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 generally relate to systems and methods for warehouse operations in a supply chain network. More particularly, the present disclosure relates to a system and a method for optimized product slotting in a warehouse that is efficient, provides optimal storage location for articles/products, and reduces congestion among operations staff in the warehouse.

BACKGROUND OF INVENTION
[0003] The following description of the 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 is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
[0004] A warehouse is a facility that is spread over a vast area and used to store a multitude of stock keeping units (SKUs). In large warehouses, hundreds of different varieties of products/articles stored may include thousands of products. In the current retail scenario, fueled by e-commerce, inflow of products into the warehouse and outflow of products from the warehouse is substantial. Considering the heavy volume of products and continuous operations, it is vital that space in the warehouse is optimally utilized and the products stored are easily picked up by operations staff from the warehouse.
[0005] Typically, operations that take place in a warehouse can be classified into inbound operations and outbound operations. Inbound operations involve the process of receiving goods from a supplier and storing the goods in shelves, pallets, or any suitable storage area. Outbound operations involve picking products/articles from the shelves when customer orders are received, packing products, and dispatching them to the customers. To ensure a high throughput, it is essential that the inbound and outbound operations at the warehouse are quick and efficient.
[0006] Typically, when products are put away for storage in the warehouse, products of the same category and/or brand are stored together. This approach, however, fails to consider the fact that customer order patterns are based on their needs and may not necessarily be based on their brand preferences. For example, a customer is more likely to order rice of brand A and flour of brand B rather than rice of brand A and rice of brand B. In this example situation, there is a possibility that the rice of brand A and the flour of brand B are placed in different areas of the warehouse, far away from each other. Subsequently, when a picker/warehouse staff is assigned a task to pick items ordered by this customer, he/she needs to travel a long distance to pick the rice and flour SKUs. At a large scale at which warehouse operations are executed, many pickers are required to travel long distances to pick many products/articles every day leading to gross inefficiencies.
[0007] US11074547B2 describes assignment of orders to pickers so that tote constraints and other constraints are satisfied. However, the system does not recommend any storage/slotting method for products but only recommends a picking method.
[0008] US11004032B2 describes setting of rack opening or rack height in the warehouse so that storage on the rack is maximized. It involves determining pallet heights and considers safety factors, rack positioning, and forklift constraints. However, the system does not describe specific association rules between products and optimal placement of products for picking.
[0009] WO 2022/040809 A1 describes a system and a method to automate warehouse operations (including packing) using robotic arms and automated guided vehicles. However, the system and method does not describe efficient storage of products in the aisle to increase picking efficiency in the warehouse.
[0010] US 2020/0349478 A1 describes grouping of items into batches and assigns batches to pickers so that picking velocity is increased. However, the distance is considered only after products have been stored and not as a factor to determine product storage locations. Further, the conventional system does not recommend any storage method.
[0011] US 2022/0129848 describes a computerized method for assigning items to pickers by calculating distance between items and pickers. However, the system does consider product association, aisle congestion, and distance to the outbound area.
[0012] There is, therefore, a need in the art to provide a system and a method that can mitigate the problems associated with the prior arts.

OBJECTS OF THE INVENTION
[0013] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are listed herein below.
[0014] It is an object of the present disclosure to provide a system and a method that provides optimal slot or storage location for each product(s) in the warehouse so that products that are ordered together by customers are placed together and placed closer to the outbound area.
[0015] It is an object of the present disclosure to provide a system and a method that places products in bins such that constraints pertaining to bin capacity, product shape, size, and weight are satisfied.
[0016] It is an object of the present disclosure to provide a system and a method that minimizes the congestion when pickers come into the aisles to pick products for packing and dispatch them to customers.
[0017] It is an object of the present disclosure to provide a system and a method that ensures an improvement in the picking productivity by reducing the distance that pickers travel to pick products from shelves in the warehouse.
[0018] It is an object of the present disclosure to provide a system and a method that uses a constraint programming technique to generate optimal product placements in the warehouse for picking.

SUMMARY
[0019] 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.
[0020] In an aspect, the present disclosure relates to a system for an optimized product slotting in a warehouse. The system may include one or more processors communicatively coupled with one or more computing devices via 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 receive one or more product inputs via the one or more computing devices. The one or more product inputs may be based on one or more product data. The one or more processors may identify one or more product cohorts based on the received one or more product inputs using one or more primary techniques to generate one or more product metrics. Further, the one or more processors may recommend the optimized product slotting through an optimization engine based on the generated one or more product metrics.
[0021] In an embodiment, the one or more product data may include at least one of orders, product dimensions, bin locations, and one or more warehouse floor-level distances.
[0022] In an embodiment, the one or more primary techniques used by the one or more processors may include an association rule mining technique for the identification of the one or more product cohorts.
[0023] In an embodiment, a constraint programming optimization technique may be used by the optimization engine to recommend the optimized product slotting.
[0024] In an embodiment, the generated one or more product metrics may include at least one of a nearness score, an association effect, and a congestion score.
[0025] In an embodiment, the one or more processors may be configured to generate a table associated with the optimized product slotting.
[0026] In an embodiment, the table may include at least one of a location, a product stored at the location, and a new product recommended by the optimization engine to be stored at the location.
[0027] In an aspect, the present disclosure relates to a method for an optimized product slotting. The method may include receiving, by one or more processors, one or more product inputs from one or more computing devices communicatively coupled with the one or more processors through a network. The one or more product inputs may be based on one or more product data. The method may include identifying, by the one or more processors, one or more product cohorts based on the received one or more product inputs using one or more primary techniques to generate one or more product metrics. The method may include recommending, by the one or more processors, the optimized product slotting through an optimization engine based on the generated one or more product metrics.
[0028] In an embodiment, the one or more primary techniques may include an association rule mining technique for the identification of the one or more product cohorts.
[0029] In an embodiment, the method may include using, by the optimization engine, a constraint programming optimization technique to recommend the optimized product slotting.
[0030] In an embodiment, the one or more product metrics may include at least one of a nearness score, an association effect, and a congestion score.
[0031] In an embodiment, the method may include generating, by the one or more processors, a table associated with the optimized product slotting.
[0032] In an embodiment, the table may include at least one of a location, a product stored at the location, and a new product recommended by the optimization engine to be stored at the location.
[0033] In an aspect, the present disclosure relates to a user equipment (UE) configured to transmit one or more product inputs to one or more processors in a system. The one or more processors are communicatively coupled with the UE via a network and configured to receive the one or more product inputs from the UE, where the one or more product inputs are based on one or more product data, identify one or more product cohorts based on the received one or more product inputs using one or more primary techniques to generate one or more product metrics, and recommend and display, on the UE, an optimized product slotting through an optimization engine based on the generated one or more product metrics.

BRIEF DESCRIPTION OF DRAWINGS
[0034] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems 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.
[0035] FIG. 1 illustrates an exemplary network architecture (100) of a proposed system (106), in accordance with an embodiment of the present disclosure.
[0036] FIG. 2 illustrates an exemplary block diagram (200) of a proposed system (106), in accordance with an embodiment of the present disclosure.
[0037] FIG. 3 illustrates an exemplary product placement (300) in a warehouse, in accordance with an embodiment of the present disclosure.
[0038] FIG. 4 illustrates an exemplary process of product placement (400) in a warehouse, in accordance with an embodiment of the present disclosure.
[0039] FIG. 5 illustrates an exemplary computer system (500) in which or with which a proposed system (106) may be implemented, in accordance with an embodiment of the present disclosure.
[0040] The foregoing shall be more apparent from the following more detailed description of the disclosure.

BRIEF DESCRIPTION OF THE INVENTION
[0041] 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.
[0042] 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.
[0043] 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 to avoid obscuring the embodiments.
[0044] Also, it is noted that individual embodiments may be described as a process that 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.
[0045] 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.
[0046] 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.
[0047] 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 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.
[0048] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGs. 1-5.
[0049] FIG. 1 illustrates an exemplary network architecture (100) of a proposed system (106), in accordance with an embodiment of the present disclosure.
[0050] As illustrated, the network architecture (100) may include a system (106) for optimized product slotting in a warehouse. The system (106) may be connected to one or more computing devices (102-1, 102-2…102-N) through a network (104). A person skilled in the art will understand that the one or more computing devices (102-1, 102-2…102-N) may be collectively referred as computing devices (102) and individually referred as computing device (102). Further, the computing devices (102) may also be known as user equipment (UE) (102) that may include, but not be limited to, a mobile, a laptop, etc. Further, the computing devices (102) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, audio aid, microphone, or keyboard. The computing devices (102) may include a mobile phone, smartphone, virtual reality (VR) devices, augmented reality (AR) devices, a laptop, a general-purpose computer, a desktop, personal digital assistants, a tablet computer, and a mainframe computer. Additionally, input devices for receiving input from a user such as a touchpad, touch-enabled screen, electronic pen, and the like may be used.
[0051] A person of ordinary skill in the art will appreciate that the computing devices or UEs (102) may not be restricted to the mentioned devices and various other devices may be used.
[0052] In an embodiment, the network (104) 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. The network (104) may also 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, a private network, 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 fiber optic network, or some combination thereof.
[0053] Referring to FIG. 1, the system (106) may receive one or more product inputs through the computing devices (102). The product inputs may be based on one or more product data such as, but not limited to, orders, product dimensions, bin locations, and warehouse floor-level distances.
[0054] In an embodiment, the system (106) may identify one or more product cohorts based on the received one or more product inputs using one or more primary techniques to generate one or more product metrics. Further, the system (106) may recommend an optimized product slotting through an optimization engine (108) based on the generated one or more product metrics.
[0055] In an embodiment, the optimization engine (108) included in the system (106) may recommend the optimized product slotting through a constraint programming optimization technique. The recommendations generated by the optimization engine (108) may place associated articles (i.e., articles that are ordered frequently together by customers) nearer to each other and reduce congestion in the aisles. Further, the recommendations may reduce the distance travelled by a picker and satisfy bin mapping constraints i.e., place products in bins such that constraints pertaining to bin capacity, product shape, size, weight, etc. are satisfied.
[0056] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture 100 may perform functions described as being performed by one or more other components of the network architecture 100.
[0057] FIG. 2 illustrates an exemplary block diagram (200) of a proposed system (106), in accordance with an embodiment of the present disclosure.
[0058] Referring to FIG. 2, the system (106) may comprise one or more processor(s) (202) that 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). 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.
[0059] In an embodiment, the system (106) may include an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output (I/O) devices, storage devices, and the like. The interface(s) (206) may also provide a communication pathway for one or more components of the system (106). Examples of such components include, but are not limited to, processing engine(s) (208), an optimization engine (210), and a database (212).
[0060] 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 (106) may comprise 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 (106) and the processing resource. In other examples, the processing engine(s) (208), the optimization engine (210), and the database (212) may be implemented by electronic circuitry. A person ordinary skilled in the art will understand that the optimization engine (210) described in FIG. 2 may be similar to the optimization engine (108) of FIG. 1 in its functionality.
[0061] In an embodiment, the one or more processors (202) may receive one or more product inputs from computing devices such as the one or more computing devices (102) of FIG. 1. The one or more product inputs may be based on one or more product data such as, but not limited to, one or more orders, one or more product dimensions, one or more bin locations, and one or more warehouse floor-level distances.
[0062] In an embodiment, the one or more processors (202) may identify one or more product cohorts based on the received one or more product inputs using one or more primary techniques to generate one or more product metrics. Further, the one or more processors (202) may store the received one or more product inputs in the database (212). The generated one or more product metrics may comprise, but not be limited to, a nearness score, an association effect, and a congestion score based on the identified one or more product cohorts.
[0063] In an embodiment, the one or more primary techniques used by the one or more processors (202) may comprise an association rule mining technique for the identification of the one or more product cohorts. Further, the one or more processors (202) may recommend an optimized product slotting through the optimization engine (210) based on the generated one or more product metrics.
[0064] In an embodiment, a constraint programming optimization technique may be used by the optimization engine (210) to recommend the optimized product slotting. Further, the one or more processors (202) may generate a table associated with the optimized product slotting. The table may include, but not be limited to, a location, a product stored at the location, and a new product recommended by the optimization engine (210) to be stored at the location.
[0065] It may be appreciated that the block diagram (200) may be modular and flexible to accommodate any kind of changes in the system (106).
[0066] FIG. 3 illustrates an exemplary mechanism for product placement (300) in a warehouse, in accordance with an embodiment of the present disclosure.
[0067] As illustrated in FIG. 3, a user/customer (302) may provide one or more product inputs to a system (306) through a computing device (304). A person skilled in the art will understand that the computing device (304) may be similar to the computing devices (102) of FIG. 1 in its functionality. Further, the system (306) may be similar to the system (106) of FIGs. 1-2 in its functionality.
[0068] In an embodiment, the one or more product inputs may include data sources (308) such as one or more orders, one or more product dimensions, one or more bin locations, and one or more warehouse floor-level distances between multiple points. Further, the system (306) may include processing (310) of the one or more product inputs. The system (306) may identify product cohorts based on association rules, calculate the metrics (frequency, association effect, nearness score, and congestion score), and provide an optimal product slotting recommendation. The system (306) may generate an output (312) in the form of a table containing the location name/label, the article originally stored at the location, and the product/article recommended by an optimization engine (210) (as described in FIGs. 1 and 2) to be stored at that location. The output (312) may be viewed by the user/customer (302) on the computer screen or printed out as a physical copy on paper.
[0069] FIG. 4 illustrates an exemplary process of product placement (400) in a warehouse, in accordance with an embodiment of the present disclosure.
[0070] As illustrated in FIG. 4, a system (402) may include order line data containing details of every line item in every order requested by customers. The order line data may include order line details (404) and a product master (406) provided to a bin mapping module (412). Order details (404) may include, but not be limited to, order date, ordered items, quantity, etc. The product master (406) may contain details such as, but not limited to, product name, category, dimensions, etc. The bin mapping module (412) as illustrated in FIG. 4 may map products to storage areas based on order pattern and/or product dimensions, and determine the right storage configuration for each product. Further, the bin mapping module (412) may store the mapped products in pallets and shelves to be picked up by assigned pickers of the warehouse.
[0071] Further, an association rules mining module (414) may identify product cohorts based on particular items ordered together and more frequently ordered items by customers. The order line details (404) and outputs from the bin mapping module (412) may be provided as inputs to the association rules mining module (414). The association rules mining module (414) may process the inputs and generate association rules between products.
[0072] Referring to FIG. 4, a constraint programming optimizer model (416) may receive inputs from the association rules mining module (414). Further, order line details (404), bin master (408), and layout distances (410) may be provided as inputs to the constraint programming optimizer model (416). Bin master (408) may contain data such as, but not limited to, bin size, capacity, location, etc. Layout distances (410) may include distances between multiple points in the warehouse and from the origin (outbound point). Based on the inputs, the constraint programming optimizer model (416) may recommend the optimal storage locations for each product in the warehouse based on distance (416-1), congestion (416-2), and association (416-3). The outputs from the constraint programming optimizer model (416) may be provided to a slotting recommendation module (418).
[0073] The constraint programming optimizer model (416) may utilize the following product metrics for optimization:
a. Nearness score =
b. Association effect = Sum of Euclidean distance between two associated articles
c. Congestion effect = Standard deviation of the orders in each aisle at the day level
[0074] The nearness score is expected to be high while the association effect is expected to be low. Further, the congestion effect is expected to be low.
[0075] The slotting/storage recommendations may be made on the following principles as represented in Table 1.

S.No. Cases Slotting Decisions
1 HFHFHA SA but in HFZ
2 HFLFHA SA but HF will be in HFZ, LF can be in either HFZ or LFZ
3 LFLFHA SA, but in any zone
4 LFLFLA Any zone
5 HFLFLA HF in HFZ, LF in any zone
6 HFHFLA HFZ

• HF: High Frequency
• LF: Low Frequency
• HA: High Association
• LA: Low Association
• SA: Same Aisle
• HFZ: High Frequency Zone
• LFZ: Low Frequency Zone
Under the ‘Cases’ column in Table 1:
• The first 2 letters may denote the frequency (high/low) of the first product in the cohort.
• The next 2 letters may denote the frequency (high/low) of the second product in the cohort.
• The last 2 letters may denote the association (high/low) of the 2 products in the cohort.
[0076] The above table (for example, the first row) may be read as: if there are 2 products, both having high frequency as well as a high association score, then both the products should be stored in the same aisle in a High Frequency Zone.
[0077] FIG. 5 illustrates an exemplary computer system (500) in which or with which the proposed system may be implemented, in accordance with an embodiment of the present disclosure.
[0078] As shown in FIG. 5, the computer system (500) may include an external storage device (510), a bus (520), a main memory (530), a read-only memory (540), a mass storage device (550), a communication port(s) (560), and a processor (570). A person skilled in the art will appreciate that the computer system (500) may include more than one processor and communication ports. The communication port (560) 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 (500) connects. The main memory (530) may be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. The read-only memory (540) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chip for storing static information e.g., start-up or basic input/output system (BIOS) instructions for the processor (570). The mass storage device (550) may be any current or future mass storage solution, which can be used to store information and/or instructions.
[0079] The bus (520) may communicatively couple the processor(s) (570) with the other memory, storage, and communication blocks. Optionally, operator and administrative interfaces, e.g., a display, keyboard, and cursor control device may also be coupled to the bus (520) to support direct operator interaction with the computer system (500). Other operator and administrative interfaces can be provided through network connections connected through the communication port (560). In no way should the aforementioned exemplary computer system (500) limit the scope of the present disclosure.
[0080] 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 is to be implemented merely as illustrative of the disclosure and not as a limitation.

ADVANTAGES OF THE INVENTION
[0081] The present disclosure provides a system and a method that considers data such as orders, product dimensions, bin locations, and warehouse floor-level distances between multiple points to provide product slotting/storage recommendations.
[0082] The present disclosure provides a system and a method that recommends product slotting/storage recommendations based on an advanced association rule mining technique.
[0083] The present disclosure provides a system and a method that improves the ease of picking for pickers and thereby increases picking productivity by placing together items that are frequently ordered together by customers.
[0084] The present disclosure provides a system and a method that provides recommendations such that frequently ordered items are placed closer to the outbound area of the warehouse so that said items are be picked and dispatched quickly.
[0085] The present disclosure provides a system and a method that ensures that high demand items are not clustered in one area of the warehouse, thereby preventing congestion during picking.
[0086] The present disclosure provides a system and a method that is focused on optimizing inbound operations so that outbound operations are smoother and faster.

, Claims:1. A system (106) for an optimized product slotting, the system (106) comprising:
one or more processors (202); and
a memory (204) coupled with the one or more processors (202), 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 product inputs via one or more computing devices (102) communicatively coupled with the one or more processors (202) through a network (104), wherein the one or more product inputs are based on one or more product data;
identify, using one or more primary techniques, one or more product cohorts based on the received one or more product inputs to generate one or more product metrics; and
recommend the optimized product slotting through an optimization engine (108) based on the generated one or more product metrics.
2. The system (106) as claimed in claim 1, wherein the one or more product data comprises at least one of orders, product dimensions, bin locations, and warehouse floor-level distances.
3. The system (106) as claimed in claim 1, wherein the one or more primary techniques used by the one or more processors (202) comprise an association rule mining technique for the identification of the one or more product cohorts.
4. The system (106) as claimed in claim 1, wherein the one or more processors (202) are configured to recommend the optimized product slotting using a constraint programming optimization technique.
5. The system (110) as claimed in claim 1, wherein the generated one or more product metrics comprise at least one of a nearness score, an association effect, and a congestion score.
6. The system (106) as claimed in claim 1, wherein the one or more processors (202) are configured to generate a table associated with the optimized product slotting.
7. The system (106) as claimed in claim 6, wherein the table comprises at least one of a location, a product stored at the location, and a new product recommended by the optimization engine (108) to be stored at the location.
8. A method for an optimized product slotting, the method comprising:
receiving, by one or more processors (202), one or more product inputs from one or more computing devices (102) communicatively coupled with the one or more processors (202) through a network (104), wherein the one or more product inputs are based on one or more product data;
identifying, by the one or more processors (202), one or more product cohorts based on the received one or more product inputs using one or more primary techniques to generate one or more product metrics; and
recommending, by the one or more processors (202), the optimized product slotting through an optimization engine (108) based on the generated one or more product metrics.
9. The method as claimed in claim 8, wherein the one or more primary techniques comprise an association rule mining technique for identifying the one or more product cohorts.
10. The method as claimed in claim 8, comprising using, by the optimization engine (108), a constraint programming optimization technique for recommending the optimized product slotting.
11. The method as claimed in claim 8, wherein the one or more product metrics comprise at least one of a nearness score, an association effect, and a congestion score.
12. The method as claimed in claim 8, comprising generating, by the one or more processors (202), a table associated with the optimized product slotting.
13. The method as claimed in claim 12, wherein the table comprises at least one of a location, a product stored at the location, and a new product recommended by the optimization engine (108) to be stored at the location.
14. A user equipment (UE) (102) for an optimized product slotting, said UE (102) comprising:
one or more primary processors communicatively coupled to one or more processors (202) in a system (106), the one or more primary processors coupled with a memory, wherein said memory stores instructions which when executed by the one or more primary processors causes the UE (102) to:
transmit one or more product inputs to the one or more processors (202),
wherein the one or more processors (202) are communicatively coupled with the UE (102) via a network (104) and configured to:
receive the one or more product inputs from the UE (102), wherein the one or more product inputs are based on one or more product data;
identify one or more product cohorts based on the received one or more product inputs using one or more primary techniques to generate one or more product metrics; and
recommend and display, on the UE (102), the optimized product slotting through an optimization engine (108) based on the generated one or more product metrics.