Description:FIELD OF INVENTION
[0001] The embodiments of the present disclosure herein relate to a predictive analysis using machine learning and artificial intelligence, and more particularly forecasting demand of a plurality of product items.

BACKGROUND OF THE INVENTION
[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] Demand forecasting is known as the process of making future estimations in relation to customer demand over a specific period. Generally, demand forecasting will consider historical data and other analytical information to produce the most accurate predictions. More specifically, the methods of demand forecasting entails using predictive analytics of historical data to understand and predict customer demand in order to understand key economic conditions and assist in making crucial supply decisions to optimise business profitability.
[0004] Conventional forecasting methods take a one-size-fits-all approach i.e., one forecasting algorithm is applied to all products. Though they are based on historical data, these approaches fail to consider the highly complicated and hidden patterns in the data that occur due to changes in customer preferences and buying patterns, geographical dynamics etc. One algorithm will not be capable of capturing all of these different patterns. Also, if a product is newly introduced in the market, forecasting and assortment planning for that product is driven mostly by instinct and experience rather than data. Due to these and several other reasons, most of the time, conventional approaches to demand forecasting do not give accurate results.
[0005] The invention solves this problem in 2 ways. First, it determines whether the product is an existing product or a new one that has just been introduced in the market. If the product is new, a Bass Diffusion Model is used to forecast its sales. The advantage of this model is that it provides a fair estimate of how many customers are going to buy the product and how quickly/slowly the product will be adopted in the market over the next few months after launch. Second, for existing products which have already been in the market, each product will have a different demand pattern. To make an accurate forecast, it is very essential to capture the irregularity in the demand. In the proposed solution, this irregularity is captured in 2 ways – variability in the demand quantity, variability in the demand timing. Based on these 2 parameters, products are classified into 4 categories – smooth, erratic, intermittent and lumpy. For products falling into the erratic, intermittent and lumpy categories, 2 different machine learning algorithms will be applied after applying another technique called as the Fourier Smoothening technique. This step will remove noise/irregularities in the data so that it is easier for the forecasting algorithm to deduce patterns. The results from these 2 algorithms will be compared and the more accurate result set will be selected. This will be fed as an input to the assortment planner. Additionally, in case the demand data for existing products has missing values, the invention uses a Kalman Filter to intelligently impute those values so that the accuracy of the forecast is not compromised. In this way, the invention uses not one, but several methods to get a high-quality demand forecast.
[0006] There is therefore a need in the art to provide a method and a system that can overcome the shortcomings of the existing prior art.

OBJECTS OF THE PRESENT DISCLOSURE
[0007] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0008] An object of the present disclosure is to provide for a method and system to classify each product into new or existing product.
[0009] An object of the present disclosure is to provide for a method and system that provides a fair estimate of how many customers are going to buy the product and how quickly/slowly the product will be adopted in the market over the next few months after launch.
[0010] An object of the present disclosure is to provide for a method and system that determines demand pattern for existing products which have already been in the market.
[0011] An object of the present disclosure is to provide for a method and system that captures irregularity in demand quantity, and demand timing.
[0012] An object of the present disclosure is to provide for a method and system that removes noise/irregularities in the data so that it is easier for the forecasting algorithm to deduce patterns.
[0013] An object of the present disclosure is to provide for a method and system that intelligently imputes missing values so that the accuracy of the forecast is not compromised.
[0014] An object of the present disclosure is to provide for a method and system that uses several methods to get a high-quality demand forecast.

SUMMARY
[0015] This section is provided to introduce certain objects and aspects of the present invention 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.
[0016] In an aspect, the present disclosure provides a system for facilitating forecasting demand of a plurality of product. The system may include a processor coupled to one or more computing devices in a network. The processor may be further coupled with a memory that stores instructions which when executed by the processor may cause the system to receive a set of parameters from the one or more computing devices, the set of parameters associated with the plurality of product items, receive a historical log of the plurality of product items from a database. Based on the received set of parameters and the received historical log of execution, classify, by using an artificial intelligence (AI) engine, the plurality of product items as a new product item or an old product item and predict, by the AI engine, a future demand for each new product item based on a predefined set of instructions. Furthermore, the system may be configured to forecast, by the AI engine, any or a combination of a number of users going to buy the new product and rate at which the new product will be adopted over a predefined time after launch of the new product. The forecast may be based on an estimation of the new product item associated with the predicted future demand.
[0017] In an embodiment, if any of the plurality of product items is classified as the old product item, the system may be further configured to check for one or more missing values associated with the old product item. The one or more missing values may be estimated based on a second set of instructions.
[0018] In an embodiment, the system may be further configured to classify the plurality of product items into at least four groups based on a demand pattern associated with a variability in demand quantity and a variability in demand timing.
[0019] In an embodiment, the system may be further configured to smoothen the demand pattern to remove one or more irregularities/noise in the demand pattern.
[0020] In an embodiment, the system may be configured to apply one or more forecasting modules on the plurality of product items to handle the volatility in the demand data.
[0021] In an embodiment, the system may be configured to train the smoothened demand pattern and generate a trained model by using a neural network module.
[0022] In an embodiment, the system may be configured to detect one or more errors obtained from a plurality of trained demand pattern obtained from the trained model and compare the one or more errors of each trained demand pattern with subsequent trained demand pattern.
[0023] In an aspect, the present disclosure provides a user equipment (UE) for facilitating forecasting demand of a plurality of product. The UE may include a processor coupled to one or more computing devices in a network. The processor may be further coupled with a memory that stores instructions which when executed by the processor may cause the UE to receive a set of parameters from the one or more computing devices, the set of parameters associated with the plurality of product items, receive a historical log of the plurality of product items from a database. Based on the received set of parameters and the received historical log of execution, classify, by using an artificial intelligence (AI) engine, the plurality of product items as a new product item or an old product item and predict, by the AI engine, a future demand for each new product item based on a predefined set of instructions. Furthermore, the UE may be configured to forecast, by the AI engine, any or a combination of a number of users going to buy the new product and rate at which the new product will be adopted over a predefined time after launch of the new product. The forecast may be based on an estimation of the new product item associated with the predicted future demand.
[0024] In an aspect, the present disclosure provides a method for facilitating forecasting demand of a plurality of product. The method may include the steps of: receiving, by a processor, a set of parameters from one or more computing devices, the set of parameters associated with the plurality of product items. The processor may be coupled to the one or more computing devices in a network and further coupled with a memory that stores instructions which are executed by the processor. The method may include the step of receiving, by the processor, a historical log of the plurality of product items from a database. Based on the received set of parameters and the received historical log of execution, the method may include the step of classifying, by using an artificial intelligence (AI) engine, the plurality of product items as a new product item or an old product item and the step of predicting, by the AI engine, a future demand for each new product item based on a predefined set of instructions. Furthermore, the method may include the step of forecasting, by the AI engine, any or a combination of a number of users going to buy said new product and rate at which the new product will be adopted over a predefined time after launch of the said new product. The forecast may be based on an estimation of the new product item associated with the predicted future demand.

BRIEF DESCRIPTION OF DRAWINGS
[0025] The accompanying drawings, which are incorporated herein, and constitute a part of this invention, 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 invention. 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 invention of such drawings includes the invention of electrical components, electronic components or circuitry commonly used to implement such components.
[0026] FIG. 1 illustrates an exemplary network architecture in which or with which the proposed system of the present disclosure can be implemented, in accordance with an embodiment of the present disclosure.
[0027] FIG. 2A illustrates an exemplary representation of the system (110) for forecasting demand of a plurality of product items, in accordance with an embodiment of the present disclosure.
[0028] FIG. 2B illustrates an exemplary representation of the user equipment (UE) (108) for forecasting demand of a plurality of product items, in accordance with an embodiment of the present disclosure.
[0029] FIG. 3 illustrates an exemplary method flow diagram for forecasting demand of a plurality of product items, in accordance with an embodiment of the present disclosure.
[0030] FIG. 4 illustrates exemplary representation of a high-level description of a product item, in accordance with an embodiment of the present disclosure.
[0031] FIG. 5 illustrates an exemplary representation of description of the proposed process flow, in accordance with an embodiment of the present disclosure.
[0032] FIG. 6 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.
[0033] The foregoing shall be more apparent from the following more detailed description of the invention.

DETAILED DESCRIPTION OF INVENTION
[0034] 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.
[0035] 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 invention as set forth.
[0036] The present invention provides a robust and effective solution to an entity or an organization by providing an estimation of the demand for product items. The product items can be of any type such as electronics, fashion products, grocery items, cosmetics and the like.
[0037] Referring to FIG. 1 that illustrates an exemplary network architecture (100) in which or with which the proposed system (110) (interchangeably referred to as the system (110)) of the present disclosure can be implemented, in accordance with an embodiment of the present disclosure. As illustrated, the exemplary architecture (100) includes one or more communicably coupled computing devices (104-1, 104-2,…104-N) that communicate across a network (106) (note that although only network 106 connections have been labelled in FIG. 1, one or more of the other indicated connections between components can also be considered part of network 106). In some implementations, the system (110) or portions of the system (110) can operate within a cloud-computing-based environment associated with a centralised server (112). As an example, and not by way of limitation, the user computing device (104) may be operatively coupled to the centralised server (112) through the network (106) and may be associated with the entity (114). Examples of the user computing devices (104) can include, but are not limited to a smart phone, a portable computer, a personal digital assistant, a handheld phone and the like.
[0038] The system (110) may further be operatively coupled to a second computing device (108) (also referred to as the user computing device or user equipment (UE) hereinafter) associated with the entity (114). The entity (114) may include a company, a hospital, an organisation, a university, a lab facility, a business enterprise, or any other facility that may require features associated with a plurality of product items. In some implementations, the system (110) may also be associated with the UE (108). The UE (108) can include a handheld device, a smart phone, a laptop, a palm top and the like. Further, the system (110) may also be communicatively coupled to the one or more first computing devices (104) via a communication network (106).
[0039] In an aspect, the system (110) may receive a set of parameters from the one or more computing devices (104) associated with the plurality of product items. The system (110) may further receive a historical log of the plurality of product items from a database (210). The historical log may include historical sales/demand, product characteristics, customer characteristics, store locations and the like. Based on the received set of parameters and the received historical log of execution, the system (110) may be configured to classify, by using an artificial intelligence (AI) engine (214), the plurality of product items as a new product item or an old product item.
[0040] In an embodiment, the system (110) may be configured to predict, by the AI engine (214), a future demand for each new product item based on a predefined set of instructions and thereby forecast any or a combination of a number of users going to buy said new product and rate at which the new product will be adopted over a predefined time after launch of the new product. In an embodiment, the forecast may be based on an estimation of the new product item associated with the predicted future demand. The estimation may be based in a Bass diffusion model but not limited to it.
[0041] In an embodiment, if any of the plurality of product items is classified as the old product item, the system may check for one or more missing values associated with the old product item. The one or more missing values may be estimated based on a second set of instructions.
[0042] In an embodiment, the system may further classify the plurality of product items into at least four groups based on a demand pattern associated with a variability in demand quantity and a variability in demand timing. This process helps in making an accurate forecast. The at least four groups may include smooth, erratic, intermittent and lumpy groups but not limited to the like.
[0043] In an embodiment, the system may further smoothen the demand pattern to remove one or more irregularities/noise in the demand pattern. For example, for products falling into the erratic, intermittent and lumpy groups, a Fourier Smoothening technique but not limited to it may be used.
[0044] In an embodiment, the system may apply one or more forecasting modules on the plurality of product items to handle the volatility in the demand data.
[0045] In another embodiment, the system may train the smoothened demand pattern and generate a trained model by using a neural network module and detect one or more errors obtained from a plurality of trained demand pattern obtained from the trained model and compare the one or more errors of each trained demand pattern with subsequent trained demand pattern.
[0046] In an embodiment, the one or more computing devices (104) may communicate with the system (110) via set of executable instructions residing on any operating system, including but not limited to, Android TM, iOS TM, Kai OS TM and the like. In an embodiment, to one or more computing devices (104), may include, but not limited to, any electrical, electronic, electro-mechanical or an equipment or a combination of one or more of the above devices such as mobile phone, smartphone, 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 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, input devices for receiving input from a user such as touch pad, touch enabled screen, electronic pen, receiving devices for receiving any audio or visual signal in any range of frequencies and transmitting devices that can transmit any audio or visual signal in any range of frequencies. It may be appreciated that the one or more computing devices (104) may not be restricted to the mentioned devices and various other devices may be used. A smart computing device may be one of the appropriate systems for storing data and other private/sensitive information.
[0047] In an embodiment, the system (110) may include a processor coupled with a memory, wherein the memory may store instructions which when executed by the one or more processors may cause the system to access content stored in a network.
[0048] FIG. 2A with reference to FIG. 1, illustrates an exemplary representation of system (110) for forecasting demand of a plurality of product items, in accordance with an embodiment of the present disclosure. In an aspect, 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 RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0049] 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 I/O devices, storage devices, and the like. The interface(s) 206 may facilitate communication of the system (110). The interface(s) 204 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.
[0050] 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.
[0051] The processing engine (208) may include one or more engines selected from any of a data acquisition engine (212), an artificial intelligence (AI) engine (214), and other engines (216). The processing engine (208) may further include a Neural Network and Bass diffusion, Fourier smoothening, Kalman filter algorithms.
[0052] FIG. 2B illustrates an exemplary representation (220) of the user equipment (UE) (108), in accordance with an embodiment of the present disclosure. In an aspect, the UE (108) may comprise an edge processor (222). The edge processor (222) 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 edge processor(s) (222) may be configured to fetch and execute computer-readable instructions stored in a memory (224) of the UE (108). The memory (224) 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 (224) may comprise any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0053] In an embodiment, the UE (108) may include an interface(s) 226. The interface(s) 206 may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 206 may facilitate communication of the UE (108). Examples of such components include, but are not limited to, processing engine(s) 228 and a database (230).
[0054] The processing engine(s) (228) 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) (228). 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) (228) may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (228) 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) (228). In such examples, the UE (108) 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 UE (108) and the processing resource. In other examples, the processing engine(s) (228) may be implemented by electronic circuitry.
[0055] The processing engine (228) may include one or more engines selected from any of a data acquisition engine (232), an artificial intelligence (AI) engine (234), and other engines (236).
[0056] FIG. 3 illustrates an exemplary representation of the proposed method (250) for demand forecasting of a plurality of product items, in accordance with an embodiment of the present disclosure. The method (300) may include at 302, the step of receiving, by a processor (202), a set of parameters from one or more computing devices (104), the set of parameters associated with the plurality of product items.
[0057] The method (300) may also include at 304, the step of receiving, by the processor, a historical log of the plurality of product items from a database. Based on the received set of parameters and the received historical log of execution, the method (300) may include at 306, the step of classifying, by using an artificial intelligence (AI) engine, the plurality of product items as a new product item or an old product item, and at 308, the step of predicting, by the AI engine, a future demand for each new product item based on a predefined set of instructions.
[0058] Furthermore, the method may include at 310, the step of forecasting, by the AI engine, any or a combination of a number of users going to buy the new product and rate at which the new product will be adopted over a predefined time after launch of the said new product. The forecast may be based on an estimation of the new product item associated with the predicted future demand.
[0059] FIG. 4 illustrate exemplary representation of a high-level description of a product item, in accordance with an embodiment of the present disclosure. As illustrated, a user (102) typically from demand planning team of an entity such as a retail organisation who will interact with the product and the system. A computing system (104) such as a Computer (desktop/laptop) system in which the proposed method is run. The proposed method may include the following 3 components. At first, an input (402) to the computing device (104) that may include one or more data sources such as historical sales/demand, product characteristics, customer characteristics, store locations and the like.
[0060] In an embodiment, a processing module (404) may contain the mechanism which classifies products as new/existing, performs demand pattern-based classification, applies the suitable machine learning algorithm and compares the results so obtained. An output (406) may be the result of the application of the steps mentioned above. It is essentially a table containing the number of units of each product (stock keeping unit) estimated by the algorithm as demand at each store. The output (406) can be viewed by the user (102) on the computer screen or printed out as a physical copy on paper.
[0061] FIG. 5 illustrates an exemplary representation of description of the proposed process flow, in accordance with an embodiment of the present disclosure. In an embodiment, a first step is to classify a product (502) into new (504) or old (506) based on availability of historical sales data for the product (502). If the product (502) has just been launched or yet to be launched in the market, demand data will not be available for the same. This will be classified as a new product (504). Other products (for which historical demand data is available) will be classified as old products (506).
[0062] In an embodiment, if the product has been classified as a new product (504), the Bass Diffusion model (508) will be applied to forecast the demand. This model estimates how many customers are going to buy the product and how quickly/slowly the product will be adopted in the market over the next few months after launch.
[0063] In another embodiment, if the product (502) has been classified as an old or existing product (506), the data will be checked for any data gaps or missing values. In case any data point is missing, the Kalman Filter method (510) will be applied on this data. This method estimates the missing value based on the value of the variable observed at other different points in time. The product (502) is classified based on the demand pattern into four groups (522) based on the variability in the demand quantity and the variability in the demand timing (512). The variability in the demand quantity is measured by the average demand interval i.e., the average frequency at which the product is sold. The variability in the demand timing is measured by the square of the coefficient of variation. The coefficient of variation is the standard deviation of the product demand divided by the mean demand quantity: The four groups are shown in Table 1.
TABLE 1
S.No Square of Coefficient of Variation Average Demand Interval Demand Pattern Type
1. Less than 0.49 Less than 1.32 Smooth
2. Less than 0.49 Greater than or equal to 1.32 Intermittent
3. Greater than or equal to 0.49 Less than 1.32 Erratic
4. Greater than or equal to 0.49 Greater than or equal to 1.32 Lumpy

[0064] In an embodiment, if, for a product, the demand class determined is not ‘Smooth’, Fourier Smoothening technique (524) is applied. This technique removes the irregularities/noise in the data so that the demand signal is smoothened out and in parallel this machine learning algorithms may be executed in parallel. Since it is very much possible that the demand signal of many products has considerable variation which cannot be gauged by normal forecasting methods, special models will be applied on all products to forecast the demand. Specifically, the GARCH (Generalized Autoregressive Conditionally Heteroscedastic) methods (532) will be applied. This is done because this model is capable of handling the volatility in the demand data.
[0065] In an embodiment, once the non-’Smooth’ products have been converted into ‘Smooth’ products, a neural network model (of multilayer perceptron) (526) will be applied to the demand data to get the forecasted demand. In an embodiment, the error metrics of the resultant forecasts may be measured and compared with each other (528). The error is measured by the Root Mean Squared Error (RMSE). The set of forecast results for which the value of the RMSE is lower as determined will be shown as the output (528) to the user.
[0066] FIG. 6 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure. As shown in FIG. 6, computer system 600 can 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 660, and a processor 670. A person skilled in the art will appreciate that the computer system may include more than one processor and communication ports. Processor 660 may include various modules associated with embodiments of the present invention. Communication port 660 may be chosen depending on a network or any network to which computer system connects. Memory 630 can be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read-only memory 640 can be any static storage device(s). Mass storage 650 may be any current or future mass storage solution, which can be used to store information and/or instructions.
[0067] Bus 620 communicatively couples processor(s) 670 with the other memory, storage and communication blocks.
[0068] Optionally, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to bus 620 to support direct operator interaction with a computer system. Other operator and administrative interfaces can be provided through network connections connected through communication port 660. Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.
[0069] 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 invention. These and other changes in the preferred embodiments of the invention 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 invention and not as limitation.
[0070] 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, IC layout design, and/or trade dress protection, belonging to Jio Platforms Limited (JPL) or its affiliates (herein after 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.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0071] The present disclosure provides for a method and system to provide for a method and system to classify each product into new or existing product.
[0072] The present disclosure provides for a method and system that provides a fair estimate of how many customers are going to buy the product and how quickly/slowly the product will be adopted in the market over the next few months after launch.
[0073] The present disclosure provides for a method and system that determines demand pattern for existing products which have already been in the market.
[0074] The present disclosure provides for a method and system that captures irregularity in demand quantity, and demand timing.
[0075] The present disclosure provides for a method and system that removes noise/irregularities in the data so that it is easier for the forecasting algorithm to deduce patterns.
[0076] The present disclosure provides for a method and system that intelligently imputes missing values so that the accuracy of the forecast is not compromised.
[0077] The present disclosure provides for a method and system that uses several methods to get a high-quality demand forecast.
, Claims:1. A system (110) for facilitating forecasting demand of a plurality of product, the system (110) comprising:
a processor (202) coupled to one or more computing devices (104) in a network (106), wherein the processor (202) is further coupled with a memory (204), wherein said memory stores instructions which when executed by the processor (202) causes the system (110) to:
receive a set of parameters from the one or more computing devices (104), the set of parameters associated with the plurality of product items;
receive a historical log of the plurality of product items from a database;
based on the received set of parameters and the received historical log of execution, classify, by using an artificial intelligence (AI) engine, the plurality of product items as a new product item or an old product item;
predict, by the AI engine, a future demand for each new product item based on a predefined set of instructions;
forecast, by the AI engine, any or a combination of a number of users going to buy said new product and rate at which the new product will be adopted over a predefined time after launch of the said new product, wherein the forecast is based on an estimation of the new product item associated with the predicted future demand.
2. The system as claimed in claim 1, wherein if any of the plurality of product items is classified as the old product item, the system is further configured to check for one or more missing values associated with the old product item, wherein the one or more missing values are estimated based on a second set of instructions.
3. The system as claimed in claim 1, wherein the system is further configured to classify the plurality of product items into at least four groups based on a demand pattern associated with a variability in demand quantity and a variability in demand timing.
4. The system as claimed in claim 3, wherein the system is further configured to smoothen the demand pattern to remove one or more irregularities/noise in the demand pattern.
5. The system as claimed in claim 1, wherein the system is configured to apply one or more forecasting modules on the plurality of product items to handle the volatility in the demand data.
6. The system as claimed in claim 5, wherein the system is configured to train the smoothened demand pattern and generate a trained model by using a neural network module.
7. The system as claimed in claim 1, wherein the system is configured to detect one or more errors obtained from a plurality of trained demand pattern obtained from the trained model and compare the one or more errors of each trained demand pattern with subsequent trained demand pattern.
8. A user equipment (UE) (108) for facilitating forecasting demand of a plurality of product, the UE (108) comprising:
a processor (222) coupled to one or more computing devices (104) in a network (106), wherein the processor (222) is further coupled with a memory (224), wherein said memory stores instructions which when executed by the processor (222) causes the UE (110) to:
receive a set of parameters from the one or more computing devices (104), the set of parameters associated with the plurality of product items;

receive a historical log of the plurality of product items from a database;

based on the received set of parameters and the received historical log of execution, classify, by using an artificial intelligence (AI) engine, the plurality of product items as a new product item or an old product item;
predict, by the AI engine, a future demand for each new product item based on a predefined set of instructions;
forecast, by the AI engine, any or a combination of a number of users going to buy said new product and rate at which the new product will be adopted over a predefined time after launch of the said new product, wherein the forecast is based on an estimation of the new product item associated with the predicted future demand.
9. A method (300) for facilitating forecasting demand of a plurality of product, the method (300) comprising the steps of:
receiving, by a processor (202), a set of parameters from one or more computing devices (104), the set of parameters associated with the plurality of product items, wherein the processor (202) is coupled to the one or more computing devices (104) in a network (106), wherein the processor (202) is further coupled with a memory (204), wherein said memory stores instructions which are executed by the processor (202);
receiving, by the processor, a historical log of the plurality of product items from a database;
based on the received set of parameters and the received historical log of execution, classifying, by using an artificial intelligence (AI) engine, the plurality of product items as a new product item or an old product item;
predicting, by the AI engine, a future demand for each new product item based on a predefined set of instructions; and,
forecasting, by the AI engine, any or a combination of a number of users going to buy said new product and rate at which the new product will be adopted over a predefined time after launch of the said new product, wherein the forecast is based on an estimation of the new product item associated with the predicted future demand.
10. The method as claimed in claim 9, wherein if any of the plurality of product items is classified as the old product item, the method further comprises the step of checking for one or more missing values associated with the old product item, wherein the one or more missing values are estimated based on a second set of instructions.
11. The method as claimed in claim 9, wherein the method further comprises the step of classifying the plurality of product items into at least four groups based on a demand pattern associated with a variability in demand quantity and a variability in demand timing.
12. The method as claimed in claim 11, wherein the method further comprises the step of smoothening the demand pattern to remove one or more irregularities/noise in the demand pattern.
13. The method as claimed in claim 9, wherein the method further comprises the step of applying one or more forecasting modules on the plurality of product items to handle the volatility in the demand data.
14. The method as claimed in claim 5, wherein the method further comprises the step of training the smoothened demand pattern and generate a trained model by using a neural network module.
15. The method as claimed in claim 1, wherein the method further comprises the step of detecting one or more errors obtained from a plurality of trained demand pattern obtained from the trained model and compare the one or more errors of each trained demand pattern with subsequent trained demand pattern.