1. A method for managing a merchandise, wherein the merchandise comprising one or more products (406) is stored in a warehouse (106), wherein a collaboration system (102) enables exchange of the merchandise between a first merchant (101) and one or more second merchants (103), the method comprising: obtaining, by the collaboration system (102), one or more details (401) and a forecast error value (402), associated with the one or more products (406) of the merchandise from the first merchant (101); determining, by the collaboration system (102), a first score (407) indicative of a product profile associated with each of the one or more products (406) of the merchandise based on at least one of, the one or more details (401), and a sales history (403) associated with each of the one or more products (406); computing, by the collaboration system (102), one of a second score (408) indicative of a holding expense or a third score (409) indicative of loss value associated with each of the one or more products (406) of the merchandise, wherein the computing of one of the second score (408) or the third score (409) is based on a comparison between the forecast error value (402) and a predefined threshold; determining, by the collaboration system (102), one or more clusters (410) for the one or more products (406) of the merchandise based on the first score (407) and, the second score (408) or the third score (409); and estimating, by the collaboration system (102), a compensation value (411) for each of the one or more products (406) of the merchandise based on the one or more clusters (410) and the forecast error value (402), wherein each of the one or more products (406) of the merchandise are exchanged with the one or more second merchants (103) based on the compensation value (411).

2. The method as claimed in claim 1, wherein the one or more details (401) associated with the merchandise comprises at least one of a count value, a date, and a geographical location, associated with each of the one or more products (406) of the merchandise.

3. The method as claimed in claim 2, wherein the count value indicates a quantity of each of the one or more products (406) to be purchased or sold, the date indicates one of an expiry date associated with each of the one or more products (406) to be sold or a time interval for purchasing each of the one or more products (406).

4. The method as claimed in claim 1, wherein the forecast error value (402) indicates a deviation in estimating a demand associated with each of the one or more products (406) of the merchandise from the first merchant (101).

5. The method as claimed in claim 1, wherein determining the first score (407) comprises: categorizing one or more products (406) of the merchandise into one or more groups (405) based on at least one of the one or more details (401), the sales history (403) associated with each of the one or more products (406) using a segmentation technique; and determining the first score (407) associated with each of the one or more products (406) of the merchandise indicative of the product profile based on the one or more groups (405) using the segmentation technique.

6. The method as claimed in claim 1 , wherein the second score (408) is computed when the forecast error value (402) is deviating from the predefined threshold in a first direction, wherein the second score (408) is computed based on at least one of a storage expense, a purchased expense, an expense associated with insurance for each of the one or more products (406) using one or more statistical techniques.

7. The method as claimed in claim 1 , wherein the third score (409) is computed when the forecast error value (402) is deviating from the predefined threshold in a second direction, wherein the third score (409) is computed based on at least one of the sales history (403) associated with each of the one or more products (406) using a time-series analysis.

8. The method as claimed in claim 1, wherein determining the one or more clusters (410) comprises: categorizing the one or more products (406) of the merchandise into the one or more clusters (410) based on the first score (407) and the second score (408) using a clustering technique, when the forecast error value (402) is deviating from the predefined threshold in a first direction; or categorizing the one or more products (406) of the merchandise into the one or more clusters (410) based on the first score (407) and the third score (409) using the clustering technique, when the forecast error value (402) is deviating from the predefined threshold in a second direction.

9. The method as claimed in claim 1, further comprises determining one of a selling price or a procuring price for each of the one or more products (406) of the merchandise using the compensation value (411).

10. A collaboration system (102) for managing a merchandise, wherein the merchandise comprising one or more products (406) is stored in a warehouse (106), wherein the collaboration system (102) enables exchange of the merchandise between a first merchant (101) and one or more second merchants (103), the collaboration system (102) comprises: a processor (203); and a memory (202) communicatively coupled to the processor (203), wherein the memory (202) stores the processor (203) instructions, which, on execution, causes the processor (203) to: obtain one or more details (401) and a forecast error value (402), associated with the one or more products (406) of the merchandise from the first merchant (101); determine a first score (407) indicative of a product profile associated with each of the one or more products (406) of the merchandise based on at least one of, the one or more details (401), and a sales history (403) associated with each of the one or more products (406); compute one of a second score (408) indicative of a holding expense or a third score (409) indicative of loss value associated with each of the one or more products (406) of the merchandise, wherein one of the second score (408) or the third score (409) is computed based on a comparison between the forecast error value (402) and a predefined threshold; determine one or more clusters (410) for the one or more products (406) of the merchandise based on the first score (407) and, the second score (408) or the third score (409); and estimate a compensation value (411) for each of the one or more products (406) of the merchandise based on the one or more clusters (410) and the forecast error value (402), wherein each of the one or more products (406) of the merchandise are exchanged with the one or more second merchants (103) based on the compensation value (411).

11. The collaboration system (102) as claimed in claim 10, wherein the processor (203) is configured to determine the first score (407) comprises: categorizing one or more products (406) of the merchandise into one or more groups (405) based on at least one of the one or more details (401), the sales history (403) associated with each of the one or more products (406) using a segmentation technique; and determining the first score (407) associated with each of the one or more products (406) of the merchandise indicative of the product profile based on the one or more groups (405) using the segmentation technique.

12. The collaboration system (102) as claimed in claim 10, wherein the processor (203) is configured to compute the second score (408) based on at least one of a storage expense, a purchased expense, an expense associated with insurance for each of the one or more products (406) using one or more statistical techniques, when the forecast error value (402) is deviating from the predefined threshold in a first direction.

13. The collaboration system (102) as claimed in claim 10, wherein the processor (203) is configured to compute the third score (409) based on at least one of the sales history (403) associated with each of the one or more products (406) using a time-series analysis, when the forecast error value (402) is deviating from the predefined threshold in a second direction.

14. The collaboration system (102) as claimed in claim 10, wherein the processor (203) is configured to determine the one or more clusters (410) comprises: categorizing the one or more products (406) of the merchandise into the one or more clusters (410) based on the first score (407) and the second score (408) using a clustering technique, when the forecast error value (402) is deviating from the predefined threshold in a first direction; or categorizing the one or more products (406) of the merchandise into the one or more clusters (410) based on the first score (407) and the third score (409) using the clustering technique, when the forecast error value (402) is deviating from the predefined threshold in a second direction.

15. The collaboration system (102) as claimed in claim 10, wherein the processor (203) is configured to determine one of a selling price or a procuring price for each of the one or more products (406) of the merchandise using the compensation value (411).

TITLE: “SYSTEM AND METHOD FOR MANAGING MERCHANDISE IN A

WAREHOUSE”

TECHNICAL FIELD

The present disclosure relates to the field of inventory management Particularly, but not exclusively, the present disclosure relates to a system and method for managing merchandise in a warehouse.

BACKGROUND

Generally, merchants store a plurality of products in warehouses, or in inventory facilities to provide a wide selection of products that are readily available for delivery to customers. Storing the plurality of products in the warehouses provide numerous benefits to merchants, for example, accommodating variations in customer's demand and/or a manufacturer or distributor's ability to supply the plurality of products. The merchants replenish the plurality of products in the warehouse using forecasting information associated with the plurality of products. The forecasting information is obtained based on the demand for the plurality of products, the cost of the plurality of products, the amount of area required to store the plurality of products in the warehouse, and the like. Further, if the existing plurality of products are sold and the plurality of products have not been replenished in the warehouse, then a customer requirement may not be met, thus reducing customer experience. Alternatively, if the plurality of products has been replenished in the warehouse and the existing plurality of products is un-sold, then the merchant faces an economic loss. Further, the un-sold products lead to wastage.

Therefore, there is a need to efficiently manage the plurality of products in the warehouse to reduce the economic loss to the merchant, reduce wastage of one or more products, and provide increased customer satisfaction.

The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgment or any form of suggestion that this information forms the prior art already known to a person skilled in the art

SUMMARY

Additional features and advantages are realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure.

Disclosed herein is a method of managing a merchandise, where the merchandise comprising one or more products is stored in a warehouse. A collaboration system enables exchange of the merchandise between a first merchant and one or more second merchants. The method comprising obtaining one or more details and a forecast error value, associated with the one or more products of the merchandise from the first merchant. Further, the method comprises determining a first score indicative of a product profile associated with each of the one or more products of the merchandise based on at least one of, the one or more details, and a sales history associated with each of the one or more products. Furthermore, the method comprises computing one of a second score indicative of a holding expense or a third score indicative of loss value associated with each of the one or more products of the merchandise, wherein the computing of one of the second score or the third score is based on a comparison between the forecast error value and a predefined threshold. Thereafter, the method comprises determining one or more clusters for the one or more products of the merchandise based on the first score and, the second score or the third score. Finally, the method comprises estimating a compensation value for each of the one or more products of the merchandise based on the one or more clusters and the forecast error value, wherein each of the one or more products of the merchandise are exchanged with the one or more second merchants based on the compensation value.

Further, the present disclosure discloses a collaboration system for managing a merchandise, wherein the merchandise comprising one or more products is stored in a warehouse. The collaboration system enables exchange of the merchandise between a first merchant and one or more second merchants, the collaboration system comprises a processor and a memory communicatively connected to the processor. The memory stores processor executable instructions which on execution cause the processor to obtain one or more details and a forecast error value, associated with the one or more products of the merchandise from the first merchant. Further, the instructions cause the processor to determine a first score indicative of a product profile associated with each of the one or more products of the merchandise based on at least one of, the one or more details, and a sales history associated with each of the one or more products. Furthermore, the

instructions cause the processor to compute one of a second score indicative of a holding expense or a third score indicative of loss value associated with each of the one or more products of the merchandise, wherein one of the second score or the third score is computed based on a comparison between the forecast error value and a predefined threshold. Thereafter, the instructions cause the processor to determine one or more clusters for the one or more products of the merchandise based on the first score and, the second score or the third score. Finally, the instructions cause the processor to estimate a compensation value for each of the one or more products of the merchandise based on the one or more clusters and the forecast error value, wherein each of the one or more products of the merchandise are exchanged with the one or more second merchants based on the compensation value.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features may become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

The novel features and characteristics of the disclosure are set forth in the appended claims. The disclosure itself, however, as well as a preferred mode of use, further objectives and advantages thereof, may best be understood by reference to the following detailed description of an illustrative embodiment when read in conjunction with the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. In the figures, the left-most digits) of a reference number identifies the figure in which the reference number first appears. One or more embodiments are now described, by way of example only, with reference to the accompanying figures wherein like reference numerals represent like elements and in which:

FIGURE 1 shows an exemplary environment for managing a merchandise stored in a warehouse, in accordance with some embodiments of the present disclosure;

FIGURE 2 shows a detailed block diagram of a collaboration system for managing a merchandise, in accordance with some embodiments of the present disclosure;

FIGURE 3 shows a flowchart illustrating a method for managing a merchandise, in accordance with some embodiment of the present disclosure;

FIGURE 4A shows an exemplary table having one or more details and forecast error value of a merchandise, in accordance with some embodiments of the present disclosure;

FIGURE 4B shows an exemplary table having sales history obtained from a first merchant, in accordance with some embodiments of the present disclosure;

FIGURE 4C shows an exemplary table having Recency, Frequency, and Monetary values for one or more products, in accordance with some embodiments of the present disclosure;

FIGURE 4D shows an exemplary graph illustrating categorizing one or more products into one or more groups, in accordance with some embodiments of the present disclosure;

FIGURE 4E shows an exemplary table having a first score determined for one or more products, in accordance with some embodiments of the present disclosure;

FIGURE 4F shows an exemplary table having a second score determined for one or more products, in accordance with some embodiments of the present disclosure;

FIGURE 4G shows an exemplary table having a third score determined for one or more products, in accordance with some embodiments of the present disclosure;

FIGURE 4H shows an exemplary graph illustrating one or more clusters determined using the first score and the second score for one or more products, in accordance with some embodiments of the present disclosure;

FIGURE 41 shows an exemplary graph illustrating one or more clusters determined using the first score and the third score for one or more products, in accordance with some embodiments of the present disclosure;

FIGURE 4J and FIGURE 4K shows an exemplary graph illustrating compensation value determined for the one or more clusters using the forecast error value for one or more products, in accordance with some embodiments of the present disclosure; and

FIGURE 5 shows a general-purpose computer system for managing merchandise stored in a warehouse, in accordance with embodiments of the present disclosure.

It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it may be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether or not such computer or processor is explicitly shown.

DETAILED DESCRIPTION

In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and may be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure.

The terms “comprises”, “includes” “comprising”, “including” or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device, or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises... a” or “includes... a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments

are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

FIGURE 1 shows an exemplary environment for managing a merchandise in a warehouse, in accordance with some embodiments of the present disclosure.

The environment comprises a first merchant (101), one or more second merchants (103 A, 103B 103N), a collaboration system (102), a communication network (105), a database (104) and one or more financial institutions (107). The first merchant (101) may be associated with a warehouse (106) for storing merchandise. The merchandise comprises one or more products. In an embodiment, the first merchant (101) may sell the one or more products to one or more customers. For example, the one or more products may include at least one of groceries, apparel, furniture, toys, electronic commodities, electro-mechanical goods, and the like. The person skilled in the art appreciates the inclusion of various articles or goods or commodities in addition to the examples provided above to denote the one or more products and the examples provided above for the one or more products should not be considered as a limitation for the present disclosure. Further, the first merchant (101) may replenish the one or more products from the merchandise stored in the warehouse (106) based on forecast information using at least one of a min/max replenishment technique, a top-off replenishment technique, a periodic replenishment technique, and the like. The forecast information may indicate a quantity of the one or more products required to be stored in the warehouse (106) for fulfilling future orders by the one or more customers. In addition, the forecast information may also indicate the time duration for selling the one or more products to the one or more customers. The forecast information for each of the one or more products may be obtained by the first merchant (101) using at least one of a customer demand, a product life-cycle, a price of the one or more products, a sales history of the one or more products, a product delivery time, and the like. The forecast information may be determined using at least one of a statistical analysis, Artificial Intelligence (Al) based techniques such as regression, time-series prediction, and the like. For example, the forecast information may indicate a requirement of 1000 pairs of shoes to be stored in the warehouse (106) and the forecast information may predict a time duration of 1 month for selling the 1000 pairs of shoes. The forecast information obtained by the first

merchant (101) may not be accurate and includes a forecast error value. The forecast error value may be determined using at least one of Mean Absolute Percent Error (MAPE) technique, a mean absolute deviation technique, and the like. The person skilled in the art appreciates the use of one or more existing techniques for determining the forecast information and the forecast error value in addition to the above-mentioned examples. In one embodiment, the forecast error value is computed using the below equation:

where the forecasted requirement value indicates forecasted/ estimated demand of the one or more products and the actual value indicates an actual demand of the one or more products. In an embodiment, the forecasted demand is determined based on the quantity of the one or more products being sold by the first merchant (101).

In one embodiment, the forecast error value indicates a deviation in estimating a demand of the one or more products of the merchandise from the actual demand. In a first example, the forecast error value of +4.2 indicates an excess quantity of one or more products is stored in the warehouse (106) as compared to the quantity of one or more products required according to the actual demand from the one or more customers for the corresponding one or more products. In a second example, the forecast error value of -2.8 indicates an inadequate quantity of the one or more products is stored in the warehouse (106) as compared to the quantity of the one or more products required according to the actual demand from the one or more customers for the corresponding one or more products. In one embodiment, based on the forecast error value, the first merchant (101) may sell the one or more products to the one or more second merchants (103 A, 103B, ..., 103N, collectively denoted as 103) or buy the one or more products from the one or more second merchants (103) via the collaboration system (102). The existing collaboration systems facilitate the trade between the first merchant (101) and the plurality of second merchants (103). However, the existing collaboration systems do not allow dynamic merchant selection according to the forecast error value. The merchants are fixed in the existing collaboration systems, thus restricting the trade between different merchants. Hence, merchants suffer inventory loss due to the limitations of the existing collaboration systems.

In an embodiment, the collaboration system (102) obtains one or more details and the forecast error value, associated with the one or more products of the merchandise from the first merchant (101). The one or more details and the forecast error value associated with the one or more products may be obtained from the database (104) associated with the first merchant (101). The one or more details may include a count value indicative of a quantity of each of the one or more products to be purchased from one or more second merchants (103) or sold to one or more second merchants (103), a date indicative of one of an expiry date associated with each of the one or more products in excess or a time interval for purchasing each of the one or more products which are inadequate in the warehouse (106), a geographical location associated with the warehouse (106). For example, the one or more details may include “Purchase 56 quantities of Product-A within 3 days having a forecast error of 9.21”, “Sell 128 quantities of Product-D having an expiry date of DD-MM-YYYY and the forecast error is 3.47’ and the like.

In an embodiment, the collaboration system (102) determines a first score indicative of a product profile associated with each of the one or more products of the merchandise. The first score is determined based on at least one of, the one or more details, and a sales history associated with each of the one or more products. For example, the sales history may include the quantity of each of the one or more products sold by the first merchant (101) within the past 1 week, 2 weeks, 1 month, 1.5 months, 6 months, and the like, a selling price associated with each of the one or more products sold by the first merchant (101), a procuring price associated with each of the one or more products, and the like. An example of a product profile may include, “1593 quantities Product-C have been sold in 4 days for a price of 12USD per quantity”. The sales history may be obtained periodically from the database (104) associated with the first merchant (101). The first score indicative of the product profile may be determined based on at least one of a frequency of selling the one or more products by the first merchant (101 ), a time duration elapsed after selling each of the one or more products, a quantity of each of the one or more products sold, an amount of money for which each of the one or more products are sold by the first merchant (101) and the like using the sales history and the one or more details. For example, the first score for “Product-A, Product-D, and Product-G” may be 3.3, 5.0, 2.7 respectively, where a higher value of the first score may indicate a higher possibility of sales of the product . The first score of 5.0 may indicate a higher frequency of the “Product-D” being sold by the first merchant (101).

In an embodiment, the collaboration system (102) computes one of a second score indicative of a holding expense or a third score indicative of loss value associated with each of the one or more products of the merchandise. Further, the computing of one of the second score or the third score is based on a comparison between the forecast error value and a predefined threshold. The predefined threshold is a number (such as +1.56, -2.43, 0, +15.4, -39.1, and the like) indicating an optimal value of the forecast error value for each of the one or more products. In a first example, when the forecast error value is deviating from the predefined threshold in a first direction (such as increasing in a positive direction), the collaboration system (102) may compute the second score indicative of the holding expense. The holding expense denotes a cost incurred by the first merchant (101) by storing the one or more products in the warehouse (106) which may remain unsold. In a second example, when the forecast error value is deviating from the predefined threshold in a second direction (such as decreasing in a negative direction), the collaboration system (102) may compute the third score indicative of the loss value. The loss value denotes a reduction in customer satisfaction with the first merchant (101) due to non-availability of the one or more products required by the one or more customers. In addition, the loss value may also indicate a financial loss incurred by the first merchant (101) due to non-availability of the one or more products required by the one or more customers. In a first example, the second score for the one or more products may be “2.45 for Product-A”, “57.2 for product-F’, and the like, where a higher value of the second score may indicate a higher holding expense to the first merchant (101). In a second example, the third score for the one or more products may be “49.1 for Product-B”, “7.34 for product-D”, and the like, where a higher value of the third score may indicate an increased reduction in the customer satisfaction and/or the financial loss incurred by the first merchant (101).

In an embodiment, the collaboration system (102) determines one or more clusters for the one or more products of the merchandise based on the first score and, the second score or the third score. In a first example, the collaboration system (102) determines the one or more clusters for the one or more products for which the second score was determined using the first score and the second score, where each of the one or more clusters includes one or more products having a similar first score and the second score. The “Product-M” and “Product-K” having the first score and the second score of [5.6, 10.2] and [5.0, 9.61] respectively may be categorized into one cluster from the one or more clusters. In a second example, the collaboration system (102) determines the one or more clusters for the one or more products for which the third score was determined using the first score and the third score, where each of the one or more clusters includes one or more products having a similar first score and the third score. The “Product-G”, “Product-H” and “Product-E” having the first score and the third score of [17.2, 3.6], [16.8, 4.1], and [17.9, 2.9] respectively may be categorized into one cluster from the one or more clusters.

In an embodiment, the collaboration system (102) estimates a compensation value for each of the one or more products of the merchandise based on the one or more clusters and the forecast error value. In a first example, when the one or more clusters are determined using the first and the second score, the compensation value for each of the one or more products in the one or more clusters is directly proportional to the forecast error value (i.e. as the forecast error value increases the compensation value also increases). In a second example, when the one or more clusters are determined using the first and the third score, the compensation value for each of the one or more products in the one or more clusters is inversely proportional to the forecast error value (i.e. as the forecast error value increases the compensation value decreases). The compensation value indicates a discount provided on an actual price (such as the procured price, maximum selling price, and the like) associated with each of the one or more products when the one or more clusters are determined using the first and the second score. Alternatively, the compensation value indicates an additional price based on the actual price associated with each of the one or more products when the one or more clusters are determined using the first and the third score.

Further, each of the one or more products of the merchandise is exchanged with the one or more second merchants (103) based on the compensation value. In a first example, the first merchant (101) may sell each of the one or more products to the one or more second merchants (103) at the selling price (i.e. the actual price - the compensation value) when each of the one or more products are clustered based on the first score and the second score. In a second example, the first merchant (101) may buy each of the one or more products from the one or more second merchants (103) at the procuring price (i.e. the actual price + the compensation value) when each of the one or more products are clustered based on the first score and the third score.

In an embodiment, the first merchant (101) and the one or more second merchants (103) communicate with each other using the communication network (105) via the collaboration system (102). The first merchant (101) and the one or more second merchants (103) may include at least one of a wholesale merchant, a retail merchant, an e-commerce merchant, an affiliate merchant, and the like. Further, the communication network (105) may include, for example, a direct interconnection, e-commerce network, a peer to peer (P2P) network, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, Wi-Fi, cellular network, wired network, short-range communication and the like using a wired interface or a wireless interface. Further, the first merchant (101) and the one or more second merchants (103) may be a user communicating with the collaboration system (102) via a server, a computer, a laptop, a smartphone and the like. In one embodiment, the collaboration system (102) may co-ordinate with the one or more financial institutions (107) for example, an issuer bank, an acquirer bank and the like, for processing a payment transaction associated with the exchange of the merchandise between the first merchant (101) and the one or more second merchants (103). For example, the first merchant (101) and the one or more second merchants (103) may be associated with respective financial institutions (e.g., 107a and 107b). While the first merchant is buying products from a second merchant (103a), the collaboration system (102) may co-ordinate with the financial institution (107a) associated with the first merchant (101) to make a payment to the second merchant (103a). The collaboration system (102) may further co-ordinate with the financial institution (103b) associated with the second merchant (103a) to ensure successful payment is made.

FIGURE 2 shows a detailed block diagram of the collaboration system (102), in accordance with some embodiments of the present disclosure.

The collaboration system (102) may include Central Processing Unit (“CPU” or “processor”) (203) and a memory (202) storing instructions executable by the processor (203). The processor (203) may include at least one data processor for executing program components for executing user (101) or system-generated requests. The memory (202) may be communicatively coupled to the processor (203). The collaboration system (102) further includes an Input/ Output (I/O) interface (201). The I/O interface (201) may be coupled with the processor (203) through which an input signal or/and an output signal may be communicated. In one embodiment, the

collaboration system (102) may receive the one or more details, the forecast error value, the sales histoiy, and the predefined threshold, through the I/O interface (201).

In some implementations, the collaboration system (102) may include data (204) and modules (209). As an example, the data (204) and modules (209) may be stored in the memory (202) configured in the collaboration system (102). In one embodiment, the data (204) may include, for example, a product data (205), a score data (206), a price data (207) and other data (208). In the illustrated FIGURE 2, the data (204) are described herein in detail.

In an embodiment, the product data (205) may include the one or more details and the forecast error value, associated with each of the one or more products of the merchandise. The forecast error value indicates the deviation in estimating the demand associated with each of the one or more products of the merchandise sold by the first merchant (101). The one or more details associated with the merchandise includes at least one of the count value, the date, and the geographical location, associated with each of the one or more products of the merchandise. The count value indicates the quantity of each of the one or more products to be purchased from one or more second merchants (103) or sold to one or more second merchants (103). The date indicates one of the expiry date associated with each of the one or more products in excess or a time interval for purchasing each of the one or more products which are inadequate in the warehouse (106). The geographical location associated with the warehouse (106). Further, the product data (205) may include the sales histoiy associated with each of the one or more products. The sales history may include the quantity of each of the one or more products sold by the first merchant (101) within the past 1 week, 2 weeks, 1 month, 1.5 months, 6 months, and the like, a selling price associated with each of the one or more products sold by the first merchant (101), a procuring price associated with each of the one or more products sold by the first merchant (101), and the like.

In an embodiment, the score data (206) may include the first score indicative of the product profile, the second score indicative of the holding expense, and the third score indicative of the loss value, associated with each of the one or more products of the merchandise. For example, the first score, the second score, and the third score may be represented using at least one of a single number, a vector of numbers, and the like.

In an embodiment, the price data (207) may include the compensation value for each of the one or more products of the merchandise. The compensation value may be denoted in terms of a percentage, the amount of money, and the like. Further, the price data (207) may include the selling price for the one or more products to be sold by the first merchant (101) to the one or more second merchants (103) and the procuring price for the one or more products to be purchased by the first merchant (101) from the one or more second merchants (103).

In an embodiment, the other data (208) may include the predefined threshold, one or more clusters for the one or more products of the merchandise, and the like.

In some embodiments, data (204) may be stored in the memory (202) in form of various data structures. Additionally, the data (204) may be organized using data models, such as relational or hierarchical data models. The other data (208) may store data, including temporary data and temporary files, generated by the modules (209) for performing the various functions of the collaboration system (102).

In some embodiments, the data (204) stored in the memory (202) may be processed by the modules (209) of the collaboration system (102). The modules (209) may be stored within the memory (202). In an example, the modules (209) communicatively coupled to the processor (203) configured in the computing system (102), may also be present outside the memory (202) as shown in FIGURE 2 and implemented as hardware. As used herein, the term modules (209) may refer to an Application Specific Integrated Circuit (ASIC), a FPGA (Field Programmable Gate Array), an electronic circuit, a processor (203) (shared, dedicated, or group), and memory (202) that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. In some other embodiments, the modules (209) may be implemented using at least one of ASICs and FPGAs.

In one implementation, the modules (209) may include, for example, a communication module (210), a first score determination module (211), a second and third score determination module (212), a clustering module (213), an estimating module (214), and other module (215). It may be appreciated that such the aforementioned modules (209) may be represented as a single module or a combination of different modules (209).

In an embodiment, the communication module (210) is configured for obtaining the one or more details, the forecast error value, and the sales history associated with the one or more products of the merchandise from the first merchant (101). The one or more details may be obtained as an input from the first merchant (101) via a user interface. In another embodiment, the one or more details may be obtained from the database (104) associated with the first merchant (101). Further, the communication module (210) is used for notifying the compensation value, the selling price, and the procuring price associated with each of the one or more products to the first merchant (101) and the one or more second merchants (103) via the user interface.

In an embodiment, the first score determination module (211) is configured for categorizing one or more products of the merchandise into one or more groups based on at least one of the one or more details, the sales history associated with each of the one or more products using a segmentation technique. Further, the first score determination module (211) is configured for determining the first score associated with each of the one or more products of the merchandise indicative of the product profile based on the one or more groups using the segmentation technique. For example, the segmentation technique may include at least one of a Recency-Frequency-Monetary (RFM) technique, centroid based clustering technique, logistic regression, decision trees, density-based spatial clustering technique, and the like.

In an embodiment, the second and third score determination module (212) is configured for computing the second score based on at least one of a storage expense, a purchased expense, an expense associated with insurance for each of the one or more products using one or more statistical techniques. For example, the second score may be determined as a mathematical product of the storage expense, the purchased expense, the expense associated with insurance for each of the one or more products. The person skilled in the art appreciates the use of one or more statistical techniques for determining the second score. Further, the second and third score determination module (212) is used for computing the third score based on at least one of the sales history associated with each of the one or more products using the time-series analysis technique.

In an embodiment, the clustering module (213) is configured for categorizing the one or more products of the merchandise into the one or more clusters based on the first score and the second score using a clustering technique, when the forecast error value is deviating from the

predefined threshold in a first direction. Further, the clustering module (213) is used for categorizing the one or more products of the merchandise into the one or more clusters based on the first score and the third score using the clustering technique, when the forecast error value is deviating from the predefined threshold in a second direction. The clustering technique may be based on unsupervised learning techniques. Examples of unsupervised techniques may include but not limited to, centroid based clustering (such as k-means), density based clustering (such as DBSCAN and OPTICS), connectivity based clustering (such as hierarchical clustering), distribution based clustering (such as expectation-maximization), and the like.

In an embodiment, the estimating module (214) is configured for determining the compensation value for the one or more products based on the first score, the second or the third score, and the forecast error value. Further, the estimating module (214) is configured for determining one of the selling price or the procuring price for each of the one or more products of the merchandise using the compensation value. For example, the selling price or the procuring price may be determined by adding or subtracting the compensation value to the actual price (such as the procured price, maximum selling price, and the like) associated with each of the one or more products of the merchandise.

In an embodiment, the other module (215) may include a comparator to compare the forecast error value with the predefined threshold for each of the one or more products and a decision module to decide whether the second score should be computed or the third score should be computed. Further, the comparator is configured to compare the forecast error value with the predefined threshold for each of the one or more products and the decision module may be configured to decide whether each of the one or more products should be sold or procured. Furthermore, the other module (215) is used to perform one or more data pre-processing analysis on the one or more details and the sales history such as normalization, aggregation, outlier detection, and the like before computing the first score, second score, and the third score.

FIGURE 3 shows a flowchart illustrating the method of managing the merchandise stored in the warehouse (106), in accordance with some embodiment of the present disclosure.

The order in which the method (300) may be described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to

implement the method. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described herein. Furthermore, the method may be implemented in any suitable hardware, software, firmware, or combination thereof.

At the step (301), the collaboration system (102) obtains the one or more details and the forecast error value, associated with the one or more products of the merchandise from the first merchant (101).

In an embodiment, the one or more details (401 ) associated with the merchandise as shown in FIGURE 4A includes at least one of the count value, the date, and the geographical location, associated with each of the one or more products of the merchandise sold by the first merchant (101). The count value indicates the quantity of each of the one or more products to be purchased or sold, the date indicates one of the expiry date associated with each of the one or more products to be sold, or the time interval for purchasing each of the one or more products. For example, the one or more details (401) as shown in FIGURE 4A indicate “1673 quantities of Product-B are to be sold to one or more second merchants (103) within 1 month from a present date”. The person skilled in the art appreciates the inclusion of other information such as a procuring price for each of the one or more products to be sold, the actual price of each of the one or more products, and the like.

In an embodiment, the forecast error value (402) as shown in FIGURE 4A indicates the deviation in estimating the demand associated with each of the one or more products of the merchandise. In a first example, a forecast error value (402) in the first direction may indicate a higher quantity of one or more products is stored in excess in the warehouse (106) which may be sold by the first merchant (101) to the one or more second merchants (103). warehouse (106). In a second example, a forecast error value (402) in the second direction may indicate a higher quantity requirement or demand for one or more products which may be procured by the first merchant (101) (alternatively, a higher reduction in the customer satisfaction level if the one or more products are not procured within the date associated with the one or more products). In a third example, a lower forecast error value (402) for selling each of the one or more products may indicate a fewer quantity of one or more products stored in excess in the warehouse (106). In a fourth example, a lower forecast error value (402) for buying each of the one or more products may indicate a fewer quantity requirement or demand for the one or more products sold by the first merchant (101) (alternatively, a lesser reduction in the customer satisfaction level if the one or more products are not procured within the date associated with the one or more products).

Referring back to FIGURE 3, at the step (302), the collaboration system (102) determines the first score indicative of the product profile associated with each of the one or more products of the merchandise based on at least one of, the one or more details (401), and the sales history associated with each of the one or more products.

In an embodiment, the sales history (403) as shown in FIGURE 4B, for example, may include the quantity of each of the one or more products sold by the first merchant (101) within a time period such as past 1 week, 2 weeks, 1 month, 1.5 months, 6 months, and the like, a selling price associated with each of the one or more products sold by the first merchant (101), a procuring price associated with each of the one or more products sold by the first merchant (101), and the like. The person skilled in the art appreciates the inclusion of information pertaining to the sales history (403) in addition to the information shown in FIGURE 4B.

In an embodiment, for determining the first score, the collaboration system (102) categorizes the one or more products of the merchandise into one or more groups (405) based on at least one of the one or more details (401), the sales history (403) associated with each of the one or more products using a segmentation technique. For example, the segmentation technique may include at least one of a Recency-Frequency-Monetary (RFM) technique, centroid based clustering technique, logistic regression, decision trees, density-based spatial clustering technique, and the like. The person skilled in the art appreciates the use of other segmentation techniques in addition to the above mention examples for categorizing the one or more products of the merchandise into one or more groups (405) and for determining the first score. In a first example, using the RFM technique, the collaboration system (102) determines the Recency (R), Frequency (F), and Monetary (M) values (404) from the one or more details (401), and the sales history (403), associated with each of the one or more products as shown in FIGURE 4C Further, using the Recency (R), Frequency (F), and Monetary (M) values (404), the collaboration system (102) categorizes the one or more products (406) of the merchandise into one or more groups (405) as shown in FIGURE 4D.

In an embodiment, after categorizing the one or more products into one or more groups (405), the collaboration system (102) determines the first score (407) for each of the one or more products of the merchandise indicative of the product profile based on the one or more groups (405) using the segmentation technique as shown in FIGURE 4E. For example, the first score (407) may indicate at least one of the R-score, the F-score, the M-score, the RFM-score, or a combination thereof. The F-score may be indicative of the frequency of selling the one or more products by the first merchant (101), the R-score may be indicative of the time duration elapsed after selling each of the one or more products, the M-score may be indicative of the amount of money for which each of the one or more products are sold by the first merchant (101) and the RFM-score may be indicative of a weighted average of the R-score, F-score and the M-score. In one embodiment, the first score (407) may be determined for each of the one or more products of the merchandise. In another embodiment, the first score (407) may be determined for each of the one or more groups (405) comprising one or more products.

At the step 303, the collaboration system (102) computes one of the second score indicative of the holding expense or the third score indicative of the loss value associated with each of the one or more products of the merchandise, where the computing of one of the second score or the third score is based on a comparison between the forecast error value (402) and a predefined threshold.

In an embodiment, the second score (408) is computed when the forecast error value (402) is deviating from the predefined threshold in the first direction as shown in FIGURE 4F. The forecast error value (402) deviating from the predefined threshold in the first direction indicates an excess quantity of the one or more products stored in the warehouse (106) of the first merchant (101) which may remain unsold before the expiry date of the one or more products. The predefined threshold is a number including a positive or a negative number, for example, 15.6, 32.7, 15.0, -2.3, 0, -21.0, and the like. In a first example, if the first direction denotes a positive direction, then the second score (408) is computed when the forecast error value (402) (e.g., 45) is greater than the predefined threshold (e.g., 22.3). In a second example, if the first direction denotes a negative direction, then the second score (408) is computed when the forecast error value (402) (e.g., 9.5) is smaller than the predefined threshold (e.g., 22.3). The second score (408) is computed based on at least one of a storage expense, a purchased expense, an expense associated with insurance for

each of the one or more products using one or more statistical techniques. For example, the second score (408) may be determined as a mathematical product of the storage expense, the purchased expense, the expense associated with insurance for each of the one or more products. The person skilled in the art appreciates the use of one or more statistical techniques for determining the second score (408).

In an embodiment, the third score (409) is computed when the forecast error value (402) is deviating from the predefined threshold in the second direction as shown in FIGURE 4G. The forecast error value (402) deviating from the predefined threshold in the second direction indicates the requirement of the one or more products in addition to the one or more products stored in the warehouse (106) of the first merchant (101). In a first example, if the second direction denotes a positive direction, then the third score (409) is computed when the forecast error value (402) (e.g., 18.2) is larger than the predefined threshold (e.g., 7.1). In a second example, if the second direction denotes a negative direction, then the third score (409) is computed when the forecast error value (402) (e.g., -1.8) is smaller than the predefined threshold (e.g., 7.1). The third score (409) is computed based on at least one of the sales history (403) associated with each of the one or more products using the time-series analysis. Further, the person skilled in the art understands that the first direction and the second direction are opposite to each other. For example, when the first direction is positive, the second direction is negative and vice versa.

Referring back to FIGURE 3, at the step 304, the collaboration system (102) determines the one or more clusters for the one or more products of the merchandise based on the first score (407) and, the second score (408) or the third score (409).

In an embodiment, when the forecast error value (402) is deviating from the predefined threshold in the first direction, the collaboration system (102) categorizes the one or more products (406) of the merchandise into one or more clusters (410) based on the first score (407) and the second score (408) using the clustering technique as shown in FIGURE 4H. The clustering technique may be based on unsupervised learning techniques, for example, centroid based clustering (such as k-means), density based clustering (such as DBSCAN and OPTICS), connectivity based clustering (such as hierarchical clustering), distribution based clustering (such as expectation-maximization), and the like.

In an embodiment, when the forecast error value (402) is deviating from the predefined threshold in a second direction, the collaboration system (102) categorizes the one or more products (406) of the merchandise into one or more clusters (410) based on the first score (407) and the third score (409) using the clustering technique as shown in FIGURE 41. Further, the one or more clusters (410) includes one or more products (406) of the merchandise having a similar first score (407) and second score (408) or third score (409).

At the step 305, the collaboration system (102) estimates the compensation value for each of the one or more products (406) of the merchandise based on the one or more clusters (410) and the forecast error value (402). Each of the one or more products (406) of the merchandise are exchanged with the one or more second merchants (103) for the compensation value.

In an embodiment, when the one or more clusters (410) are formed using the first score (407) or the second score (408), the collaboration system (102) estimates the compensation value (411) (denoted as CV in the figures) for each cluster, where the CV is directly proportional to the forecast error value (402) as shown in FIGURE 4J. For example, as the forecast error value (402) increases, the compensation value (411) also increases. The compensation value (411) may be determined as a percentage, the amount of money, and the like. The compensation value (411) indicates the discount provided on the actual price (such as the procured price, maximum selling price, and the like) associated with each of the one or more products (406). Further, the collaboration system (102) determines the price (i.e. selling price) for each of the one or more products (406) of the merchandise using the compensation value (411). In one embodiment, the selling price may be determined as the difference between the actual price and the compensation value (411) (i.e. the actual price - the compensation value (411)). For example, if the compensation value (411) for “Product-B” is estimated as “15%” and the actual price is “674USD”, then the selling price for “Product-B” is determined as “674 - 101.1 = 572.9USD”. The collaboration system (102) provides the selling price associated with each of the one or more products (406) to the one or more second merchants (103). Furthermore, the one or more second merchants (103) may provide consent or dissent to buy each of the one or more products (406) from the first merchant (101) based on the selling price.

In an embodiment, when the one or more clusters (410) are formed using the first score (407) and the third score (409), the collaboration system (102) estimates the compensation value (411) (denoted as CV in the figures) which is inversely proportional to the forecast error value (402) as shown in FIGURE 4K For example, as the forecast error value (402) increases, the compensation value (411) decreases. The compensation value (411) indicates the additional price based on the actual price associated with each of the one or more products (406). For example, if the compensation value (411) for “Product- A” is estimated as “6%” and the actual price is “72.5USD”, then the procuring price for “Product- A” is determined as “72.5 - 4.35 = 68.15 USD”. In one embodiment, the procuring price may be determined as the aggregation of the actual price and the compensation value (411) (i.e. the actual price + the compensation value (411)). The collaboration system (102) provides the procuring price associated with each of the one or more products (406) to the one or more second merchants (103). Furthermore, the one or more second merchants (103) may provide the consent or the dissent to sell each of the one or more products (406) to the first merchant (101) based on the procuring price.

The method of managing the merchandise stored in the warehouse (106) provides the compensation value (411) for each of the one or more products (406) of the merchandise to be sold or procured by the first merchant (101) based on the forecast error value (402). Further, the collaboration system (102) helps the first merchant (101) reduce the out-of-stock problems in the warehouse (106) by determining the procuring price for each of the one or more products (406) to be procured from the one or more second merchants (103). The first merchant (101) by procuring each of the one or more products (406) from the one or more second merchants (103) provides an increased customer satisfaction. The collaboration system (102) helps the first merchant (101) reduce the excess stock problems in the warehouse (106) by determining the selling price for each of the one or more products (406) to be sold to the one or more second merchants (103). The selling price and the procuring price is determined dynamically for exchanging the one or more products (406) with one or more second merchants (103) based on the forecast error value (402). The exchange of one or more products (406) increases the sales throughput for both the first merchant (101) and the one or more second merchants (103), when the one or more products (406) are procured which are inadequate with the first merchant (101) and the one or more second merchants (103). The exchange of one or more products (406) decreases the wastage of one or more products (406) for both the first merchant (101) and the second merchant, when the one or more products (406) are sold within the expiry date which is in excess with the first merchant (101) and the one or more second merchants (103). The first merchant (101) may sell or procure the one or more

products (406) by selecting the one or more second merchants (103) based on the selling price and the procuring price respectively, when a plurality of second merchants are willing to buy or sell the one or more products. Further, the collaboration system (102) may select at least one second merchant from the one or more second merchants (103) to sell the one or more products (406) based on at least one of a timestamp associated with a request for buying the one or more products (406) initiated by the one or more second merchants (103), the geographical location associated with the one or more second merchants (103), a quantity associated with the request for buying the one or more products (406) from the one or more second merchants (103). The at least one second merchant selected by the collaboration system (102) is provided to the first merchant (101). Similarly, the collaboration system (102) may select at least one second merchant from the one or more second merchants (103) willing to sell the one or more products (406) to the first merchant (101) based on at least one of a timestamp associated with the willingness to sell the one or more products (406) initiated by the one or more second merchants (103), the geographical location associated with the one or more second merchants (103), the quantity associated with the notification for selling the one or more products (406) from the one or more second merchants (103). The at least one second merchant selected by the collaboration system (102) is provided to the first merchant (101). For example, the collaboration system (102) may select at least one second merchant based on a first-come-first serve based technique, a time interval set by the first merchant (101) to respond for buying or selling the one or more products (406), a location of the one or more second merchants within a pro-defined radius set by the first merchant (101), the quantity associated with the one or more products (406) being greater than a predefined quantity set by the first merchant (101).

COMPUTER SYSTEM

FIGURE 5 illustrates a block diagram of an exemplary computer system (500) for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system (500) may be used to implement the method of managing the merchandise stored in the warehouse (106). The computer system (500) may comprise a central processing unit (“CPU” or “processor”) (502). The processor (502) may comprise at least one data processor for executing program components for dynamic resource allocation at run time. The processor (502) may include specialized processing units such as integrated system (bus) controllers, memory (502) management control units, floating point units, graphics processing units, digital signal processing units, etc.

The processor (502) may be disposed in communication with one or more input/output (I/O) devices (not shown) via I/O interface (501). The I/O interface (501) may employ communication protocols/methods such as, without limitation, audio, analog, digital, monoaural, RCA, stereo, IEEE-(1394), serial bus, universal serial bus (USB), infrared, PS/2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA, IEEE 8O2.n /b/g/n/x, Bluetooth, cellular (e.g., codedivision multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc.

Using the I/O interface (501), the computer system (500) may communicate with one or more I/O devices. For example, the input device (510) may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, stylus, scanner, storage device, transceiver, video device/source, etc. The output device (511) may be a printer, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, Plasma display panel (PDP), Organic light-emitting diode display (OLED) or the like), audio speaker, etc.

In some embodiments, the computer system (500) is connected to the service operator through a communication network (509). The processor (502) may be disposed in communication with the communication network (509) via a network interface (503). The network interface (503) may communicate with the communication network (509). The network interface (503) may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/Internet protocol (TCP/IP), token ring, IEEE 802.1 la/b/g/n/x, etc. The communication network (509) may include, without limitation, a direct interconnection, e-commerce network, a peer to peer (P2P) network, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, Wi-Fi, etc. Using the network interface (503) and the communication network (509), the computer system (500) may communicate with the one or more service operators.

In some embodiments, the processor (502) may be disposed in communication with a memory (505) (e.g., RAM, ROM, etc. not shown in Figure 7 via a storage interface (504). The storage interface (504) may connect to memory (505) including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as serial advanced technology attachment (SATA), Integrated Drive Electronics (IDE), IEEE- 1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.

The memory (505) may store a collection of program or database (104) components, including, without limitation, user interface (506), an operating system (507), web server (508) etc. In some embodiments, computer system (500) may store user/application data (506), such as the data, variables, records, etc. as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase.

The operating system (507) may facilitate resource management and operation of the computer system (500). Examples of operating systems include, without limitation, APPLE® MACINTOSH® OS X®, UNIX®, UNIX-like system distributions (E.G, BERKELEY SOFTWARE DISTRIBUTION® (BSD), FREEBSD®, NETBSD®, OPENBSD, etc.), LINUX® DISTRIBUTIONS (E.G, RED HAT®, UBUNTU®, KUBUNTU®, etc.), IBM®OS/2®, MICROSOFT® WINDOWS® (XP®, VISTA®/7/8, 10 etc.), APPLE® IOS®, GOOGLE™ ANDROID™, BLACKBERRY® OS, or the like.

In some embodiments, the computer system (500) may implement a web browser (not shown in Figure) stored program component. The web browser may be a hypertext viewing application, such as MICROSOFT® INTERNET EXPLORER®, GOOGLE™ CHROME™, MOZILLA® FIREFOX®, APPLE® SAFARI®, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), etc. Web browsers (508) may utilize facilities such as AJAX, DHTML, ADOBE® FLASH®, JAVASCRIPT®, JAVA®, Application Programming Interfaces (APIs), etc. In some embodiments, the computer system (500) may implement a mail server stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as Active Server Pages (ASP), ACTIVEX®, ANSI® C++/C#, MICROSOFT®, NET, CGI SCRIPTS, JAVA®, JAVASCRIPT®, PERL®, PHP, PYTHON®, WEBOBJECTS®, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), MICROSOFT® Exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system (500) may implement a mail client stored program component. The mail client may be a mail viewing application, such as APPLE® MAIL, MICROSOFT® ENTOURAGE®, MICROSOFT® OUTLOOK®, MOZILLA® THUNDERBIRD®, etc.

Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. A computer-readable storage medium refers to any type of physical memory (505) on which information or data readable by a processor (502) may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processors to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., non-transitory. Examples include Random Access memory (RAM), Read-Only memory (ROM), volatile memory, non-volatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.

In an embodiment, the computer system (500) may comprise remote devices (512). The remote devices (512) may indicate the first merchant (101) and the one or more second merchants (103). The computer system (500) may receive the one or more details (401), the forecast error value (402), the sales history (403) and the like from the remote devices (512) through the communication network (509).

The terms "an embodiment", "embodiment", "embodiments", "the embodiment", "the embodiments", "one or more embodiments", "some embodiments", and "one embodiment" mean "one or more (but not all) embodiments of the inventions)" unless expressly specified otherwise.

The terms "including", "comprising", “having” and variations thereof mean "including but not limited to", unless expressly specified otherwise.

The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms "a", "an" and "the" mean "one or more", unless expressly specified otherwise.

A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.

When a single device or article is described herein, it may be readily apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it may be readily apparent that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.

The illustrated operations of FIGURE 3 show certain events occurring in a certain order. In alternative embodiments, certain operations may be performed in a different order, modified, or removed. Moreover, steps may be added to the above described logic and still conform to the described embodiments. Further, operations described herein may occur sequentially or certain operations may be processed in parallel. Yet further, operations may be performed by a single processing unit or by distributed processing units.

Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

While various aspects and embodiments have been disclosed herein, other aspects and embodiments may be apparent to those skilled in the art The various aspects and embodiments

disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

REFERRAL NUMERALS:
We Claim:

1. A method for managing a merchandise, wherein the merchandise comprising one or more products (406) is stored in a warehouse (106), wherein a collaboration system (102) enables exchange of the merchandise between a first merchant (101) and one or more second merchants (103), the method comprising:

obtaining, by the collaboration system (102), one or more details (401) and a forecast error value (402), associated with the one or more products (406) of the merchandise from the first merchant (101);

determining, by the collaboration system (102), a first score (407) indicative of a product profile associated with each of the one or more products (406) of the merchandise based on at least one of, the one or more details (401), and a sales history (403) associated with each of the one or more products (406);

computing, by the collaboration system (102), one of a second score (408) indicative of a holding expense or a third score (409) indicative of loss value associated with each of the one or more products (406) of the merchandise, wherein the computing of one of the second score (408) or the third score (409) is based on a comparison between the forecast error value (402) and a predefined threshold;

determining, by the collaboration system (102), one or more clusters (410) for the one or more products (406) of the merchandise based on the first score (407) and, the second score (408) or the third score (409); and

estimating, by the collaboration system (102), a compensation value (411) for each of the one or more products (406) of the merchandise based on the one or more clusters (410) and the forecast error value (402), wherein each of the one or more products (406) of the merchandise are exchanged with the one or more second merchants (103) based on the compensation value (411).

2. The method as claimed in claim 1, wherein the one or more details (401) associated with the merchandise comprises at least one of a count value, a date, and a geographical location, associated with each of the one or more products (406) of the merchandise.

3. The method as claimed in claim 2, wherein the count value indicates a quantity of each of the one or more products (406) to be purchased or sold, the date indicates one of an expiry date

associated with each of the one or more products (406) to be sold or a time interval for purchasing each of the one or more products (406).

4. The method as claimed in claim 1, wherein the forecast error value (402) indicates a deviation in estimating a demand associated with each of the one or more products (406) of the merchandise from the first merchant (101).

5. The method as claimed in claim 1, wherein determining the first score (407) comprises:

categorizing one or more products (406) of the merchandise into one or more groups (405) based on at least one of the one or more details (401), the sales history (403) associated with each of the one or more products (406) using a segmentation technique; and

determining the first score (407) associated with each of the one or more products (406) of the merchandise indicative of the product profile based on the one or more groups (405) using the segmentation technique.

6. The method as claimed in claim 1 , wherein the second score (408) is computed when the forecast error value (402) is deviating from the predefined threshold in a first direction, wherein the second score (408) is computed based on at least one of a storage expense, a purchased expense, an expense associated with insurance for each of the one or more products (406) using one or more statistical techniques.

7. The method as claimed in claim 1 , wherein the third score (409) is computed when the forecast error value (402) is deviating from the predefined threshold in a second direction, wherein the third score (409) is computed based on at least one of the sales history (403) associated with each of the one or more products (406) using a time-series analysis.

8. The method as claimed in claim 1, wherein determining the one or more clusters (410) comprises:

categorizing the one or more products (406) of the merchandise into the one or more clusters (410) based on the first score (407) and the second score (408) using a clustering technique, when the forecast error value (402) is deviating from the predefined threshold in a first direction; or

categorizing the one or more products (406) of the merchandise into the one or more clusters (410) based on the first score (407) and the third score (409) using the clustering technique, when the forecast error value (402) is deviating from the predefined threshold in a second direction.

9. The method as claimed in claim 1, further comprises determining one of a selling price or a procuring price for each of the one or more products (406) of the merchandise using the compensation value (411).

10. A collaboration system (102) for managing a merchandise, wherein the merchandise comprising one or more products (406) is stored in a warehouse (106), wherein the collaboration system (102) enables exchange of the merchandise between a first merchant (101) and one or more second merchants (103), the collaboration system (102) comprises:

a processor (203); and

a memory (202) communicatively coupled to the processor (203), wherein the memory (202) stores the processor (203) instructions, which, on execution, causes the processor (203) to:

obtain one or more details (401) and a forecast error value (402), associated with the one or more products (406) of the merchandise from the first merchant (101);

determine a first score (407) indicative of a product profile associated with each of the one or more products (406) of the merchandise based on at least one of, the one or more details (401), and a sales history (403) associated with each of the one or more products (406);

compute one of a second score (408) indicative of a holding expense or a third score (409) indicative of loss value associated with each of the one or more products (406) of the merchandise, wherein one of the second score (408) or the third score (409) is computed based on a comparison between the forecast error value (402) and a predefined threshold;

determine one or more clusters (410) for the one or more products (406) of the merchandise based on the first score (407) and, the second score (408) or the third score (409); and

estimate a compensation value (411) for each of the one or more products (406) of the merchandise based on the one or more clusters (410) and the forecast error value (402),

wherein each of the one or more products (406) of the merchandise are exchanged with the one or more second merchants (103) based on the compensation value (411).

11. The collaboration system (102) as claimed in claim 10, wherein the processor (203) is configured to determine the first score (407) comprises:

categorizing one or more products (406) of the merchandise into one or more groups

(405) based on at least one of the one or more details (401), the sales history (403) associated with each of the one or more products (406) using a segmentation technique; and determining the first score (407) associated with each of the one or more products (406) of the merchandise indicative of the product profile based on the one or more groups (405) using the segmentation technique.

12. The collaboration system (102) as claimed in claim 10, wherein the processor (203) is configured to compute the second score (408) based on at least one of a storage expense, a purchased expense, an expense associated with insurance for each of the one or more products

(406) using one or more statistical techniques, when the forecast error value (402) is deviating from the predefined threshold in a first direction.

13. The collaboration system (102) as claimed in claim 10, wherein the processor (203) is configured to compute the third score (409) based on at least one of the sales history (403) associated with each of the one or more products (406) using a time-series analysis, when the forecast error value (402) is deviating from the predefined threshold in a second direction.

14. The collaboration system (102) as claimed in claim 10, wherein the processor (203) is configured to determine the one or more clusters (410) comprises:

categorizing the one or more products (406) of the merchandise into the one or more clusters (410) based on the first score (407) and the second score (408) using a clustering technique, when the forecast error value (402) is deviating from the predefined threshold in a first direction; or

categorizing the one or more products (406) of the merchandise into the one or more clusters (410) based on the first score (407) and the third score (409) using the clustering technique, when the forecast error value (402) is deviating from the predefined threshold in a second direction.

15. The collaboration system (102) as claimed in claim 10, wherein the processor (203) is configured to determine one of a selling price or a procuring price for each of the one or more products (406) of the merchandise using the compensation value (411).