Logistics service supply chain elasticity evaluation method

Abstract

The invention discloses a logistics service supply chain elastic evaluation method, which comprises a multi-dimensional comprehensive evaluation system, a dynamic adaptation and real-time feedback mechanism, deep mining of historical data in combination with big data and an algorithm, combination of the method and a decision support system, integration of sustainable development and environmental protection factors, emphasis of cooperation and information sharing, and realization of flexible evaluation of a logistics service supply chain. A customized evaluation scheme is provided, data transparency and traceability are ensured by using a block chain technology, a comprehensive score S is calculated, an investigation period T is set to be continuously recorded and the score is periodically updated to serve as a continuous cooperation evaluation basis, and after the investigation period is finished, whether cooperation continues or not is determined according to an average score Savg and score fluctuation sigma. If the average score is higher than the threshold and fluctuates within an acceptable range, maintaining or enhancing cooperation; otherwise, adjusting the strategy; the method not only improves the elasticity and sustainability of a supply chain, but also promotes long-term stable cooperation through a scoring mechanism, promotes the healthy development of the logistics service industry, and achieves a win-win situation of economic benefits and social benefits.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics services, and in particular to a logistics service supply chain elasticity evaluation method. Background Art

[0002] With the rapid development of globalization and e-commerce, the logistics supply chain plays an increasingly important role in the circulation of goods. However, the logistics supply chain faces numerous challenges, such as market fluctuations, natural disasters, and technical failures. These challenges can lead to supply chain disruptions and seriously affect the continuity and reliability of logistics services. Therefore, assessing the resilience of the logistics supply chain—its ability to recover and adapt in the face of emergencies—has become a key issue.

[0003] Traditional logistics service supply chain resilience evaluation methods often focus on static, single-dimensional assessments, ignoring the dynamic and complex nature of supply chains. Furthermore, existing evaluation methods lack a continuous collaborative evaluation mechanism and are unable to adjust collaborative strategies based on the actual performance of the supply chain, thus limiting supply chain optimization and upgrading.

[0004] Therefore, we propose a logistics service supply chain resilience evaluation method. Summary of the Invention

[0005] The purpose of the present invention is to provide a logistics service supply chain elasticity evaluation method to solve the problems raised in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a logistics service supply chain elasticity evaluation method, comprising the following method steps:

[0007] Step 1: Build a multi-dimensional comprehensive evaluation system that covers key links in the logistics service supply chain, including transportation, warehousing, distribution, and information processing, and quantify the resilience of each link;

[0008] Step 2: Design a dynamic adaptation and real-time feedback mechanism to dynamically adjust evaluation criteria and weights based on changes in market environment, customer needs, emergencies, and other factors;

[0009] Step 3: Leverage big data technology and artificial intelligence algorithms to conduct in-depth mining and analysis of historical data from the logistics service supply chain, establish a predictive model, and anticipate the risks and challenges the supply chain may face in the future.

[0010] Step 4: Integrate the resilience evaluation method with a decision support system to provide decision makers in the logistics service supply chain with an intuitive and easy-to-use tool to evaluate the impact of different decision options on supply chain resilience through system simulation and simulation;

[0011] Step 5: Integrate sustainable development and environmental protection factors into the evaluation of logistics service supply chain resilience, assessing the supply chain's ability to address environmental risks, reduce carbon emissions, and improve resource efficiency.

[0012] Step 6: Emphasize collaboration and information sharing among organizations in the logistics service supply chain, establish a collaborative evaluation mechanism, and promote communication and collaboration among organizations;

[0013] Step 7: Provide customized evaluation plans for different types of logistics service supply chains, and design the evaluation plans in a modular manner to facilitate flexible combination and adjustment according to different needs;

[0014] Step 8: Leverage blockchain technology to achieve transparency and traceability of data in the logistics service supply chain, ensuring the fairness and credibility of evaluation results;

[0015] Step 9: Calculate the comprehensive score S of the logistics service supply chain and set a probation period T based on the comprehensive score S and its fluctuations. During the probation period, continuously record and regularly update the comprehensive score of the supply chain as the basis for ongoing cooperation evaluation.

[0016] Step 10: After the inspection period T ends, the supply chain's average score S_avg and score fluctuation σ during the inspection period are used as the main basis for deciding whether to continue cooperation with the supply chain. If the average score is higher than the preset cooperation threshold and the score fluctuation is within an acceptable range, the cooperation relationship is maintained or strengthened; otherwise, the cooperation strategy is considered to be adjusted.

[0017] As a preferred embodiment of the present invention, the quantitative formula is: E_i=f(x_i,y_i,z_i), where E_i is the elasticity score of the i-th link, and x_i, y_i, and z_i are the key factors affecting the elasticity of the link.

[0018] As a preferred embodiment of the present invention, the weight adjustment formula is: W_i'=g(ΔM, ΔC, ΔE)*W_i, where W_i' is the adjusted weight, W_i is the original weight, ΔM, ΔC, ΔE are the changes in market environment, customer demand, and emergencies, respectively, and g is the weight adjustment function.

[0019] As a preferred embodiment of the present invention, the prediction model is: R=h(D), where R is the predicted risk, D is the historical data, and h is the prediction function.

[0020] As a preferred embodiment of the present invention, the impact assessment formula is: ΔE=I(P,D);

[0021] Where ΔE is the elastic impact, P is the decision plan, D is the decision environment, and I is the impact evaluation function.

[0022] As a preferred embodiment of the present invention, the environmental assessment formula is: G = Σ_i = 1^n k_i * E_i',

[0023] Where G is the environmental protection score, k_i is the weight of the i-th environmental protection indicator, and E_i' is the quantitative score of the i-th environmental protection indicator.

[0024] As a preferred embodiment of the present invention, the collaborative evaluation formula is: C = j (T, I, P),

[0025] Where C is the collaboration score, T is the collaboration time, I is the degree of information sharing, P is the collaborative project, and j is the collaboration evaluation function.

[0026] As a preferred embodiment of the present invention, the customized evaluation scheme is: M=l(N, S, R),

[0027] Where M is the evaluation scheme, N is the supply chain type, S is the service demand, R is the evaluation requirement, and l is the customization function;

[0028] The data transparency formula is: T = m(B), where T is data transparency, B is blockchain data, and m is the transparency calculation function;

[0029] The formula for calculating the comprehensive score is: S = Σ_i = 1T S_t / T, where S_t is the score at the tth inspection time point and T is the inspection period; the formula for calculating the score fluctuation is: σ = √[(Σ_t = 12) / T].

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] This invention achieves a multi-dimensional, dynamic, quantitative assessment of supply chain resilience, significantly improving its accuracy and practicality. By building a comprehensive evaluation system covering key links such as transportation, warehousing, distribution, and information processing, and combining big data and artificial intelligence algorithms, this invention can accurately predict supply chain risks, provide strong support to decision makers, optimize the decision-making process, and improve supply chain operational efficiency.

[0032] In addition, this method incorporates sustainable development and environmental protection factors, ensuring the supply chain's ability to respond to environmental risks, reduce carbon emissions, and improve resource utilization efficiency, which is in line with the green development trend of modern logistics services. At the same time, it emphasizes collaboration and information sharing among organizations in the supply chain, establishes a collaborative evaluation mechanism, and enhances the collaborative efficiency and overall competitiveness of the supply chain.

[0033] The introduction of a scoring mechanism is a highlight of this invention. By continuously recording and regularly updating the comprehensive score of the supply chain as the basis for ongoing cooperation evaluation, it not only provides a scientific basis for the selection and management of partners, but also encourages all participants in the supply chain to continuously improve their performance and form an atmosphere of healthy competition and cooperation. This mechanism helps to promote the stability and long-term cooperation of the supply chain, reduce cooperation risks, and improve overall benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0035] Figure 1 This is a flow chart of a logistics service supply chain elasticity evaluation method of the present invention. DETAILED DESCRIPTION

[0036] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0037] Example

[0038] A logistics service supply chain elasticity evaluation method

[0039] The specific implementation methods are as follows:

[0040] 1. Build a multi-dimensional comprehensive evaluation system

[0041] We have established a multi-dimensional comprehensive evaluation system for key links in the logistics service supply chain, including transportation, warehousing, distribution, and information processing. Specifically, we have established key influencing factors for each link and set quantitative indicators and scoring standards for them; the details are as follows:

[0042] Transportation: Key factors include transportation efficiency, transportation costs, and transportation safety. Quantitative indicators include transportation efficiency (E_t) = transportation mileage / transportation time, and the scoring criteria are set based on the numerical range of E_t.

[0043] Warehousing: Key influencing factors include storage capacity C_w, storage cost Cost_w, and storage efficiency E_w. Quantitative indicators such as storage cost Cost_w = cost per unit storage area × storage area. The scoring criteria are also set based on the numerical range of Cost_w.

[0044] Delivery: Key factors include delivery speed V_d, delivery accuracy A_d, delivery cost Cost_d, etc. Quantitative indicators and scoring criteria are similar;

[0045] Information processing: Key influencing factors include information processing capability (P_i), information security (S_i), and information sharing level (H_i). Quantitative indicators include information processing capability (P_i) = information processing speed × information processing accuracy. Scoring criteria are based on the numerical range of P_i.

[0046] 2. Realize dynamic adaptation and real-time feedback mechanism

[0047] Design a dynamic adaptation and real-time feedback mechanism to dynamically adjust the evaluation criteria and weights based on changes in market environment, customer needs, emergencies, and other factors. The adjustment formula is as follows:

[0048] W_new=g(ΔM, ΔC, ΔE)×W_old

[0049] Among them, W_new is the adjusted weight, W_old is the original weight, ΔM, ΔC, and ΔE are the changes in market environment, customer demand, and emergencies, respectively, and g is the weight adjustment function.

[0050] 3. Leverage big data and artificial intelligence algorithms

[0051] We have established a forecasting model using big data technology and artificial intelligence algorithms to predict the risks and challenges that the logistics service supply chain may face in the future. The forecasting model is as follows:

[0052] R=h(D)

[0053] Among them, R is the predicted risk, D is the historical data, and h is the prediction function.

[0054] 4. Develop a decision support system

[0055] By combining the elasticity assessment method with a decision support system, a corresponding software system was developed. This system can provide decision support based on the assessment results and prediction model. The impact assessment formula is as follows:

[0056] ΔE=I(P,D)

[0057] Among them, ΔE is the elastic impact, P is the decision plan, D is the decision environment, and I is the impact evaluation function.

[0058] 5. Consider sustainable development and environmental protection factors

[0059] When evaluating the resilience of the logistics service supply chain, we fully consider sustainable development and environmental protection factors. The environmental protection assessment formula is as follows:

[0060] G=Σ_i=1^n k_i*E_i'

[0061] Among them, G is the environmental protection score, k_i is the weight of the i-th environmental protection indicator, and E_i' is the quantitative score of the i-th environmental protection indicator;

[0062] 6. Establish a collaborative cooperation and information sharing mechanism

[0063] Establish a collaborative cooperation and information sharing mechanism, set collaborative evaluation standards and information sharing requirements. Collaborative evaluation formulas are as follows:

[0064] C=j(T,I,P)

[0065] Among them, C is the collaboration score, T is the collaboration time, I is the information sharing degree, P is the collaborative project, and j is the collaboration evaluation function;

[0066] 7. Develop a customized evaluation plan

[0067] We have developed customized evaluation schemes for different types of logistics service supply chains. The customized evaluation scheme formula is as follows:

[0068] M=l(N,S,R)

[0069] Among them, M is the evaluation scheme, N is the supply chain type, S is the service demand, R is the evaluation requirement, and l is the customization function.

[0070] 8. Apply blockchain technology

[0071] Blockchain technology is used to achieve transparency and traceability of data in the logistics service supply chain. The data transparency formula is as follows:

[0072] T=m(B)

[0073] Among them, T is data transparency, B is blockchain data, and m is the transparency calculation function.

[0074] 9. Implement a continuous cooperation evaluation mechanism

[0075] Implement a continuous cooperation evaluation mechanism, set an inspection period T, and continuously record and regularly update the comprehensive score S of the logistics service supply chain. The comprehensive score calculation formula is as follows:

[0076] S=Σ_t=1^T S_t / T

[0077] Where S_t is the score at the tth inspection point. After the inspection period T, the supply chain's performance is evaluated based on its average score S_avg and the score fluctuation σ during the inspection period. If the average score exceeds the preset cooperation threshold and the score fluctuation is within an acceptable range, the partnership is maintained or strengthened; otherwise, adjustments to the cooperation strategy or the search for new partners are considered.

[0078] In summary, the logistics service supply chain resilience evaluation method integrated with continuous cooperation evaluation proposed in the present invention not only improves the resilience and sustainability of the supply chain, but also promotes long-term and stable cooperative relationships among all supply chain participants through the scoring mechanism, jointly promotes the healthy development of the logistics service industry, and achieves a win-win situation in economic and social benefits.

[0079] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention. Therefore, the embodiments should be regarded as illustrative and non-restrictive in all respects. The scope of the present invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be included therein.

[0080] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

Claims

1. A logistics service supply chain elasticity evaluation method, characterized by: The method comprises the following steps: Step 1: Build a multi-dimensional comprehensive evaluation system that covers key links in the logistics service supply chain, including transportation, warehousing, distribution, and information processing, and quantify the resilience of each link; Step 2: Design a dynamic adaptation and real-time feedback mechanism to dynamically adjust evaluation criteria and weights based on changes in market environment, customer needs, emergencies, and other factors; Step 3: Leverage big data technology and artificial intelligence algorithms to conduct in-depth mining and analysis of historical data from the logistics service supply chain, establish a predictive model, and anticipate the risks and challenges the supply chain may face in the future. Step 4: Integrate the resilience evaluation method with a decision support system to provide decision makers in the logistics service supply chain with an intuitive and easy-to-use tool to evaluate the impact of different decision options on supply chain resilience through system simulation and simulation; Step 5: Integrate sustainable development and environmental protection factors into the evaluation of logistics service supply chain resilience, assessing the supply chain's ability to address environmental risks, reduce carbon emissions, and improve resource efficiency. Step 6: Emphasize collaboration and information sharing among organizations in the logistics service supply chain, establish a collaborative evaluation mechanism, and promote communication and collaboration among organizations; Step 7: Provide customized evaluation plans for different types of logistics service supply chains, and design the evaluation plans in a modular manner to facilitate flexible combination and adjustment according to different needs; Step 8: Leverage blockchain technology to achieve transparency and traceability of data in the logistics service supply chain, ensuring the fairness and credibility of evaluation results; Step 9: Calculate the comprehensive score S of the logistics service supply chain and set a probation period T based on the comprehensive score S and its fluctuations. During the probation period, continuously record and regularly update the comprehensive score of the supply chain as the basis for ongoing cooperation evaluation. Step 10: After the inspection period T ends, the supply chain's average score S_avg and score fluctuation σ during the inspection period are used as the main basis for deciding whether to continue cooperation with the supply chain. If the average score is higher than the preset cooperation threshold and the score fluctuation is within an acceptable range, the cooperation relationship is maintained or strengthened; otherwise, the cooperation strategy is considered to be adjusted.

2. A logistics service supply chain elasticity evaluation method according to claim 1, characterized in that: The quantitative formula is: E_i=f(x_i,y_i,z_i), where E_i is the elasticity score of the i-th link, and x_i, y_i, and z_i are the key factors affecting the elasticity of the link.

3. A logistics service supply chain elasticity evaluation method according to claim 1, characterized in that: The weight adjustment formula is: W_i'=g(ΔM, ΔC, ΔE)*W_i, where W_i' is the adjusted weight, W_i is the original weight, ΔM, ΔC, and ΔE are the changes in market environment, customer demand, and emergencies, respectively, and g is the weight adjustment function.

4. The logistics service supply chain elasticity evaluation method according to claim 1, characterized in that: The prediction model is: R = h(D), where R is the predicted risk, D is the historical data, and h is the prediction function.

5. The logistics service supply chain elasticity evaluation method according to claim 1, characterized in that: The impact assessment formula is: ΔE=I(P,D), Where ΔE is the elastic impact, P is the decision plan, D is the decision environment, and I is the impact evaluation function.

6. A logistics service supply chain elasticity evaluation method according to claim 1, characterized in that: The environmental assessment formula is: G = Σ_i = 1^n k_i*E_i', Where G is the environmental protection score, k_i is the weight of the i-th environmental protection indicator, and E_i' is the quantitative score of the i-th environmental protection indicator.

7. A logistics service supply chain elasticity evaluation method according to claim 1, characterized in that: The collaborative evaluation formula is: C = j(T, I, P), Where C is the collaboration score, T is the collaboration time, I is the degree of information sharing, P is the collaborative project, and j is the collaboration evaluation function.

8. The logistics service supply chain elasticity evaluation method according to claim 1, characterized in that: The customized evaluation scheme is: M=l(N, S, R), Where M is the evaluation scheme, N is the supply chain type, S is the service demand, R is the evaluation requirement, and l is the customization function; The data transparency formula is: T = m(B), where T is data transparency, B is blockchain data, and m is the transparency calculation function; The formula for calculating the comprehensive score is: S = Σ_i = 1T S_t / T, where S_t is the score at the tth inspection time point and T is the inspection period; the formula for calculating the score fluctuation is: σ = √[(Σ_t = 12) / T].

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