Description:RESERVATION OF RIGHTS
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 DISCLOSURE
[0001] The embodiments of the present disclosure generally relate to a warehouse operations management. In particular, the present disclosure relates to a manpower planning system for estimating the manpower or human resource required in a warehouse using artificial intelligence and machine learning based architecture.

BACKGROUND OF DISCLOSURE
[0002] 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.
[0003] For any retail organization, a warehouse is the starting point in the supply chain for goods procured from vendors. In such organization warehouse operations management plays an important role since any delay in this first leg will adversely impact the entire downstream supply chain.
[0004] Warehouse operations management becomes even more critical during deep-discount sale events. During such events there will be a huge stock of products in the warehouse. To handle such huge stock of products additional manpower or human resource will be needed in the warehouse around the sale time for example, 1 or 2 months. It is not financially sound to maintain a high level of warehouse manpower throughout the year to cater for the demand during such events, therefore, warehouse manpower or human resource is hired on a contractual basis to service the high demand period. Warehouses store a variety of products, and each product category requires a different set of skills for different tasks such as picking and packing lead, creating a big challenge in estimating the optimal manpower or human resource hiring requirement.
[0005] 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.

SUMMARY
[0006] 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.
[0007] In an aspect, the present disclosure relates to a system for estimating optimal manpower or human resource requirement during a high demand period. The system includes one or more processors and a memory operatively coupled to the one or more processors, where the memory includes processor-executable instructions, which on execution, cause the one or more processors to receive a first set of data including user defined inputs, for example, without limitations, planning horizon, identity of the warehouse, per person hiring cost, and a total hiring budget through a user interface, receive a second set of data related to a warehouse operation from a database, for example, without limitations, historical demand, benchmark productivity levels, working hours, budget, warehouse operations data, attendance details, predict a demand forecast data and a worker absentee data based on the received first and second set of data, and estimate an optimized human resource deployment plan based on the predicted data.
[0008] In an embodiment, the optimized human resource deployment plan provides a number of manpower or human resource to be hired based at least on the first set of data and the second set of data.
[0009] In another embodiment, the optimized human resource deployment plan includes a mix of newly hired manpower or human resource and existing manpower or human resource.
[0010] In another embodiment, the optimized human resource deployment plan is based on user priorities such as, but not limited to, high moving product or products ordered by a large number of users.
[0011] In another aspect, the present disclosure relates to a method for determining a manpower or human resource deployment plan for a warehouse. The method includes receiving, by one or more processors, a first set of data including user defined inputs through a user interface of a computing device, receiving, by the one or more processors, a second set of data related to a warehouse operation from a database, predicting, by the one or more processors, a demand forecast data and a worker absenteeism data based on the received first and second set of data, and generating, by the one or more processors, an optimized manpower or human resource deployment plan based on the predicted data.
[0012] In an embodiment, the method includes generating, by the one or more processors, the optimized manpower or human resource deployment plan based on a Mixed Integer Linear Programming (MILP) optimization model, where the optimized manpower or human resource deployment plan includes at least one of providing a number of manpower or human resource to be hired based on the first set of data, providing a mix of newly hired manpower or human resource and existing manpower or human resource, and deploying manpower or human resource based on user priorities.
[0013] In an embodiment, the method includes displaying, by the one or more processors, the generated optimized manpower or human resource deployment plan on the user interface of the computing device.
[0014] In another aspect, the present disclosure relates to a user equipment including one or more processors, and a memory coupled to the one or more processors, where the memory includes processor-executable instructions, which on execution, cause the one or more processors to transmit a first set of data including user defined inputs through a user interface of the user equipment, and receive an optimized human resource deployment plan through the user interface, where the optimized human resource deployment plan is based on the first set of data and a second set of data, and where the second set of data is related to a warehouse operation.

OBJECTS OF THE PRESENT DISCLOSURE
[0015] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0016] An object of the present disclosure is to provide an optimized worker deployment plan in a warehouse during a peak sale period based on the demand forecast and absenteeism data of the worker.
[0017] An object of the present disclosure is to provide the optimized deployment plan based on the available cost budget.
[0018] An object of the present disclosure is to provide the optimized deployment plan based on user requirements.

BRIEF DESCRIPTION OF DRAWINGS
[0019] 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.
[0020] FIG. 1 illustrates an exemplar y network architecture (100) in which or with which a proposed system may be implemented, in accordance with an embodiment of the present disclosure.
[0021] FIG. 2 illustrates an exemplary representation (200) of the proposed system for estimating optimal human resource requirement, in accordance with an embodiment of the present disclosure.
[0022] FIG. 3 illustrates a sequence diagram of a network architecture (300) in which or with which embodiments of the present disclosure may be implemented.
[0023] FIG. 4 illustrates an exemplary architecture (400) in which or with which the embodiments of the present disclosure may be implemented.
[0024] FIG. 5 illustrates an example method (500) for estimating optimal human resource requirement in a network, in accordance with an embodiment of the present disclosure.
[0025] FIG. 6 illustrates an exemplary computer system (600) in which or with which embodiments of the present disclosure may be implemented.
[0026] The foregoing shall be more apparent from the following more detailed description of the disclosure.

DETAILED DESCRIPTION OF DISCLOSURE
[0027] 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.
[0028] 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.
[0029] 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.
[0030] 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.
[0031] 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.
[0032] 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.
[0033] 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.
[0034] The present disclosure provides a robust and effective solution for estimating optimal manpower or human resource requirements in a warehouse during high demand period such as, but not limited to a deep-discount sale. In particular, the disclosed solution provides a deployment plan estimating the number of workers required for a particular day for a particular category of products during high demand period based on a comprehensive set of parameters such as, without limitations, a forecast related to product demand, rotational weekly holiday for workers, benchmark productivity levels, worker absenteeism etc. Further, the disclosed solution provides the manpower or human resource estimation keeping the hiring cost at a minimum and if a given manpower or human resource cannot be accommodated in the given budget the solution also provides recommendation for deploying manpower or human resource based on user preferences for example, deploying more manpower or human resource for products having high demand.
[0035] The present disclosure also provides simulating manpower or human resource deployment plans for different scenarios such as during high demand, high absenteeism etc. The disclosed solution may also be used in manpower or human resource planning in various places such a, without limitations package sortation centres, factories etc.
[0036] Embodiments of the present disclosure relate to optimal estimation of manpower or human resource deployment in a network. In particular, a system may be provided for manpower or human resource deployment plans based on the optimal estimation. In an embodiment, the present disclosure relates to product warehouse environments requiring more manpower or human resource during a high demand period.
[0037] In accordance with the embodiments described herein, a user (e.g., a warehouse operations manager or a human resource manager) may interact with a device connected to a network to specify inputs pertinent to the manpower or human resource planning. For example, without limitations, the device may be a computer system having a user interface with clickable buttons and text fields with which the user can interact and specify inputs pertinent to the manpower or human resource planning exercise.
[0038] Accordingly, the present disclosure allows access to different data sources to estimate the optimal manpower or human resource plan. Data sources include, but are not limited to, historical sales/demand for different products and benchmark productivity levels as per standard operating procedures/manuals for different tasks. These data points may typically be stored in a relational database management system and may be edited/altered by the user via the user interface. Further, the disclosed solution may use an artificial intelligence (AI) triggered system for estimating the optimal manpower or human resource plan. One or more AI modules may be employed for estimating various parameters to enable estimation of the optimized manpower or human resource plan. For example, a first AI module may forecast the sales volume of each product that is expected to be dispatched from the warehouse thereby providing a demand forecast. A second AL module predicts whether a warehouse worker is going to be absent on a given day. This may include an Extreme Gradient Boosting (XGBoost) algorithm or a similar algorithm or a collection of similar algorithms that predicts the likelihood of absenteeism. The disclosed solution further provides a decision making AI module or a manpower or human resource deployment planning module employing a Mixed Integer Linear Programming (MILP) optimization model for generating the manpower or human resource hiring and deployment plan. The deployment plan includes essentially the number of new persons that need to be hired and the number of experienced and new persons that need to be deployed in each product category on each day of the planning horizon based on an available budget. Other like benefits and advantages are provided by the disclosed solution, which will be discussed in detail throughout the disclosure.
[0039] Certain terms and phrases have been used throughout the disclosure and will have the following meanings in the context of the ongoing disclosure.
[0040] The various embodiments throughout the disclosure will be explained in more detail with reference to FIGs. 1-6.
[0041] FIG. 1 illustrates an exemplary network architecture (100) in which or with which embodiments of the present disclosure may be implemented.
[0042] Referring to FIG. 1, the network architecture (100) may include at least one computing devices (104) operable by a user (102) deployed in an environment. In an embodiment, the computing device (104) may interoperate with any other computing device (not shown) that may be present in the network architecture (100). In an embodiment, the computing devices (104) may be referred to as a user equipment (UE). A person of ordinary skill in the art will appreciate that the terms “computing device(s)” and “UE” may be used interchangeably throughout the disclosure.
[0043] In an embodiment, the computing device (104) may include, but are not limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a phablet device, and so on), a wearable computer device (e.g., a head-mounted display computer device, a head-mounted camera device, a wristwatch computer device, and so on), a Global Positioning System (GPS) device, a laptop computer, a tablet computer, or another type of portable computer, a media playing device, a portable gaming system, and/or any other type of computer device (104) with wireless communication capabilities, and the like. In an embodiment, the computing devices (104) may include, but are not limited to, any electrical, electronic, electro-mechanical, or an equipment, or a combination of one or more of the above devices such as virtual reality (VR) devices, augmented reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, mainframe computer, or any other computing device, wherein the computing device (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, a keyboard, and input devices for receiving input from a user (102) such as touch pad, touch enabled screen, electronic pen, and the like.
[0044] In an embodiment, the computing devices (104) may include one or more of the following components: sensor, radio frequency identification (RFID) technology, GPS technology, mechanisms for real-time acquisition of data, passive or interactive interface, mechanisms of outputting and/or inputting sound, light, heat, electricity, mechanical force, chemical presence, biological presence, location, time, identity, other information, or any combination thereof.
[0045] A person of ordinary skill in the art will appreciate that the computing device (104) may include, but not be limited by, intelligent, multi-sensing, network-connected devices, that can integrate seamlessly with each other and/or with a central server or a cloud-computing system or any other device that is network-connected.
[0046] A person of ordinary skill in the art will appreciate that the computing device or UE (104) may not be restricted to the mentioned devices and various other devices may be used.
[0047] Referring to FIG. 1, the computing device (104) may communicate with a system (110), for example, an estimation system, through a network (106). In an embodiment, the network (406) may include at least one of a Fourth Generation (4G) network, a Fifth Generation (5G) network, or the like. The network (106) may enable the computing device (104) to communicate between devices (not shown) and/or with the system (110). In an exemplary embodiment, the network (106) may incorporate one or more of a plurality of standard or proprietary protocols including, but not limited to, Wi-Fi, ZigBee, or the like. In another embodiment, the network (106) may be implemented as, or include, any of a variety of different communication technologies such as a wide area network (WAN), a local area network (LAN), a wireless network, a mobile network, a Virtual Private Network (VPN), the Internet, the Public Switched Telephone Network (PSTN), or the like.
[0048] Referring to FIG. 1, the system (110) may include artificial intelligence (AI) engines/modules (108-1, 108-2) in which or with which the embodiments of the present disclosure may be implemented. In particular, the system (110), and as such, the AI modules (108-1, 108-2) facilitates estimating the manpower or human resource deployment plan in the network architecture (100) based on input provided by the user (102) in a user interface of the computing device (104). The network (100) for example, without limitation, include a communication network within a warehouse, factory, storage facility etc. Further, the system (110) may be operatively coupled to a server (not shown).
[0049] In accordance with an embodiment of the present invention, a user (102) for example, a warehouse operations manager or a human resources manager may specify a set of inputs, pertinent to a manpower or human resource deployment planning exercise through a user interface system present in the computing device (104). The set of input may include for example, without limitations, planning horizon, warehouse for which the manpower or human resource needs to be planned, per person hiring cost, total hiring budget etc. Additionally, the user (102) may also have an option to edit certain pre-loaded data points. For example, for a planning horizon specified by the user (102), the computing device (104) may by default, show a demand forecast. If the user (102) proposes to estimate the manpower or human resource for a higher demand, he/she can adjust the demand values on the user interface provided in the computing device (104). Upon receiving the input from the user (102) the AI modules (108-1, 108-2) proceed with predicting at least two data points based on the user input and data from a set of databases (not shown). The data from the database might include, for example without any limitations, historical demand, benchmark productivity levels, working hours, cost, budget, warehouse operations data, employee details attendance, etc. The first AI module 108-1 may be configured to predict demand forecast data based on the user input and the information from the database and the second AI module (108-2) may be configured to predict worker absenteeism data based on the user input and the information from the database. The data from the two AI modules (108-1, 108-2) may be passed to a main decision-making module (112) which generates the most optimal manpower or human resource hiring and deployment plan that satisfies the given set of inputs. The decision-making module (112) includes Mixed Integer Linear Programming (MILP) optimization model for estimating the manpower or human resource deployment plan. The estimated plan may be communicated back from system (110) to the computing device (104) such that said plan can be viewed by the user (102) on the user interface or can be printed on paper as a physical copy.
[0050] 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).
[0051] FIG. 2 illustrates an exemplary representation (200) of the system (110), in accordance with embodiments of the present disclosure.
[0052] For example, the system (110) may include one or more processor(s) (202). The one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog 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 Electrically Erasable Programmable Read-only Memory (EPROM), flash memory, and the like.
[0053] 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 for 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 unit/engine(s) (208) and a database (210).
[0054] The processing unit/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 include 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. In an aspect, the database (210) may comprise data that may be either stored or generated as a result of functionalities implemented by any of the components of the processor (202) or the processing engines (208).
[0055] In an embodiment, the processing engine (208) may include engines that receive data from one or more computing device via a network such as the computing device (104) via the network (106) (e.g., via the Internet) of FIG. 1, to index the data, to analyze the data, and/or to generate statistics based on the analysis or as part of the analysis. In an embodiment, the analyzed data may be stored at the database (210). In an embodiment, the processing engine (208) may include one or more modules/engines such as, but not limited to, an acquisition engine (212), one or more AI engine/modules (214), a decision making engine/module (218) and other engine(s) (216). A person of ordinary skill in the art will understand that the AI engine(s)/module(s) (214) may be similar in its functionality with the AI engine/modules (108-1, 108-2) of FIG. 1, and hence, may not be described in detail again for the sake of brevity.
[0056] Referring to FIG. 2, the database (210) may store the data, i.e., a set of data parameters associated with historical demand, benchmark productivity levels, working hours, cost, budget, warehouse operations data, employee details attendance, etc. that may be used in estimating the manpower or human resource deployment plan. In an embodiment, the database (210) may or may not reside in the system (110). In an embodiment, the system (110) may be operatively coupled with the database (210).
[0057] By way of example but not limitation, the one or more processor(s) (202) may receive a user input pertinent to estimating the manpower or human resource deployment plan. In an embodiment, the user input might be received using any suitable input/output device associated with the user interface.
[0058] In an embodiment, the one or more processor(s) (202) of the system (110) may cause the acquisition engine (212) to extract the set of data parameters from the database (210) for enabling prediction of data points by one or more AI module(s) 214 which is further used by the decision-making module (218) to estimate the manpower or human resource deployment plan. In particular, the set of data parameters gained from user input along with the set of parameters from database (210) may be analyzed as a whole to arrive at the estimation. In an embodiment, the one or more processor(s) (202) may cause the AI engine (214) to pre-process the set of data parameters in one or more batches. As described with reference to FIG. 2 above, the AI engine (214) may utilise one or more machine learning models to pre-process the set of data parameters. In an embodiment, the AI engine (214) may perform pre-processing of the set of data parameters to form data in a proper time-series with equal intervals of time, for example, intervals of 1 minute. In an embodiment, results of the pre-processing or analysis may thereafter be transmitted back to the computing device (104), to other devices, to a server providing a web page to a user (102) of the computing device (104), or to other non-device entities.
[0059] In an embodiment, based on the pre-processing, the one or more processor(s) (202) may cause the AI engine(s) (214) to predict demand forecast during a deep-discount sale period and worker absenteeism during such period which in turn may be used by the decision-making module (218) for estimating the deployment plan.
[0060] A person of ordinary skill in the art will appreciate that the exemplary representation (200) may be modular and flexible to accommodate any kind of changes in the system (110). In an embodiment, the data may get collected meticulously and deposited in a cloud-based data lake to be processed to extract actionable insights. Therefore, the aspect of predictive maintenance can be accomplished.
[0061] FIG. 3 illustrates a sequence diagram of a network architecture (300) in which or with which embodiments of the present disclosure may be implemented.
[0062] Referring to FIG. 3, the network architecture (300) comprises devices, database, computing system, ML module, and the like. For example, the network architecture (300) comprises a user (302), a device (304), a system (306), database (308), and a manpower or human resource planning module/engine (310). A person of ordinary skill in the art will understand that the user (302) and the device (304) may be similar in their functionality to the user (102) and the computing device (104) of FIG. 1, respectively, and hence, may not be described in detail again for the sake of brevity. Further, a person of ordinary skill in the art will understand that the system (306), database (308), and manpower or human resource planning module (310), are similar to the system (110), database (210), and decision-making module (218), of FIG. 2 respectively, and hence, may not be described in detail again for the sake of brevity.
[0063] Referring to FIG. 3 the user (302) may be associated with the device (304). Once the user (302) inputs a set of parameters on a user interface of the device (304) the user selection is communicated to the system (306) in step 1. Further, in step 2, the system (306) receives data from the database (308) to predict demand forecast and worker absenteeism which is communicated to the manpower or human resource planning module (310) at steps 3 and 4. The manpower planning module (310) includes a mixed integer linear programming (MILP) model performing the estimation. The MILP uses the demand forecast data and the worker absenteeism prediction data to worker/manpower deployment plan and is communicated to the user (302) through the user interface in the device (304) at step 5.
[0064] Thus, the present disclosure allows for estimating manpower deployment plan based on the input from the user (302) and the set of data parameters from the database (308).
[0065] A person of ordinary skill in the art will appreciate that the architecture (300) may be modular and flexible to accommodate any kind of changes. Although FIG. 3 shows exemplary components of the architecture (300), in other embodiments, the architecture (300) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 3. Additionally, or alternatively, one or more components of the architecture (300) may perform functions described as being performed by one or more other components of the architecture (300).
[0066] FIG. 4 illustrates an exemplary architecture (400) in which or with which the embodiments of the present disclosure may be implemented.
[0067] Referring to FIG. 4, the exemplary components of the architecture (400), as implemented in the system (110) of FIG. 1, includes a data layer (406), an AI/ML layer (408), an optimization layer or core solution layer (410), and result/output (412). A user (402) interacts with device (404) to provide a set of user inputs. The device (404) communicated with the data layer (406), wherein the data layer provides database parameters as discusses above with respect to database 210 of FIG. 2. The AI/ML layer (408) includes a demand forecast module (408-1) and a worker absenteeism/churn module (408-2), wherein the two modules uses the user input and the data from the data layer (406) to predict demand forecast and absenteeism, respectively. In an embodiment, the worker absenteeism/churn module (408-2) may include Extreme Gradient Boosting (XGBoost) algorithm to predict the likelihood of absenteeism. The predicted data from the AI/ML layer (408) is passed on to the optimization layer (410) which uses Mixed Integer Linear Programming (MILP) optimization model to generate the manpower hiring and deployment plan. The generated plan is thus passed as a result/output (412) to the user (402).
[0068] FIG. 5 illustrates an example method (500) for providing an estimation of a manpower or human resource deployment plan based on one or more user inputs in a network, in accordance with an embodiment of the present disclosure.
[0069] At step 502, the method (500) may include receiving user input for manpower or human resource planning. The user input includes planning horizon, warehouse for which the manpower or human resource needs to be planned, per person hiring cost, total hiring budget etc. Upon receiving the user input at step 502, the method proceeds with acquiring data from database for different data point at step 504. The data from the database includes historical demand, benchmark productivity levels, working hours, cost, budget, warehouse operations data, employee details attendance, etc. upon acquiring data from the database at step 504, the method proceeds to predict a forecast demand associated with a product at step 506 and predict worker absenteeism factor at step 508. After the prediction at steps 506, 508 manpower or human resource deployment plan is generated at step 508. The manpower or human resource deployment plan is generated by keeping the hiring cost at a minimum and if a given manpower or human resource cannot be accommodated in the given budget then the plan is generated such that the manpower or human resource is deployed based on user preferences for example, deploying more manpower or human resource for products having high demand. The generated deployment plan is displayed to the user at step 512.
[0070] FIG. 6 illustrates an exemplary computer system (600) in which or with which embodiments of the present disclosure may be utilized. As shown in FIG. 7, 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), 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 processor (670) may include various modules associated with embodiments of the present disclosure. The communication port(s) (760) may be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port(s) (660) may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (700) 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) including, 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 may be used to store information and/or instructions.
[0071] The bus (620) communicatively couples the processor (670) with the other memory, storage, and communication blocks. The bus (620) can 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, for 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).
[0072] 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 may be provided through network connections connected through the communication port(s) (660). In no way should the aforementioned exemplary computer system (600) limit the scope of the present disclosure.
[0073] Thus, the present disclosure enables generation of a cost-effective manpower or human resource deployment plan in a warehouse or a factory or a storage setting during a peak sale season. The present disclosure facilitates a budget friendly deployment plan at the same time keeping the user interest as priority.
[0074] 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 PRESENT DISCLOSURE
[0075] The present disclosure estimates the number of additional human resource to be hired such that the minimum cost is incurred.
[0076] The present disclosure recommends the mix of new and experienced manpower to be deployed in each product category.
[0077] The present disclosure considers a comprehensive set of parameters such as the demand forecast, rotational weekly holiday for workers, benchmark productivity levels, worker absenteeism etc. for the estimation.
[0078] The present disclosure recommends deploying the limited manpower or human resource based on the user’s priorities. E.g., higher manpower or human resource will be allocated to high priority product categories, for example, without limitations, products that are fast moving or that have been ordered by more number of users or customers.
[0079] The present disclosure may be used to simulate manpower or human resource deployment plans for different scenarios such as high demand, high absenteeism etc.
[0080] The present disclosure may be extended to other environments such as package sortation centres, factories etc.

, Claims:1. A system (110) for determining a human resource deployment plan for a warehouse network (106), said system (110) comprising:
one or more processors (202); and
a memory (204) operatively coupled to the one or more processors (202), wherein the memory (204) comprises processor-executable instructions, which on execution, cause the one or more processors (202) to:
receive a first set of data comprising user defined inputs through a user interface;
receive a second set of data related to a warehouse operation from a database (210);
predict a demand forecast data and a worker absentee data based on the received first and second set of data; and
estimate an optimized human resource deployment plan based on the predicted data.
2. The system (110) as claimed in claim 1, wherein the first set of data comprises at least one of planning horizon, identity of the warehouse, per person hiring cost, and a total hiring budget.
3. The system (110) as claimed in claim 1, wherein the second set of data comprises at least one of historical demand, benchmark productivity levels, working hours, budget, warehouse operations data, and attendance details.
4. The system (110) as claimed in claim 1, wherein the optimized human resource deployment plan provides a number of human resource to be hired based at least on the first set of data and the second set of data.
5. The system (110) as claimed in claim 4, wherein the optimized human resource deployment plan comprises a mix of newly hired human resource and existing human resource.
6. The system (110) as claimed in claim 1, wherein the optimized human resource deployment plan is based on user priorities.

7. A method (500) for determining a human resource deployment plan for a warehouse, said method (500) comprising:
receiving (502), by one or more processors (202), a first set of data comprising user defined inputs through a user interface of a computing device (104);
receiving (504), by the one or more processors (202), a second set of data related to a warehouse operation from a database (210);
predicting, by the one or more processors (202), a demand forecast data (506) and a worker absenteeism data (508) based on the received first and second set of data; and
generating (510), by the one or more processors (202), an optimized human resource deployment plan based on the predicted data.
8. The method (500) as claimed in claim 7, comprising generating, by the one or more processors (202), the optimized human resource deployment plan based on a Mixed Integer Linear Programming (MILP) optimization model.
9. The method (500) as claimed in claim 7, wherein generating, by the one or more processors (202), the optimized human resource deployment plan comprises at least one of:
providing a number of human resource to be hired based on the first set of data;
providing a mix of newly hired human resource and existing human resource; and
deploying human resource based on user priorities.
10. The method (500) as claimed in claim 7, comprising displaying (512), by the one or more processors, the generated optimized human resource deployment plan on the user interface of the computing device (104).
11. A user equipment (104), comprising:
one or more processors; and
a memory operatively coupled to the one or more processors, wherein the memory comprises processor-executable instructions, which on execution, cause the one or more processors to:
transmit a first set of data comprising user defined inputs through a user interface of the user equipment; and
receive an optimized human resource deployment plan through the user interface, wherein the optimized human resource deployment plan is based on the first set of data and a second set of data, and wherein the second set of data is related to a warehouse operation.