Elsevier

Decision Support Systems

Volume 48, Issue 2, January 2010, Pages 331-341
Decision Support Systems

Comparative study of adaptability and flexibility in distributed manufacturing supply chains

https://doi.org/10.1016/j.dss.2009.09.001Get rights and content

Abstract

Supply chains need to be flexible and adaptive because their operations are always subject to a variety of uncertainties like customer demand and supplier capacity, particularly for Make-to-Order (MTO) supply chains since their flow of materials is only triggered by customer orders. The main objective of this paper is to study how flexibility and adaptability in delivery quantity and due date can improve the performance in a network of two-level multi-product MTO supply chains. Effect of uncertain customer demand and also supplier capacity, and supplier's capacity utilization are studied. Flexibility and adaptability are realized based on two proposed coordination mechanisms. Agent-based simulation is employed in this study to model the operations of supply chains. Performance of the system is measured in terms of a number of cost items and customer demand fill rate. Simulation results indicate that introduction of such flexibility and adaptability can improve the aforementioned performance. However, there is a trade-off in selecting the coordination mechanism between adaptability and flexibility subject to capacity utilization. Details and concluding remarks are discussed in this paper.

Introduction

Manufacturers are now operating as nodes in a network of suppliers, customers, and other specialized service functions. Independent companies usually work in a distributed or decentralized manner in such a complex network. These companies probably have no right to access sensitive information of the others. If any operational parameter, e.g. customer demand or supplier capacity studied in this paper, is uncertain, the situation becomes more complicated and traditional mathematical optimization techniques are unable to provide an optimal solution for such problems. There is a need for supply chain members to adapt to such uncertain environment in order to reduce any adverse impacts [24].

To accomplish this objective, information should be acquired, distributed, and interpreted [6]. However, information sharing is not always possible because it is not uncommon for companies to keep important information confidentially. In this connection, one key research motivation of this paper is to study the effect of adaptability without information sharing in distributed supply chains. Choi et al. [7] defined adaptive behavior as the mechanism for assessing the current state of its external domain and incorporated this into its “decision” about future action in order to maintain autonomous and reasoning capabilities. In this paper, this is realized in terms of a coordination mechanism among supply chain members.

More specifically, an adaptive coordination strategy (or sometimes referred to as a mechanism) with quantity flexibility is proposed for distributed, multi-product manufacturing supply chain networks, subject to uncertainties in customer demand, supplier capacity and also supplier's capacity utilization. Quantity flexibility is a coordination mechanism that allows a buyer to place an order earlier, or to make a commitment for a minimum quantity to be purchased from the supplier, who then provides the buyer with flexibility to adjust the order quantity later, subject to the most updated and accurate demand information [30]. On the other hand, the proposed adaptability coordination mechanism allows a supplier to plan for production by adapting to the most recent information. A comparative study is conducted to contrast the two approaches.

In this study, agent-based simulation is employed to model the operations of the supply chain under study. Section 2 reviews related literature. A theoretical model with multiple suppliers and multiple customers in a multi-product supply chain network is formulated in Section 3. The two coordination mechanisms with quantity flexibility and adaptability are also discussed in Section 3. Section 4 summarizes the simulation results for a selected supply chain configuration. Section 5 is the concluding section.

Section snippets

Review of the literature

Coordination of activities across a network of suppliers is essential for reacting quickly to uncertain environments. Helo [11] advocates that manufacturing firms could gain such competitive advantage via flexibility. It is particularly important for Make-to-Order (MTO) supply networks since their flow of materials is triggered by dynamic customer orders, and there is little buffer inventory to protect the system from coordination inefficiencies [20]. Generally, an MTO strategy can reduce

Problem formulation

Two-echelon supply chain with multiple suppliers and customers is studied in this paper. The customers face uncertain demand as well as uncertain supply (see discussion later). In the model without coordination, the customers needs to make an inventory replenishment order based on stochastic order-up-to policy while the suppliers bid for an order based on their own capacity (which is not known to the customers) at the time the order is announced. Expected lead time is then reported to the

Results and discussions

A simulation study has been carried out to verify the usefulness of the proposed supply chain coordination with flexibility and adaptability, and to provide a comparative study to show whether flexibility alone, or both flexibility and adaptability would be better. The simulation program is written in JAVA. In order to improve the robustness of the simulation results, a number of precautions have been carried out before actual measurements have been recorded.

Firstly, each simulation setting is

Conclusion

To remain competitive, organizations must be able to move fast and quickly adapt to change. Moreover, they must be able to reconfigure their key business processes with changing market conditions. Enterprises must respond to new requirements quickly without interrupting the course of business. Such changes must be mapped to the business object level and related to existing enterprise models. As product life cycles are shortening, adoption of MTO production strategy is inevitable. However, a

Hing Kai Chan received his BEng degree in Electrical and Electronic Engineering, MSc degree in Industrial Engineering and Industrial Management, PhD degree, all from the University of Hong Kong. He also earned a bachelor degree in Economics and Management from London School of Economics and Political Science. He is now a lecturer in the Norwich Business School, University of East Anglia, UK. Prior to joining the school in 2007, he was a project leader (carrying the title of research associate)

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    Hing Kai Chan received his BEng degree in Electrical and Electronic Engineering, MSc degree in Industrial Engineering and Industrial Management, PhD degree, all from the University of Hong Kong. He also earned a bachelor degree in Economics and Management from London School of Economics and Political Science. He is now a lecturer in the Norwich Business School, University of East Anglia, UK. Prior to joining the school in 2007, he was a project leader (carrying the title of research associate) of a government funded research project at Hong Kong Polytechnic University. His current research interests include industrial informatics, supply chain modeling and simulation, advanced industrial or manufacturing systems, and applications of soft computing on intelligent industrial systems and supply chains. He is also keen on research topics related to manufacturing or operations strategy like scheduling of flexible manufacturing systems, which are his early research interests.

    Felix Chan received his BSc in Mechanical Engineering with First Class Honour at Brighton Polytechnic (now University). He obtained his MSc in Advanced Applied Mechanics and PhD at Imperial College of Science and Technology, University of London. He was a research fellow for 2 years in the Department of Design, Manufacture and Engineering Management, University of Strathclyde, then a senior lecturer at the School of Manufacturing and Mechanical Engineering, University of South Australia. Prior to joining the Department of Industrial and Systems Engineering of the Hong Kong Polytechnic University in 2009, Dr. Chan was an Associate Professor at the Department of Industrial and Manufacturing Systems Engineering, the University of Hong Kong. His research areas cover modeling and simulation in advanced manufacturing systems and supply chain networks, supply chain performance measurement systems, intelligent distribution methodology with multicriterion decision-making approach for logistics, and supply chain management.

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