Tobacco enterprise performance assessment method

Abstract

The invention relates to the technical field of performance appraisal, in particular to a tobacco enterprise performance appraisal method. According to the technical scheme, the method comprises the following steps: constructing a five-dimensional performance assessment index system comprising a supply chain collaboration dimension, a financial operation dimension, a green development dimension, an innovation driving dimension and a customer service dimension, and carrying out the subjective and objective comprehensive weighting method based on the combination of an analytic hierarchy process and an entropy weight method. And determining the initial weight of each level index in the five-dimensional index system. According to the method, a supply chain collaborative dynamic weight adjustment mechanism is established, collaborative performance is deeply associated with assessment of all dimensions, cross-department collaboration is stimulated, a subjective and objective comprehensive weighting method optimized through conflict analysis is adopted, the scientificity of index weight distribution is improved, the Internet of Things and a service system are integrated, accurate data collection is achieved, and the system performance is improved. An automatic management closed loop from assessment and early warning to suggestion improvement is constructed, and differential decision-making requirements are met through a multi-level interactive report, so that the unification of performance assessment and operation optimization is realized.

Description

Technical Field

[0001] This invention relates to the field of performance appraisal technology, and in particular to a performance appraisal method for tobacco companies. Background Technology

[0002] Under the current monopoly system in the tobacco industry, provincial industrial and commercial companies, as core nodes in the supply chain, respectively undertake the key functions of cigarette production and wholesale sales. Their performance appraisal systems play a crucial role in guiding corporate behavior and ensuring the stable operation of the industry. In today's fiercely competitive market environment, corporate performance appraisal has become an important tool for improving management and driving strategic implementation. Traditional performance appraisal systems, such as the Balanced Scorecard (BSC) and Key Performance Indicators (KPIs), mainly focus on internal dimensions such as financial, customer, internal processes, and learning and growth. These methods, to some extent, decompose corporate strategic goals into actionable quantitative indicators, but their initial design primarily served the internal management of individual enterprises and failed to fully consider the key characteristic of modern supply chain networks where corporate performance is highly dependent on the efficiency of collaboration with upstream and downstream partners.

[0003] As supply chain management concepts become more deeply ingrained, some advanced companies have begun to incorporate supply chain-related indicators into their performance evaluation systems. However, existing methods mostly treat these indicators as static, independent evaluation items. For example, "inventory turnover rate" or "order fulfillment rate" are simply assessed as ordinary KPIs. The limitation of this approach is that it fails to reflect the leverage effect of excellent supply chain operations on the entire enterprise system (such as cost control, innovation response, and customer satisfaction). The quality of supply chain collaboration performance only affects the score of this single dimension, failing to dynamically adjust and influence the evaluation weights of other core dimensions. This results in the performance evaluation failing to fully realize its potential and making it difficult to incentivize departments to actively engage in cross-organizational and cross-departmental collaborative practices.

[0004] In terms of setting indicator weights, subjective weighting methods such as the Analytic Hierarchy Process (AHP) rely on expert judgment and are easily influenced by personal experience and preferences. While objective weighting methods such as the entropy weighting method are based on data, they only reflect the dispersion of the data itself and ignore the high correlation that may exist between indicators (i.e., information overlap). This can lead to repeated calculation of evaluation information and weaken the scientific nature of weight allocation and the accuracy of results.

[0005] At the data level, although systems such as Enterprise Resource Planning (ERP) and Supply Chain Management (SCM) are widely used, the assessment data largely relies on the data entered into these systems after the fact, lacking real-time and objective data support from Internet of Things (IoT) sensing devices. For example, route optimization and carbon emission accounting in the logistics and transportation process still rely heavily on manual reports and static estimates, resulting in coarse data granularity, poor timeliness, and low reliability.

[0006] Furthermore, existing performance appraisal processes generally end with score calculation and report generation. There is a lack of systematic and automated linkage between appraisal results and subsequent resource allocation and process optimization actions. Managers often need to invest significant additional effort in analyzing reports, identifying problems, and developing improvement plans, making continuous self-optimization difficult.

[0007] Therefore, the industry urgently needs a new performance appraisal method that can deeply integrate supply chain collaboration characteristics, dynamically reflect collaborative value, be based on real-time objective data, and automatically drive a closed-loop management system. This method aims to overcome the shortcomings of existing technologies and truly empower enterprises to gain a sustainable advantage in a complex supply chain competitive environment. We propose a performance appraisal method for tobacco companies. Summary of the Invention

[0008] The purpose of this invention is to address the problems existing in the background art by proposing a performance evaluation method for tobacco enterprises.

[0009] This application provides a performance evaluation method that incorporates environmental characteristics, comprising the following steps: S1. Construct a five-dimensional performance evaluation indicator system that includes supply chain collaboration, financial operations, green development, innovation-driven development, and customer service. Among them, the supply chain collaboration dimension is a prerequisite and foundational dimension, and its evaluation results directly affect the final score weight of other dimensions. S2. Based on the subjective and objective comprehensive weighting method combining the analytic hierarchy process and the entropy weighting method, the initial weights of each level of indicators in the five-dimensional indicator system are determined. S3. Establish a dynamic adjustment mechanism for supply chain collaboration. Based on the assessment scores of the supply chain collaboration dimension, adjust the effective weights of the financial operation dimension, green development dimension, innovation-driven dimension and customer service dimension in real time through a preset dynamic weight adjustment function. The dynamic weight adjustment function is: in, For the first Effective weights after adjustment of each dimension For its initial weight, This represents the actual score for the supply chain collaboration dimension. As the benchmark score for the supply chain collaboration dimension, As the sensitivity coefficient for supply chain collaboration, and ; S4. Data Acquisition and Preprocessing: Collect raw data of each indicator through enterprise resource planning system, supply chain management system, Internet of Things sensing devices and data exchange interfaces of upstream and downstream enterprises, and perform standardization and dimensionless processing on the data. S5. Performance appraisal calculation: Substitute the preprocessed data into the five-dimensional performance appraisal indicator system, combine the effective weights of each dimension obtained in step S3, and use the weighted summation method to calculate the final comprehensive performance score of the appraisee. S6. Generate and output a multi-level performance profile report, which should include at least a comprehensive score radar chart, supply chain collaboration bottleneck analysis, comparison of strengths and weaknesses in each dimension, and a progress trend line based on historical scores.

[0010] Optionally, in step S1, the supply chain collaboration dimension specifically includes: Inventory coordination indicators include the matching degree between the industrial cigarette inventory turnover days and the commercial social inventory sales ratio. It is calculated as the reciprocal of |industrial inventory turnover days - (commercial inventory sales ratio × 30)| and normalized. Logistics coordination metrics include the on-time rate of industrial shipment plans and commercial delivery expectations, as well as the timeliness of emergency adjustment response based on shared transportation information between industry and commerce. Information collaboration metrics include the accuracy of monthly demand forecasts transmitted by commercial enterprises to industrial enterprises, as well as the frequency and depth of market information sharing between the two parties through data platforms; The data collection for the logistics collaboration indicators comes from a logistics cloud platform shared by industry and commerce. By comparing the "planned shipping time" of industrial delivery orders with the "actual arrival time" of commercial receipt records, and combining GPS trajectory data to verify the rationality of the transportation process, the on-time rate and response time are automatically calculated.

[0011] Optionally, the subjective and objective combined weighting method described in step S2 specifically includes: Using the analytic hierarchy process (AHP), the company's strategic management team compares the indicators pairwise to construct a judgment matrix and calculates the subjective weights of each indicator. ; By using the entropy weight method, based on historical indicator data within a preset period, the information entropy of each indicator is calculated, and then the objective weight of each indicator is determined. ; Calculate the overall weight using a linear combination method ,in, This is the preference coefficient, with a value ranging from 0.4 to 0.6; After calculating the objective weights, conflict analysis between indicators is introduced, and the indicators are calculated based on the correlation coefficient matrix. Conflict volume ,in, Indicates the first The number of conflicts in the indicators This represents the total number of indicators involved in the conflict analysis. It means the first Item and the first The correlation coefficients between the indicators are then used to correct the objective weights obtained by the entropy weight method. ; in, This represents the new objective weight vector after conflict analysis correction, which reduces the overall weight ratio of highly correlated indicator groups in objective weighting, thereby improving the differentiation and scientific nature of weight allocation.

[0012] Optionally, in step S3, the supply chain collaboration sensitivity coefficient The value is set according to the differentiated role of the assessed entity in the supply chain. For provincial industrial companies or commercial companies located at the core nodes of the supply chain, The value is higher than that of enterprises at the supply chain support nodes, for departments directly responsible for production and sales coordination, warehousing and transportation. The value is higher than that of other internal support departments; The benchmark score It is not a fixed value, but is determined by weighting the advanced value of the industry's supply chain collaboration dimension in the same period and the moving average score of this dimension over the past M periods in this unit. ,in, This represents the dynamic benchmark score for the supply chain collaboration dimension. As an industry-leading benchmark, This indicates the industry's advanced value in terms of supply chain collaboration during the same period. This represents the moving average score of the assessed entity's supply chain collaboration dimension over the past M periods (e.g., M=4, representing one year), where M represents the number of historical periods covered in the calculation of the moving average, so that the baseline dynamically adapts to the highest level in the industry and its own historical progress.

[0013] Optionally, in step S1, the financial operations dimension includes specific indicators for cost reduction and efficiency improvement, and the calculation method for these indicators is as follows: Cost reduction and efficiency improvement contribution value = (Total actual controllable costs in the current period - Total actual controllable costs in the previous period) / Total actual controllable costs in the previous period × (-100%) + Additional benefits achieved through management innovation in the current period / Total revenue in the current period × 100%; Among them, controllable costs are defined by constructing a cost behavior analysis model. The cost behavior analysis model identifies the driving relationship between various costs and business volume based on historical data, divides costs into fixed costs, variable costs and mixed costs, and decomposes mixed costs using regression analysis. Only the variable costs and some mixed costs that are directly affected by the management behavior of the assessed entity are included in the scope of controllable costs.

[0014] Optionally, in step S1, the innovation-driven dimension includes an innovation achievement transformation efficiency indicator, which is calculated as follows: Innovation achievement transformation efficiency = (Number of innovation projects that have been successfully commercialized and generated profits / Total number of innovation projects approved in the year) × (New profits generated by innovation projects / Total investment in innovation projects in the year) × 100%; The "new profits generated by innovative projects" are calculated using a contribution separation model. This model establishes a benchmark profit forecast curve that is unrelated to the innovative project. The positive difference between the actual profit after project implementation and the benchmark forecast profit is deducted from the expected growth factors brought about by natural industry growth and routine operating efforts. The remaining part is attributed to the innovative project. The benchmark profit forecast curve is fitted using time series analysis combined with macroeconomic variables.

[0015] Optionally, in step S4, the data collection via IoT sensing devices specifically includes: By deploying RFID readers and weight-sensing pallets in industrial warehouses and commercial distribution centers, real-time inbound and outbound data and inventory dynamics of cigarettes are collected, and these data are used as the core data source for inventory coordination indicators in the aforementioned supply chain coordination dimension. By using GPS and OBD interfaces installed on delivery vehicles, vehicle driving routes and fuel consumption data are collected and used as a common data source for logistics cost control indicators in the financial operation dimension and carbon emission indicators in the green development dimension. Using the GPS trajectory data, the difference between the theoretically optimal delivery route and the actual driving route is calculated through a path optimization algorithm, and this difference is used as a negative adjustment factor for logistics cost control indicators. At the same time, based on real-time fuel consumption data collected by the OBD interface and the carbon emission coefficient calibrated by the vehicle model, the carbon emission of a single delivery task is dynamically calculated to achieve accurate measurement of carbon emissions.

[0016] Optionally, after step S5, the following steps are also included: Step S5A: Performance feedback and adaptive adjustment of goals; The overall score and scores for each dimension of this assessment are compared with the preset target values ​​to generate a gap analysis. If the score of the same dimension is consistently lower than the target value for N consecutive assessment cycles, an early warning will be automatically triggered, and the key performance indicators under that dimension will be automatically included in the key improvement target library for the next cycle. At the same time, a high-priority improvement task will be generated in the system task dashboard. Step S5B: Resource allocation and process optimization suggestions are generated. Based on the gap analysis and key improvement target library, if the root cause of the problem lies in the supply chain collaboration dimension, the system automatically analyzes and recommends cross-organizational resource allocation solutions or process optimization nodes. The resource allocation solutions include sharing warehousing resources or transportation resources with upstream and downstream enterprises, and the process optimization nodes include adjusting order cut-off times or optimizing the ordering process.

[0017] Optionally, the value of N is 2 or 3, and the tasks in the key improvement target library are forcibly associated with the monthly work plans of departments and individuals. Failure to complete the tasks will directly affect the basic score of the corresponding dimension in the next cycle. The generation of resource allocation and process optimization suggestions is specifically achieved by constructing a "Supply Chain Performance Gap - Collaboration Model" case library. The "Supply Chain Performance Gap - Collaboration Model" case library stores typical models and key parameters that have successfully improved supply chain performance through industrial and commercial collaboration in the past. The system performs model matching in the case library based on the specific gap in the current supply chain collaboration dimension and recommends the most relevant collaboration optimization solution.

[0018] Optionally, the multi-level performance profile report described in step S6 can be presented in a differentiated manner for managers at different levels: For senior managers, the report focuses on strategic alignment analysis, demonstrating the linkage between supply chain synergy and financial operations, customer service satisfaction, and the ability of an efficient supply chain to support innovation investment. For middle-level department heads, the report focuses on comparing process indicators with outcome indicators, and highlights the deviation analysis of their department's score from the company average, especially the contribution or bottlenecks in cross-departmental / cross-enterprise collaboration. For frontline employees, the report mainly uses visual charts to clearly mark their personal strengths, areas for improvement, and comparisons with the team's average level. Furthermore, the report includes an embedded interactive data exploration module, allowing managers to click on abnormal data points in the report and drill down to the lowest level of original data vouchers, including but not limited to specific order records, inventory count sheets, logistics trajectory maps, or cross-enterprise data exchange logs, enabling full traceability of performance evaluation data.

[0019] Compared with the prior art, the present invention has the following beneficial technical effects: Through a dynamic weighting adjustment mechanism, the performance in the supply chain collaboration dimension is directly linked to the assessment weights of other dimensions. When an entity performs exceptionally well in supply chain collaboration, the system automatically increases its effective weights in dimensions such as finance, innovation, green practices, and customer service, thereby gaining a greater advantage in the final score.

[0020] Based on a comprehensive weighting system combining subjective and objective factors, this method introduces indicator conflict analysis to correct objective weights. It identifies and strengthens the weights of indicators that provide independent information and possess unique evaluative significance, effectively resolving the problem of double-counting caused by high correlation between indicators. This results in a more reasonable weight allocation, more differentiated assessment results, and a more accurate reflection of the true performance level of the assessed individuals.

[0021] By integrating ERP, SCM systems, IoT devices, and cross-enterprise data interfaces, the system achieves automated collection and processing of performance evaluation data. Utilizing IoT devices such as RFID, GPS, and OBD to collect real-time operational data, combined with in-depth accounting methods such as path optimization algorithms and cost behavior analysis models, the system ensures that performance evaluations are based on objective, accurate, and traceable data, significantly improving the accuracy and reliability of the evaluations.

[0022] This invention can automatically perform gap analysis after assessment, trigger early warnings, and forcibly link improvement goals with work plans. When supply chain coordination issues are identified, the system can further match and recommend validated optimization solutions from a pre-set case library.

[0023] By generating multi-level, interactive performance profile reports, the system can provide targeted decision support for managers at different levels. Senior managers can gain insights into strategic linkages, middle managers can identify process bottlenecks, and frontline employees can clarify their personal improvement directions. The data drill-down function embedded in the reports allows any abnormal scores to be traced back to the underlying original evidence, enhancing the transparency of performance evaluation and management penetration.

[0024] In summary, this invention establishes a dynamic weight adjustment mechanism for supply chain collaboration, deeply linking collaborative performance with assessments across various dimensions to incentivize cross-departmental cooperation. It employs a comprehensive subjective and objective weighting method optimized through conflict analysis to enhance the scientific rigor of indicator weight allocation. Furthermore, it integrates the Internet of Things with business systems to achieve precise data collection, constructing an automated management loop from assessment and early warning to improvement suggestions. Finally, it meets differentiated decision-making needs through multi-level interactive reports, achieving a unified approach to performance evaluation and operational optimization. Attached Figure Description

[0025] Figure 1 A flowchart of a performance evaluation method incorporating environmental characteristics is provided according to the present invention. Detailed Implementation

[0026] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention.

[0027] Example This embodiment provides a performance appraisal method that incorporates environmental characteristics. This implementation is applicable to industrial enterprises, commercial enterprises, and upstream and downstream collaborative entities in the cigarette supply chain. By constructing a five-dimensional indicator system and integrating subjective and objective weighting with a dynamic weight adjustment mechanism, it achieves performance appraisal that balances strategic orientation, market responsiveness, and sustainable development. It breaks through the limitations of traditional single-dimensional appraisals, making the appraisal results more aligned with the actual operational scenarios of the supply chain through multi-dimensional collaboration and dynamic adjustment. Simultaneously, it provides accurate data support for enterprise resource allocation and process optimization, avoiding decision-making biases caused by appraisals. The implementation process relies on Enterprise Resource Planning (ERP) systems, Supply Chain Management (SCM) systems, logistics cloud platforms, and IoT sensor networks to ensure real-time data collection, dynamic weight adjustment, and the practicality of the appraisal results. Figure 1 The method will now be explained in detail.

[0028] I. Construct a five-dimensional performance evaluation indicator system, comprising supply chain collaboration, financial operations, green development, innovation-driven development, and customer service dimensions. The detailed rules and calculation methods for each dimension strictly adhere to the corresponding claims, as detailed below: Supply chain collaboration dimension: This dimension specifically includes inventory collaboration indicators, logistics collaboration indicators, and information collaboration indicators. Inventory Coordination Indicator: This indicator calculates the matching degree between the industrial cigarette inventory turnover days and the commercial social inventory-to-sales ratio. The formula is: Matching Degree = 1 / |Industrial Inventory Turnover Days - (Commercial Inventory-to-Sales Ratio × 30)|, with the results normalized. This indicator quantifies the degree of matching between industrial and commercial inventories through "inverse absolute value and normalization," avoiding inventory backlogs or stockouts.

[0029] Logistics collaboration metrics include the on-time rate of industrial shipment plans and commercial delivery expectations (on-time rate = number of orders whose actual delivery time matches the planned shipment time / total number of orders × 100%), and the emergency response time for industrial and commercial adjustments (response time = time from triggering abnormal information to confirming the adjustment plan). The logistics collaboration metric data comes from a shared logistics cloud platform used by industrial and commercial entities. It is verified by comparing the "planned shipment time" on the delivery note, the "actual delivery time" in the receiving record, and combining GPS trajectory data to ensure the rationality of transportation and the authenticity and traceability of the data.

[0030] Information collaboration indicators include the accuracy of monthly demand forecasts transmitted by commercial enterprises to industrial enterprises (accuracy = 1 - |forecasted demand - actual supply| / actual supply × 100%), the frequency (number of times per month) and depth (number of dimensions of shared data) of market information shared by both parties through the data platform, and the weighted sum of the two and normalized.

[0031] Financial Operations Dimension: Includes specific indicators for cost reduction and efficiency improvement, calculated as follows: Cost Reduction and Efficiency Improvement Contribution Value = (Total Actual Controllable Costs in the Current Period - Total Actual Controllable Costs in the Previous Period) / Total Actual Controllable Costs in the Previous Period × (-100%) + Additional Benefits Achieved Through Management Innovation in the Current Period / Total Revenue in the Current Period × 100%. Controllable costs are defined using a "Cost Behavior Analysis Model": Based on historical data, the relationship between cost and business volume is identified, and costs are divided into fixed costs, variable costs, and mixed costs. Mixed costs are decomposed using regression analysis, and only variable costs directly affected by the management behavior of the assessed entity and some mixed costs are included in the controllable cost category to ensure fairness in the assessment. Additionally, a logistics cost control indicator is added (Logistics Cost Control Rate = Actual Logistics Cost / Theoretical Optimal Logistics Cost × 100%, where the theoretical optimal cost is calculated using a path optimization algorithm).

[0032] Green development dimension: The core indicator is carbon emission, which is calculated dynamically based on real-time fuel consumption data collected by the OBD interface of the delivery vehicle and the carbon emission coefficient calibrated by the vehicle model. The emission amount of a single delivery task is calculated dynamically according to the formula: carbon emission amount = fuel consumption × carbon emission coefficient. Supplementary indicators include energy utilization efficiency (energy utilization efficiency = effective energy consumption / total energy consumption × 100%) and environmental protection investment ratio (environmental protection investment ratio = environmental protection investment amount / total investment amount × 100%).

[0033] Innovation-driven dimension: Includes an indicator of innovation achievement transformation efficiency, calculated as follows: Innovation achievement transformation efficiency = (Number of innovation projects successfully commercialized and generating profits / Total number of innovation projects initiated in the year) × (New profits generated by innovation projects / Total investment in innovation projects in the year) × 100%. The "new profits generated by innovation projects" are calculated using a "contribution separation model": A time series analysis method is used to fit a "benchmark profit forecast curve unrelated to innovation projects" to macroeconomic variables. The positive difference between the actual profit after project implementation and the benchmark forecast profit is calculated. After deducting natural industry growth factors (using the industry average growth rate) and expected growth from routine operating efforts (using the company's average growth rate over the past 3 years), the remaining portion is attributed to the innovation project, avoiding the misjudgment of non-innovation factors as innovation value.

[0034] Customer service dimensions include customer satisfaction rating (based on a 10-point scale from a survey of retail customers), order fulfillment rate (order fulfillment rate = number of actual delivered orders / number of customer-requested orders × 100%), and after-sales response timeliness (after-sales response timeliness = average time from customer feedback to resolution). This focuses on customer experience and avoids prioritizing internal efficiency over external needs.

[0035] II. Determining Initial Weights Based on a Comprehensive Subjective and Objective Weighting Method: The initial weights of each level of indicators in the five-dimensional performance appraisal indicator system are calculated using this method. The specific process is as follows: Calculating subjective weights (Analytic Hierarchy Process, AHP): The corporate strategic management team compares the importance of each indicator pairwise, constructing a judgment matrix using a 1-9 scale. The rationality of the judgment matrix is ​​verified through a consistency test (CR < 0.1) (if it fails, the comparison results are readjusted). After passing the consistency test, the eigenvectors are calculated to obtain the subjective weights of each indicator. This step integrates the company's strategic intent into the weighting allocation, ensuring that the assessment focus aligns with strategic objectives.

[0036] The objective weights are calculated and adjusted using the entropy weighting method and conflict analysis, specifically including: Initial objective weight calculation: Based on historical indicator data for a preset period (nearly 12 assessment periods), the information entropy of each indicator is calculated after data standardization. ,according to / , Using the indicator sequence number, the initial objective weights of each indicator are obtained. This avoids biases caused by human experience.

[0037] Conflict Analysis and Weight Adjustment: Calculating Indicators Based on the Correlation Coefficient Matrix Conflict volume k=1 to , The total number of indicators involved in the conflict analysis. As an indicator With indicators The correlation coefficient; then according to The initial objective weights are corrected to obtain the corrected objective weights. This step reduces the interference of information redundancy between indicators on the weights, ensuring that the objective weights reflect the unique contributions of the indicators.

[0038] Calculate the initial composite weights: The composite weights are calculated using a linear combination method, and the formula is as follows: ,in This is the preference coefficient, ranging from 0.4 to 0.6 (enterprises can adjust this according to their strategic orientation: emphasizing strategic execution). Taking 0.6, emphasizing objective data... (Take 0.4), to achieve a balance between subjective strategy and objective data.

[0039] III. Establish a dynamic adjustment mechanism for supply chain collaboration, and adjust the effective weights of the financial operations, green development, innovation-driven, and customer service dimensions based on the assessment scores of the supply chain collaboration dimension, as detailed below: Determine key parameters: Supply chain collaboration sensitivity coefficient : The value is set according to the differentiated role of the assessed entity in the supply chain—core nodes of the supply chain (provincial industrial companies, commercial companies). Values ​​of 0.3-0.5 are 0.1-0.2 higher than those for supply chain support node enterprises; this applies to departments directly responsible for production and sales coordination, warehousing, and transportation. The value of 0.4-0.6 is higher than that of other internal support departments (0.1-0.3), which is in line with the actual logic of the supply chain that "core drives the whole".

[0040] Supply chain collaboration dimension benchmark score , It is not a fixed value; it is determined through weighted calculation, and the formula is as follows: ,in: This is an industry-leading benchmark, ranging from 0.5 to 0.7. This represents the advanced value for supply chain collaboration within the industry during the same period. The advanced value is the average of the top 10 companies in the industry. This is the moving average score of the assessed entity's supply chain collaboration dimension over the past M periods, where M=4 represents one year. This setting benchmarks against industry best practices while also considering its own historical performance, ensuring dynamic adaptation of the baseline.

[0041] Perform weight adjustment: First, calculate the actual score for the supply chain collaboration dimension. The result is obtained by multiplying the preprocessed scores of each indicator under this dimension by the calculated comprehensive weight of the internal indicators of this dimension, and then summing the results. .

[0042] Substituting into the dynamic weight adjustment function: the dynamic weight adjustment function is ,in For the first The effective weights after adjustment for each dimension to be adjusted (financial, green, innovation, customer service) This is the initial overall weight for this dimension. .

[0043] Weight normalization: After adjustment, ensure that the sum of the effective weights of the four dimensions to be adjusted is 1. If it exceeds this, normalize it proportionally to avoid abnormal weight sums.

[0044] IV. Data collection and preprocessing to ensure the real-time nature, accuracy, and comparability of the assessment data, as detailed below: Multi-channel data collection: Data collection through system interfaces, including financial data (controllable costs, total revenue, and additional benefits from management innovation) and production data collected through Enterprise Resource Planning (ERP) systems; order data and supply and demand forecast data collected through Supply Chain Management (SCM) systems; and collaborative feedback data collected through data exchange interfaces between upstream and downstream enterprises, enabling automatic data synchronization across systems and reducing human error.

[0045] IoT device data collection: Core data is collected through the following devices: RFID readers and weight-sensing pallets are deployed in industrial warehouses and commercial distribution centers to collect real-time data on the entry and exit of cigarettes and inventory dynamics, serving as the core data source for inventory coordination indicators in the supply chain coordination dimension. GPS and OBD interfaces are installed on delivery vehicles to collect vehicle driving routes and fuel consumption data. GPS data is used to calculate the "path difference" of logistics cost control indicators (path difference = actual driving route length / theoretical optimal route length × 100%, as a negative adjustment factor). OBD fuel consumption data is combined with the vehicle carbon emission coefficient to calculate carbon emissions in the green development dimension, realizing "one data for multiple uses".

[0046] Logistics cloud platform data collection: By using the logistics cloud platform shared by industry and commerce, the "planned shipping time" of industrial delivery orders is compared with the "actual arrival time" of commercial receipts. Combined with GPS trajectory data, the rationality of the transportation process is verified, and the on-time rate and emergency adjustment response time of logistics coordination indicators are automatically calculated without manual reconciliation.

[0047] Data preprocessing: Data cleaning: Remove outliers (such as invalid data caused by GPS positioning deviation or equipment failure), use linear interpolation to supplement missing data, and eliminate interference from invalid data.

[0048] Standardization and dimensionlessness: The Z-score method is used to process indicators with different dimensions. , The mean of the indicators. The standard deviation of the indicators is used to ensure that the scores of all indicators are in the range of [0, 100], which meets the requirements for weighted summation.

[0049] V. Performance Appraisal Calculation, as follows: Substitute the preprocessed data into the five-dimensional performance appraisal indicator system, and calculate the final comprehensive performance score according to the following logic: Calculate the scores for each dimension: Supply chain collaboration dimension score = preprocessed score of each indicator in this dimension × sum of the comprehensive weights of the corresponding indicators; Initial scores for the four dimensions of finance, green, innovation, and customer service = preprocessed score of each indicator in each dimension × sum of the comprehensive weights of the corresponding indicators.

[0050] Calculate the overall performance score: Overall performance score = Supply chain collaboration dimension score × its initial overall weight + Σ (Initial scores of financial / green / innovation / customer service dimensions × corresponding adjusted effective weights of the respective dimensions) The weighted summation method is used to ensure that the score fully reflects the performance and synergistic impact of each dimension. The full score is 100 points, which facilitates horizontal (different objects) and vertical (different periods) comparisons.

[0051] This embodiment also includes performance feedback and adaptive target adjustment, as detailed below: Gaps analysis: Compare the overall score and scores of each dimension of this assessment with the preset target values ​​(based on industry standards and corporate strategic goals) to identify the score gaps and their causes (e.g., "low on-time delivery rate of logistics collaboration leads to failure to meet supply chain collaboration score").

[0052] Triggering warnings and setting improvement targets: If the score of the same dimension continues to be lower than the target value for N consecutive assessment cycles (N=2 or 3), the system will automatically trigger a warning and automatically include the key performance indicators (KPIs) under that dimension into the key improvement target pool for the next cycle.

[0053] Mandatory task association: Tasks in the key improvement target library are forcibly associated with the monthly work plans of departments and individuals. Failure to complete them will directly affect the base score of the corresponding dimension in the next cycle (e.g., if one core improvement task is not completed, 5 points will be deducted from the base score of the corresponding dimension), ensuring that improvement goals are achieved.

[0054] Furthermore, this embodiment also includes the generation of resource allocation and process optimization suggestions, as detailed below: Root cause identification: Based on the above gap analysis and key improvement target library, if the root cause of the problem lies in the supply chain collaboration dimension, then proceed to the suggestion generation process.

[0055] Optimization suggestions are generated by constructing a case library of "Supply Chain Performance Gap - Collaboration Models." This case library stores historical examples of successful supply chain performance improvements through business collaboration (e.g., "Sharing warehousing resources to reduce inventory turnover days," "Capacity allocation to improve on-time delivery") and key parameters. Based on the current specific gap in supply chain collaboration (e.g., "Low inventory collaboration score"), the system performs pattern matching within the case library and recommends the most relevant collaboration optimization solutions. Resource allocation plan: Share warehousing resources and transportation resources with upstream and downstream enterprises; Process optimization steps: Adjusting order cut-off time and optimizing the ordering process.

[0056] Task tracking: Recommended solutions are synchronized to the system task dashboard, generating high-priority improvement tasks that are led by the supply chain management department and implemented collaboratively by upstream and downstream enterprises to ensure the solutions are effectively implemented.

[0057] VI. Generate and output multi-level performance profile reports. These reports must include "an overall score radar chart, supply chain collaboration bottleneck analysis, comparison of strengths and weaknesses across various dimensions, and a progress trend line based on historical scores," and should be presented in a differentiated manner for managers at different levels, as detailed below: Differentiated report content: Senior Management Report: Focuses on Strategic Alignment Analysis – Demonstrating the linkage between supply chain collaboration level and financial operations, customer service satisfaction, and the ability of an efficient supply chain to support innovation investment. A 10% reduction in supply chain collaboration costs leads to a 5% increase in innovation investment, supporting strategic decision-making.

[0058] Mid-level department head report: Focus on the comparison between process indicators and outcome indicators - show the deviation of the department's score from the company average, clarify the contribution of cross-department / cross-enterprise collaboration, the information sharing frequency compliance rate is 90%, which is 8% higher than the company average, or the number of order response delays due to bottlenecks is 5 times, resulting in the logistics collaboration score being 12% lower than the company average, focusing on process optimization.

[0059] Frontline employee reports: primarily using visual charts (bar charts, line charts) – clearly marking individual strengths (score ≥ 80 points), areas for improvement (score < 60 points), and comparisons with the team average, indicating the impact of individual task completion on departmental scores, thus improving the focus of work.

[0060] Interactive data exploration function: The report is embedded with an interactive data exploration module—when managers click on abnormal data points in the report ("sudden increase in carbon emissions in a certain period" or "low inventory coordination score"), they can drill down to the lowest level of original data vouchers (including order records, inventory count sheets, logistics trajectory maps, and cross-enterprise data exchange logs), realizing full traceability of performance evaluation data and reducing evaluation disputes.

[0061] VII. Implementation of Safeguard Measures: To ensure that the above steps are strictly implemented in accordance with the claims, the following safeguard measures need to be established: System support: Build a unified data platform integrating ERP, SCM, logistics cloud platform and IoT devices, and develop "weight calculation module, dynamic adjustment module, performance calculation module and report generation module" to automate each step, reduce manual intervention and ensure that real-time adjustment and data preprocessing requirements are implemented.

[0062] Data security assurance: Establish a cross-enterprise data sharing security mechanism—adopt encrypted transmission technology (SSL protocol) to protect the data transmission process, and adopt hierarchical access control (senior managers can view all data, while junior employees can only view their personal data) to protect trade secrets, eliminate concerns about the company's reluctance to transmit data, and support IoT data collection and data traceability.

[0063] Personnel training support: Specialized training is provided for personnel at different levels—strategic management is trained on the AHP judgment matrix construction method, middle-level department heads are trained on gap analysis and improvement plan implementation processes, and front-line employees are trained on the operation methods of IoT devices that collect data through IoT sensing devices, ensuring that personnel at all levels master the core logic of the assessment methods.

[0064] It is worth noting that this invention introduces a dynamic adjustment mechanism for supply chain collaboration, directly linking supply chain collaboration performance with the assessment weights of other dimensions. When the assessed entity performs exceptionally well in the supply chain collaboration dimension, the effective weights of its financial, innovation, green, and customer service dimensions are increased, resulting in a greater advantage in the final comprehensive score. This design transforms supply chain collaboration from a single assessment indicator into a lever that amplifies overall performance, incentivizing assessed units to proactively strengthen cross-organizational and cross-departmental collaboration, rather than focusing solely on optimizing internal functions. Based on a comprehensive weighting of subjective and objective factors, this invention introduces indicator conflict analysis to correct objective weights. This process identifies and strengthens the weights of indicators with low overlap with other indicators and independent evaluative significance, making the entire indicator system more scientifically structured and ensuring that weight allocation better reflects the independent contribution of each indicator. This effectively avoids the problem of double-counting caused by high correlation between indicators, improving the differentiation and accuracy of the evaluation results. This invention achieves closed-loop management from data collection and calculation to feedback. By integrating ERP, SCM, IoT devices, and cross-enterprise data interfaces, it ensures the breadth and authenticity of the assessment data sources. In particular, leveraging IoT data (such as GPS and OBD) not only serves performance evaluation but also directly drives operational optimization. For example, it can guide improvements in logistics efficiency through route difference analysis. After the evaluation, the system can automatically trigger early warnings and include weak links in key improvement targets, forcibly linking them to work plans to form a continuous optimization cycle of evaluation, feedback, improvement, and re-evaluation.

[0065] The multi-level, interactive performance profile reports generated by the method of this invention meet the decision-making needs of managers at different levels. Senior managers can gain insights into strategic linkages, middle managers can pinpoint process bottlenecks, and frontline employees can identify areas for personal improvement. The report's embedded data drill-down function allows any abnormal scores to be traced back to the underlying original documentation, which not only greatly enhances the transparency and credibility of the assessment but also provides a powerful tool for root cause analysis. The "Supply Chain Performance Gap - Collaboration Model" case library constructed by this invention enables the system to learn from historical successes and provide validated and specific collaborative optimization suggestions for current performance gaps. This transcends the limitations of traditional assessments that merely point out problems; it proactively guides management actions and promotes cross-organizational process reengineering and resource optimization, such as sharing warehousing capacity and adjusting order processes, thereby upgrading the performance appraisal system from an evaluation tool into a decision support and operational optimization platform.

[0066] Furthermore, in traditional performance appraisals, each dimension is independent and has fixed weights, which can easily lead to departments sacrificing overall interests to optimize local indicators. This invention introduces a dynamic weight adjustment function, allowing the score of the supply chain collaboration dimension to influence the effective weights of other dimensions in real time. This mechanism creatively places supply chain collaboration at the core of the assessment system: when a unit performs exceptionally well in supply chain collaboration, the system automatically increases its assessment weights in other dimensions such as finance and innovation, thus giving it a greater reward in the final score. This incentivizes all departments to proactively break down barriers, because strengthening collaboration not only improves the score in its own dimension but also amplifies its overall performance results, thereby promoting consistency in cross-departmental goals and unity of action in the system. This invention does not stop at a simple combination of subjective and objective weights but further introduces indicator conflict analysis based on a correlation coefficient matrix. This method can identify indicators that provide independent information and represent unique management aspects, and strengthen them during weighting. This creatively solves the information redundancy problem commonly found in multi-indicator evaluation systems, enabling the assessment results to more clearly and accurately reflect the true capabilities and shortcomings of the assessed entities, providing a reliable basis for precise management decisions.

[0067] This invention integrates IoT sensor data and cross-system business data, and designs in-depth accounting models such as cost reduction and efficiency improvement contribution values ​​and innovation achievement transformation efficiency, enabling performance evaluation to be based on real-time, authentic, and traceable data. More importantly, the system can automatically perform gap analysis after evaluation, trigger early warnings, and forcibly link improvement goals with work plans. When supply chain collaboration issues are identified, the system can further match and recommend validated optimization solutions (such as shared warehousing and order process adjustments) from a pre-set "performance gap-collaboration model" case library. By generating multi-level reports with different focuses and embedding interactive data exploration functions, this invention creates a shared understanding of performance results between management and execution levels. Senior managers can intuitively understand the strategic linkage between supply chain collaboration and finance, innovation, etc.; middle managers can accurately locate bottlenecks in cross-departmental processes; and frontline employees can clearly identify their individual areas for improvement. Any abnormal data can be drilled down to the lowest level of business vouchers. This transparency not only enhances the credibility of the assessment, but also shifts the focus of all employees from arguing about the scores to discussing business facts and improvement measures, thereby forming a collaborative force for improvement within the organization.

[0068] The above specific embodiments are merely several optional embodiments of the present invention. Based on the technical solutions of the present invention and the relevant teachings of the above embodiments, those skilled in the art can make various alternative improvements and combinations to the above specific embodiments.

Claims

1. A performance evaluation method incorporating environmental features, characterized by, The method comprises the following steps: S1, constructing a five-dimensional performance evaluation index system including supply chain coordination dimension, financial operation dimension, green development dimension, innovation driving dimension and customer service dimension; S2, determining the initial weights of the indexes at each level in the five-dimensional index system based on a subjective and objective comprehensive weighting method combining analytic hierarchy process and entropy weight method; S3, establishing a supply chain coordination dynamic adjustment mechanism, based on the evaluation score of the supply chain coordination dimension, adjusting the effective weights of the financial operation dimension, the green development dimension, the innovation driving dimension and the customer service dimension in real time through a preset dynamic weight adjustment function; The dynamic weight adjustment function is: wherein, is the effective weight of the th dimension adjustment, is its initial weight, is the actual score of the supply chain collaboration dimension, is the benchmark score of the supply chain collaboration dimension, is the supply chain collaboration sensitivity coefficient, and ; S4, data collection and preprocessing, collecting the original data of each index through enterprise resource planning system, supply chain management system, Internet of Things sensing equipment and upstream and downstream enterprise data exchange interface, and standardizing and dimensionless processing the data; S5, performance evaluation calculation, substituting the preprocessed data into the five-dimensional performance evaluation index system, combining the effective weights of each dimension calculated in step S3, and calculating the final comprehensive performance score of the evaluated object by using the weighted summation method; S6, generating and outputting a multi-level performance portrait report, the report at least including a comprehensive score radar chart, a supply chain coordination bottleneck analysis, a comparison of advantages and disadvantages of each dimension, and a progress trend line based on historical scores.

2. The performance evaluation method in combination with environmental characteristics according to claim 1, characterized in that, In step S1, the supply chain coordination dimension specifically includes: An inventory coordination index, including the matching degree of industrial cigarette inventory turnover days and commercial social inventory storage-sales ratio, which is calculated as the reciprocal of |industrial inventory turnover days-(commercial storage-sales ratio x 30)| and normalized; A logistics coordination index, including the on-time rate of industrial delivery plan and commercial expected arrival, and the emergency adjustment response time based on shared transportation in-transit information between industry and commerce; An information coordination index, including the monthly demand forecast accuracy rate of commercial enterprises to industrial enterprises, and the frequency and depth of sharing market information between the two parties through data platforms; The data collection part of the logistics coordination index is derived from the logistics cloud platform shared by industry and commerce, by comparing the "scheduled shipping time" of the industrial delivery order with the "actual arrival time" of the commercial receipt record, and combining GPS trajectory data to verify the rationality of the transportation process, to automatically calculate the on-time rate and response time.

3. The performance evaluation method in combination with environmental characteristics according to claim 1, characterized in that, The subjective and objective comprehensive weighting method in step S2 specifically includes: The subjective weight of each index is calculated by comparing the indexes two by two through the analytic hierarchy process, constructing a judgment matrix, and calculating the subjective weight of each index ; The information entropy of each index is calculated based on historical index data in a preset period by an entropy weight method, and then the objective weight of each index is determined ; The comprehensive weight is calculated in a linear combination manner wherein, is a preference coefficient, and the value range is 0.4 to 0.6; After calculating the objective weights, conflict analysis between indicators is introduced, and the indicators are calculated based on the correlation coefficient matrix. Conflict volume ,in, Indicates the first The number of conflicts in the indicators This represents the total number of indicators involved in the conflict analysis. It means the first Item and the first The correlation coefficients between the indicators are then used to correct the objective weights obtained by the entropy weight method. ; wherein, represents the new objective weight vector after the conflictive analysis correction.

4. The performance evaluation method in combination with environmental characteristics according to claim 1, characterized in that, In step S3, the supply chain coordination sensitivity coefficient is set according to the role difference of the object being examined in the supply chain. For a provincial industrial company or a commercial company that is a core node in the supply chain, the value is higher than that of a supply chain auxiliary node enterprise. For a department directly responsible for production and sales connection, warehouse storage and transportation, the value is higher than that of other internal support departments. The benchmark score is not a fixed value, but is determined by weighting calculation based on the advanced value of the industry contemporaneous supply chain collaboration dimension and the moving average score of the dimension of the unit in the past M periods, that is, , wherein, represents the dynamic benchmark score of the supply chain collaboration dimension, is the industry leading benchmark coefficient, represents the advanced value of the industry contemporaneous supply chain collaboration dimension, represents the moving average score of the supply chain collaboration dimension of the unit being assessed in the past M periods, and M represents the number of historical periods covered in the calculation of the moving average, so that the benchmark line dynamically adapts to the highest level of the industry and the historical progress of itself.

5. The performance evaluation method in combination with environmental characteristics according to claim 1, characterized in that, In step S1, the financial operation dimension includes a cost reduction and efficiency improvement special index, and the calculation method of the cost reduction and efficiency improvement special index is: Cost reduction and efficiency improvement contribution value=(actual controllable total cost this period-actual controllable total cost last period) / actual controllable total cost last period x(-100%)+additional benefits achieved through management innovation this period / total revenue this period x 100%; Wherein, the controllable cost is defined by constructing a cost behavior analysis model, the cost behavior analysis model identifies the cause-and-effect relationship between each cost and business volume based on historical data, divides the cost into fixed cost, variable cost and mixed cost, and decomposes the mixed cost by regression analysis method; only the variable cost part and part of the mixed cost directly affected by the management behavior of the evaluated object are included in the controllable cost category.

6. The performance evaluation method in combination with environmental characteristics according to claim 1, characterized in that, In step S1, the innovation driving dimension includes an innovation achievement transformation efficiency indicator, and the calculation method of the innovation achievement transformation efficiency indicator is: Innovation achievement transformation efficiency=(number of successfully commercialized and profit-generating innovation projects / total number of annual innovation projects)×(newly added profit brought by innovation projects / total investment of annual innovation projects)×100%; The newly added profit brought by the innovation project is calculated by using a contribution separation model. The contribution separation model establishes a benchmark profit prediction curve irrelevant to the innovation project. The positive difference between the actual profit after the implementation of the project and the benchmark predicted profit is deducted from the expected growth caused by the natural growth factor and the conventional operation effort, and the remaining part is attributed to the innovation project. The benchmark profit prediction curve is fitted by using time series analysis combined with macroeconomic variables.

7. The performance evaluation method in combination with environmental characteristics according to claim 1, characterized in that, In step S4, the data collected by the Internet of Things sensing device specifically includes: Real-time warehouse in-out data and inventory dynamics of cigarettes are collected by RFID readers and weight sensing trays deployed in industrial warehouses and commercial distribution centers, and are used as core data sources of the inventory coordination indicator in the supply chain coordination dimension; Vehicle driving path and fuel consumption data are collected by GPS and OBD interfaces installed on distribution vehicles, and are used as common data sources of the logistics cost control indicator in the financial operation dimension and the carbon emission indicator in the green development dimension; The difference between the theoretical optimal distribution path and the actual driving path is calculated by using the GPS trajectory data through a path optimization algorithm, and this difference is used as a negative adjustment factor of the logistics cost control indicator. At the same time, based on the real-time fuel consumption data collected by the OBD interface and the carbon emission coefficient calibrated according to the vehicle model, the carbon emission of a single distribution task is dynamically calculated.

8. The performance evaluation method in combination with environmental characteristics according to claim 1, characterized in that, After step S5, it further includes: Step S5A, performance feedback and target adaptive adjustment; The comprehensive score and the score of each dimension of this assessment are compared with the preset target value to generate a gap analysis; If the score of the same dimension is continuously lower than the target value in N consecutive assessment periods, a warning is automatically triggered, and the key performance indicators under this dimension are automatically included in the key improvement target library of the next period, and a high-priority improvement task is generated in the system task board; Step S5B, resource allocation and process optimization suggestion generation; based on the gap analysis and the key improvement target library, if the problem is rooted in the supply chain coordination dimension, the system automatically analyzes and recommends a cross-organizational resource allocation scheme or a process optimization node; the resource allocation scheme includes sharing warehouse resources or transportation resources with upstream and downstream enterprises, and the process optimization node includes adjusting the order cut-off time or optimizing the order placement process.

9. The performance evaluation method in combination with environmental characteristics according to claim 8, characterized in that, The value of N is 2 or 3, and the tasks in the key improvement target library are forcibly associated with the monthly work plans of departments and individuals, and the uncompleted situation will directly affect the basic score of the corresponding dimension in the next period; The resource allocation and process optimization suggestion generation is specifically realized by constructing a "supply chain performance gap-collaboration mode" case library. The "supply chain performance gap-collaboration mode" case library stores typical modes and key parameters that successfully improve supply chain performance through industry and business collaboration in history. The system performs mode matching in the case library based on the specific gap of the current supply chain collaboration dimension, and recommends the most relevant collaboration optimization scheme.

10. The performance evaluation method in combination with environmental characteristics according to claim 1, characterized in that, The multi-level performance portrait report in step S6 is presented differently for managers at different levels: For senior managers, the report focuses on strategic matching degree analysis, showing the linkage between supply chain collaboration level and financial operation, customer service satisfaction, and the ability of efficient supply chain to repay innovation input. For middle-level department heads, the report focuses on the comparison between process indicators and result indicators, and highlights the deviation analysis of the department score from the average level of the whole company, especially the contribution or blocking point in cross-department / cross-enterprise collaboration. For grassroots employees, the report mainly uses visual charts, clearly marking individual strengths, areas for improvement, and comparison with team average level. Moreover, the report has an embedded interactive data exploration module, allowing managers to click on abnormal data points in the report to drill down to the bottom layer of original data vouchers, including but not limited to specific order records, inventory inventory records, logistics track charts, or cross-enterprise data exchange logs, realizing the full traceability of performance evaluation data.

Images