Research On The Optimization Of Supply Chain Resilience Measures In The Machinery Manufacturing Industry Considering Customer Demands And Risks

As a pillar industry for the development of China’s national economy,the machinery manufacturing industry has made remarkable achievements in recent years through continuous accumulation.However,it is undeniable that it is also facing some problems in the development process,for example: the upstream and downstream supply chains of machinery manufacturing enterprises are susceptible to various contingencies and risks,making their supply chain resilience constantly under attack.In addition,in the process of development of machinery manufacturing enterprises,they also face the pressure and challenge of diversifying customer needs.In this context,it is of great importance to the development of the machinery manufacturing industry that machine manufacturers are committed to creating more effective and feasible supply chain resilience measures.At the same time,academic research on supply chain resilience measures is emerging,but most of them start with the analysis of the level of performance of resilience,while few studies consider the dual role of supply chain risk factors and customer demand.Therefore,how to build a set of scientific and reasonable supply chain resilience measures to reduce the various risks in the supply chain and meet customer demand is an important issue that must be considered in the future long-term development of machinery manufacturing enterprises,and it is also an important issue that needs to be addressed.In view of this,this study applies the Kano model,DANP(a combination of DEMATEL and ANP methods),QFD(Quality Function Deployment)multi-attribute decision model and non-linear programming model to the study of supply chain resilience measures optimisation in the machinery manufacturing industry,taking into account both customer demand and risk factors.Firstly,the Kano model is used to analyse customer needs qualitatively and quantitatively,and to determine the priority of each consumer need and customer satisfaction;the DANP is used to identify key risk factors and to analyse the causal relationships between them.Secondly,the results obtained from the Kano model and DANP were incorporated into the QFD so that client needs were translated into risk factors and further into resilience measures.In addition,a non-linear programming model is established with the aim of finding the optimal resilience solution under the conditions of minimum cost investment,maximum customer satisfaction,etc.,as a means to optimise the supply chain resilience measures for the machinery manufacturing industry.Finally,the optimisation solution is validated with a business case study.The study finds that the most urgent tasks for the case company to reduce supply chain risks and meet customer demands are to develop key supply chain resilience measures,namely:strengthening supplier management,establishing a professional supply chain risk management department and enhancing upstream and downstream cooperation in the supply chain.This study conducted in-depth interviews with experts from the supply chain and upstream and downstream of the machinery manufacturing industry to identify the strategic resilience measures that machinery manufacturers should focus on.The resilience measures proposed in this study can further improve the risk resistance and customer satisfaction of the supply chain of machinery manufacturing enterprises,thus promoting high-quality development of machinery manufacturing enterprises.In addition,the practical application of the idea of optimising resilience measures in the machinery manufacturing industry proposed in this study can be a reference for other industries in the study of optimising resilience measures in the supply chain.