations of special purpose hardware and computer
instructions.
[0078] While the present disclosure has been described in detail
with reference to exemplary embodiments, those skilled in the art
will appreciate that various modifications and substitutions can be
made thereto without departing from the spirit and scope of the
disclosure as set forth in the appended claims.
* * * * *