Claims:We claim:

1. A computer system comprising:
one or more processors (206); and
a memory (204) coupled to the one or more processors (206), the memory (204) for storing instructions which, when executed by the one or more processors (206), cause the one or more processors (206) to perform a method for predicting inventory of a retailer in real time, the method comprising:
receiving, at an order prediction system (108), past data from a second one or more communication devices (110) associated with a plurality of retailers (112), wherein the past data is received from a plurality of sources in real time;
creating, at the order prediction system (108), a profile of each of the plurality of retailers (112), wherein the profile of each of the plurality of retailers (112) is created with facilitation of the past data of each of the plurality of retailers (112), wherein the profile of each of the plurality of retailers (112) is created based on set of parameters;
analyzing, at the order prediction system (108), the profile of each of the plurality of retailers (112), current inventory data and sales data of each of the plurality of retailers (112) using hardware-run machine learning techniques, wherein the analysis is done after receiving the current inventory data and the sales data, wherein the analysis is done in real time;
predicting, at the order prediction system (108), one or more items for each of the plurality of retailers (112) based on the analysis, wherein the one or more items corresponds to item or products to be required by each of the plurality of retailers (112) for sale, wherein prediction of the one or more items is done for each of the plurality of retailers (112), wherein the prediction is done at a specified period of time; and
displaying, at the order prediction system (108), the predicted one or more items to a user (102) on a first one or more communication devices (104), wherein the first one or more communication devices (104) are associated with the user (102), and wherein the predicted one or more items for each of the plurality of retailers (112) is displayed to the user (102) for pushing the predicted one or more items for sale to each of the plurality of retailers (112).

2. The computer system as recited in claim 1, wherein the past data comprises past inventory data, past orders, past shortages, past seasonal orders, past festival, past sales, past festival sale, past day level products, past abundances, upselling data and cross-selling data.

3. The computer system as recited in claim 1, wherein the plurality of sources comprises point of sale devices (POS), user devices, vendor databases, third party databases and retailer database.

4. The computer system as recited in claim 1, wherein the set of parameters comprises retailer demographic, stock keeping unit (SKU), product brand, retailer constraints, quality of product, quantity of product ordered, order frequency, quantity sold, seasonal order and order cancelled.

5. The computer system as recited in claim 1, wherein the current inventory data comprises data associated with inventory stock available at retail stores of each of the plurality of retailers.

6. The computer system as recited in claim 1, wherein the sales data comprises product name, brand name, quantity sold, quantity ordered, customer feedback, retailer feedback, season of sale, retailer constraints, location population, number of footfall, upselling data and cross-selling data.
7. The computer system as recited in claim 1, wherein the profile of each of the plurality of retailers (112) comprises name, address, sales of retailer, retailer order frequency, demographic information, geographic location, location population, footfall, stock keeping unit, upselling offer and cross-selling offer.
8. The computer system as recited in claim 1, further comprising receiving, at the order prediction system (108), the current inventory data of the plurality of retailers (112), wherein the current inventory data is received from a plurality of vendors or point of sale devices of each of the plurality of retailers (112), wherein the current inventory data is received in real time.
9. The computer system as recited in claim 1, further comprising receiving, at the order prediction system (108), the sales data of the plurality of retailers (112), wherein the sales data is received from third party databases, user devices and point of sale devices.
10. The computer system as recited in claim 1, further comprising displaying at the order prediction system (108), a list on the first one or more communication devices (104), wherein the list comprises data associated with the plurality of retailers (112), wherein each of the plurality of retailers (112) in the list is to place order for the one or more items identified by the order prediction system (108).
, Description:METHOD AND SYSTEM TO PREDICT OUTLET LEVEL AND SKU LEVEL SALE FOR SALES ASSISTANCE

TECHNICAL FIELD

[0001] The present disclosure relates to a field of inventory management. More specifically, the present disclosure relates to a method and system to predict outlet level and SKU level sale for sales assistance.

BACKGROUND

[0002] Inventory for a retail store at micro level is maintained by ordering the items on daily basis. The micro level corresponds to small region or village where a sales person visits the retail store for taking order on daily basis. The sales person visits each store on daily basis for taking order from the retailer. The sales person’s daily revenue depends on number of items ordered by the retailer for which the sales person tries visiting more number of retail store in order to maximize the sales. The sales person sometimes may not receive any order from the retail store as the inventory is already present with the retailer. In certain cases, the sales person may not visit some retail store on daily basis in order to take orders from other retail store present in other region for maximizing the sales.

SUMMARY

[0003] In one aspect, the present disclosure provides a computer system. The computer system includes one or more processors and a memory. The memory is coupled to the one or more processors. The memory stores instructions. The instructions are executed by the one or more processors. The execution of instructions causes the one or more processors to perform a method to predict inventory of a retailer in real time. The method may include a first step to receive past data from a second one or more communication devices associated with a plurality of retailers. In addition, the method may include a second step to create a profile of each of the plurality of retailers. Further, the method may include a third step to analyze the profile of each of the plurality of retailers and current inventory data and sales data of each of the plurality of retailers using hardware-run machine learning techniques. Furthermore, the method may include a fourth step to predict one or more items for each of the plurality of retailers based on the analysis. Moreover, the method may include a fifth step to display the predicted one or more items to a user on a first one or more communication devices. The past data is received from a plurality of sources in real time. The profile of each of the plurality of retailers is created with facilitation of the past data of each of the plurality of retailers. The profile of each of the plurality of retailers is created based on set of parameters. The analysis is done after receiving the current inventory data and the sales data in real time. The one or more items correspond to item or products to be required by each of the plurality of retailers for sale. The prediction of the one or more items is done for each of the plurality of retailers. The prediction is done at a specified period of time. The first one or more communication devices are associated with the user. The predicted one or more items for each of the plurality of retailers is displayed to the user for pushing the predicted one or more items for sale to each of the plurality of retailers.

[0004] In an embodiment of the present disclosure, the past data may include past inventory data, past orders, past shortages, past seasonal orders, past festival, past sales, past festival sale, past day level products, past abundances, upselling data and cross-selling data.

[0005] In an embodiment of the present disclosure, the plurality of sources may include point of sale devices (POS), user devices, vendor databases, third party databases and retailer database.

[0006] In an embodiment of the present disclosure, the set of parameters may include retailer demographic, stock keeping unit (SKU), product brand, retailer constraints, quality of product, quantity of product ordered, order frequency, quantity sold, seasonal order and order cancelled.

[0007] In an embodiment of the present disclosure, the current inventory data may include data associated with inventory stock available at retail stores of each of the plurality of retailers.

[0008] In an embodiment of the present disclosure, the sales data may include product name, brand name, quantity sold, quantity ordered, customer feedback, retailer feedback, season of sale, retailer constraints, location population, number of footfall, upselling data and cross-selling data.

[0009] In an embodiment of the present disclosure, the profile of each of the plurality of retailers may include name, address, sales of retailer, retailer order frequency, demographic information, geographic location, population of the location, footfall, stock keeping unit, upselling offer and cross-selling offer.

[0010] In an embodiment of the present disclosure, the method may include step to receive the current inventory data from the plurality of retailers. The current inventory data may be received from one or more vendors or point of sale devices of the each of the plurality of retailers. The current inventory data is received in real time.

[0011] In an embodiment of the present disclosure, the method may include step to receive the sales data from the plurality of retailers, third party databases, user devices and point of sale devices.

[0012] In an embodiment of the present disclosure, the method may include step to display a list on the first one or more communication devices. The list comprises data associated with the plurality of retailers. Each of the plurality of retailers in the list is to place order for the one or more items identified by the order prediction system.

OBJECT OF THE DISCLOSURE

[0013] The principal object of this invention is to predict the inventory of a retailer in real time in order to increase sale of the sales person.

[0014] Another object of this invention is to predict the inventory of each of the retailer for a given date and time.

[0015] Yet another object of this invention is to reduce the efforts of the sales person and maximize sale of the sales person.

[0016] Yet another object of this invention is to increase the sales of vendor by predicting items which will be ordered by the retailer.

[0017] Yet another object of this invention is to maintain inventory of the vendor by predicting the order of the retailer.

[0018] Yet another object of this invention is to provide suggestions to the sales person for increasing the sales beat.

[0019] Yet another object of this invention is to prevent shortfall of the inventory of the vendor.

[0020] Yet another object of this invention is to predict the sale of the sales person at each retailer or outlet on a given date and time.

BRIEF DESCRIPTION OF THE FIGURES

[0021] Having thus described the invention in general terms, reference will now be made to the accompanying drawings, which are not necessarily drawn to scale, and wherein

[0022] FIG. 1 illustrates an interactive computing environment to predict the inventory of a retailer in real time, in accordance with the various embodiments of the present disclosure; and

[0023] FIG. 2 illustrates a block diagram of a computing device, in accordance with various embodiments of the present disclosure.

[0024] It should be noted that the accompanying figures are intended to present illustrations of exemplary embodiments of the present disclosure. These figures are not intended to limit the scope of the present disclosure. It should also be noted that accompanying figures are not necessarily drawn to scale.

DETAILED DESCRIPTION

[0025] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present technology. It will be apparent, however, to one skilled in the art that the present technology can be practiced without these specific details. In other instances, structures and devices are shown in block diagram form in order to avoid obscuring the present technology only.

[0026] Reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present technology. The appearance of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Moreover, various features are described which may be exhibited by some embodiments and not by others. Similarly, various requirements are described which may be requirements for some embodiments but not for other embodiment.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by ordinary skilled in the art to which this invention belongs. The materials, methods, and examples provided herein are illustrative only and not intended to be limiting.

[0028] FIG. 1 illustrates a block diagram 100 of an interactive computing environment to predict the inventory of a retailer in real time, in accordance with the various embodiments of the present disclosure. The interactive computing environment 100 includes a user 102, first one or more communication devices 104, a communication network 106, an order prediction system 108 and second one or more communication devices 110. In addition, the interactive computing environment 100 includes a plurality of retailers 112, a server 114 and a database 116.

[0029] The interactive computing environment 100 includes the user 102 who is a person interested in knowing the future inventory of the retailer. The user 102 is a sales person who takes order from the stores on daily basis. The sales person works as a representative of one or more vendors. The daily sale of the sales person depends on the number of stores visited by the sales person. The sales person visits every store in order to take more number of orders in order to increase sale. In an embodiment of the present disclosure, the user 102 is the vendor of the one or more vendors who wants to know the future inventory of the retailer in order to send inventory based on the prediction and avoid shortage of inventory. In an embodiment of the present disclosure, the one or more vendors are person who sells products to the retailers. The user 102 is any person who is interested in knowing the future inventory of the retailer. The user 102 is associated with the first one or more communication devices 104.

[0030] The interactive computing environment 100 includes the one or more communication devices used for interaction by the user or the retailers. In an embodiment of the present disclosure, the one or more communication devices are a portable communication device. In an example, the portable communication device includes laptop, smartphone, tablet, PDA and the like. In another embodiment of the present disclosure, the one or more communication devices are a fixed communication device. In an example, the fixed communication device includes a desktop, a workstation, point of sale devices and the like. In an embodiment of the present disclosure, the one or more communication devices are connected global positioning system (GPS) enabled devices. In general, the global positioning system (GPS) facilitates to identify the location of the each of the one or more communication devices.

[0031] In an embodiment of the present disclosure, the one or more communication devices include an advanced vision display panel. The advanced vision display panel includes organic light-emitting diode (OLED), active-matrix organic light-emitting diode (AMOLED), Super active-matrix organic light-emitting diode (AMOLED), Retina display, haptic touch screen display and the like. In another embodiment of the present disclosure, the one or more communication devices include a basic display panel. The basic display panel includes but may not be limited to liquid crystal display (LCD), capacitive touch screen LCD, resistive touch screen LCD, thin film transistor liquid crystal display (TFT-LCD) and the like. In addition, the one or more communication devices perform computing operations based on operating system installed inside the one or more communication devices. In general, the operating system is system software that manages computer hardware and software resources and provides common services for computer programs. In addition, the operating system acts as an interface for software installed inside the one or more communication devices to interact with hardware components of the one or more communication devices.

[0032] In an embodiment of the present disclosure, the operating system installed inside the one or more communication devices is a mobile operating system. In an embodiment of the present disclosure, the one or more communication devices performs computing operations based on any suitable operating system designed for the one or more communication devices. In an example, the operating system includes Windows operating system from Microsoft, Android operating system from Google, and Symbian operating system from Nokia. In another example, the operating system includes Bada operating system from Samsung Electronics, ios operating system from Apple and BlackBerry operating system from BlackBerry. However, the operating system is not limited to above mentioned operating systems. In an embodiment of the present disclosure, the one or more communication devices operates on any version of particular operating system of above mentioned operating systems.

[0033] In order to clearly explain the embodiment of the present disclosure using FIG. 1, the one or more communication devices is used as the first one or more communication devices 104 and the second one or more communication devices 110. However, the one or more communication devices may be any number of communication devices based on requirement for the prediction of inventory of the plurality of retailers 112. The first one or more communication devices 104 are the one or more communication devices which are present with the user 102. The second one or more communication devices 110 are the one or more communication devices which are present with the plurality of retailers 112.

[0034] In an embodiment of the present disclosure, the plurality of retailers 112 is shop keeper who gives order to the user 102 in order to keep inventory. The plurality of retailers 112 keep inventory for performing direct sale to customers. The inventory of the plurality of retailers 112 includes the products which can be purchased by the customers. The plurality of retailers 112 buy products from the one or more vendors and sell the products to the customers. The plurality of retailers 112 gives order to the user 102 on daily basis when the user 102 arrives for taking the order. In an embodiment of the present disclosure, the user 102 may arrive at anytime for taking order from the plurality of retailers 112 based on requirements of the plurality of retailers 112.

[0035] In an embodiment of the present disclosure, the plurality of retailers 112 includes but may not be limited to small retail store, departmental stores, supermarket, drug store, specialty store and direct retailer. In another embodiment of the present disclosure, the plurality of retailers 112 includes but may not be limited to stall, retailers, warehouse stores and grocery stores. The plurality of retailers 112 are associated with the second one or more communication devices 110. The second one or more communication devices 110 and the first one or more communication devices 104 are associated with the communication network 106.

[0036] In addition, the interactive computing environment 100 includes the communication network 106 which enables transfer of information to and from the one or more communication devices. The communication network 106 is used to connect to the order prediction system 108. Also, the communication network 106 provides network connectivity to the one or more communication devices. In an example, the communication network 106 uses protocol for connecting the one or more communication devices to the order prediction system 108. The communication network 106 connects the one or more communication devices to the order prediction system 108 using 2G, 3G, 4G, Wifi and the like. In an embodiment of the present disclosure, the medium for communication may be internet, infrared, microwave, radio frequency (RF) and the like.

[0037] In another embodiment of the present disclosure, the communication network 106 may be any type of network that provides internet connectivity to the one or more communication devices. In an embodiment of the present disclosure, the communication network 106 is a wireless mobile network. In another embodiment of the present disclosure, the communication network 106 is a wired network connection. In yet another embodiment of the present disclosure, the communication network 106 is a combination of the wireless and the wired network for optimum throughput of data transmission.

[0038] Further, the interactive computing environment 100 includes the order prediction system 108 which is used to predict the inventory of the plurality of retailers 110. The order prediction system 108 predicts outlet level and SKU level sale for sales assistance. The order prediction system 108 performs one or more tasks in order to predict the inventory of the plurality of retailers 112 in real time. The order prediction system 108 is associated with the first one or more communication devices 104 and the second one or more communication devices 110 through the communication network 106. In an embodiment of the present disclosure, the order prediction system 108 is present as a separate combination of hardware and software. In another embodiment of the present disclosure, the order prediction system 108 is present inside the first one or more communication devices 104.

[0039] The order prediction system 108 receives past data from the second one or more communication devices 110 associated with the plurality of retailers 112. The past data is received from a plurality of sources. In an embodiment of the present disclosure, the past data include but may not be limited to past inventory data, past orders, past shortages, past seasonal orders, past festival, and past sales. In another embodiment of the present disclosure, the past data includes but may not be limited to past festival sale, past day level products, past abundances, upselling data and cross-selling data. In another embodiment of the present disclosure, the past data includes but may not be limited to location population, number of footfall, retailer name and retailer address. In an embodiment of the present disclosure, the location information is received from the second one or more communication devices 110. The number of footfall corresponds to the number of customer at each of the plurality of retailer for the shopping.

[0040] The past inventory data includes the stock keeping unit (SKU) of the products which has been ordered by the plurality of retailers 112. The past orders include the order which had been given to the user 102 by the plurality of retailers 112. The past shortages includes information regarding the stock keeping unit (SKU) which run out of stock of the plurality of retailers 112. In an embodiment of the present disclosure, the past shortages includes stock keeping unit (SKU), period of shortage, shortage quantity, brand name, fulfillment status, one or more items, month of shortage and the like.

[0041] The past seasonal orders include information regarding the orders which had been placed by the plurality of retailers 112 during a particular time period. In an embodiment of the present disclosure, the past seasonal orders include but may not be limited to stock keeping unit (SKU), time of order, brand name and seasonal period. In another embodiment of the present disclosure, the past seasonal orders include stock keeping unit (SKU), brand name, seasonal quantity, quantity sold, quantity purchased, the one or more items ordered and the like. The past festival includes the list of past festival for each year. The past festival sale includes the amount of sale made during the festival season by each of the plurality of retailers 112 for each of the products. The past sales include but may not be limited to stock keeping unit (SKU), the one or more items, order purchased, quantity sold, stock left, sold period, upselling offer, cross-selling offer.

[0042] The past day level products include daily products which are ordered by the plurality of retailers 112. In an example, the past day level products include products like dairy products, vegetables, fruits, meat, bread and the like. The past abundances include those products which were ordered by the plurality of retailers 112 but were not sold to the customers. The upselling data include the data related to purchase of the products by the customers based on the inducement by the user 102 to buy more expensive products. In general, the upselling is a marketing technique to induce the customers to purchase more expensive items, upgrades or add-ons for making a profitable sale. In an example, the plurality of retailers 112 may induce the customer to buy “trimmer” instead of shaving cream and shaving brush. The cross-selling data include the data related to the purchase of the products by the customers by selling additional products to the customers. In general the cross-selling is the practice of selling additional product or service to the customer by the plurality of retailers 112.

[0043] The plurality of sources includes point of sale devices (POS), user devices, vendor databases, third party databases and retailer database. In general, the point of sale devices (POS) or the point of purchase is the place to calculate the amount of purchase for the customer based on the one or more items purchased by the customer. The user devices include connected devices which are being used by plurality of retailers 112 in the store. In an example, the retailer X has a store Y which has four communication devices (laptop, tablet, mobile phone, and computer) than the user devices include the four communication devices.

[0044] The third party databases are databases which provide information regarding the sell, purchase, shortage, inventory data, cross-selling offer used, upselling offer used, point of sale information and other brand databases. In an example, the third party databases includes M retailer database, N retailer database, O vendor database, P vendor database which integrate with the order prediction system 108. The order prediction system 108 integrates with the third party databases in order to receive information. The integration is done when the order prediction system 108 receives the connection request from the third party databases in real time through communication network. The order prediction system 108 accepts the connection request from the third party databases and receives the data from the third part databases.

[0045] In addition, the order prediction system 108 creates a profile of each of the plurality of retailers 112. The profile of each of the plurality of retailers 112 is created with facilitation of the past data of each of the plurality of retailers 112. The profile of each of the plurality of retailers 112 is created based on set of parameters. The set of parameters includes but may not be limited to retailer demographic, stock keeping unit (SKU), product brand, retailer constraints and quality of product. In an embodiment of the present disclosure, the set of parameters includes but may not be limited to quantity of product ordered, order frequency, quantity sold, seasonal order and order cancelled. In an embodiment of the present disclosure, the profile of each of the plurality of retailers includes name, address, sales of retailer, retailer order frequency, demographic information and geographic location. In another embodiment of the present disclosure, the profile of each of the plurality of retailers includes location population, footfall, stock keeping unit (SKU), the upselling offer and the cross-selling offer. In an embodiment of the present disclosure, the footfall corresponds to the number of customer that entered during a specified period of time at each of the plurality of retailer for the shopping. The specified period of time includes but may not be limited to weekly, monthly, quarterly, yearly, daily, hourly, and the like. In an embodiment of the present disclosure, the specified period of time may be any time and date for which the prediction is required by the user 102.

[0046] Further, the order prediction system 108 receives current inventory data of the plurality of retailers 112. The current inventory data is received from the point of sale devices of each of the plurality of retailers 112. The current inventory data is received in real time. The current inventory data includes but may not be limited to stock keeping unit (SKU), brand name, quantity available, quantity sold, quantity purchased, daily quantity sold, upselling offer and cross-selling offer. In an embodiment of the present disclosure, the current inventory data includes but may not be limited to cold storage products, abundance product, less purchased product and the like.

[0047] Furthermore, the order prediction system 108 receives sales data of the plurality of retailers 112. The sales data is received from the third party databases, user devices and the point of sale devices. The sales data is received in real time. The sales data includes information regarding the sale of each of the plurality of retailers 112. The sales data is received after establishing a connection with the third party databases. The sales data of the plurality of retailers 112 is received on daily basis. In an embodiment of the present disclosure, the sales data of the plurality of retailers 112 is received on periodic basis based on the requirement of the order prediction system 108. The sales data includes but may not be limited to product name, brand name, quantity sold, quantity ordered, customer feedback, retailer feedback, season of sale, retailer constraint, upselling data and cross-selling data. In another embodiment of the present disclosure, the sales data includes but may not be limited to quantity of product ordered, order frequency, quantity sold, seasonal order and order cancelled.The season of sale include the season during which the sales was made for the product.

[0048] Moreover, the order prediction system 108 analyze the profile of each of the plurality of retailers 112, the current inventory data and the sales data of each of the plurality of retailers 112 using hardware-run machine learning techniques. The analysis is done after receiving the current inventory data and the sales data of the plurality of retailers 112. The analysis is done in real time. In an embodiment of the present disclosure, the analysis is done based on usage pattern of each of the plurality of retailers 112. In another embodiment of the present disclosure, the analysis is done based on the geographical location of the stores of the plurality of retailers 112. In another embodiment of the present disclosure, the analysis is done based on the population of the customers present near the stores of the plurality of retailers 112. In another embodiment of the present disclosure, the analysis is done based on brand preferences of the customers present near the stores of the plurality of retailers 112. In another embodiment of the present disclosure, the analysis is done based on types of product purchased by the customers near the stores of the plurality of retailers 112. In another embodiment of the present discourse, the analysis is done based on selection of one or more of the above mentioned basis for performing the analysis of the profile of each of the plurality of retailers 112.

[0049] Also, the order prediction system 108 predicts the one or more items or the products for each of the plurality of retailers based on the analysis. The one or more items correspond to products to be required by each of the plurality of retailers 112 for sale. In an embodiment of the present disclosure, the one or more items correspond to the products with minimal stock in the inventory of each of the plurality of retailers 112. The minimal stock corresponds to the one or more items which are less in the inventory of the plurality of retailers 112. The prediction is done by identifying one or more patterns based on the analysis of the profile of each of the plurality of retailers 112, the current inventory data and the sales data. The order prediction system 108 predicts the one or more items by comparing the stock of the one or more items with the pre-defined threshold of each of the one or more items for each of the plurality of retailers 112. In an embodiment of the present disclosure, the pre-defined threshold for each of the one or more items or the stock keeping unit (SKU) is defined by the user 102. In another embodiment of the present disclosure, the pre-defined threshold is identified by the order prediction system 108 based on the analysis in real time. In an embodiment of the present disclosure, the order prediction system 108 predicts the sale of each of the plurality of retailers 112 at the specified period of time.
[0050] Also, the order prediction system 108 displays the predicted one or more items to the user 102on the first one or more communication devices 104. The first one or more communication devices 104 are associated with the user 102. The predicted one or more items for each of the plurality of retailers 112 is displayed to the user 102 for pushing the predicted one or more items for sale to each of the plurality of retailers 112. The predicted one or more items are displayed to the user 102 so that the user 102 goes to the each of the plurality of retailers 112 for taking order. In an embodiment of the present disclosure, the order prediction system 108 displays the promotion of new product or the one or more items. In an example, the one or more items include green peas, than the one or more items related to the green peas will be displayed to the user 102.

[0051] In an embodiment of the present disclosure, the order prediction system 108 display list on the first one or more communication devices 104 in real time. The list comprises data associated with the plurality of retailers 112. Each of the plurality of retailers 112 in the list is to place order for the one or more items predicted by the order prediction system 108. In an embodiment of the present disclosure, the list include but may not be limited to predicted sale which will be done by each of the plurality of retailers 112 based on the identified one or more items at the specified period of time.

[0052] In an embodiment of the present disclosure, the order prediction system 108 updates the past data, current inventory data and the sales data. The order prediction system 108 updates data in real time. In another embodiment of the present disclosure, the order prediction system 108 stores the past data, current inventory data and the sales data. In addition, the order prediction system 108 stores the identified one or more patterns in order to train itself based on the past prediction of the inventory by the order prediction system 108. The order prediction system 108 stores data in real time.

[0053] In another embodiment of the present disclosure, the order prediction system 108 alerts the user 102 on the first one or more communication devices 104. The alert is sent to the user 102 when inventory of the plurality of retailers 112 reaches below the pre-defined threshold for the one or more items present in the inventory of the plurality of retailers 112. The alert is sent to the user 102 based on the analysis of the current inventory data, the sales data and the past data. The order prediction system 108 alters the user 102 in real time. In an example, the user 102 is the sales person or the vendor of the plurality of vendors. The alert is sent by sending notification to the first one or more communication devices 104. Also, the order prediction system 108 is associated with the server 114. In an embodiment of the present disclosure, the order prediction system 108 transmits data to the server 114. In an embodiment of the present disclosure, the order prediction system 208 is located in the server 114.

[0054] Also, the interactive computing environment 100 includes the server 114 to handle each operation and task performed by the order prediction system 108. The server 114 stores one or more instructions for performing the various operations of the order prediction system 108. In an embodiment of the present disclosure, the server 114 is a cloud server which is built, hosted and delivered through a cloud computing platform. In general, cloud computing is a process of using remote network server which are hosted on the internet to store, manage, and process data. The use of cloud server helps to access the order prediction system 108 from anywhere using the Internet. The server 114 is associated with the database 116.

[0055] The interactive computing environment 100 includes the database 116 where all the information is stored for accessing by the order prediction system 108. The database 116 includes data which is pre-stored in the database 116 and data collected in real-time. The database 116 may be a cloud database or any other database based on the requirement for the order prediction system 108. The data is stored in the database 116 in form of tables. The tables are matrix which stored different type of data. In an example, one table may store data related to the user 102 and in other table the data related to the plurality of retailers 112 is stored in the database 116.

[0056] FIG. 2 illustrates the block diagram of a computing device 200, in accordance with various embodiments of the present disclosure. The computing device 200 includes a bus 202 that directly or indirectly couples the following devices: memory 204, one or more processors 206, one or more presentation components 208, one or more input/output (I/O) ports 210, one or more input/output (I/O) components 212 and an illustrative power supply 214. The bus 202 represents what may be one or more busses (such as an address bus, data bus, or combination thereof). Although the various blocks of FIG. 2 are shown with lines for the sake of clarity, in reality, delineating various components is not so clear, and metaphorically, the lines would more accurately be grey and fuzzy. For example, one may consider a presentation component such as a display device to be an I/O component. Also, processors have memory. The inventors recognize that such is the nature of the art, and reiterate that the diagram of FIG. 2 is merely illustrative of an exemplary computing device 200 that can be used in connection with one or more embodiments of the present invention. Distinction is not made between such categories as “workstation,” “server,” “laptop,” “hand-held device,” etc., as all are contemplated within the scope of FIG. 2 and reference to “computing device.”

[0057] The computing device 200 typically includes a variety of computer-readable media. The computer-readable media can be any available media that can be accessed by the computing device 200 and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, the computer-readable media may comprise computer readable storage media and communication media. The computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. The computer storage media includes, but is not limited to, non-transitory computer-readable storage medium that stores program code and/or data for short periods of time such as register memory, processor cache and random access memory (RAM), or any other medium which can be used to store the desired information and which can be accessed by the computing device 200. The computer storage media includes, but is not limited to, non-transitory computer readable storage medium that stores program code and/or data for longer periods of time, such as secondary or persistent long term storage, like read only memory (ROM), EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computing device 200.

[0058] The communication media typically embodies computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media. The computing device 200 includes one or more processors that read data from various entities such as the memory 204 or I/O components 212. The one or more presentation components 208 present data indications to a user or other device. Exemplary presentation components include a display device, speaker, printing component, vibrating component, etc. The one or more I/O ports 210 allow the computing device 200 to be logically coupled to other devices including the one or more I/O components 212, some of which may be built in. Illustrative components include a microphone, joystick, game pad, satellite dish, scanner, printer, wireless device, etc.

[0059] It may be noted that the foregoing description has been explained with help of one user 102 for the explanation purpose only. In an embodiment of the present disclosure, the interactive computing environment 100 may contain any number of users associated with any number of communication devices.

[0060] The foregoing descriptions of specific embodiments of the present technology have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present technology to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the present technology and its practical application, to thereby enable others skilled in the art to best utilize the present technology and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the application or implementation without departing from the spirit or scope of the claims of the present technology.

[0061] While several possible embodiments of the invention have been described above and illustrated in some cases, it should be interpreted and understood as to have been presented only by way of illustration and example, but not by limitation. Thus, the breadth and scope of a preferred embodiment should not be limited by any of the above-described exemplary embodiments.