Simulation based model for component replenishment in multi-product ATO systems with shared resources



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With increasing product complexity and customization, Assemble-to-Order (ATO) systems have gained a lot of popularity in recent years. ATO systems have the advantage of delivering customer orders at shorter leadtimes by manufacturing components to stock. However, for an on-time delivery of the final assembled product, the corresponding components must be replenished and be available when needed for assembly in a timely yet cost-effective manner. This research investigates the production and subcontracting decisions in the multi-product ATO systems. We also provide insights on the following main research questions: (1) how to allocate shred in-house resource among various components? and (2) how does randomness in the service times impact these decisions? We consider a manufacturing system where the components can either be shared manufactured in house or can be procured by dedicated subcontractors, with each having finite manufacturing capacity. In addition, the components have stochastic lead times, and component availability is critical to satisfying the demand of the final product. Using, Monte Carlo simulation approach, we encompass a wide range of possible scenarios and provide insights on when to use shared resource for producing one component versus another, when it is optimal to source components from outside vendors. Using numerical experiments, we analyze different practical scenarios: (i) In-house manufacturer is cheaper, (ii) External sub-contractor is cheaper, (iii) Using shifted exponential distribution (adding a constant delay in exponential distribution). Further, we observe that if the service times are shifted exponential distribution then the optimal policy tends to subcontract more often compared to when the service times are exponential distribution.



Assemble-to-Order, Supply Chain, Simulation, Multi-Product

Graduation Month



Master of Science


Department of Industrial & Manufacturing Systems Engineering

Major Professor

Ashesh K. Sinha