Inventory and Production Decisions for an Assemble-to-Order System with Uncertain Demand and Limited Assembly Capacity

Operations Research (Impact Factor: 1.74). 12/2006; 54(6):1137-1150. DOI: 10.1287/opre.1060.0335
Source: DBLP


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-

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    • "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.
    European Journal of Operational Research 12/2011; 215(3):590-603. DOI:10.1016/j.ejor.2011.07.007 · 2.36 Impact Factor
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    • "However finding semi-finished products is of interest for the manufacturing company. Determining a set of modules to be pre-assembled and stocked is a complex optimization problem (Da Cunha et al., 2007; Fu et al., 2006; Jiao and Zhang, 2004; Kusiak and Huang, 1996; Swaminathan and Tayur, 1998) in which multiple costs have to be considered and balanced. Da Cunha et al. (2007) developed a linear cost function and heuristic algorithms to find the optimal module combinations that could reduce the mean number of assembly operations. "
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    ABSTRACT: This paper presents a framework for finding optimal modules in a delayed product differentiation scenario. Historical product sales data is utilized to estimate demand probability and customer preferences. Then this information is used by a multiple-objective optimization model to form modules. An evolutionary computation approach is applied to solve the optimization model and find the Pareto-optimal solutions. An industrial case study illustrates the ideas presented in the paper. The mean number of assembly operations and expected pre-assembly costs are the two competing objectives that are optimized in the case study. The mean number of assembly operations can be significantly reduced while incurring relatively small increases in the expected pre-assembly cost.
    European Journal of Operational Research 02/2010; 201(1-201):123-128. DOI:10.1016/j.ejor.2009.02.013 · 2.36 Impact Factor
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    • "Our paper addresses the component stocking policy in a single-order assembly system where the product price depends on delivery time. A number of research papers have studied the optimal component stocking policies for single-order assembly systems; see, e.g., Chu et al. (1993), Fu et al. (2006), Gurnani et al. (1996), Hopp and Spearman (1993), Kumar (1989), Shore (1995), Song et al. (2000), Gerchak et al. (1994), and Yano (1987). Recently, Hsu et al. (2006) analyze a single-order assembly system with a delivery time-dependent pricing structure. "
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    ABSTRACT: We consider a contract manufacturer who procures multiple components from independent suppliers to produce an assemble-to-order customized product for a client. The unit price of the product depends on the manufacturer's delivery lead time. We explore how the manufacturer can use a vendor-managed consignment inventory (VMCI) scheme to manage the underlying risk and coordinate independent suppliers' decisions on the production quantities of their components under demand uncertainty. We formulate the problem as a Stackelberg game played by the manufacturer against her component suppliers to determine her pricing policy for suppliers' consignment inventories. We further develop an efficient algorithm for finding the manufacturer's optimal pricing scheme. Our results provide useful insights for managing components in these types of assemble-to-order environments and for understanding how component production cost and procurement lead times affect individual firms' performance in decentralized assembly channels.
    Management Science 12/2008; 54(12):1997-2011. DOI:10.1287/mnsc.1080.0934 · 2.48 Impact Factor
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