Abstract
This article proposes a decision support system to improve the management of the middle-of-life phase according to product life cycle (LC) cost evaluation. A new maintenance approach integrates information gathered from different actors in order to obtain data which may impact on the whole value chain. A specific algorithm calculating residual LC costs is used for the optimisation of maintenance interventions of a fleet of trucks. The proposed system enables to create new value-transforming information into knowledge available for all LC phases and meant to support improvements in product and service quality, efficiency and sustainability. The solution described here goes in the direction of predictive maintenance for trucks aiming to reduce the number of unexpected stops and minimise product LC costs, avoiding component breakdowns.
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