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1667. Achieving emergent symmetry in high tech supply chains
Invited abstract in session MD-39: Stochastic Models in Manufacturing, stream Stochastic Modelling.
Monday, 14:30-16:00Room: 35 (building: 306)
Authors (first author is the speaker)
| 1. | Mirjam Meijer
|
| Kühne Logistics University | |
| 2. | Dennis Schol
|
| Eindhoven University of Technology | |
| 3. | Willem van Jaarsveld
|
| Eindhoven University of Technology | |
| 4. | Maria Vlasiou
|
| University of Twente | |
| 5. | Bert Zwart
|
| CWI |
Abstract
For manufacturers of high-tech products, such as aircraft, lithography machines, etc., it is of key importance that all required components are available at the time of assembly. This is a major challenge, as the number of components required for assembling such complex systems is enormous and most of them are sourced at external suppliers. For a supplier, the risk of over-investing in costly capacity is high, while the lost revenue of one missing component is much lower than the value of the manufacturer's end-product. Therefore, each supplier will trade off the cost of investing in additional capacity against the risk of being unable to deliver. Ultimately, the overall delay in delivery of the end-product is determined by the supplier with the largest backlog. To reduce the risk of component shortages, the manufacturer aims to motivate suppliers to invest in excess production capacity, for example by charging a fee for each time unit the supplier delivers past a previously agreed lead time. We model a manufacturing system consisting of a manufacturer and a large number of suppliers, each producing a single component that is required in the manufacturer's assembly process of the end-product. We consider the decentralized optimization problem where each supplier determines their own optimal service rate. We analyze how the contract parameters can be set such that the suppliers choose the service rates that are asymptotically optimal for the supply chain as a whole.
Keywords
- Stochastic Optimization
- Large Scale Optimization
- Manufacturing
Status: accepted
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