Van den Broeke, Maud; Boute, Robert; Van Mieghem, Jan (2018)
Product platforms are assets that are shared by multiple products. We study the optimal investment in platform flexibility. Each platform type is characterized by its functionality that determines its R&D investment and unit production cost, as well as the customization cost to produce the end products from the platform. The firm can invest in a portfolio of specialized platforms that align with the functionalities of a specific product and flexible platforms that cover the functionalities of a product range at lower customization cost. We characterize the optimal platform portfolio strategy using an ex-ante investment versus ex-post production customization tradeoff curve and show comparative statics of these costs, demand forecast, and the decision
maker's regret and risk attitude. Flexible platforms provide operational hedging for risk-averse decision makers who thus should invest more than risk-neutral counterparts. In contrast to manufacturing flexibility, the regret of sub-optimal investments increases as demand is more negatively correlated.
Poppe, Joeri; Boute, Robert; Lambrecht, Marc (Elsevier, 2018)
Condition-based maintenance (CBM) makes use of the actual condition of the component to decide when to maintain and/or replace the component, thereby maximising the lifetime of the machine, while minimising the number of service interventions. In this paper we combine CBM on one (monitored) component, with periodic preventive maintenance (PM) and corrective maintenance (CM) on the other components of the same machine/system. We implement two thresholds on the degradation
level to decide when to service the monitored component: when the degradation level of the monitored component surpasses a first ‘opportunistic’ threshold, the monitored component will be serviced together with other components, for instance with a (planned) PM intervention, or upon breakdown of another component, requiring CM. In case none of these opportunities have taken place, and the degradation level surpasses a second ‘intervention’ threshold, an additional maintenance intervention
is planned for the monitored component in order to prevent a failure. Both thresholds are optimised to minimise the total expected maintenance costs of the monitored component, or to minimise the downtime of the machine due to maintenance on the monitored component. We perform an extensive numerical experiment to demonstrate the potential gains of this hybrid policy with two thresholds compared to using a traditional PM policy, and we identify its key drivers of performance. We also benchmark our results when only one threshold is implemented. Our model is validated and applied at an OEM in the compressed air and generator industry.
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