This paper proposes a multiattribute decision-making model for supplier selection under the service-oriented manufacturing, which can be used to effectively evaluate each candidate supplier. The supplier selection index system under the service-oriented manufacturing is proposed, and the interval evaluation matrix is established. In view of the mixed attribute of evaluation index, we construct a method that converts mixed attribute value to interval number. In order to avoid the subjectivity of the weight and make alternatives be provided with more discrimination, we use a combination model based on the deviation function model and the interval relative entropy ranking method to evaluate each candidate supplier. Finally, an application example is given to verify the correctness and practicability of the proposed decision-making model.

Manufacturing serves as a vital indicator of a nation’s economic strength, technological advancement, and comprehensive competitiveness. In the context of the VUCA (Volatility, Uncertainty, Complexity, Ambiguity) business environment and globalization, uncertain market demand has intensified supply chain disruption risks, necessitating resilience strategies to enhance supply chain stability. This study proposes five resilience strategies—establishing an information sharing system, multi-sourcing, alternative suppliers, safety stock, and alternative transportation plans—while integrating sustainability requirements. A multi-objective mixed-integer optimization model was developed to balance cost efficiency, resilience, and environmental sustainability. Comparative analysis reveals that the resilience-embedded model outperforms traditional approaches in both cost control and risk mitigation capabilities. The impact of parameter variations on the model results was examined through sensitivity analysis. The findings demonstrate that the proposed optimization model effectively enhances supply chain resilience—mitigating cost fluctuations while maintaining robust demand fulfillment under uncertainties.

Promoting high-quality development of the manufacturing industry is the key to achieving sustainable economic development. The development of the manufacturing industry is affected by a variety of factors, and it is particularly important to reflect on its level of development from multiple perspectives. This paper built an evaluation indicator system for the manufacturing industry’s high-quality development using the structural equation model. An improved TOPSIS evaluation model was constructed based on the combined weighting of the level difference maximization method. The time series arithmetic mean operator was introduced to assign weight to time, and the quadratic weighted integration enabled a dynamic evaluation. The level of high-quality development of China’s manufacturing industry from 2018 to 2022 was assessed both statically and dynamically. According to the findings, the overall high-quality development of China’s manufacturing industry exhibited a trend of first declining and then increasing when the current status was only element taken into account. Regionally, there was a sequential decline from the east to the central part, followed by the western and northeastern regions. When focusing solely on the degree of growth, it was higher in the northeast and lower in the east. However, the regional characteristics declined when both the current status and the degree of growth were considered. Evaluating the high-quality development level of the manufacturing industry can provide references and lessons for promoting regional coordinated and sustainable development of China’s manufacturing industry.

Shapely value is a method of determining the importance of individuals in the collective, avoiding the equal allocation of profit. The profit allocation on SOM (service-oriented manufacturing) alliance between agricultural machinery manufacturing enterprises and suppliers under the SOM mode is a complex problem restricted by many factors, but the Shapely value method does not consider the differences of member enterprises. Therefore, factors that affect the profit allocation are given in this paper, such as input level, effort level, innovation level, risk factor, and value-added factor. Based on these factors, the Euclidean distance is used to modify the traditional TOPSIS method to determine the profit allocation correction coefficient, and the GRA (grey relational analysis) is introduced into the TOPSIS method to calculate the closeness degree, which reflects the position relationship and consistency of data curve. Based on the modified Shapley value method, a profit allocation method of agricultural machinery service-oriented manufacturing alliance is constructed. Finally, an application example is given to verify the effectiveness of the proposed method.

The identification of customer requirements (CRs) priority is the beginning of Product-service system (PSS) design, which identifies the direction of enterprise resource allocation. In order to accurately reflect customer’s intrinsic requirements in PSS design, a hierarchical model based on the means-end chain theory is constructed to capture CRs from the perspective of customer expectations in this paper, and then a method combining cloud model, fuzzy Decision-making Trial and Evaluation Laboratory (DEMATEL) with improved analytical Kano (A-Kano) model is proposed to identify importance priority. Cloud model and Relative Preference Relation of the triangular fuzzy number are applied to identify the basic importance with solving the problem of fuzziness and randomness in expert evaluation information. Considering the directional influence relationship among CRs, the fuzzy DEMATEL method is introduced to modify the basic importance. Based on the category criterion of CRs, an improved A-Kano model is constructed, and the importance index and satisfaction index based on customer satisfaction are introduced to identify the final importance. A case study is carried out to verify the scientificity and feasibility of the proposed method.

The identification of customer requirements (CRs) priority is the beginning of Product-service system (PSS) design, which identifies the direction of enterprise resource allocation. In order to accurately reflect customer’s intrinsic requirements in PSS design, a hierarchical model based on the means-end chain theory is constructed to capture CRs from the perspective of customer expectations in this paper, and then a method combining cloud model, fuzzy Decision-making Trial and Evaluation Laboratory (DEMATEL) with improved analytical Kano (A-Kano) model is proposed to identify importance priority. Cloud model and Relative Preference Relation of the triangular fuzzy number are applied to identify the basic importance with solving the problem of fuzziness and randomness in expert evaluation information. Considering the directional influence relationship among CRs, the fuzzy DEMATEL method is introduced to modify the basic importance. Based on the category criterion of CRs, an improved A-Kano model is constructed, and the importance index and satisfaction index based on customer satisfaction are introduced to identify the final importance. A case study is carried out to verify the scientificity and feasibility of the proposed method.