Multi-level dynamic fuzzy comprehensive evaluation method in the automotive supply chain performance evaluation

The application of cloud computing brings not only enormous economic benefits but also security risks. According to the safety risk problems faced by cloud computing environment, this paper on the basis of the experience of traditional risk analysis methods, qualitatively researches the main factors of cloud computing risk, and adopts multi-level fuzzy evaluation method to analyze multiple decision objectives, which is combined with Analytic Hierarchy Process (AHP) method and fuzzy evaluation theory; Then using this method to construct a model and give an example of calculation in cloud computing, the result of this example shows that the risk assessment method used in this paper is effective, and it has a certain practical application value.

In recent years, with the emergence of new technologies, big data, artificial intelligence, and other technologies have had a greater impact on supply chain management. Among them, big data analysis capability, as one of the important capabilities that supply chain enterprises should have, has a particularly significant impact on supply chain resilience management. From the perspective of performance management, based on supply chain resilience theory, the relationship between the supply chain performance management level, supply chain collaboration, and other supply chain resilience elements, as well as big data analysis capability and supply chain performance can be analyzed to study the impact of big data analysis capability on supply chain performance of enterprises of different scales. The impact on the level of supply chain performance is being studied. This paper investigates the problem of supply chain performance evaluation and optimization based on the LMBP algorithm and provides some references for supply chain performance evaluation and optimization.

Purpose Existing supply chain (SC) performance models are not able to cope with the potential of intensive SC digitalisation and establish a relationship between decisions and decision criteria. The purpose of this paper is to develop an integrated knowledge-based system (KBS) that creates a link between decisions and decision criteria (attributes) and evaluates the overall SC performance. Design/methodology/approach The proposed KBS is grounded on the fuzzy analytic hierarchy process (fuzzy AHP), which establishes a relationship between short-term and long-term decisions and SC performance criteria (short-term and long-term) for accurate and integrated Overall SC performance evaluation. Findings The proposed KBS evaluates the overall SC performance, establishes a relationship between decisions (long-term and short-term) and decision criteria of SC functions and provides decision makers with a view of the impact of their short-term or long-term decisions on overall SC performance. The proposed system was implemented in a case company where the authors were able to develop a SC performance monitoring dashboard for the company’s top managers and operational managers. Practical implications The proposed KBS assists organisations and decision makers in evaluating their overall SC performance and helps in identifying underperforming SC functions and their associated criteria. It may also be considered as a tool for benchmarking SC performance against competitors. It can efficiently point to improvement directions and help decision makers improve overall SC performance. Originality/value The proposed KBS provides a holistic and integrated approach, establishes a relationship between decisions and decision criteria and evaluates overall SC performance, which is one of the main limitations in existing supply chain performance measurement systems.

Since single evaluation method is inefficient, establishing a teaching quality evaluation algorithm with real-time optimization and intelligent learning becomes a major trend for current automatic evaluation. Based on the study of evaluation index system of classroom teaching quality in colleges, this paper proposes an intelligent evaluation model for the quality of classroom teaching in colleges and applies it to evaluation practice of intelligent teaching system. It avoids the problem of difficult determination on weights a and simplifies the evaluation process. The improved multilevel fuzzy comprehensive evaluation method is adopted to perform evaluation on the quality of classroom teaching. The case analysis shows that the model is an effective method to evaluate the quality of classroom teaching in colleges, and it realizes the high efficiency, network and intelligence of the teaching quality evaluation.

Effective and comprehensive evaluation of cold stress is critical for healthy dairy cow breeding in the winter. Previous studies on dairy cow cold stress have considered thermal environmental factors but not physiological factors or air quality. Therefore, this study aimed to propose a multilevel fuzzy comprehensive evaluation (FCE) method for cold stress in dairy cows based on the analytic hierarchy process (AHP) and a genetic algorithm (GA). First, the AHP was used to construct an evaluation index system for cold stress in dairy cows from 3 dimensions: thermal environment (temperature, relative humidity, wind speed, and illumination), physiological factors (respiratory rate, body surface temperature), and air quality [NH3, CO2, inhalable particulate matter (PM10)]. Second, the consistency test of the judgment matrix was transformed into a nonlinear constrained optimization problem and solved using the GA. Next, based on fuzzy set theory, the comment set and membership function were established to classify the degree of cold stress into 5 levels: none, mild, moderate, high, and extreme. Then, the degree of cold stress in cows was obtained using multilevel fuzzy comprehensive judgment. To investigate the effect of illumination indicators on cold stress in dairy cows, 24 prelactation cows from the south and north sides were selected for a 117-d comprehensive cold stress evaluation. The results showed that the mean mild cold stress durations were 605.3 h (25.22 d) and 725.5 h (30.23 d) and the moderate cold stress durations were 67.2 h (2.8 d) and 96 h (4.0 d) on the south and north sides, respectively. Simultaneously, generalized linear mixed model showed that there were significant correlations between the daily cold stress duration and milk yield, feeding time, lying time, and active steps in the cows on both sides. This method can reasonably indicate cow cold stress conditions and better guide cold protection practices in actual production.

Automotive Returnable Container Management with RFID:A Simulation Approach

Aiming at the inaccuracy and blindness of the traditional detection and assessment methods for the quality of emulsion explosives, a method for evaluating the quality of emulsion explosives based on soft measurement and multilevel fuzzy evaluation is proposed. The soft-sensing model of BP neural network can predict the online unmeasured performance indicators of detonation velocity and detonation online. The multilevel fuzzy evaluation method establishes the reliable multilevel fuzzy evaluation system based on the key parameters of the production process and expert experience. Experiments show that this soft-sensing model have made the convergence quickly and the accuracy highly,and can accurately predicts the detonation velocity and brisance of emulsion explosives online. In the last part, the design of quality assessment system can provide a new idea for solving the point of quality blindness detection and assessment.

The respective operation performance of node enterprises and the coordination performance between them commonly constitutes the internal performance of the supply chain. The custom satisfaction degree, which depends on the products and service provided by the supply chain system to the ultimate customers, determines the external performance. The existing studies on performance evaluation of supply chains mainly focus on one aspect of the both, on the internal performance or on the external performance, which are not incomplete and unsystematic. So, It has important practical significance to study the performance evaluation of supply chains as one thing. The paper applied rough set theory to analyse the evaluation index system of supply chains' total performance. The model used Johnson reducer algorithm to reduce the initial decision table and, through calculating the importance of the attributes in the reduced decision table, identified the weight coefficients of various evaluation indexes, effectively overcoming the subjectivity of weight coefficient determination, which provided basis for decision making of evaluating on the performance of supply chains.

Supply chain management is the main model of corporate production, operation and market competition. Performance evaluation of supply chain provides important and strategic guidance for the effective implementation of supply chain management. According to designing principles of performance evaluation index in supply chain, this paper constructed a performance evaluation index system of supply chain. In view of the uncertainty and fuzziness of indexes in evaluating supply chain performance, this paper combined improved entropy method and fuzzy matter-element theory to establish a fuzzy-matter model for evaluating supply chain performance. The weight of index was endowed by improved entropy method in the process of performance evaluation, and this method could avoid the influences of objective factors. The case study indicates that the evaluation method based on fuzzy matter-element model is simple and workable, and provides a new way for the evaluation of supply chain performance.

Conducting the performance evaluation of equipment supports parts supply chain timely and accurately can provide reliable information for equipment support commander to adjust equipment spare parts inventory policy. Fuzzy comprehensive hierarchy analysis method is used to evaluate the supply chain performance of equipment spare parts. The performance evaluation index system of equipment spare supply chain is established firstly based on the analysis of all the influence factors. The application steps of fuzzy comprehensive hierarchy analysis method to supply chain performance evaluation are illustrated in detail. A performance evaluation example is conducted at last. The evaluation result demonstrates that the provided method can give the performance level, which verifies the correctness and validity of the proposed method and provides an efficient method for supply chain performance evaluation of equipment spare parts.

Prefabricated building is an objective requirement to achieve sustainable development of the construction industry. However, it should be noted that Chinese enterprises are characterized by an immature supply chain management mechanism, and weak environmental protection awareness and social responsibility awareness. Therefore, from the perspective of sustainable development, a performance evaluation system for a prefabricated building supply chain was established based on SEM (Structural Equation Model) and virtual frontier SBM–DEA (Slacks-Based Measure and Data Envelopment Analysis). Upon summarization of a great deal of literatures, the most influential 34 indexes were selected, after which the weight calculation and index screening were performed using SEM method. Second, the performance evaluation was conducted using the virtual SBM–DEA method. Horizontally, a comparison is made on the performance and total performance of the four sub-units (supply chain operation, economic benefit, environmental protection and social liabilities) in the supply chain; vertically, the dynamic changes of the supply chain in time dimension are assessed. After the evaluation system was applied into enterprises, research results show that factors affecting the performance of the corporate supply chain are ranked as: supply chain operation > economic benefits > environmental protection > social responsibility. At the same time, the performance of 14 supply chains was evaluated, in order to provide guidance for supply chain management in enterprises.

This chapter discusses the external factors considered in supply chain performance evaluation in the manufacturing industry. We first provide a conceptualization of these factors and then discuss their background. The fundamental goal of this chapter is to provide an overview of those external elements that impact on supply chain performance and to explain how they are traditionally evaluated. Through its content, this chapter makes reference to the following topics: Supply chain risk, including demand risks, supply risks, and process production risk. Manufacturing practices in the supply chain, especially lean manufacturing practices. Regional factors affecting the supply chain, including the government, services, regional costs, regional infrastructure, quality of life, proximity, and human resources characteristics.

In 2020, the textile industry contributed nearly 7% of Indonesia's gross domestic product. The garment industry is still dominated by small and medium enterprises (SMEs) among the textile products. Although these SMEs are considered one of the economic pillars in Indonesia, many challenges require strategical scale-up to improve their competitiveness. One of the aspects to be improved is supply chain performance, as the supply chain controls material, information, and financial flow from both supply and demand sides. This study seeks to measure and evaluate supply chain performance in the garment industry, focusing the case on small and medium-scale enterprises. The Supply Chain Operation Reference (SCOR) is used for Key Performance Index (KPI) determinants. Performance measurement starts by determining the criteria based on the performance measurement literature and expert opinion. Then, the weight of each criterion on the performance score is determined using the Analytical Hierarchy Process (AHP). Paired comparison questionnaires for the criteria weighting were distributed to experts, and the answers were analyzed. The final performance value is obtained by multiplying the weight with the normalized performance value using the Snorm-De Boer formula. This study obtained 23 indicators from five processes: plan, make, source, deliver, and return, with the final value of SCM performance classified as good. The result can evaluate the company's current condition and propose a strategy to improve its performance.

Purpose: The study sought to establish the effects of end user involvement in the supply chain performance of higher learning institutions in Kenya.Methodology: The study was conducted at Chuka University procurement department and user departments comprising of 64 respondents from user departments and procurement. The study employed cross-sectional research design. Structured questionnaires were used for data collection. In order to determine the validity and reliability of the questionnaire, pretesting of the research instruments was conducted. To establish the validity of the research instrument, content validity was used while internal consistency method was used to determine the reliability. Correlation analysis followed by multivariate regression analysis was conducted between the independent variables and the dependent variable.Results: Results showed strong statistical significance between supply chain performance and procurement planning. The study also established that there was a strong statistical significance between supply chain performance and specification preparation. It was also established that there was strong statistical significance between supply chain performance and monitoring and evaluation. Lastly, there was strong statistical significance between supply chain performance and inspection and receipt of goods. The findings also revealed that the four independent variables under study only contributed to 63.4% of the dependent variable’s outcome..Conclusion: The study concludes that procurement planning, specification preparation, monitoring and evaluation, and inspection and receipt of goods are good measures of the end user involvement in the effective performance of supply chain, but that more studies need to be done to unearth the hidden variables contributing to the remaining 36.6% factors/variables, in order to get a full picture of the impact of the end-users on supply chain performance. Contribution to policy and practice: Research institutes and scholars will gain vital insights from the study when they want to research further and lastly government together with all its agencies will also gain important information that will inform the policies they come up with in future

This paper presents a fuzzy-based methodology using triangular fuzzy numbers to determine the weight of influencing factors and construct decision matrices in order to reduce the subjective uncertainty associated with influencing factors and decision-makers in traditional criticality analysis. A multi-level evaluation system is established to realize the hierarchical criticality quantitative analysis for failure modes of components and the system. The bogie system as a case example is used to demonstrate the proposed method and the quantitative criticality index of the bogie system can be obtained by using the multi-level fuzzy comprehensive evaluation method. The results show that the proposed method can be used effectively for quantitative criticality analysis of the systems.