Developing a resilient supply chain in complex product systems through investment in reliability and cooperative contracts

In recent years, finding mitigation strategies for supply chain disruptions has become one of the most critical challenges for businesses. This issue is crucial for complex product industries because of their role in the modern economy, few suppliers, and their need for high investment in research and development (R&D). This paper studies a resilient supply chain in complex product systems to overcome its specific challenges through supplier reliability enhancement and cooperative contracts. Utilising a game theoretic approach and analytical models, this paper aims to improve the supply chain performance from the resilience perspective while considering R&D investment, supplier learning effect, buyer fairness concern, and market sensitivity to the product’s technology. Investment in supplier reliability enhancement with different contracts is proposed to mitigate disruption risks for a two-echelon supply chain. Analytical mathematical models have been developed, and a simulation approach has been used in optimisation. The results show how proposed contracts effectively increase supply chain performance from financial and resilience perspectives. Moreover, the market sensitivity to the product’s technological level and the sensitivity to the price could adversely affect performance. The buyer’s fairness concern also improves the profit loss while decreasing the service level slightly

Economic pricing of complex products in a competitive closed-loop supply chain network under uncertainty: A case study of CoPS industry

The development of technology, globalization of the economy and the unpredictable behavior of customers have eventuated in a dynamic and competitive environment in the complex product systems (CoPS) market. Besides, CoPS economic pricing is one of the key factors that dramatically reduces production costs and increases competitiveness. In this regard, this paper unveils a hybrid data envelopment analysis (DEA)-fuzzy mathematical model for economic pricing of CoPS in a competitive closed-loop supply chain network under uncertainty. In the first stage, different CoPS suppliers are evaluated exploiting a DEA model based on a set of economic, technical, and geographical criteria. The advantage of this evaluation is choosing appropriate suppliers, and reducing the complexity of the original model. Next, using a robust optimization model, the strategic and tactical decisions are simultaneously determined, providing a fully optimal solution to the model. In the concerned model, the costs and capacities of facilities are considered to be hemmed in by uncertainty. Eventually, to evaluate the proposed approach, a case study is conducted to derive the important managerial results. The numerical results corroborate that the presented robust model is capable of providing a stable structure under different realizations

A Novel Robust Network Data Envelopment Analysis Approach for Performance Assessment of Mutual Funds under Uncertainty

Mutual fund (MF) is one of the applicable and popular tools in investment market. The aim of this paper is to propose an approach for performance evaluation of mutual fund by considering internal structure and financial data uncertainty. To reach this goal, the robust network data envelopment analysis (RNDEA) is presented for extended two-stage structure. In the RNDEA method, leader-follower (non-cooperative game) and robust optimization approaches are applied in order to modeling network data envelopment analysis (NDEA) and dealing with uncertainty, respectively. The proposed RNDEA approach is implemented for performance assessment of 15 mutual funds. Illustrative results show that presented method is applicable and effective for performance evaluation and ranking of MFs in the presence of uncertain data. Also, the results reveal that the discriminatory power of robust NDEA approach is more than the discriminatory power of deterministic NDEA models