Inventory and Production Decisions for an Assemble-to-Order System with Uncertain Demand and Limited Assembly Capacity
George Mason University, 페어팩스, Virginia, United States Operations Research
(Impact Factor: 1.74).
12/2006; 54(6):1137-1150. DOI: 10.1287/opre.1060.0335
Abstract This paper considers an inventory and production planning problem for a contract manu- facturer who anticipates an order of a single product but with uncertain quantity. To meet the challenges of long component procurement lead times and limited assembly capacity, which may render production time insucient,to assemble total order quantity, the manufacturer may need to procure components or even assemble some quantities of the final product before receiving the confirmation of the actual order quantity. We present profit-maximization models that make optimal inventory and production decisions in the above assemble-to-order environment. We also consider the option of outsourcing that the manufacturer can outsource part of his produc- tion to an external facility which also has limited capacity. We establish structural properties of optimal solutions and develop ecient,solution procedures for the proposed problems. We also provide sensitivity analysis of the optimal decisions and some managerial insights. Subject classifications: Inventory/production: assemble-to-order systems, component procure- ment lead times, demand uncertainty, capacity management, outsourcing. This research is supported in part by Hong Kong RGC Earmark Grant HKUST 6153/04E and the Doctoral Dis-
Available from: Zumbul Atan
- "The authors develop a model to determine the modules to be pre-assembled to minimize the customer utility per cost of implementing the modules. Similar to Fu et al. (2006), Inman and Schmeling (2003) consider limited assembly capacity but in a multiple end products setting. Their results are based on a case study performed within the automobile industry where each individual product is matched to a customer order. "
Available from: Dezhi Zhang
- "Second, after confirming the actual customer demand, the manufacturer may need to assemble more final products to fulfil the needs of customers as much as possible  . "
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ABSTRACT: This paper presents an optimization decision model for a production system that comprises the hybrid make-to-stock/assemble-to-order (MTS/ATO) organization mode with demand uncertainty, which can be described as a two-stage decision model. In the first decision stage (i.e., before acquiring the actual demand information of the customer), we have studied the optimal quantities of the finished products and components, while in the second stage (i.e., after acquiring the actual demand information of the customer), we have made the optimal decision on the assignment of components to satisfy the remaining demand. The optimal conditions on production and inventory decision are deduced, as well as the bounds of the total procurement quantity of the components in the ATO phase and final products generated in the MTS phase. Finally, an example is given to illustrate the above optimal model. The findings are shown as follows: the hybrid MTS and ATO production system reduces uncertain demand risk by arranging MTS phase and ATO phase reasonably and improves the expected profit of manufacturer; applying the strategy of component commonality can reduce the total inventory level, as well as the risk induced by the lower accurate demand forecasting.
Available from: Jennifer Shang
- "Bollapragada et al. (2004) consider uncertain lead time for random demand and supply capacity in assembly Systems. Fu et al. (2006), Kazaz (2004), Akartunali and Miller (2009), and Li et al. (2009) have also addressed such issues. "
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ABSTRACT: This research is motivated by an automobile manufacturing supply chain network. It involves a multi-echelon production system with material supply, component fabrication, manufacturing, and final product distribution activities. We address the production planning issue by considering bill of materials and the trade-offs between inventories, production costs and customer service level. Due to its complexity, an integrated solution framework which combines scatter evolutionary algorithm, fuzzy programming and stochastic chance-constrained programming are combined to jointly take up the issue. We conduct a computational study to evaluate the model. Numerical results using the proposed algorithm confirm the advantage of the integrated planning approach. Compared with other solution methodologies, the supply chain profits from the proposed approach consistently outperform, in some cases up to 13% better. The impacts of uncertainty in demand, material price, and other parameters on the performance of the supply chain are studied through sensitivity analysis. We found the proposed model is effective in developing robust production plans under various market conditions.
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