Block chain technology applicability evaluation method for large-scale manufacturing industry

Abstract

The invention provides a block chain technology applicability evaluation method for a large-scale manufacturing industry, and relates to the technical field of block chains. According to the method, the expected income of the enterprise when the block chain is not adopted or the block chain is adopted is calculated on the basis of the game theory from the possible recall event as a starting point, some references are provided for the enterprise, and the enterprise can make a more reasonable decision better for the problem of whether to adopt the block chain or not. In the data processing link, processing such as least squares and normalization needs to be carried out on input data to obtain some important parameters; suggestions are given from the perspective of the game theory, calculation can be carried out on the basis of estimating behaviors of other enterprises, and the result can better reflect reality.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a method for evaluating the applicability of blockchain technology to a large-scale manufacturing industry. Background Art

[0002] The rapid development of large-scale manufacturing has brought challenges to product quality control. Traditional traceability systems have information islands and data falsification risks, making it difficult to achieve accurate recall. Blockchain technology, with its decentralized and tamper-proof characteristics, provides a new solution for traceability, which can break information islands, improve traceability efficiency and reliability, and reduce the risk of falsification. However, the high cost of blockchain has become a major obstacle to enterprise adoption. At present, some literature has studied the blockchain adoption strategy of enterprises and given some suggestions for enterprise decision-making. First, a game model is constructed, that is, the demand function of the enterprise is determined, and the enterprise's profit function is written. After that, the equilibrium solution (optimal solution) of the profit function is solved using game theory. Finally, by comparing and analyzing the equilibrium solutions, some conclusions on enterprise decision-making are drawn. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a blockchain technology applicability evaluation method for large-scale manufacturing industries in response to the deficiencies of the above-mentioned prior art. Starting from possible recall events and based on game theory, the expected benefits of enterprises before or after adopting blockchain are calculated, providing some references for enterprises so that they can make more reasonable decisions on whether to adopt blockchain.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A method for evaluating the applicability of blockchain technology in large-scale manufacturing industries. First, the enterprise inputs data on its own positioning and the sales volume, wholesale price, retail price, R&D investment, traceability system construction and maintenance cost, product qualification rate, recall compensation rate and sample data of the number of product recalls in the corresponding period of the series of products that need to be managed through blockchain within n cycles into the blockchain traceability system, where the positioning includes manufacturers and retailers;

[0006] After that, the blockchain traceability system starts to process the input data, including least squares estimation and normalization, to obtain parameters for subsequent analysis;

[0007] After processing the data, the expected benefits of enterprises before and after adopting blockchain are calculated, and the equilibrium solution is obtained using Stackelberg game theory;

[0008] Finally, the parameters obtained after previously processing the data are brought into the equilibrium solution, and recommendations are given to enterprises on whether to adopt blockchain or not.

[0009] Furthermore, the construction and maintenance costs of the traceability system include the costs required for the implementation of RFID (Radio Frequency Identification) technology and the labor costs of system operation, but do not include the amount of compensation;

[0010] The recall compensation ratio is the ratio of the compensation per unit product to the wholesale price / retail price;

[0011] If no recall event has occurred, the sample of the number of product recalls in the corresponding period is empty.

[0012] Furthermore, the least squares estimation specifically includes:

[0013] In microeconomics, the demand function is defined as D = A-bp, where D is the actual sales volume, A is the market potential, that is, the maximum demand under ideal conditions, and b represents the consumer's sensitivity to price, and p is the retail price. Using n groups of retail prices and sales volumes, and then using the least squares method, the coefficients A and b are estimated; at this time, in represents the estimated value of the consumer's sensitivity to price, represents the estimated value of market potential, p i represents the i-th data in the retail price data set, represents the average value of all data in the retail price data set, D i represents the i-th data in the sales volume data set, Represents the average value of all data in the sales volume data group;

[0014] Next, solve for the unit upgrade cost c m ; The total R&D cost is defined as C m =C+c m D, among which C m is the total R&D investment, C is the fixed investment, c m is the unit upgrade cost; using the R&D investment and sales volume of n groups, and then using the least squares method, the parameter c is estimated m and D; at this time, in, represents the estimated value of the unit upgrade cost, D i represents the i-th data in the sales volume data set, Represents the average value of all data in the sales data set. represents the i-th data in the total R&D investment data set, It represents the average value of all data in the total R&D investment data group;

[0015] Then calculate the recall accuracy and total traceability cost coefficient; set n1 as the sample size of the recall event, x as the number of recalled products, then the accuracy of each recall is Among them, x i is the i-th data in the recalled product quantity data set, r i represents the qualified rate of the i-th batch of products; the total traceability cost is defined as Using the quadratic function fitting method to solve the coefficient c t1 、c t2 and c t3 .

[0016] Furthermore, the normalization process is specifically as follows:

[0017] Assume A = 1, then in represents the estimated value of the coefficient of the quadratic term of the total cost function of tracing, c t1 Represents the coefficient of the quadratic term of the total cost function of tracing; represents the estimated value of the coefficient of the first-order term of the total cost function of tracing, c t2 Represents the coefficient of the first-order term of the total cost function of tracing; represents the estimated value of the constant coefficient of the total cost function of tracing, c t3 represents the constant term coefficient of the total cost function of tracing; if there is no recall accident, directly assume that c t1 =0.2, c t2 =0, c t3 =0, then define other variables, the product qualification rate is Wholesale price is w i Represents the i-th data in the wholesale price data set; it is assumed that the wholesale price is constant while the retail price changes.

[0018] Furthermore, in the calculation of the expected benefits of the enterprise before and after adopting the blockchain, a game model consisting of a manufacturer and a retailer is considered; the benefit function of the enterprise before adopting the blockchain is:

[0019]

[0020] Among them, the subscript M refers to the manufacturer, the subscript R refers to the retailer, and the superscript N indicates that the blockchain is not adopted. Respectively represent the benefits of manufacturers and retailers when they do not adopt blockchain; represents the product demand in the first stage, when the manufacturer needs to decide on the traceability efforts to minimize the “fewer recalls” ratio, and the retailer needs to decide on the retail price. Sell ​​products to consumers; k represents the compensation rate, which is the ratio of the unit compensation amount to the first-stage retail price, determined by the losses caused to consumers by product defects and the company's recall policy; represents the product demand in the second stage. At this time, in order to prevent the production of defective products, the manufacturer firstly increases the unit cost c m After upgrading the product, the product is sold to retailers at wholesale price w, and the retailers sell it at retail price Selling products to consumers; The market potential will be reduced, assuming that the reduction in demand is proportional to the recall accuracy;

[0021] Using Stackelberg game theory to solve the equilibrium solution, we get:

[0022]

[0023] in, represents the optimal revenue of the manufacturer when blockchain is not adopted, Represents the optimal revenue of retailers when blockchain is not adopted.

[0024] Furthermore, in the calculation of the expected benefits of an enterprise before and after adopting blockchain, the benefit function of the enterprise when adopting blockchain is:

[0025]

[0026] Among them, the superscript B indicates the situation of adopting blockchain. represents the benefits to manufacturers when adopting blockchain, Represents the benefits to retailers if they adopt blockchain; θ is the growth rate of market potential brought by the adoption of blockchain; c b The unit cost of blockchain adoption for enterprises; represents the retail price in the first phase, represents the retail price in the second phase;

[0027] Compared with the situation before blockchain was adopted, assuming that after blockchain was adopted, enterprises can conduct accurate recalls, that is, enterprises can recall all problematic products without missing any recalls;

[0028] Using Stackelberg game theory to solve the equilibrium solution, we get:

[0029]

[0030] in, They represent the optimal benefits of manufacturers and retailers under the adoption of blockchain.

[0031] Furthermore, the suggestions given to enterprises on whether to adopt or not adopt blockchain are:

[0032] For manufacturers, if It is recommended that enterprises adopt blockchain, otherwise, they should not adopt blockchain; for retailers, if It is recommended that enterprises adopt blockchain, otherwise, they should not adopt blockchain.

[0033] The beneficial effect of adopting the above technical solution is that the blockchain technology applicability assessment method for large-scale manufacturing industries provided by the present invention takes game theory as its theoretical basis, takes into account the impact of recall events on the company's blockchain adoption decision, and converts actual data into model data through some reasonable data processing methods. On the premise of taking into account the actual game scenarios, some suggestions are made to the company, so that the company can make a better decision on whether to adopt blockchain. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flow chart of a method for evaluating the applicability of blockchain technology for large-scale manufacturing industries provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0036] like Figure 1 As shown, the method of this embodiment is described as follows.

[0037] First, the enterprise needs to input the data of its own positioning and the sales volume, wholesale price, retail price, R&D investment of the series of products that need to be managed through blockchain within n cycles, the construction and maintenance cost of the traceability system (including the cost of implementing technologies such as RFID, the labor cost of the system operator, etc., excluding the amount of compensation), product qualification rate, recall compensation ratio (the ratio of unit product compensation to wholesale price / retail price) and the sample of the number of product recalls in the corresponding cycle (if a recall event has occurred) into the blockchain traceability system, where the positioning includes manufacturers (hereinafter referred to as M) and retailers (hereinafter referred to as R). n is preferably greater than 5, and the larger the better, which can help the system obtain more accurate parameters. The cycle can be arbitrary, it can be one day, one week, or even one year, but it is best to ensure the sample size, so as to ensure the accuracy of the results.

[0038] After that, the blockchain traceability system starts to process the data. In microeconomics, the demand function is defined as D=A-bp, where D is the actual sales volume, A is the market potential, that is, the maximum demand under ideal conditions, and b represents the consumer's sensitivity to price. The larger b is, the greater the demand fluctuation caused by price increases or decreases; p is the retail price. This embodiment uses n groups of retail prices and sales volumes, and then uses the least squares method to estimate the coefficients A and b. At this time, in represents the estimated value of the consumer's sensitivity to price, represents the estimated value of market potential, p i represents the i-th data in the retail price data set, represents the average value of all data in the retail price data set, D i represents the i-th data in the sales volume data set, Represents the average value of all data in the sales volume data set.

[0039] Next, solve for the unit upgrade cost c m The total R&D cost is defined as C m =C+c m D, among which C m is the total R&D investment, C is the fixed investment, c m This embodiment uses the R&D investment and sales volume of n groups and the least squares method to estimate the coefficient c m and D, where c m is the parameter required for this embodiment. Then calculate the recall accuracy and the total traceability cost coefficient; in this embodiment, n1 is set as the sample size of the recall event, x is the number of recalled products, and the accuracy of each recall is Among them, x i is the i-th data in the recalled product quantity data set, r i represents the qualified rate of the i-th batch of products. The total traceability cost is defined as This embodiment uses a quadratic function fitting method to solve c t1 、c t2 、c t3 .

[0040] After calculating the above parameters, these parameters are normalized. In this embodiment, it is assumed that A = 1, then If there has been no recall, then assume that c t1 =0.2, c t2 =0, c t3 = 0. Then, define other variables, product qualification rate Wholesale Price This embodiment assumes that the wholesale price is constant and the retail price changes, because in general situations, the wholesale price will not change dramatically over time, while the retail price will change relatively greatly.

[0041] After processing the data, we start to calculate the expected benefits of enterprises when they do not adopt blockchain and when they adopt blockchain. This example considers a game model consisting of a manufacturer and a retailer. First, the benefit function when blockchain is not adopted is written as:

[0042]

[0043] Among them, M is the manufacturer, R is the retailer, represents the product demand in the first stage, when the manufacturer needs to decide on the traceability efforts to minimize the “fewer recalls” ratio, and the retailer needs to decide on the retail price. Sell ​​products to consumers; k represents the compensation rate, which is the ratio of the unit compensation amount to the first-stage retail price, determined by the losses caused to consumers by product defects and the company's recall policy; represents the product demand in the second stage. At this time, in order to prevent the production of defective products, the manufacturer firstly increases the unit cost c m After upgrading the product, the product is sold to retailers at wholesale price w, and the retailers sell it at retail price Sell ​​products to consumers. It is worth noting that The market potential will be reduced because the recall is not accurate enough, which will lead to reduced consumer trust in the enterprise, that is, reduced reputation of the enterprise, which will further lead to reduced demand. This embodiment assumes that the reduction in demand is proportional to the recall accuracy.

[0044] Using Stackelberg game theory to solve the equilibrium solution, we get:

[0045]

[0046] in, represents the optimal revenue of the manufacturer when blockchain is not adopted, Represents the optimal revenue of retailers when blockchain is not adopted.

[0047] After that, the profit function when adopting the blockchain is:

[0048]

[0049] Among them, B represents the situation of adopting blockchain. represents the benefits to manufacturers when adopting blockchain, Represents the benefits to retailers if blockchain is adopted. Where θ is the growth rate of market potential brought by the adoption of blockchain (this part appears because consumers generally trust blockchain and therefore believe the information uploaded to blockchain). In this embodiment, it is assumed that θ = 0.2. b is the unit cost that enterprises have to bear when adopting blockchain. In this example, it is assumed that c b is 0.2. represents the retail price in the first phase, Indicates the retail price in the second stage.

[0050] Compared with the situation when blockchain is not adopted, this embodiment assumes that after adopting blockchain, the enterprise can conduct accurate recall, that is, the enterprise can recall all problematic products without missing any recall.

[0051] Using Stackelberg game theory to solve the equilibrium solution, we get:

[0052]

[0053] in, They represent the optimal benefits of manufacturers and retailers under the adoption of blockchain.

[0054] Finally, the previously obtained parameters are brought into the equilibrium solution. For the manufacturer, if It is recommended that enterprises adopt blockchain, otherwise, they should not adopt blockchain; for retailers, if It is recommended that enterprises adopt blockchain, otherwise, they should not adopt blockchain.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope defined by the claims of the present invention.

Claims

1. A method for evaluating the applicability of blockchain technology for large-scale manufacturing industries, characterized by: First, the enterprise inputs its own positioning and the sales volume, wholesale price, retail price, R&D investment, traceability system construction and maintenance cost, product qualification rate, recall compensation ratio and sample data of the number of product recalls in the corresponding period of the series of products that need to be managed through blockchain within n cycles into the blockchain traceability system, where the positioning includes manufacturers and retailers; After that, the blockchain traceability system starts to process the input data, including least squares estimation and normalization, to obtain parameters for subsequent analysis; After processing the data, the expected benefits of enterprises before and after adopting blockchain are calculated, and the equilibrium solution is obtained using Stackelberg game theory; Finally, the parameters obtained after previously processing the data are brought into the equilibrium solution, and recommendations are given to enterprises on whether to adopt blockchain or not.

2. The blockchain technology applicability assessment method for large-scale manufacturing industry according to claim 1 is characterized by: The construction and maintenance costs of the traceability system include the costs required for the implementation of RFID (Radio Frequency Identification) technology and the labor costs of system operation, but do not include the amount of compensation; The recall compensation ratio is the ratio of the compensation per unit product to the wholesale price / retail price; If no recall event has occurred, the sample of the number of product recalls in the corresponding period is empty.

3. The blockchain technology applicability assessment method for large-scale manufacturing industry according to claim 2 is characterized by: The least squares estimation specifically includes: In microeconomics, the demand function is defined as D = A-bp, where D is the actual sales volume, A is the market potential, that is, the maximum demand under ideal conditions, and b represents the consumer's sensitivity to price, and p is the retail price. Using n groups of retail prices and sales volumes, and then using the least squares method, the coefficients A and b are estimated; at this time, in represents the estimated value of the consumer's sensitivity to price, represents the estimated value of market potential, p i represents the i-th data in the retail price data set, represents the average value of all data in the retail price data set, D i represents the i-th data in the sales volume data set, Represents the average value of all data in the sales volume data group; Next, solve for the unit upgrade cost c m ; The total R&D cost is defined as C m =C+c m D, among which C m is the total R&D investment, C is the fixed investment, c m is the unit upgrade cost; using the R&D investment and sales volume of n groups, and then using the least squares method, the parameter c is estimated m and D; at this time, in, represents the estimated value of the unit upgrade cost, D i represents the i-th data in the sales volume data set, Represents the average value of all data in the sales data set. represents the i-th data in the total R&D investment data set, It represents the average value of all data in the total R&D investment data group; Then calculate the recall accuracy and total traceability cost coefficient; set n1 as the sample size of the recall event, x as the number of recalled products, then the accuracy of each recall is Among them, x i is the i-th data in the recalled product quantity data set, r i represents the qualified rate of the i-th batch of products; the total traceability cost is defined as Using the quadratic function fitting method to solve the coefficient c t1 、c t2 and c t3 .

4. The blockchain technology applicability assessment method for large-scale manufacturing industry according to claim 3 is characterized by: The normalization process is specifically as follows: Assume A = 1, then in represents the estimated value of the coefficient of the quadratic term of the total cost function of tracing, c t1 Represents the coefficient of the quadratic term of the total cost function of tracing; represents the estimated value of the coefficient of the first-order term of the total cost function of tracing, c t2 Represents the coefficient of the first-order term of the total cost function of tracing; represents the estimated value of the constant coefficient of the total cost function of tracing, c t3 represents the constant term coefficient of the total cost function of tracing; if there is no recall accident, directly assume that c t1 =0.2, c t2 =0, c t3 =0, then define other variables, the product qualification rate is Wholesale price is w i Represents the i-th data in the wholesale price data set; it is assumed that the wholesale price is constant while the retail price changes.

5. The blockchain technology applicability assessment method for large-scale manufacturing industry according to claim 4 is characterized by: In the calculation of the expected benefits of enterprises before and after adopting blockchain, a game model consisting of a manufacturer and a retailer is considered; the benefit function of the enterprise before adopting blockchain is: Among them, the subscript M refers to the manufacturer, the subscript R refers to the retailer, and the superscript N indicates that the blockchain is not adopted. Respectively represent the benefits of manufacturers and retailers when they do not adopt blockchain; represents the product demand in the first stage, when the manufacturer needs to decide on the traceability efforts to minimize the "fewer recalls" ratio, and the retailer needs to decide on the retail price. Sell ​​products to consumers; k represents the compensation rate, which is the ratio of the unit compensation amount to the first-stage retail price, determined by the losses caused to consumers by product defects and the company's recall policy; represents the product demand in the second stage. At this time, in order to prevent the production of defective products, the manufacturer firstly increases the unit cost c m After upgrading the product, the product is sold to retailers at wholesale price w, and the retailers sell it at retail price Selling products to consumers; The market potential will be reduced, assuming that the reduction in demand is proportional to the recall accuracy; Using Stackelberg game theory to solve the equilibrium solution, we get: in, represents the optimal revenue of the manufacturer when blockchain is not adopted, Represents the optimal revenue of retailers when blockchain is not adopted.

6. The blockchain technology applicability assessment method for large-scale manufacturing industry according to claim 5 is characterized by: In the calculation of the expected benefits of enterprises before and after adopting blockchain, the benefit function of enterprises when adopting blockchain is: Among them, the superscript B indicates the situation of adopting blockchain. represents the benefits to manufacturers when adopting blockchain, Represents the benefits to retailers if they adopt blockchain; θ is the growth rate of market potential brought by the adoption of blockchain; c b The unit cost of blockchain adoption for enterprises; represents the retail price in the first phase, represents the retail price in the second phase; Compared with the situation before blockchain was adopted, assuming that after blockchain was adopted, enterprises can conduct accurate recalls, that is, enterprises can recall all problematic products without missing any recalls; Using Stackelberg game theory to solve the equilibrium solution, we get: in, They represent the optimal benefits of manufacturers and retailers under the adoption of blockchain.

7. The blockchain technology applicability assessment method for large-scale manufacturing industry according to claim 6 is characterized by: The recommendations for enterprises to adopt or not adopt blockchain are: For manufacturers, if It is recommended that enterprises adopt blockchain, otherwise, they should not adopt blockchain; for retailers, if It is recommended that enterprises adopt blockchain, otherwise, they should not adopt blockchain.

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