(518a) Centralized Vs. Decentralized Supply Chain Management Optimization

Authors: 
Sahay, N., Aspen Technology
Ierapetritou, M. G., Rutgers, The State University of New Jersey


Centralized vs. Decentralized Supply Chain Management Optimization

Nihar Sahay, Marianthi Ierapetritou

Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ

A supply chain (SC) is a network of suppliers, production facilities, warehouses and markets designed to acquire raw materials, manufacture and store and distribute products among the markets. The entire process is driven by the demand generated at the markets1.  Organizations often have complex distributed supply chains and operating them optimally becomes a challenging task. One of the major problems in managing supply chain networks is the lack of collaboration among the different entities including raw material suppliers, production sites, warehouses and retailers. The problem is that the different decision makers do not have access to the information regarding the state of the entire SC network, and in addition they usually operate under different objective functions. However, it has been shown that greater efficiency and reduced costs can be achieved through proper coordination among the entities in terms of material, financial and information flow.2, 3

Decision-making in a supply chain network can be performed in a centralized or a decentralized way. In a centralized structure, there exists a central authority responsible for decision-making, whereas in a decentralized structure the individual entities can make their own decisions. In practice no supply chain can be completely centralized or decentralized and both approaches have their advantages and disadvantages. Most commonly the strategic decisions are usually made centrally while operation decisions are decentralized. The performance of each approach has been found to depend on the specific environment and the particular decisions.4, 5

The effects of centralization in different supply chain problems have been studied in the past using different approaches. Chen and Chen6 study the effects of centralization and decentralization on the multi-item replenishment problem in a two-echelon supply chain. They proposed both centralized and decentralized decision models and proved the optimal properties of both the models to minimize costs. Duan and Liao7 determined optimal replenishment policies of capacitated supply chains operating under the centralized and decentralized control strategies using a simulation based optimization framework. They concluded that it is beneficial to adopt centralized control and proposed a mechanism to coordinate the decentralized system so that each player in the chain is benefited. Behdani et al.8 studied the centralized and decentralized management of abnormal situations in a multi-plant enterprise using an agent based approach. They considered disruptions in a multi-plant enterprise and studied alternative policies for coping with them. To model the complex system of a multi-plant enterprise, they used an agent based simulation model.

Simulation models have been widely used to represent complex supply chains. Among them Agent based models have been proven to be very suitable for representing the different entities of the supply chain which have their own individual goals and policies while capturing the complex interactions among the entities9. It has also been shown that hybrid simulation based optimization models can be utilized to achieve the optimal supply chain operations10. They are used to overcome the computational complexity associated with solving a large scale supply chain problem and allow the simulation of the complex interactions among the entities describing more accurately the way a supply chain entity would actually behave.

In this work, we compare the behavior of a small scale supply chain operating under the centralized and decentralized strategies. The suitability of the two strategies under different scenarios is explored. Hybrid simulation based optimization approach is used to achieve the optimal operation of the supply chain in both cases. The hybrid approach involves the development of an agent based simulation model, which captures the behavior of the individual entities in the supply chain. These are stochastic models with uncertain demand, transportation times, and machine breakdowns. The entities are modeled as intelligent agents that aim to maximize their individual profits based on their set of goals and policies. Different simulation models are developed for the centralized and decentralized supply chains as the two strategies alter the behavior and objectives of the individual entities. The simulation models are coupled with an optimization model iteratively so that the solution is directed towards optimality. The optimization model of the problem has been developed in GAMS while the simulation model has been developed using the JAVA Repast tool. The hybrid models enable us to evaluate the optimal behavior of the supply chain following the two different control strategies under different scenarios.

References:

1.            Beamon, B.M. Supply chain design and analysis:: Models and methods. International Journal of Production Economics. 1998, 55, 281-294.

2.         Stadtler, H. Supply chain management and advanced planning––basics, overview and challenges. European Journal of Operational Research. 2005, 163, 575-588.

3.         Varma, V.A.;Reklaitis, G.V.;Blau, G.E.;Pekny, J.F. Enterprise-wide modeling & optimization—An overview of emerging research challenges and opportunities. Computers & Chemical Engineering. 2007, 31, 692-711.

4.         Chang, M.-H.;Harrington, J.E., Jr. Centralization vs. Decentralization in a Multi-Unit Organization: A Computational Model of a Retail Chain as a Multi-Agent Adaptive System. Management Science. 2000, 46, 1427-1440.

5.         Saharidis, G.K.D.;Kouikoglou, V.S.;Dallery, Y. Centralized and decentralized control polices for a two-stage stochastic supply chain with subcontracting. International Journal of Production Economics. 2009, 117, 117-126.

6.            Chen, J.-M.;Chen, T.-H. The multi-item replenishment problem in a two-echelon supply chain: the effect of centralization versus decentralization. Computers & Operations Research. 2005, 32, 3191-3207.

7.         Duan, Q.;Warren Liao, T. Optimization of replenishment policies for decentralized and centralized capacitated supply chains under various demands. International Journal of Production Economics. 2013, 142, 194-204.

8.         Behdani, B.;Lukszo, Z.;Adhitya, A.;Srinivasan, R., Decentralized vs. centralized management of abnormal situations in a multi-plant enterprise using an agent-based approach, in Computer Aided Chemical Engineering, S. Pierucci and G.B. Ferraris, Editors. 2010, Elsevier. p. 1219-1224.

9.         Lee, J.H.;Kim, C.O. Multi-agent systems applications in manufacturing systems and supply chain management: a review paper. International Journal of Production Research. 2007, 46, 233-265.

10.       Nikolopoulou, A.;Ierapetritou, M.G. Hybrid simulation based optimization approach for supply chain management. Computers & Chemical Engineering. 2012, 47, 183-193.