Description:RESERVATION OF RIGHTS
[0001] A portion of the disclosure of this patent document contains material, which is subject to intellectual property rights such as, but are not limited to, copyright, design, trademark, integrated circuit (IC) layout design, and/or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (hereinafter referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.

FIELD OF INVENTION
[0002] The embodiments of the present disclosure herein relate to predictive analysis using mathematical optimization and artificial intelligence. More particularly, the present disclosure provides a system and a method for assortment planning of a plurality of products.

BACKGROUND OF INVENTION
[0003] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.
[0004] Retail assortment can vary significantly based on consumer demands. Even by looking at sales data of previous years, it is difficult to accurately determine the size and variety of an assortment. Accuracy is of paramount importance, because the decision has significant cost implications. Additionally, there will be restrictions such as minimum order quantity from suppliers, demand satisfaction requirements, store/shelf storage space, etc.
[0005] For example, a store may be selling a dozen brands of shirts, each brand having shirts of many styles and designs. Within each style or design, there will be shirts of many colours and within each colour, the store may need to stock shirts of different sizes. In this situation, the store may face with the problem of determining how many shirts of each brand, style, colour, and size should be stocked in the store. This, in itself, is a complicated decision to make. When the store is not a single entity, but part of a chain of many stores, the decision is complicated further. Storing equal quantities of all variations of shirts will result in either the stores ending up with a lot of unsold products at the end of the season or the stores not being able to satisfy a significant portion of customer demand. This happens because of several reasons, most notably, because of differences in customer preferences. For example, shirts of extra-large size will not sell as much as will shirts of medium size. Similarly, customers in metro cities will have a different preference from customers in tier 2 or tier 3 cities in terms of shirt designs, brands, etc. Also, certain colours will not sell as much as others due to customer liking. The problem is applicable not just to the sale of shirts, but to any product category that has a huge variety of products, for example, mobile phones, televisions, processed foods, and many others.
[0006] Typically, assortment planning is a manual process. The category/merchandise managers look at sales data of past years and then use their experience and judgement to decide what quantities of each product should be stocked. In certain cases, conventional supply chain models such as a reorder point model or an order up to model along with safety stock calculation is used. As mentioned before, this approach does not yield the most optimal results in terms of sales and profits.
[0007] Prior art US20220188747A1 mentions various sales forecasting techniques and includes a graph-based approach and a Djikstra’s technique. In particular, the prior art mentions a combine-to-leverage (CTL) score and a popularity score to group items based on probability and provides a rating system for each set of items or products. However, the prior art does not specify a concrete set of techniques and the sequence in which those techniques have to be implemented.
[0008] Prior art US8527321B2 calculates sales volume for initial assortment and incremental/decremental sales volume as per the desired assortment for various products. Additionally, the prior art specifies an interaction unit and a presentation unit which simulate the interaction between products such as cannibalization and demand transference. However, the prior art does not specify the cost component associated with the products.
[0009] Prior art US11449882B2 visualizes a product similarity and the effect of transferable demand. Further, the prior art specifies modification of a product assortment by adding or removing a product, observes the resulting increase or decrease in sales volume, and calculates the transferable demand. Additionally, the prior art specifies a similarity matrix, which is calculated based on product attributes such as size and colour and similar products grouped together. However, the prior art does not identify the least cost assortment and reduce inventory costs for various products.
[0010] Prior art US20150170167A1 calculates new product metrics based on certain metrics for existing products (geometric mean of their average attribute values). Further, the prior art forecasts the demand based on the calculated metrics. However, the prior art does not calculate the cost of procuring and carrying an assortment.
[0011] Prior art US11403574B1 uses a Pareto optimality type of mechanism that allows a user to specify sales, margin, and weights for each objective. Additionally, the prior art ranks constraints and items in the assortment and involves demand forecasting, and constraints while including a demand transfer component. However, the prior art does not consider inventory costs or specify an optimization technique for various products.
[0012] Prior art US20140058781A1 ranks products and prepares a list based on the rank. Further, the prior art uses data points to rank the products and plans the assortment. Additionally, the prior art utilizes a genetic algorithm and a Generalized Reduced Gradient (GRG) to rank the products. However, the prior art does not select the assortment with the least inventory cost for various products.
[0013] Prior art US20210295251A1 provides a user interface that is mainly concerned with adding, removing, and editing attributes for new products. Further, the prior art uses multiple drag-and-drop elements, adds colours, and selects images for various products. However, the prior art does not specify an optimization technique for reducing the cost for various product assortments.
[0014] Prior art EP1459226A1 specifies a consensus-based inventory planning methodology. Further, the prior art specifies a user interface component where a supplier and a merchandizer each select a plan based on the demand forecast, and then create a production plan. However, the prior art does not utilize an assortment technique for product planning.
[0015] Prior art US20190347606A1 focuses on inventory management and handling of multiple products. Further, the prior art involves a predetermined optimal inventory and a transfer order component. However, the prior art does not utilize an assortment technique for product planning.
[0016] There is, therefore, a need in the art to provide a method and a system that can overcome the shortcomings of the existing prior arts.

OBJECTS OF THE INVENTION
[0017] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0018] An object of the present disclosure is to provide a method and a system that uses a predicting algorithm for processing large amounts of data in a short span of time.
[0019] An object of the present disclosure is to provide a method and a system that specifies an estimated time for placing an order with multiple suppliers.
[0020] An object of the present disclosure is to provide a method and a system that considers a cost angle while providing a computerised optimisation solution.
[0021] An object of the present disclosure is to provide a method and a system that automates the manual process of assortment planning and identifies the least cost assortment after examining all possible product combinations.
[0022] An object of the present disclosure is to provide a method and a system that is applicable to the sale of any product across a large geographical area.

SUMMARY
[0023] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0024] In an aspect, the present disclosure relates to a system that may include one or more processors operatively coupled to one or more computing devices. The one or more computing devices may be associated with one or more users and may be connected to the one or more processors through a network. The one or more processors may be coupled with a memory that stores instructions to be executed by the one or more processors. The one or more processors may be configured to receive one or more input parameters from the one or more computing devices. The one or more input parameters may be indicative of one or more product inputs provided by the one or more users through the one or more computing devices. Further, the one or more processors may be configured to extract a first set of attributes from the one or more input parameters. The first set of attributes may be indicative of indicative of the one or more product quantity combinations. Furthermore, the one or more processors may be configured to extract a second set of attributes based on the first set of attributes. The second set of attributes may be indicative of one or more inventory cost values for the one or more product quantity combinations. Based on the first set of attributes, and the second set of attributes, the one or more processors may be configured to predict, through an artificial intelligence (AI) engine, a minimum cost associated with the one or more product quantity combinations. The one or more processors may also be configured to generate the assortment planning of the one or more product quantity combinations based on the one or more orders.
[0025] In an embodiment, the one or more product inputs may comprise any or a combination of a product and store detail, a demand forecast, an inventory cost, and a business constraint.
[0026] In an embodiment, the one or more product quantity combinations may comprise one or more products, one or more stores, and one or more combinations.
[0027] In an embodiment, the one or more techniques used by the AI engine may comprise an ant colony optimization technique to generate the assortment planning of the one or more product quantity combinations.
[0028] In an embodiment, the one or more processors may be configured to record a store, a product, an order quantity, and an order date associated with the assortment planning of the one or more product quantity combinations.
[0029] In an embodiment, the one or more processors may be configured to generate an estimated time associated with the one or more orders to be presented to the one or more suppliers.
[0030] In an aspect, a method for assortment planning for one or more product quantity combinations may include receiving, by a processor, one or more input parameters from one or more computing devices. The one or more computing devices may be associated with one or more users and may be connected to the processor through a network. The one or more input parameters may be indicative of one or more product inputs provided by the one or more users through the one or more computing devices. Further, the method may include extracting, by the processor, a first set of attributes from the one or more input parameters. The first set of attributes may be indicative of the one or more product quantity combinations.
[0031] The method may further include extracting, by the processor, a second set of attributes based on the first set of attributes. The second set of attributes may be indicative of one or more inventory cost values for the one or more product quantity combinations. The method may include predicting, by the processor, based on the first set of attributes, and the second set of attributes, through an artificial intelligence (AI) engine, a minimum cost associated with the one or more product quantity combinations. The method may further include generating, by the one or more processors, one or more orders through the AI engine to be presented to one or more suppliers for the one or more product quantity combinations. The method may further include generating, by the one or more processors, the assortment planning of the one or more product quantity combinations based on the one or more orders.
[0032] In an embodiment, the method may include any or a combination of a product and store detail, a demand forecast, an inventory cost, and a business constraint.
[0033] In an embodiment, the method may include the one or more product quantity combinations that further include one or more products, one or more stores, and one or more combinations.
[0034] In an embodiment, the method may include recording, by the one or more processors, a store, a product, an order quantity, and an order date associated with the assortment planning of the one or more product quantity combinations.
[0035] In an embodiment, the method may include generating, by the one or more processors, an estimated time associated with the one or more orders to be presented to the one or more suppliers.

BRIEF DESCRIPTION OF DRAWINGS
[0036] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes the disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[0037] FIG. 1 illustrates an exemplary architecture (100) of a proposed system (110), in accordance with an embodiment of the present disclosure.
[0038] FIG. 2 illustrates an exemplary representation (200) of a proposed system (110), in accordance with an embodiment of the present disclosure.
[0039] FIG. 3 illustrates an exemplary description of one or more product quantity combinations (300) in the proposed system (110), in accordance with an embodiment of the present disclosure.
[0040] FIG. 4 illustrates an exemplary representation of a process (400) of the proposed system (110), in accordance with an embodiment of the present disclosure.
[0041] FIG. 5 illustrates an exemplary representation of an ant colony optimization (500) of the proposed system (110), for one or more product quantity combinations.
[0042] FIG. 6 illustrates an exemplary computer system (600) in which or with which the proposed system (110) may be implemented, in in accordance with embodiments of the present disclosure.
[0043] The foregoing shall be more apparent from the following more detailed description of the disclosure.

BRIEF DESCRIPTION OF INVENTION
[0044] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0045] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.
[0046] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
[0047] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0048] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.
[0049] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0050] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0051] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGs. 1-6.
[0052] FIG. 1 illustrates an exemplary network architecture (100) in which or with which embodiments of the present disclosure may be implemented. As illustrated in FIG. 1, a plurality of computing devices (104-1, 104-2…104-N), herein referred as computing devices (104), may be connected to a system (110). The computing devices (104) may also be known as a user equipment (UE) that may include, but not be limited to, a mobile, a laptop, etc. Further, the computing devices (104) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as camera, audio aid, a microphone, or a keyboard. The computing devices (104) may include a mobile phone, smartphone, virtual reality (VR) devices, augmented reality (AR) devices, a laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, and mainframe computer. Additionally, input devices for receiving input from a user such as touch pad, touch enabled screen, electronic pen and the like may be used.
[0053] Referring to FIG. 1, the computing devices (104) may be connected to the system (110) through a network (106). One or more users (102) (herein referred as users (102)) may provide one or more input parameters indicative of one or more product inputs through the computing devices (104). The system (110) may further include an AI engine (216) for predicting a minimum cost associated with one or more product quantity combinations for the one or more product inputs using one or more techniques. Additionally, the system (10) may generate one or more orders through the AI engine (216) to be presented to one or more suppliers for the one or more product quantity combinations. Further, the system (110) may generate an assortment planning of the one or more product quantity combinations based on the one or more orders.
[0054] In an embodiment, the computing devices (104) may communicate with the system (110) through a set of executable instructions residing on any operating system including, but not limited to, Android TM and the like.
[0055] In an exemplary embodiment, a network (106) may include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. One or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth may be included by the one or more nodes. The network (106) may include, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, and a private network. Further, the network (106) may include a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a public-switched telephone network (PSTN), a cable network, a cellular network, a satellite network, a fibre optic network, or some combination thereof.
[0056] FIG. 2 illustrates an exemplary representation (200) of the system (110), in accordance with an embodiment of the present disclosure.
[0057] Referring to FIG. 2, the system (110) may comprise one or more processor(s) (202). The one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the system (110). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (204) may comprise any non-transitory storage device including, for example, volatile memory such as random-access memory (RAM), or non-volatile memory such as erasable programmable read only memory (EPROM), flash memory, and the like.
[0058] In an embodiment, the system (110) may include an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as input/output (I/O) devices, storage devices, and the like. The interface(s) (206) may facilitate communication through the system (110). The interface(s) (206) may also provide a communication pathway for one or more components of the system (110). Examples of such components include, but are not limited to, processing engine(s) (208) and a database (210).
[0059] The processing engine(s) (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (208) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the system (110) may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system (110) and the processing resource. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry.
[0060] In an embodiment, the one or more processor(s) (202) may be configured to receive one or more input parameters from computing devices, such as the computing devices (104) of FIG. 1. The processing engine(s) (208) may include one or more engines selected from any of a parameter acquisition engine (212), an extraction engine (214), and an artificial intelligence (AI) engine (216).
[0061] In an embodiment, the parameter acquisition engine (212) may receive one or more input parameters from the computing devices (104). The one or more input parameters may be indicative of one or more product inputs provided by users, such as users (102) of FIG. 1 through the computing devices (104). In an embodiment, the one or more product inputs comprise any or a combination of a product and store detail, a demand forecast, an inventory cost, and a business constraint.
[0062] In an embodiment, the extraction engine (214) may extract a first set of attributes from the one or more input parameters and store the first set of attributes in the database (210). The first set of attributes may be indicative of one or more product quantity combinations.
[0063] In an embodiment, the extraction engine (214) may extract a second set of attributes based on the first set of attributes and store the second set of attributes in the database (210). The second set of attributes may be indicative of one or more inventory cost values for the one or more product quantity combinations. Based on the first set of attributes and the second set of attributes, the one or more processor(s) (202) may predict, through the AI engine (216), a minimum cost associated with the one or more product quantity combinations. Further, in an embodiment, the one or more processor(s) (202) may generate one or more orders through the AI engine (216) to be presented to one or more suppliers for the one or more product quantity combinations. Further, the one or more processor(s) (202) may generate an assortment planning of the one or more product quantity combinations based on the one or more orders.
[0064] In an embodiment, the one or more processor(s) (202) may be configured to record a store, a product, an order quantity, and an order date associated with the assortment planning of the one or more product quantity combinations.
[0065] In an embodiment, the one or more processor(s) (202) may be configured to generate an estimated time associated with the one or more orders to be presented to the one or more suppliers.
[0066] FIG. 3 illustrates an exemplary description of one or more product quantity combinations (300) in the proposed system (110), in accordance with an embodiment of the present disclosure.
[0067] In an exemplary embodiment, users, such as the users (102) of FIG. 2, may interact with the one or more product quantity combinations through computing devices, such as the computing devices (104) of FIG. 1. In an embodiment, the users (102) may be from a category or merchandising team of a retail organisation. Further, the one or more product quantity combinations may have the following components as shown in FIG. 3.
[0068] 3a: This section of the product may contain data sources such as product and store details, demand forecast, inventory costs, and business constraints.
[0069] 3b: This section contains ant colony optimization technique which may perform iterations on the data to determine the minimum cost associated with the one or more product quantity combinations.
[0070] 3c: This is the result of the application of the ant colony optimization technique on the data. It is essentially a table containing the number of units of each product recommended by the technique to be ordered and stocked at each store. Based on the expected demand and supplier lead time to deliver the order, the table also displays when the order should be placed with the supplier. The result can be viewed by the users (102) on a computer screen or printed out as a physical copy on paper.
[0071] FIG. 4 illustrates an exemplary representation of a process (400) of the proposed system (110), in accordance with an embodiment of the present disclosure.
[0072] Referring to FIG. 4, step 1 may represent all possible combinations of stores and products that may be stored in the stores. For example, if there are two stores and three products in each store, there may be a total of six product-store combinations. For example, the one or more product quantity combinations may include Product 1 in store 1, Product 1 in store 2, Product 1 in store 3, Product 2 in store 1, Product 2 in store 2, and Product 2 in store 3. This may be annotated in FIG. 4 as P1 S1, P1 S2, and so on respectively.
[0073] FIG. 5 illustrates an exemplary representation of an ant colony optimization (500) of the proposed system (110) for one or more product quantity combinations, in accordance with an embodiment of the present disclosure. The below description refers to both FIGs. 4 and 5.
[0074] In an exemplary embodiment, the ant colony optimisation technique, called as an “ant” may include all possible product-store combinations. At step 2 of FIG. 4, as shown in FIG. 5, against each item in this list, all feasible number of combinations for each product-store combination may be listed. As shown in FIG. 5, “x” indicates the total number of products, “y” indicates the total number of stores, and “N” indicates the total number of combinations. For example, Combination 1 contains 75 units of Product 1 in Store 1, 82 units of Product 1 in Store 2, and so on. In an exemplary embodiment, the minimum and maximum values within the table may be determined by business constraints defined as follows:
• There should be at least 10 units of Product 1 in all stores
• Store 2 should contain at least 25 units of Product 2
• Store 3 has only enough space to store 15 units of Product 1, 20 units of Product 2, and 30 units of Product 3
[0075] Further, at step 3 of FIG. 4, one out of the “N” combinations may be chosen at random by each “ant”.
[0076] At step 4 of FIG. 4, as shown in FIG. 5, a total inventory cost for the selected combination may be calculated. The total inventory cost may be the cost of ordering the required number of items plus the cost of storing/holding said items in a warehouse.
[0077] At step 5 of FIG. 4, the value of a parameter called “pheromone” may be calculated. The product-store combination that gives a lower cost will have a higher pheromone value and the combination that gives a higher cost will give a lower pheromone value. Based on the pheromone value calculated, the suitable combination may be selected (step 6).
[0078] Steps 4 and 5 may be repeated multiple times (step 7) until the total cost reaches the minimum value. That particular combination of products at certain stores corresponding to this minimum cost value may be the final solution passed as an output to the users (102).
[0079] FIG. 6 illustrates an exemplary computer system (600) in accordance with embodiments of the present disclosure. As shown in FIG. 6, the computer system (600) may include an external storage device (610), a bus (620), a main memory (630), a read-only memory (640), a mass storage device (650), a communication port(s) (660), and a processor (670). A person skilled in the art will appreciate that the computer system (600) may include more than one processor and communication ports. The communication port (660) may be chosen depending on a network, such as a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (600) connects. The main memory (630) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (640) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or basic input/output system (BIOS) instructions for the processor (670). The mass storage device (650) may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage device (650) includes, but is not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces).
[0080] The bus (620) may communicatively couple the processor(s) (670) with the other memory, storage, and communication blocks. The bus (620) may be, e.g., a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), universal serial bus (USB), or the like. The bus (620) may further include connecting expansion cards, drives, and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (670) to the computer system (600).
[0081] Optionally, operator and administrative interfaces, e.g., a display, keyboard, and a cursor control device may also be coupled to the bus (620) to support direct operator interaction with the computer system (600). Other operator and administrative interfaces can be provided through network connections connected through the communication port (660). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (900) limit the scope of the present disclosure.
[0082] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter to be implemented merely as illustrative of the disclosure and not as limitation.

ADVANTAGES OF THE INVENTION
[0083] The present disclosure provides a method and a system that uses a predicting algorithm for processing large amounts of data in a short span of time.
[0084] The present disclosure provides a method and a system that specifies an estimated time for placing an order with multiple suppliers.
[0085] The present disclosure provides a method and a system that considers a cost angle while providing a computerised optimisation solution.
[0086] The present disclosure provides a method and a system that automates the manual process of assortment planning and identifies the least cost assortment after examining all possible product combinations.
[0087] The present disclosure provides a method and a system that is applicable to the sale of any product across a large geographical area.
, Claims:1. A system (110) for an assortment planning of one or more product quantity combinations, the system (110) comprising:
one or more processors (202) operatively coupled to one or more computing devices (104), the one or more processors (202) coupled with a memory (204), wherein said memory (204) stores instructions which when executed by the one or more processors (202) causes the one or more processors (202) to:
receive one or more input parameters from the one or more computing devices (104), wherein the one or more computing devices (104) are associated with one or more users (102) and are connected to the one or more processors (202) through a network (106), and
wherein the one or more input parameters are indicative of one or more product inputs provided by the one or more users (102) through the one or more computing devices (104);
extract a first set of attributes from the one or more input parameters, wherein the first set of attributes are indicative of the one or more product quantity combinations;
extract a second set of attributes based on the first set of attributes, wherein the second set of attributes are indicative of one or more inventory cost values for the one or more product quantity combinations;
based on the first set of attributes and the second set of attributes, predict, through an artificial intelligence (AI) engine (216), a minimum cost associated with the one or more product quantity combinations;
generate one or more orders through the AI engine (216) to be presented to one or more suppliers for the one or more product quantity combinations; and
generate the assortment planning of the one or more product quantity combinations based on the one or more orders.

2. The system (110) as claimed in claim 1, wherein the one or more product inputs comprise any or a combination of a product and store detail, a demand forecast, an inventory cost, and a business constraint.

3. The system (110) as claimed in claim 1, wherein the one or more product quantity combinations comprise one or more products, one or more stores, and one or more combinations.

4. The system (110) as claimed in claim 1, wherein the one or more techniques used by the AI engine (216) comprises an ant colony optimization technique to generate the assortment planning of the one or more product quantity combinations.

5. The system (110) as claimed in claim 1, wherein the one or more processors (202) are configured to record a store, a product, an order quantity, and an order date associated with the assortment planning of the one or more product quantity combinations.
6. The system (110) as claimed in claim 1, wherein the one or more processors (202) are configured to generate an estimated time associated with the one or more orders to be presented to the one or more suppliers.

7. A method for an assortment planning of one or more product quantity combinations, said method comprising:
receiving, by one or more processors (202), one or more input parameters from one or more computing devices (104),
wherein the one or more input parameters are indicative of one or more product inputs provided by one or more users (102) through the one or more computing devices (104);
extracting, by the one or more processors (202), a first set of attributes from the one or more input parameters, wherein the first set of attributes are indicative of the one or more product quantity combinations;
extracting, by the one or more processors (202), a second set of attributes based on the first set of attributes, wherein the second set of attributes are indicative of one or more inventory cost values for the one or more product quantity combinations;
predicting, by the one or more processors (202), based on the first set of attributes and the second set of attributes, through an artificial intelligence (AI) engine (216), a minimum cost associated with the one or more product quantity combinations;
generating, by the one or more processors (202), one or more orders through the AI engine (216) to be presented to one or more suppliers for the one or more product quantity combinations; and
generating, by the one or more processors (202), the assortment planning of the one or more product quantity combinations based on the one or more orders.

8. The method as claimed in claim 7, wherein the one or more product inputs comprise any or a combination of a product and store detail, a demand forecast, an inventory cost, and a business constraint.

9. The method as claimed in claim 7, wherein the one or more product quantity combinations comprise one or more products, one or more stores, and one or more combinations.

10. The method as claimed in claim 7, comprising recording, by the one or more processors (202), a store, a product, an order quantity, and an order date associated with the assortment planning of the one or more product quantity combinations.
11. The method as claimed in claim 7, comprising generating, by the one or more processors (202), an estimated time associated with the one or more orders to be presented to the one or more suppliers.

12. A user equipment (UE) (104) for an assortment planning of one or more product quantity combinations, said UE (104) comprising:
one or more processors communicatively coupled to a processor (202) comprised in a system (110), the one or more processors coupled with a memory, wherein said memory stores instructions which when executed by the one or more processors causes the UE (104) to:
transmit one or more input parameters to the processor (202), wherein the UE (104) is associated with one or more users (102) and is connected to the processor (202) through a network (106),
wherein the one or more processors (202) are configured to:
receive the one or more input parameters from the UE (104), wherein the one or more input parameters are indicative of one or more product inputs provided by the one or more users (102) through the UE (104);
extract a first set of attributes from the one or more input parameters, wherein the first set of attributes are indicative of the one or more product quantity combinations;
extract a second set of attributes based on the first set of attributes, wherein the second set of attributes are indicative of one or more inventory cost values for the one or more product quantity combinations;
based on the first set of attributes and the second set of attributes, predict, through an artificial intelligence (AI) engine (216), a minimum cost associated with the one or more product quantity combinations, wherein the AI engine (216) is configured to use one or more techniques;
generate one or more orders through the AI engine (216) to be presented to one or more suppliers for the one or more product quantity combinations; and
generate the assortment planning of the one or more product quantity combinations based on the one or more orders.