WO2024058708A1

WO2024058708A1 - Server and method for calculating supplier score

Info

Publication number: WO2024058708A1
Application number: PCT/SG2022/050661
Authority: WO
Inventors: Rajat Goswami PATIT PABAN GOSWAMI; Toshiki Ishii; Yusuke KITAJIMA
Original assignee: Hitachi, Ltd.
Priority date: 2022-09-15
Filing date: 2022-09-15
Publication date: 2024-03-21

Abstract

A server for calculating a supplier score is provided. The server comprises: a memory for storing instructions; and a processor for executing the stored instructions and configured to: receive a request for an item from a buyer; upon the receipt of the request from the buyer, for each supplier of a plurality of suppliers capable of supplying the item: collect first information about performance of a supplier; and calculate the supplier score for the supplier based on the first information.

Description

SERVER AND METHOD FOR CALCULATING SUPPLIER SCORE

TECHNICAL FIELD

[0001] Various embodiments relate to a server and a method for calculating a supplier score.

BACKGROUND

[0002] Currently, new digital supply chain finance platforms are being launched as a number of small medium enterprises may be underbanked and need credit for their operations. In the supply chain finance platform, key players may be buyers, suppliers, and financial institutions. Orders from the buyers may be fulfilled by the suppliers and the credit may be provided by the financial institutions. The supply chain finance platform may allow the suppliers to obtain the credit from the financial institutions. Presently, the financial institutions may provide the credit based on suppliers’ financial information such as credit history including balance sheet cash flow and income statements.

[0003] However, the financial institutions may still be at risk and be unable to provide the credit as the suppliers may have limited credit history with the financial institutions. In addition, due to global macroeconomics’ changes, there may be an acute demand and supply gap, and thus price of items (products) may have inflated irrationally. Many suppliers may be unable to price the items accurately and/or irrationally, and inaccurate pricing may lead to missed orders or increase the price of the items. At least one of the above situations may negatively affect the suppliers’ revenue.

[0004] A prior art for the supply chain finance platform, US Patent Publication No. US 2016/0048852 Al, discloses an apparatus comprising a demand module that determines a demand for a product offered from a plurality of suppliers; a pricing module that receives cost factors associated with the product to determine a base per unit cost of the product; a quality module that receives quality factors associated with the product to determine a per unit quality cost adder; a cost module that calculates a procurement cost of the product; a supplier module that receives production factors describing a supplier’s ability to provide the product; a social module that monitors social media for social data describing events related to a supplier’s ability to provide the product; and a procurement module that determines, based on the per unit procurement cost of the product, the production factors, and the social data, a product order allocation for each supplier that fulfils the demand. However, the prior art does not consider the supplier’s score, and also does not focus on revenue maximization of the supplier. The prior art merely focuses on data filtering based on conditions. In addition, in the prior art, the system is centralised. The centralised system may provide limited data trust and be prone to manipulation or bias towards a particular supplier.

[0005] Therefore, there may be a need to provide a server and a method for calculating a supplier score. Moreover, there may be a need to provide the server and the method for considering the calculated supplier score to dynamically price the items in a supply chain.

SUMMARY

[0006] According to various embodiments, a server for calculating a supplier score is provided. The server comprises: a memory for storing instructions; and a processor for executing the stored instructions and configured to: receive a request for an item from a buyer; upon the receipt of the request from the buyer, for each supplier of a plurality of suppliers capable of supplying the item: collect first information about performance of a supplier; and calculate the supplier score for the supplier based on the first information.

[0007] In some embodiments, the first information includes encrypted information.

[0008] In some embodiments, the processor is further configured to: collect second information about a revenue target of the supplier; calculate a distance to the revenue target for the supplier based on the first information and the second information, wherein the distance to the revenue target includes a difference between the revenue target and performance achieved by the supplier; and calculate a dynamic price for the supplier based on the supplier score and the distance to the revenue target

[0009] In some embodiments, the processor is further configured to assign weights to at least one of the supplier score and the distance to the revenue target, and calculate the dynamic price for the supplier further based on the assigned weights.

[0010] In some embodiments, the processor is further configured to collect supplier score information of the supplier from the buyer, match the supplier score and the supplier score information, and calculate the dynamic price further based on the matching.

[0011] In some embodiments, the first information and the second information are stored in a private database of the supplier. [0012] In some embodiments, the processor is further configured to, upon the receipt of the request from the buyer, trigger a smart contract to collect the first information and the second information stored in the private database, and the smart contract is configured to allow a device of the supplier to create hashed first information and hashed second information based on the first information and the second information respectively, and store the hashed first information and the hashed second information in a public database of the supplier.

[0013] In some embodiments, the supplier score is stored in the public database along with the hashed first information.

[0014] In some embodiments, the distance to the revenue target is stored in the public database along with the hashed second information.

[0015] In some embodiments, the processor is further configured to provide the supplier score as a credit score for the supplier, to one or more financial institutions.

[0016] In some embodiments, the processor is further configured to provide the supplier score to the buyer.

[0017] In some embodiments, the processor is further configured to re-calculate the supplier score based on the first information and the distance to the revenue target.

[0018] In some embodiments, the processor is further configured to use a machine learning model to calculate the supplier score, and the processor is further configured to categorise the plurality of suppliers into a predetermined number of supplier groups based on the first information, allocate a rating value to each of the supplier groups, and train the machine learning model using the allocated rating value to calculate the supplier score.

[0019] In some embodiments, the processor is further configured to use a k-means clustering algorithm to categorise the plurality of suppliers into the predetermined number of supplier groups.

[0020] According to various embodiments, a method for calculating a supplier score is provided. The method comprises: receiving a request for an item from a buyer; upon the receipt of the request from the buyer, for each supplier of a plurality of suppliers capable of supplying the item: collecting first information about performance of a supplier; and calculating the supplier score for the supplier based on the first information.

[0021] In some embodiments, the first information includes encrypted information.

[0022] In some embodiments, the method further comprises: collecting second information about a revenue target of the supplier; calculating a distance to the revenue target for the supplier based on the first information and the second information, wherein the distance to the revenue target includes a difference between the revenue target and performance achieved by the supplier; and calculating a dynamic price for the supplier based on the supplier score and the distance to the revenue target

[0023] In some embodiments, the calculating the dynamic price comprises: assigning weights to at least one of the supplier score and the distance to the revenue target; and calculating the dynamic price for the supplier further based on the assigned weights.

[0024] In some embodiments, the calculating the dynamic price comprises: collecting supplier score information of the supplier from the buyer; matching the supplier score and the supplier score information; and calculating the dynamic price further based on the matching.

[0025] In some embodiments, the first information and the second information are stored in a private database of the supplier.

[0026] In some embodiments, the method further comprises: upon the receipt of the request from the buyer, triggering a smart contract to collect the first information and the second information stored in the private database; creating hashed first information and hashed second information based on the first information and the second information respectively; and storing the hashed first information and the hashed second information in a public database of the supplier.

[0027] In some embodiments, the method further comprises: storing the supplier score in the public database along with the hashed first information.

[0028] In some embodiments, the method further comprises: storing the distance to the revenue target in the public database along with the hashed second information.

[0029] In some embodiments, the method further comprises: providing the supplier score as a credit score for the supplier, to one or more financial institutions.

[0030] In some embodiments, the method further comprises: providing the supplier score to the buyer.

[0031] In some embodiments, the method further comprises: re-calculating the supplier score based on the first information and the distance to the revenue target.

[0032] In some embodiments, the calculating the supplier score comprises: using a machine learning model to calculate the supplier score, and the method further comprises: categorising the plurality of suppliers into a predetermined number of supplier groups based on the first information; allocating a rating value to each of the supplier groups; and training the machine learning model using the allocated rating value to calculate the supplier score. [0033] In some embodiments, the categorising the plurality of suppliers into the predetermined number of supplier groups comprises: using a k-means clustering algorithm to categorise the plurality of suppliers into the predetermined number of supplier groups.

[0034] According to various embodiments, a computer program product comprising instructions to cause the server of any one of the above embodiments to execute the steps of the method of any one of the above embodiments is provided.

[0035] According to various embodiments, a computer-readable medium having stored thereon the above computer program product is provided.

[0036] According to various embodiments, a data processing apparatus configured to perform the method of any one of the above embodiments is provided.

[0037] According to various embodiments, a computer program element comprising program instructions, which, when executed by one or more processors, cause the one or more processors to perform the method of any one of the above embodiments is provided.

[0038] According to various embodiments, a computer-readable medium comprising program instructions, which, when executed by one or more processors, cause the one or more processors to perform the method of any one of the above embodiments is provided. The computer-readable medium may include a non-transitory computer-readable medium.

BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In the drawings, like reference characters generally refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the invention. In the following description, various embodiments are described with reference to the following drawings, in which:

[0040] FIG. 1 is a block diagram illustrating a system according to various embodiments.

[0041] FIG. 2 is a block diagram illustrating a server according to various embodiments.

[0042] FIG. 3 is a flow diagram illustrating a method according to various embodiments.

[0043] FIG. 4 is a flow diagram illustrating a method according to various embodiments.

[0044] FIG. 5 is a block diagram illustrating a device according to various embodiments.

[0045] FIG. 6 is a sequence diagram illustrating calculating a supplier score.

[0046] FIG. 7A is an image diagram illustrating interactions between a server, a supplier, a buyer, and a financial institution according to various embodiments. [0047] FIG. 7B is a table diagram illustrating assessing a supplier score by a financial institution according to various embodiments.

[0048] FIG. 8 is an image diagram illustrating calculating a distance to a revenue target for a supplier according to various embodiments.

[0049] FIG. 9 is an image diagram illustrating calculating a dynamic price according to various embodiments.

[0050] FIG. 10 is a table diagram illustrating calculating a dynamic price according to various embodiments.

[0051] FIG. 11 is a flow diagram illustrating a method according to various embodiments. [0052] FIG. 12 is a flow diagram illustrating a method according to various embodiments.

DESCRIPTION

[0053] Embodiments described below in context of the method are analogously valid for the server, and vice versa. Furthermore, it will be understood that the embodiments described below may be combined, for example, a part of one embodiment may be combined with a part of another embodiment.

[0054] It will be understood that any property described herein for a specific device may also hold for any device described herein. Furthermore, it will be understood that for any device described herein, not necessarily all the components described must be enclosed in the device, but only some (but not all) components may be enclosed.

[0055] It should be understood that the terms “on”, “over”, “top”, “bottom”, “down”, “side”, “back”, “left”, “right”, “front”, “lateral”, “side”, “up”, “down” etc., when used in the following description are used for convenience and to aid understanding of relative positions or directions, and not intended to limit the orientation of any device, structure or any part of any device or structure. In addition, the singular terms “a”, “an”, and “the” include plural references unless context clearly indicates otherwise. Similarly, the word “or” is intended to include “and” unless the context clearly indicates otherwise.

[0056] The term “coupled” (or “connected”) herein may be understood as electrically coupled or as mechanically coupled, for example attached or fixed, or just in contact without any fixation, and it will be understood that both direct coupling or indirect coupling (in other words: coupling without direct contact) may be provided. [0057] In order that the invention may be readily understood and put into practical effect, various embodiments will now be described by way of examples and not limitations, and with reference to the figures.

[0058] FIG. 1 is a block diagram illustrating a system 1000 including a server 100 according to various embodiments.

[0059] As shown in FIG. 1, the system 1000 may include, but is not limited to, the server 100, a device associated with a buyer 150, a plurality of devices each associated with a plurality of suppliers 160, a smart contract 170, a device associated with a financial institution 180, and a network 190 including a blockchain network. In some embodiments, the server 100 may be referred to as an SCF (supply chain finance) platform 100.

[0060] In some embodiments, the buyer 150 may be an individual or an entity who requests for an item. For example, the request for the item may include a request related to the item, including, but not limited to, at least one of a request for an inquiry of the item, a request for order of the item, and a request for pricing the item. In some embodiments, the suppliers 160 may be individuals or entities that are capable of providing the item requested by the buyer 150. In some embodiments, the financial institution 180 may be an individual or an entity that is capable of providing services as intermediaries for different kinds of financial monetary transactions. For example, the financial monetary transactions may include, but are not limited to, loans, investments, deposits, current exchanges, factoring, and reverse factoring. For example, the financial institution 180 may include a bank.

[0061] In some embodiments, the network 190 may include, but is not limited to, a Local Area Network (LAN), a Wide Area Network (WAN), a Global Area Network (GAN), or any combination thereof. The network 190 may provide a wireline communication, a wireless communication, or a combination of the wireline and wireless communication between the server 100 and the device associated with buyer 150, between the server 100 and the plurality of devices each associated with the plurality of suppliers 160, and between the server 100 and the device associated with the financial institution 180.

[0062] In some embodiments, the server 100, the device associated with the buyer 150, the plurality of devices each associated with the plurality of suppliers 160, and the device associated with the financial institution 180 may include, but are not limited to, at least one of the following: a mobile phone, a tablet computer, a laptop computer, a desktop computer, a head-mounted display, a smart watch, a server, a workstation, and a POS terminal. [0063] In some embodiments, under the blockchain network, the system 1000 may provide at least one distributed ledger across at least one of the server 100, the device associated with the buyer 150, the plurality of devices each associated with the plurality of suppliers 160, and the device associated with the financial institution 180. In some embodiments, the at least one of the server 100, the device associated with the buyer 150, the plurality of devices each associated with the plurality of suppliers 160, and the device associated with the financial institution 180 may be implemented as a plurality of nodes on the distributed ledger.

[0064] In some embodiments, the at least one of the server 100, the device associated with the buyer 150, the plurality of devices each associated with the plurality of suppliers 160, and the device associated with the financial institution 180 may maintain and/or update the distributed ledger. In some embodiments, the distributed ledger may be updated periodically or from time to time with modifications to the ledger. For example, the modifications may include, but are not limited to, an insertion or an update of a ledger entry.

[0065] In some embodiments, in the system 1000, where an event occurs with the distributed ledger, the event may be resolved based on an event resolution logic. For example, the event may include, but is not limited to, hash collision and corrupted ledger entries. In some embodiments, the event resolution logic may be distributed among the at least one of the server 100, the device associated with the buyer 150, the plurality of devices each associated with the plurality of suppliers 160, and the device associated with the financial institution 180.

[0066] In some embodiments, the at least one of the server 100, the device associated with the buyer 150, the plurality of devices each associated with the plurality of suppliers 160, and the device associated with the financial institution 180 may be utilised as a decentralized processor as well as a decentralized database. Therefore, each of the at least one of the server 100, the device associated with the buyer 150, the plurality of devices each associated with the plurality of suppliers 160, and the device associated with the financial institution 180 may be implemented as a plurality of nodes for storing a copy of the ledger. The ledger may be collaboratively maintained by anonymous peers on the blockchain network. In some other embodiments, the ledger may be only maintained and stored on a set of trusted nodes, for example devices of authorized users.

[0067] In some embodiments, the server 100, for example, implemented by a server computer, may include a communication interface 110, a processor 120, and a memory 130 (as will be described with reference to FIG. 2). In some embodiments, the server 100 may further include an output module 140. In some embodiments, the processor 120 may be referred to as a pricing engine 120. The processor 120 may include, but is not limited to, a distance calculating module 121 (also referred to as a “distance calculator”) and a supplier credit score module 122.

[0068] In some embodiments, the server 100, for example, the communication interface 110 of the server 100, may communicate with the device associated with the buyer 150 and the plurality of devices each associated with the plurality of suppliers 160 via the network 190. In some embodiments, the device associated with the buyer 150 may receive a request (demand) for the item from the buyer 150. For example, the device associated with the buyer 150 may receive a request for pricing the item from the buyer 150. The server 100, for example, the communication interface 110 of the server 100, may then receive the request for the item from the device associated with the buyer 150.

[0069] In some embodiments, the device associated with the buyer 150 may further receive information about the request from the buyer 150. As shown in FIG. 1, the information about the request may include, but is not limited to, the number of requested items (quantity), preferred price of the item (or budget), and a preferred supplier score (for example, in the form of star rating). The server 100, for example, the communication interface 110 of the server 100, may then receive the information about the request from the device associated with the buyer 150.

[0070] In some embodiments, the device associated with the buyer 150 may belong to the buyer 150. In some embodiments, the plurality of devices each associated with the plurality of suppliers 160 may belong to each of the plurality of suppliers 160. As shown in FIG. 1, for example, the plurality of suppliers 160 may include a first supplier 161, a second supplier 162, and a third supplier 163 capable of supplying the item requested by the buyer 150.

[0071] In some embodiments, upon receipt of the request from the device associated with the buyer 150, the server 100, for example, the processor 120 of the server 100, may, for each supplier of a plurality of suppliers 160 capable of supplying the item (for example, for each of the first supplier 161, the second supplier 162, and the third supplier 163), collect first information about performance of a supplier, and calculate a supplier score for the supplier based on the first information. For example, the supplier credit score module 122 may, for each supplier of a plurality of suppliers 160 capable of supplying the item, collect the first information about performance of the supplier, and calculate the supplier score for the supplier based on the first information. In this manner, the server 100 in accordance with various embodiments may calculate the supplier score for each supplier, so that the calculated supplier score may be used in various transactions. [0072] In some embodiments, the first information about the performance of the supplier may include encrypted information. In some embodiments, the processor 120 of the server 100 may use the encrypted information to calculate the supplier score for the supplier. In this manner, the first information about the performance of the supplier may be protected. In some other embodiments, the first information about the performance of the supplier may include encrypted information and non-encrypted information. For example, sensitive information among the first information about the performance of the supplier may be encrypted, and nonsensitive information among the first information about the performance of the supplier may not be encrypted. In some embodiments, the processor 120 of the server 100 may use the encrypted information and the non-encrypted information to calculate the supplier score for the supplier. In this manner, at least the sensitive information may be protected. Throughout the description, the term of “encrypted information” may be referred to as “hashed information”.

[0073] In some embodiments, after calculating the supplier score for each supplier, the server 100, for example, the processor 120 of the server 100, may further collect second information about a revenue target of the supplier, and calculate a distance to the revenue target for the supplier based on the first information and the second information. For example, the distance calculating module 121 may collect the second information about the revenue target of the supplier, and calculate the distance to the revenue target for the supplier based on the first information and the second information. In some embodiments, the distance to the revenue target may include a difference between the revenue target and performance achieved by the supplier. The server 100, for example, the processor 120 of the server 100, may further calculate a dynamic price for the supplier based on the supplier score for the supplier and the distance to the revenue target for the supplier.

[0074] In some embodiments, the server 100, for example, the output module 140 of the server 100, may output the calculated dynamic price, so that the buyer 150 may check the calculated dynamic price. For example, the output module 140 may include a display module configured to display the calculated dynamic price on a screen of the display module, so that the buyer 150 may view the calculated dynamic price.

[0075] In some embodiments, the first information and the second information may be stored in a private database of each supplier. For example, as shown in FIG. 1, the first information about performance of the first supplier 161 and the second information about the revenue target of the first supplier 161 may be stored in a private database 161b of the first supplier 161. Likewise, the first information about performance of the second supplier 162 and the second information about the revenue target of the second supplier 162 may be stored in a private database 162b of the second supplier 162, and the first information about performance of the third supplier 163 and the second information about the revenue target of the third supplier 163 may be stored in a private database 163b of the third supplier 163.

[0076] In some embodiments, upon the receipt of the request from the buyer 150, the server 100, for example, the processor 120 of the server 100, may trigger a smart contract 170 to collect the first information and the second information stored in the private database 160b of each of the plurality of suppliers 160. In some embodiments, the smart contract 170 may allow each of the plurality of devices associated with each of the plurality of suppliers 160 capable of supplying the item to create hashed first information and hashed second information based on the first information and the second information respectively, and store the hashed first information and the hashed second information in a public database 160a of each of the plurality of suppliers 160. For example, as shown in FIG. 1, the smart contract 170 may allow the device associated with the first supplier 161 to create hashed first information and hashed second information of the first supplier 161 based on the first information and the second information of the first supplier 161 respectively, and store the hashed first information and the hashed second information in a public database 161a of the first supplier 161. Likewise, the smart contract 170 may allow the device associated with the second supplier 162 to create hashed first information and hashed second information of the second supplier 162 based on the first information and the second information of the second supplier 162 respectively, and store the hashed first information and the hashed second information in a public database 162a of the second supplier 162. The smart contract 170 may allow the device associated with the third supplier 163 to create hashed first information and hashed second information of the third supplier 163 based on the first information and the second information of the third supplier 163 respectively, and store the hashed first information and the hashed second information in a public database 163 a of the third supplier 163.

[0077] In some embodiments, the processor 120 may collect the first information and the second information of the first supplier 161, the second supplier 162 and the third supplier 163 from the public database 161a of the first supplier 161, the public database 162a of the second supplier 162, and the public database 163a of the third supplier 163 respectively. For example, the processor 120 may decrypt the hashed first information and the hashed second information of the first supplier 161, the second supplier 162 and the third supplier 163 stored in the public database 161a of the first supplier 161, the public database 162a of the second supplier 162, and the public database 163a of the third supplier 163 respectively. As another example, the processor 120 may access the private database 161b of the first supplier 161, the private database 162b of the second supplier 162, and the private database 163b of the third supplier 163 via the public database 161a of the first supplier 161, the public database 162a of the second supplier 162, and the public database 163a of the third supplier 163 respectively, to collect the first information and the second information of the first supplier 161, the second supplier 162 and the third supplier 163 stored in the private database 161b of the first supplier 161, the private database 162b of the second supplier 162, and the private database 163b of the third supplier 163 respectively.

[0078] In some embodiments, the supplier score may be hashed, and the hashed supplier score may be stored in the public database 160a of each of the plurality of suppliers 160 along with the hashed first information. In some embodiments, at the same time, the hashed supplier score may be linked to the private database 160b of each of the plurality of suppliers 160 storing the first information. For example, as shown in FIG. 1, the hashed supplier score for the first supplier 161 may be stored in the public database 161a of the first supplier 161 along with the hashed first information of the first supplier 161. At the same time, the hashed supplier score for the first supplier 161 may be linked to the private database 161b of the first supplier 161 storing the first information about the performance of the first supplier 161. The hashed supplier score for the second supplier 162 may be stored in the public database 162a of the second supplier 162 along with the hashed first information of the second supplier 162. At the same time, the hashed supplier score for the second supplier 162 may be linked to the private database 162b of the second supplier 162 storing the first information about the performance of the second supplier 162. The hashed supplier score for the third supplier 163 may be stored in the public database 163 a of the third supplier 163 along with the hashed first information of the third supplier 163. At the same time, the hashed supplier score for the third supplier 163 may be linked to the private database 163b of the third supplier 163 storing the first information about the performance of the third supplier 163.

[0079] In some embodiments, the distance to the revenue target may be hashed, and the hashed distance to the revenue target may be stored in the public database 160a of each of the plurality of suppliers 160 along with the hashed second information. For example, as shown in FIG. 1, the hashed distance to the revenue target for the first supplier 161 may be stored in the public database 161a of the first supplier 161 along with the hashed second information of the first supplier 161. The hashed distance to the revenue target for the second supplier 162 may be stored in the public database 162a of the second supplier 162 along with the hashed second information of the second supplier 162. The hashed distance to the revenue target for the third supplier 163 may be stored in the public database 163 a of the third supplier 163 along with the hashed second information of the third supplier 163.

[0080] In some embodiments, the server 100, for example, the processor 120 of the server 100, may provide the supplier score as a credit score for the supplier, to one or more financial institutions 180. For example, as shown in FIG. 1, the supplier credit score module 122 may provide the supplier score for the first supplier 161, the supplier score for the second supplier 162, and the supplier score for the third supplier 163 to the device associated with the one or more financial institutions 180.

[0081] In some embodiments, the server 100, for example, the processor 120 of the server 100, may provide the supplier score to the buyer 150. For example, the supplier credit score module 122 may provide the supplier score for the first supplier 161, the supplier score for the second supplier 162, and the supplier score for the third supplier 163 to the device associated with the buyer 150.

[0082] In some embodiments, the server 100, for example, the processor 120 of the server 100, may re-calculate the supplier score based on the first information and the distance to the revenue target. For example, the supplier credit score module 122 may re-calculate the supplier score for the first supplier 161 based on the first information of the first supplier 161 and the distance to the revenue target for the first supplier 161. The supplier credit score module 122 may re-calculate the supplier score for the second supplier 162 based on the first information of the second supplier 162 and the distance to the revenue target for the second supplier 162. The supplier credit score module 122 may re-calculate the supplier score for the third supplier 163 based on the first information of the third supplier 163 and the distance to the revenue target for the third supplier 163.

[0083] As described above, the server 100 may be created on a blockchain platform such as Hyperledger fabric. In such scenario each of the suppliers 160 may upload private data or integrate to procurement/ERP (enterprise resource planning) systems, and provide access to their data related to performance. The private data may be shared via channels with the supply chain finance platform 100, while joining the blockchain network. The smart contract 170 may be executed to read the private data, create a new hash for the private data, and create a supplier score for the supplier 160 to be written on a public chain. With every transaction, the private performance data may be changed, and similarly the smart contract 170 may be configured to find the new hash, the supplier score, and the distance to the revenue target. The system 1000 may be designed to show the underlying actual private data to a final buyer 150 or the financial institution 180 after transaction. The system 1000 may be trusted as the data may not be modified by individual participants, and thus the buyer 150 and the financial institution 180 may be more assured in making business decisions.

[0084] FIG. 2 is a block diagram illustrating a server 100 according to various embodiments.

[0085] As shown in FIG. 2, the server 100, for example, implemented by a server computer, may include a communication interface 110, a processor 120, and a memory 130.

[0086] In some embodiments, the memory 130 (also referred to as a “database”) may store input data and/or output data temporarily or permanently. In some embodiments, the memory 130 may store program code which allows the server 100 to perform methods 210, 220 (as will be described with reference to FIGS. 3 and 4). In some embodiments, the program code may be embedded in a Software Development Kit (SDK). The memory 130 may include an internal memory of the server 100 and/or an external memory. The external memory may include, but is not limited to, an external storage medium, for example, a memory card, a flash drive, and a web storage.

[0087] In some embodiments, the communication interface 110 may allow one or more external devices, including a device associated with a buyer 150 and devices each associated with a plurality of suppliers 160, to communicate with the processor 120 of the server 100 via a network 190 (as described with reference to FIG. 1). In some embodiments, the communication interface 110 may transmit signals to the external devices, and/or receive signals from the external devices via the network 190.

[0088] In some embodiments, the communication interface 110 may receive a request for an item from the device associated with the buyer 150. The communication interface 110 may then send the request for the item to the processor 120.

[0089] In some embodiments, the communication interface 110 may further receive information about the request from the device associated with the buyer 150. In some embodiments, the request for the item may include a request for pricing the item, and the information about the request may include, but is not limited to, the number of requested items (quantity), preferred price of the item (or budget), and a preferred supplier score (for example, in the form of star rating).

[0090] In some embodiments, the communication interface 110 may receive concurrently the request for the item and the information about the request from the device associated with the buyer 150 via the network 190. In some other embodiments, the communication interface 110 may receive the request for the item first, and subsequently receive the information about the request, from the device associated with the buyer 150 via the network 190.

[0091] In some embodiments, the processor 120 may include, but is not limited to, a microprocessor, an analogue circuit, a digital circuit, a mixed-signal circuit, a logic circuit, an integrated circuit, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), or any combination thereof. Any other kind of implementation of the respective functions, which will be described below in further detail, may also be understood as the processor 120.

[0092] In some embodiments, the processor 120 may be connectable to the communication interface 110. In some embodiments, the processor 120 may be arranged in data or signal communication with the communication interface 110 to receive the request for the item and the information about the request from the communication interface 110.

[0093] In some embodiments, the processor 120 may receive concurrently the request for the item and the request from the communication interface 110. In some other embodiments, the processor 120 may receive the request for the item first, and subsequently receive the information about the request from the communication interface 110.

[0094] In some embodiments, upon receipt of the request from the device associated with the buyer 150, the processor 120 may, for each supplier of a plurality of suppliers 160 capable of supplying the item (for example, for each of a first supplier 161, a second supplier 162, and a third supplier 163), collect first information about performance of a supplier, and calculate a supplier score for the supplier based on the first information. For example, the processor 120 may collect first information about performance of the first supplier 161, and calculate a supplier score for the first supplier 161 based on the first information of the first supplier 161. The processor 120 may collect first information about performance of the second supplier 162, and calculate a supplier score for the second supplier 162 based on the first information of the second supplier 162. The processor 120 may collect first information about performance of the third supplier 163, and calculate a supplier score for the third supplier 163 based on the first information of the third supplier 163.

[0095] In some embodiments, the processor 120 may further collect second information about a revenue target of the supplier, and calculate a distance to the revenue target for the supplier based on the first information and the second information. In some embodiments, the distance to the revenue target may include a difference between the revenue target and performance achieved by the supplier. For example, the processor 120 may collect second information about a revenue target of the first supplier 161, and calculate a distance to the revenue target for the first supplier 161 based on the first information and the second information of the first supplier 161. The processor 120 may collect second information about a revenue target of the second supplier 162, and calculate a distance to the revenue target for the second supplier 162 based on the first information and the second information of the second supplier 162. The processor 120 may collect second information about a revenue target of the third supplier 163, and calculate a distance to the revenue target for the third supplier 163 based on the first information and the second information of the third supplier 163.

[0096] In some embodiments, the first information and the second information are stored in a private database 160b of each of the plurality of suppliers 160. In some embodiments, the processor 120 may trigger a smart contract 170 to collect the first information and the second information stored in the private database 160b of each of the plurality of suppliers 160. In some embodiments, the smart contract 170 may allow each of the plurality of devices associated with each of the plurality of suppliers 160 capable of supplying the item to create hashed first information and hashed second information based on the first information and the second information respectively, and store the hashed first information and the hashed second information in a public database 160a of each of the plurality of suppliers 160. For example, the smart contract 170 may allow the device associated with the first supplier 161 to create hashed first information and hashed second information of the first supplier 161 based on the first information and the second information of the first supplier 161 respectively, and store the hashed first information and the hashed second information in a public database 161a of the first supplier 161. The smart contract 170 may allow the device associated with the second supplier 162 to create hashed first information and hashed second information of the second supplier 162 based on the first information and the second information of the second supplier 162 respectively, and store the hashed first information and the hashed second information in a public database 162a of the second supplier 162. The smart contract 170 may allow the device associated with the third supplier 163 to create hashed first information and hashed second information of the third supplier 163 based on the first information and the second information of the third supplier 163 respectively, and store the hashed first information and the hashed second information in a public database 163a of the third supplier 163. In some embodiments, the processor 120 may collect the first information and the second information of the first supplier 161, the second supplier 162 and the third supplier 163 from the public database 161a of the first supplier 161, the public database 162a of the second supplier 162, and the public database 163a of the third supplier 163 respectively.

[0097] In some embodiments, the processor 120 may further calculate a dynamic price for the supplier based on the supplier score and the distance to the revenue target. For example, the processor 120 may calculate a dynamic price for the first supplier 161 based on the supplier score for the first supplier 161 and the distance to the revenue target for the first supplier 161. The processor 120 may calculate a dynamic price for the second supplier 162 based on the supplier score for the second supplier 162 and the distance to the revenue target for the second supplier 162. The processor 120 may calculate a dynamic price for the third supplier 163 based on the supplier score for the third supplier 163 and the distance to the revenue target for the third supplier 163.

[0098] In some embodiments, the output module 140 may output the calculated dynamic price, so that the buyer 150 may check the calculated dynamic price. For example, the output module 140 may include a display module configured to display the calculated dynamic price on a screen of the display module, so that the buyer 150 may view the calculated dynamic price. In some embodiments, the display module is configured to display information processed by the processor 120. The display module may include, but is not limited to, a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT-LCD), a light emitting diode (LED), an organic light emitting diode (OLED) display, an active-matrix organic light emitting diode (AMOLED), a plasma display panel (PDP), a quantum dot display, an electronic ink display, and a flexible display. As another example, the output module 140 may include an audio module configured to output audio signal about the calculated dynamic price. In some embodiments, the output module 140 may be implemented on the server 100. In some other embodiments, the output module 140 may be external to the server 100 and communicatively coupled to the server 100.

[0099] In some embodiments, the processor 120 may assign weights to at least one of the supplier score and the distance to the revenue target, and calculate the dynamic price for the supplier further based on the assigned weights. In some embodiments, the processor 120 may assign a first weight to the supplier score and a second weight to the distance to the revenue target. For example, the first weight may be different from the second weight. In some embodiments, the processor 120 may calculate the dynamic price for the supplier based on the supplier score given the first weight and the distance to the revenue target given the second weight. For example, the processor 120 may assign the first weight to the supplier score for the first supplier 161 and the second weight to the distance to the revenue target for the first supplier 161, and calculate the dynamic price for the first supplier 161 based on the supplier score for the first supplier 161 given the first weight and the distance to the revenue target for the first supplier 161 given the second weight. The processor 120 may assign the first weight to the supplier score for the second supplier 162 and the second weight to the distance to the revenue target for the second supplier 162, and calculate the dynamic price for the second supplier 162 based on the supplier score for the second supplier 162 given the first weight and the distance to the revenue target for the second supplier 162 given the second weight. The processor 120 may assign the first weight to the supplier score for the third supplier 163 and the second weight to the distance to the revenue target for the third supplier 163, and calculate the dynamic price for the third supplier 163 based on the supplier score for the third supplier 163 given the first weight and the distance to the revenue target for the third supplier 163 given the second weight.

[00100] In some embodiments, the processor 120 may collect supplier score information of the supplier from the buyer 150, match the supplier score and the supplier score information, and calculate the dynamic price further based on the matching. For example, the processor 120 may collect supplier score information of the first supplier 161 from the device associated with the buyer 150, match the supplier score for the first supplier 161 and the supplier score information of the first supplier 161, and calculate the dynamic price for the first supplier 161 further based on the matching. The processor 120 may collect supplier score information of the second supplier 162 from the device associated with the buyer 150, match the supplier score for the second supplier 162 and the supplier score information of the second supplier 162, and calculate the dynamic price for the second supplier 162 further based on the matching. The processor 120 may collect supplier score information of the third supplier 163 from the device associated with the buyer 150, match the supplier score for the third supplier 163 and the supplier score information of the third supplier 163, and calculate the dynamic price for the third supplier 163 further based on the matching.

[00101] In some embodiments, the processor 120 may use a machine learning model to calculate the supplier score. In some embodiments, the processor 120 may categorise the plurality of suppliers 160 into a predetermined number of supplier groups based on the first information, allocate a rating value to each of the supplier groups, and train the machine learning model using the allocated rating value to calculate the supplier score. In some embodiments, the processor 120 may use a k-means clustering algorithm to categorise the plurality of suppliers into the predetermined number of supplier groups. In some embodiments, the processor 120 may categorise the plurality of suppliers 160 into five (5) supplier groups based on the first information about performance of the plurality of suppliers 160. The processor 120 may then allocate a rating value, for example, in the form of star rating, to each of the supplier groups, and train the machine learning model using the allocated rating value, for example, in the form of star rating, to calculate the supplier score for the plurality of suppliers 160.

[00102] In some embodiments, when the buyer 150 requests for the item, the processor 120 may collect the first information about the performance of each of the plurality of suppliers 160 which is private data. In some embodiments, the first information may include, but is not limited to, information about quality, costs, delivery, and social media for each of the plurality of suppliers 160. Input data, for example, provided by the suppliers 160 and/or the supply chain finance platform 100, may include, but is not limited to, part defects, return rate on defects, a revenue target, a unit cost, inventory volume variability, time delivery, the number of delays, lead time variability, and social media data. For example, since transactions may happen in the supply chain finance platform 100, some data points such as delivery and social media rating may be derived from the supply chain finance platform 100 itself. The private data may be hashed and stored in the public database 160a of each of the plurality of suppliers 160 along with the supplier score for each of the plurality of suppliers 160. The supplier score for each of the plurality of suppliers 160 may be calculated by obtaining the first information about the performance of each of the plurality of suppliers 160 and executing an unsupervised machine learning algorithm such as the k-means clustering algorithm to form clusters. For example, five (5) clusters may be created, and each cluster may include suppliers with similar performance. As an example, the clusters may be given new star rating from 1 to 5, with 5 being the best rated suppliers. The labelled data, for example, data labelled with new star rating, may further be utilised to train a supervised machine learning model to perform multi-label classification and obtain supplier score for each of the plurality of suppliers 160.

[00103] FIG. 3 is a flow diagram illustrating a method 210 according to various embodiments. According to various embodiments, the method 210 for calculating a supplier score is provided.

[00104] In some embodiments, the method 210 may include a step 211 of receiving a request for an item from a buyer. [00105] In some embodiments, the method 210 may include a step 212 of, for each supplier of a plurality of suppliers capable of supplying the item, collecting first information about performance of a supplier.

[00106] In some embodiments, the method 210 may include a step 213 of, for each supplier of the plurality of suppliers capable of supplying the item, calculating a supplier score for the supplier based on the first information.

[00107] FIG. 4 is a flow diagram illustrating a method 220 according to various embodiments. According to various embodiments, the method 220 for dynamically pricing an item is provided.

[00108] In some embodiments, the method 220 may include a step 221 of receiving a request for an item from a buyer. For example, the request for the item may include a request for pricing the item.

[00109] In some embodiments, the method 220 may include a step 222 of, for each supplier of a plurality of suppliers capable of supplying the item, collecting first information about performance of a supplier.

[00110] In some embodiments, the method 220 may include a step 223 of, for each supplier of the plurality of suppliers capable of supplying the item, calculating a supplier score for the supplier based on the first information.

[00111] In some embodiments, the method 220 may include a step 224 of, for each supplier of the plurality of suppliers capable of supplying the item, collecting second information about a revenue target of the supplier.

[00112] In some embodiments, the method 220 may include a step 225 of, for each supplier of the plurality of suppliers capable of supplying the item, calculating a distance to the revenue target for the supplier based on the first information and the second information. In some embodiments, the distance to the revenue target may include a difference between the revenue target and performance achieved by the supplier.

[00113] In some embodiments, the method 220 may include a step 226 of, for each supplier of the plurality of suppliers capable of supplying the item, calculating a dynamic price for the supplier based on the supplier score and the distance to the revenue target.

[00114] FIG. 5 is a block diagram illustrating a device 300 according to various embodiments. [00115] In some embodiments, a system 1000 according to various embodiments includes the device 300. The device 300 may be associated with a server 100, a buyer 150, a supplier 160, or a financial institution 180 (as described with reference to FIG. 1). In some embodiments, the system 1000 may be hosted on a cloud computing platform. In some embodiments, the system 1000 may represent a web-based or cloud-based platform which may be accessed over a network 190 by the device 300.

[00116] In some embodiments, the device 300 may include, but is not limited to, at least one of the following: a mobile phone, a tablet computer, a laptop computer, a desktop computer, a head-mounted display, a smart watch, a server, a workstation, and a POS terminal. In some embodiments, the device 300 may include a single device, a plurality of devices located in close proximity, or a plurality of devices distributed over a geographic region.

[00117] As shown in FIG. 5, the device 300 may include a communication interface 310, a processor 320, a memory 330, an output module 340, and an input module 350.

[00118] In some embodiments, the embodiments about the communication interface 110 described with reference FIG. 2 may be applied to the communication interface 310. In some embodiments, the communication interface 310 may be communicatively coupled to the processor 320. In some embodiments, the communication interface 310 may include, but is not limited to, a wired network adapter, a wireless network adapter, a mobile network adapter, or other device capable of communicating to one or more different networks, including the network 190. In some embodiments, the device 300 may include the processor 320 and one or more communication interfaces 310 incorporated into or with the processor 320.

[00119] In some embodiments, the embodiments about the processor 120 described with reference to FIG. 2 may be applied to the processor 320. In some embodiments, the processor 320 may include one or more processing units (for example, in a multi-core configuration, etc.) including, but not limited to, a central processing unit (CPU), a microcontroller, a reduced instruction set computer (RISC) processor, an application specific integrated circuit (ASIC), a programmable logic device (PLD), a gate array, and/or any other circuit or processor capable of the functions described herein.

[00120] In some embodiments, the embodiments about the memory 130 described with reference to FIG. 2 may be applied to the memory 330. In some embodiments, the memory 330 may be communicatively coupled to the processor 320. In some embodiments, the memory 330 may include one or more memory devices which may allow data, instructions, etc., to be stored therein and retrieved therefrom. In some embodiments, the memory 330 may include one or more computer-readable storage media including, but not limited to, a dynamic random access memory (DRAM), a static random access memory (SRAM), a read only memory (ROM), an erasable programmable read only memory (EPROM), a solid state device, a flash drive, a CD-ROM, a thumb drive, a floppy disk, a tape, a hard disk, and/or any other type of volatile or non-volatile physical or tangible computer-readable media. In some embodiments, the memory 330 may be configured to store data including, but not limited to, transaction data, other data relating to the supplier 160, and/or other types of data and/or information suitable for use as described herein. Furthermore, in some embodiments, computer-executable instructions may be stored in the memory 330 for execution by the processor 320 to cause the processor 320 to perform one or more of the functions described herein, such that the memory 330 may be a physical, tangible, and non-transitory computer readable storage media. It may be appreciated that the memory 330 may include a variety of different memories, each implemented in one or more of the functions or processes described herein.

[00121] In some embodiments, the embodiments about the output module 140 described with reference to FIG. 2 may be applied to the output module 340. In some embodiments, the output module 340 may be communicatively coupled to the processor 320. As shown in FIG. 5, in some embodiments, the output module 340 may be implemented on the device 300. Although not shown, in some other embodiments, the output module 340 may be external to the device 300 and communicatively coupled to the device 300. In some embodiments, the output module 340 may output information, either visually or audibly to a user of the device 300, for example, the buyer 150 or the supplier 160. In some embodiments, the output module 340 may include a display module configured to display the information on a screen of the display module. In some embodiments, the display module is configured to display information processed by the processor 320. The display module may include, but is not limited to, a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT-LCD), a light emitting diode (LED), an organic light emitting diode (OLED) display, an active-matrix organic light emitting diode (AMOLED), a plasma display panel (PDP), a quantum dot display, an electronic ink display, and a flexible display. As another example, the output module 340 may include an audio module configured to output audio signal about the calculated dynamic price. In some embodiments, the output module 340 may include a plurality of output devices.

[00122] In some embodiments, the input module 350 may receive inputs from the user of the device 300 (i.e., user inputs). In some embodiments, the input module 350 may be communicatively coupled to the processor 320. In some embodiments, the input module 350 may include, but is not limited to, a keyboard, a pointing device, a mouse, a stylus, a touch sensitive panel (for example, a touch pad or a touch screen, etc.), another computing device, and/or an audio input device. In some embodiments, a touch screen, for example, included in a tablet computer, a smartphone, or similar device, may operate as both the output module 340 and the input module 350.

[00123] FIG. 6 is a sequence diagram illustrating calculating a supplier score 430.

[00124] As described with reference to FIG. 1, a server 100 (also referred to as a “supply chain finance platform”) may be hosted on a blockchain network with each participants having a copy of a ledger. In some embodiments, a smart contract 170 may be invoked to read private data including performance data 410 stored in a private database 160b of each of a plurality of suppliers 160.

[00125] As shown in FIG. 6, in some embodiments, the performance data 410 may include, but is not limited to, information about quality, costs, delivery, and social media for each of a plurality of suppliers 160. In some embodiments, an Al (artificial intelligence) model 420 may be executed to calculate the supplier score 430 for each of a plurality of suppliers 160. For example, the Al model 420 may be implemented in a processor 120 of the server 100. As another example, the Al model 420 may be implemented in a device associated with each of a plurality of suppliers 160 and controlled by the processor 120 of the server 100. In some embodiments, the Al model 420 may output the calculated supplier score 430 for each of a plurality of suppliers 160 and store the calculated supplier score 430 in a public database 160a of each of a plurality of suppliers 160, for example, along with hashed performance data of each of a plurality of suppliers 160.

[00126] FIG. 7A is an image diagram illustrating interactions 500 between a server 100, a supplier 160, a buyer 150, and a financial institution 180 according to various embodiments. FIG. 7B is a table diagram illustrating assessing a supplier score by the financial institution 180 according to various embodiments.

[00127] As shown in FIG. 7A, in some embodiments, the financial institution 180 may utilise the supplier score of the supplier 160 for flexible rate structuring during purchase order (PO) financing.

[00128] As shown in FIG. 7B, the table diagram 500 may show different impact of different supplier score and impact in bank rates. As described above, the supplier score may be utilised by the financial institution 180 as a risk score or a credit score in providing the flexible rate structuring during the purchase order financing. In some embodiments, the supplier score may be high for a less risky supplier, and the financing rate for such less risky supplier may be lower. On the other hand, the supplier score may be low for a risky supplier, and the financing rate for such risky supplier may be higher. [00129] FIG. 8 is an image diagram illustrating calculating a distance to a revenue target for a supplier 160 according to various embodiments.

[00130] As shown in FIG. 8, the distance to the revenue target for the supplier 160 may be calculated by capturing revenue details from the supplier 160. For example, a distance to a revenue target for a first supplier (SI) 161 may be calculated by capturing revenue details from the first supplier (SI) 161. A distance to a revenue target for a second supplier (S2) 162 may be calculated by capturing revenue details from the second supplier (S2) 162. A distance to a revenue target for a third supplier (S3) 163 may be calculated by capturing revenue details from the third supplier (S3) 163.

[00131] In some embodiments, the distance to the revenue target for the supplier 160 may include a difference between the revenue target and performance achieved by the supplier 160. As shown in FIG. 8, in some embodiments, a mathematical equation to calculate the distance to the revenue target for the supplier 160 is as follows:

Distance = \Revenue target - Percentage completed\

[00132] In some embodiments, the revenue target for the supplier 160 may be obtained at a start of a financial year. As the supplier 160 may keep fulfilling the orders, an absolute distance, which is an absolute difference between the revenue target and the percentage completed, may be calculated. In some embodiments, suppliers who have completed more orders may have a distance reduced, and suppliers who have received limited orders may have a high distance. The distance may be different for different suppliers. In some embodiments, the result may be normalised to have values between 0 to 1. In some embodiments, the calculated value which is hashed (i.e. the hashed distance) may be stored in a public database 160a of each of the plurality of suppliers 160 along with hashed revenue target.

[00133] FIG. 9 is an image diagram illustrating calculating a dynamic price according to various embodiments.

[00134] As shown in FIG. 9, a processor 120 (also referred to as a “pricing engine”) may calculate a dynamic price for a supplier 160, after capturing input demand data (for example, a request for an item (e.g. a request for pricing the item)) from a buyer 150 and obtaining a supplier score for the supplier 160, and summation with a distance to a revenue target for the supplier 160. In this manner, a new price for the supplier 160 may be calculated.

[00135] FIG. 10 is a table diagram illustrating calculating a dynamic price according to various embodiments. [00136] As shown in FIG. 10, the table diagram 600 may show different scenarios and impact of a distance to a revenue target and a supplier score for a supplier 160, while calculating a dynamic price for the supplier 160.

[00137] In some embodiments, the processor 120 may combine outputs of the supplier score of the supplier 160 and the distance to the revenue target for the supplier 160, so as to create a factor which may be multiplied with cost price or listed price of an item. The new price may be dependent on two (2) factors including the performance of the supplier (for example, the supplier score of the supplier 160) and the distance to the revenue target for the supplier 160. In some embodiments, suppliers with a high distance to the revenue target and high performance may lead to higher final price for the item, and suppliers with a low distance to the revenue target and low performance may lead to lower price for the item. The system 1000 according to various embodiments may reward suppliers with higher price for their item, if the suppliers may consistently keep the high performance for orders.

[00138] In some embodiments, a mathematical equation to calculate the supplier score for the supplier 160 is as follows:

Supplier Performance = fl(Quality) + f2(cost) + f3(Delivery) + f4(Social media review) [00139] In some embodiments, another mathematical equation to calculate the supplier score for the supplier 160 is as follows:

Supplier Performance = fl(P art defects , Audit report) + f2(unit cost, inventory) + f3(Timely Delivery , Delays )

[00140] In some embodiments, a mathematical equation to calculate the dynamic price for the supplier 160 is as follows:

Dynamic Price = { [distance] + [fl(Quality)+ f2(cost) +f3 (Delivery)]} x Current Price [00141] For example, as shown in the table diagram 600 of FIG. 10, for a first supplier who has the high distance and the high performance, the dynamic price may be calculated as follows:

Dynamic Price for the first supplier = (0.5 + 0.7) x 100 = 120

[00142] As another example, as shown in the table diagram 600 of FIG. 10, for a fourth supplier who has the low distance and the low performance, the dynamic price may be calculated as follows:

Dynamic Price for the fourth supplier = (0.4 + 0.6) x 96 = 96 [00143] FIG. 11 is a flow diagram illustrating a method 700 according to various embodiments. According to various embodiments, the method 700 for calculating a supplier score is provided.

[00144] In some embodiments, the method 700 may include a step 701 of obtaining supplier’s performance data.

[00145] In some embodiments, the method 700 may include a step 702 of categorising suppliers using k-means clustering algorithm.

[00146] In some embodiments, the method 700 may include a step 703 of creating a predetermined number of clusters of suppliers with similar performance.

[00147] In some embodiments, the method 700 may include a step 704 of labelling the clusters based on star rating.

[00148] In some embodiments, the method 700 may include a step 705 of utilising labelled data to train multi-label classification model.

[00149] In some embodiments, the method 700 may include a step 706 of utilising the model to obtain supplier score.

[00150] FIG. 12 is a flow diagram illustrating a method 800 according to various embodiments. According to various embodiments, the method 800 for dynamically pricing an item is provided.

[00151] In some embodiments, the method 800 may include a step 801 of obtaining supply chain data including supplier’s performance. In some embodiments, in the step 801, the supply chain data comprising the supplier’s performance data may be obtained.

[00152] In some embodiments, the method 800 may include a step 802 of extracting similar supplier. In some embodiments, in the step 802, extraction of the similar supplier may be carried out using a k-means clustering algorithm.

[00153] In some embodiments, the method 800 may include a step 803 of calculating supplier score using machine learning. In some embodiments, in the step 803, a calculation if the supplier score is performed using a supervised machine learning algorithm may be performed. [00154] In some embodiments, the method 800 may include a step 804 of calculating a distance to a revenue target.

[00155] In some embodiments, the method 800 may include a step 805 of computing a price based on past performance and the distance to the revenue target. In some embodiments, in the step 805, a new dynamic price may be calculated based on the past performance and the distance to the revenue target for the supplier. [00156] In some embodiments, the method 800 may include a step 806 of dynamically updating the price. In some embodiments, in the step 806, the price may be displayed to a buyer.

[00157] As described above, a server 100 according to various embodiments may use information such as past performance data of each supplier 160 which may be sensitive private data and hash the past performance data to store in a public chain. The server 100 according to various embodiments may create Al-based supplier score for the supplier 160 which may be tagged with the corresponding hashed past performance data. The supplier score for the supplier 160 may be used as a credit score or a supplier score for the suppliers 160 for understanding performance of the supplier 160 by a financial institution 180. The server 100 according to various embodiments may incorporate dynamic pricing. To perform the dynamic pricing, the server 100 may utilise private information such as annual revenue target of the supplier 160. The annual revenue target of the supplier 160 may be captured and stored as hashed information in the public chain. A revenue target distance calculator 121 (distance calculating module) may be utilised to determine pending target. New dynamic price of an item may be calculated by combining the supplier score of the supplier 160 along with the revenue target distance calculator 121 of the supplier 160 by assigning weights. The price may be updated after each transaction. The supplier 160 providing consistent good performance may be rewarded with higher price, to enable the supplier 160 to perform better and reach their annual target.

[00158] In this regard, in circumstances that a number of suppliers may be underbanked and face challenges in cash flow to deliver orders, the server 100 according to various embodiments may help to provide access to credit. The server 100 according to various embodiments may help the supplier 160 to obtain better price for their item based on performance. As a result, the price may be justified and stable and may also prevent inflation of price. The dynamic price calculated by the server 100 according to various embodiments may also help the supplier 160 to reach their revenue targets, and thus may create stickiness in the platform at the same time deliver quality items. A system 1000 according to various embodiments may be decentralized, and thus there may be trust in the price and the supplier scores which may be utilised by financial institutions 180. In this regard, costs may be reduced and credit assessment timeline may be shortened.

[00159] While embodiments of the invention have been particularly shown and described with reference to specific embodiments, it should be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. The scope of the invention is thus indicated by the appended claims and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced. It will be appreciated that common numerals, used in the relevant drawings, refer to components that serve a similar or the same purpose. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Unless specifically stated otherwise, the term “some” refers to one or more. Combinations such as “at least one of A, B, or C,” “one or more of A, B, or C,” “at least one of A, B, and C,” “one or more of A, B, and C,” and “A, B, C, or any combination thereof’ include any combination of A, B, and/or C, and may include multiples of A, multiples of B, or multiples of C. Specifically, combinations such as “at least one of A, B, or C,” “one or more of A, B, or C,” “at least one of A, B, and C,” “one or more of A, B, and C,” and “A, B, C, or any combination thereof’ may be A only, B only, C only, A and B, A and C, B and C, or A and B and C, where any such combinations may contain one or more member or members of A, B, or C. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. The words “module,” “mechanism,” “element,” “device,” and the like may not be a substitute for the word “means.” As such, no claim element is to be construed as a means plus function unless the element is expressly recited using the phrase “means for.”

Claims

1. A server for calculating a supplier score, the server comprising: a memory for storing instructions; and a processor for executing the stored instructions and configured to: receive a request for an item from a buyer; upon the receipt of the request from the buyer, for each supplier of a plurality of suppliers capable of supplying the item: collect first information about performance of a supplier; and calculate the supplier score for the supplier based on the first information.

2. The server according to claim 1, wherein the first information includes encrypted information.

3. The server according to claim 1 or claim 2, wherein the processor is further configured to: collect second information about a revenue target of the supplier; calculate a distance to the revenue target for the supplier based on the first information and the second information, wherein the distance to the revenue target includes a difference between the revenue target and performance achieved by the supplier; and calculate a dynamic price for the supplier based on the supplier score and the distance to the revenue target.

4. The server according to claim 3, wherein the processor is further configured to assign weights to at least one of the supplier score and the distance to the revenue target, and calculate the dynamic price for the supplier further based on the assigned weights.

5. The server according to claim 3 or claim 4, wherein the processor is further configured to collect supplier score information of the supplier from the buyer, match the supplier score and the supplier score information, and calculate the dynamic price further based on the matching.

6. The server according to any one of claims 3 to 5, wherein the first information and the second information are stored in a private database of the supplier.

7. The server according to claim 6, wherein the processor is further configured to, upon the receipt of the request from the buyer, trigger a smart contract to collect the first information and the second information stored in the private database, and the smart contract is configured to allow a device of the supplier to create hashed first information and hashed second information based on the first information and the second information respectively, and store the hashed first information and the hashed second information in a public database of the supplier.

8. The server according to claim 7, wherein the supplier score is stored in the public database along with the hashed first information.

9. The server according to claim 7 or claim 8, wherein the distance to the revenue target is stored in the public database along with the hashed second information.

10. The server according to any one of claims 3 to 9, wherein the processor is further configured to provide the supplier score as a credit score for the supplier, to one or more financial institutions.

11. The server according to any one of claims 3 to 10, wherein the processor is further configured to provide the supplier score to the buyer.

12. The server according to any one of claims 3 to 11, wherein the processor is further configured to re-calculate the supplier score based on the first information and the distance to the revenue target.

13. The server according to any one of claims 3 to 12, wherein the processor is further configured to use a machine learning model to calculate the supplier score, and the processor is further configured to categorise the plurality of suppliers into a predetermined number of supplier groups based on the first information, allocate a rating value to each of the supplier groups, and train the machine learning model using the allocated rating value to calculate the supplier score.

14. The server according to claim 13, wherein the processor is further configured to use a k-means clustering algorithm to categorise the plurality of suppliers into the predetermined number of supplier groups.

15. A method for calculating a supplier score, the method comprising: receiving a request for an item from a buyer; upon the receipt of the request from the buyer, for each supplier of a plurality of suppliers capable of supplying the item: collecting first information about performance of a supplier; and calculating the supplier score for the supplier based on the first information.

16. The method according to claim 15, wherein the first information includes encrypted information.

17. The method according to claim 15 or claim 16 further comprising: collecting second information about a revenue target of the supplier; calculating a distance to the revenue target for the supplier based on the first information and the second information, wherein the distance to the revenue target includes a difference between the revenue target and performance achieved by the supplier; and calculating a dynamic price for the supplier based on the supplier score and the distance to the revenue target.

18. The method according to claim 17 , wherein the calculating the dynamic price comprises : assigning weights to at least one of the supplier score and the distance to the revenue target; and calculating the dynamic price for the supplier further based on the assigned weights.

19. The method according to claim 17 or claim 18, wherein the calculating the dynamic price comprises: collecting supplier score information of the supplier from the buyer; matching the supplier score and the supplier score information; and calculating the dynamic price further based on the matching.

20. The method according to any one of claims 17 to 19, wherein the first information and the second information are stored in a private database of the supplier.

21. The method according to claim 20 further comprising: upon the receipt of the request from the buyer, triggering a smart contract to collect the first information and the second information stored in the private database; creating hashed first information and hashed second information based on the first information and the second information respectively; and storing the hashed first information and the hashed second information in a public database of the supplier.

22. The method according to claim 21 further comprising: storing the supplier score in the public database along with the hashed first information.

23. The method according to claim 21 or claim 22 further comprising: storing the distance to the revenue target in the public database along with the hashed second information.

24. The method according to any one of claims 17 to 23 further comprising: providing the supplier score as a credit score for the supplier, to one or more financial institutions.

25. The method according to any one of claims 17 to 24 further comprising: providing the supplier score to the buyer.

26. The method according to any one of claims 17 to 25 further comprising: re-calculating the supplier score based on the first information and the distance to the revenue target.

27. The method according to any one of claims 17 to 26, wherein the calculating the supplier score comprises: using a machine learning model to calculate the supplier score, and the method further comprises: categorising the plurality of suppliers into a predetermined number of supplier groups based on the first information; allocating a rating value to each of the supplier groups; and training the machine learning model using the allocated rating value to calculate the supplier score.

28. The method according to claim 27, wherein the categorising the plurality of suppliers into the predetermined number of supplier groups comprises: using a k-means clustering algorithm to categorise the plurality of suppliers into the predetermined number of supplier groups.

29. A computer program product, comprising instructions to cause the server according to any one of claims 1 to 14 to execute the steps of the method according to any one of claims 15 to 28.

30. A computer-readable medium having stored thereon the computer program product of claim 29.