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.

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 suppl...

AI Technical Summary

Benefits of technology

[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.

[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.

[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.

Problems solved by technology

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.

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.

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.

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.

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.

Out-of-the-shelf conditions are also a delicate situation, since they represent an impact on sales.

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.

This situation can occur if the product is out-of-stock or simply if the shelf replenishment was not made satisfactorily.

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.

Specialized sensors may have high cost and be challenging to obtain and install.

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