An object-oriented framework for modelling control policies in a supply chain. (6th January 2009)

• Record Type: Journal Article
• Authors: Wadhwa, Subhash; Madaan, Jitendra; Chawla, Anoop
• Is Part Of: International journal of value chain management. Volume 3:Number 1(2009)
• Page Start: 36
• Page End: 63
• Publication Date: 2009-01-06
• Subjects: supply chain management -- SCM -- simulation -- object-oriented modelling -- multiple entity flow -- supply chain control -- control policies -- decision making -- source selection -- inventory management -- transportation -- production planning; Business logistics -- Periodicals; 658.7
• Languages: English
• ISSNs: 1741-5357

Abstract

In this paper, a framework for an object-oriented Supply Chain (SC) environment is proposed. This framework is based on object-oriented methodology, which allows the building of complex simulation models by combining some basic or primitive objects. This proposed framework provides several advantages over existing SC systems. First of all, this system incorporates the independent decision-making at each SC node by modelling each node as an autonomous entity. In the proposed framework, independent SC nodes are connected to each other by mutual relationships. These relationships are determined using the relevant control policies of each node. These relationships may change during execution of the simulation model in accordance with the control policies. The proposed framework is generic enough to incorporate both the manufacturing and nonmanufacturing type of nodes. In this framework, the SC system is studied in terms of five entity flows: material, information, money, resource and decision. Another major contribution of this framework is the provision of a number of control policies for major SC decisions such as source selection, inventory management, transportation and production planning. This framework is also capable of modelling, simulating and analysing nondeterministic SC systems where one or more of the uncontrollable variables are stochastic. In addition, this framework can also be used for improving the performance of SCs by suggesting the optimal control policies.