The aim of this research is to provide an integrated decision-making framework for evaluating and prioritizing value chain improvement strategies in manufacturing and service industries using a combined
TOPSIS and fuzzy
TOPSIS approach. In the first step, the main and supporting activities of the value chain in the manufacturing sector are evaluated based on criteria such as operational efficiency, cost, quality, and flexibility and are ranked using the
TOPSIS method. Next, for the service sector, which is associated with more uncertainty and linguistic judgments, value chain improvement strategies are analyzed and prioritized using fuzzy
TOPSIS using criteria such as customer satisfaction, service speed, risk, and value creation. In the final stage, the results of the two parts are combined and a comprehensive model is presented for selecting the best value chain improvement strategies. The research findings show that combining classical and fuzzy methods can provide a more accurate picture of the state of the value chain in different industries and help managers make more effective strategic decisions. This framework has applicability in manufacturing industries, financial services, health, information technology and other economic sectors and can be a basis for improving the competitiveness and performance of organizations.The aim of this research is to provide an integrated decision-making framework for evaluating and prioritizing value chain improvement strategies in manufacturing and service industries using a combined
TOPSIS and fuzzy
TOPSIS approach. In the first step, the main and supporting activities of the value chain in the manufacturing sector are evaluated based on criteria such as operational efficiency, cost, quality, and flexibility and are ranked using the
TOPSIS method. Next, for the service sector, which is associated with more uncertainty and linguistic judgments, value chain improvement strategies are analyzed and prioritized using fuzzy
TOPSIS using criteria such as customer satisfaction, service speed, risk, and value creation. In the final stage, the results of the two parts are combined and a comprehensive model is presented for selecting the best value chain improvement strategies. The research findings show that combining classical and fuzzy methods can provide a more accurate picture of the state of the value chain in different industries and help managers make more effective strategic decisions. This framework has applicability in manufacturing industries, financial services, health, information technology and other economic sectors and can be a basis for improving the competitiveness and performance of organizations.
