Designing a manufacturing network with additive manufacturing using stochastic optimisation
Ahmed, R., Heese, H. S., & Kay, M. (2022, April 1). INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH.
Two of the major problems Traditional Manufacturing (TM) supply chains face are setting requisite reactive strategies to address the uncertainties in demand and the optimal placement of these buffering capacities in order to be both responsive and cost-effective. With Additive Manufacturing (AM) stepping into large-scale production at different firms, we address the aforementioned supply chain dilemmas by considering the potential role of AM in a TM supply chain network where AM facilities can act as a recourse to the TMs and, also, as a dedicated source providing responsive and cost-effective sourcing alternatives. We develop an analytical allocation rule based on cost differentials, which provides optimal sourcing decisions through sequential demand replenishment and facilitates an efficient performance evaluation of possible network configurations. We first model the scenario as a three-stage stochastic optimisation problem. We then solve it using the allocation rule and present an illustration of our analysis and the optimal supply chain network configuration. Furthermore, we derive some insights as to how different problem characteristics affect the value and usage of AM.