Cross-efficiency for advanced manufacturing technology selection: A multi-task approach

Abstract

Advanced manufacturing technologies (AMTs) are more and more used by firms to perform repetitive tasks in the production processes. As opting for an ATM represents an important investment for firms, several methodologies have been suggested to help firm decision-makers selecting the best one. A popular concept in that context is the cross-efficiency technique. In short, it endogenously selects the best ATM by computing scores using linear programmings. In this paper, we extend the cross-efficiency technique by adding a new feature: we model ATMs as multi-task processes. The multi-task approach presents two main advantages. One, it naturally gives the option to allocate inputs/costs and indicators/attributes to every task, yielding to a more realist modelling of the AMT processes. Two, AMTs can be compared for every task separately, increasing the discriminatory power of the selection process. As a consequence, the overall performances can be better understood, and, in particular, the reasons for declaring a specific AMT to be best can be investigated. We demonstrate the usefulness of our approach by considering a numerical example and two applications. In each case, we demonstrate the practical and managerial usefulness of our approach.