Multi-Echelon Supply Chain Modeling With Dynamic Continuous Review Inventory Policy

Author(s):  
K. Narayana Rao ◽  
K. Venkata Subbaiah

In this chapter, an integrated procurement, production and distribution supply chain model is developed in fuzzy environment and performance vector of the supply chain is determined by solving strategic model and tactical model iteratively. Mixed integer programming model is formulated through fuzzy goal programming approach in strategic level. In the tactical level, dynamic continuous review inventory policy for controlling of raw material inventory at supplier echelon, finished products at plant echelon and distribution center echelons is assumed. The inventory models are solved by considering the interdependency of economic order quantity and reorder point. The supply chain model, which is developed in fuzzy environment, finds compromise solution with multiple, vague and in-compatible objectives. Fuzzy goal programming techniques provide feasible solutions with flexible model formulation in decision-making problems, which involve human judgments in decision-making. Need for supply chin modeling with dynamic continuous review policy in fuzzy environment and the existing literature are outlined in Introduction. Fuzzy supply chain modeling with dynamic continuous review policy for controlling of the raw materials, finished products at plant and distribution center echelons is described in Fuzzy supply chain modeling section. Flow chart of the methodology is explained in Solution Methodology section. The proposed model is illustrated through a numerical example. Supply chain cost, Volume flexibility and unit costs are determined and presented in Results and Discussion section. Importance of the methodology and future scope is made in Conclusion section. This model finds application in the industries involving continuous production like oil and natural gas, steel manufacturing industries etc

2012 ◽  
pp. 1505-1521
Author(s):  
K. Narayana Rao ◽  
K. Venkata Subbaiah

In this chapter, an integrated procurement, production and distribution supply chain model is developed in fuzzy environment and performance vector of the supply chain is determined by solving strategic model and tactical model iteratively. Mixed integer programming model is formulated through fuzzy goal programming approach in strategic level. In the tactical level, dynamic continuous review inventory policy for controlling of raw material inventory at supplier echelon, finished products at plant echelon and distribution center echelons is assumed. The inventory models are solved by considering the interdependency of economic order quantity and reorder point. The supply chain model, which is developed in fuzzy environment, finds compromise solution with multiple, vague and in-compatible objectives. Fuzzy goal programming techniques provide feasible solutions with flexible model formulation in decision-making problems, which involve human judgments in decision-making. Need for supply chin modeling with dynamic continuous review policy in fuzzy environment and the existing literature are outlined in Introduction. Fuzzy supply chain modeling with dynamic continuous review policy for controlling of the raw materials, finished products at plant and distribution center echelons is described in Fuzzy supply chain modeling section. Flow chart of the methodology is explained in Solution Methodology section. The proposed model is illustrated through a numerical example. Supply chain cost, Volume flexibility and unit costs are determined and presented in Results and Discussion section. Importance of the methodology and future scope is made in Conclusion section. This model finds application in the industries involving continuous production like oil and natural gas, steel manufacturing industries etc


Author(s):  
Jairo R. Montoya-Torres

Supply chain performance is highly influenced by the coordination level between its members, which needs information sharing. In this paper we consider a three-echelon direct sell supply chain model and focus on the problem of coordinated decision-making between its members. Our contribution is a first approach that measures the impact of the degree of coordination between the members. Demand behavior is modeled using a geometric Brownian process. Simulation models are run in order to analyze various cooperation scenarios. Our results show a direct relation between the degree of coordination within the supply chain and the total system cost. Although this result is intuitive, our simulations allowed us to quantify such a relation and in which measure these costs are whether or not associated to imperfect coordination.


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