A new approach for the selection of advanced manufacturing technologies: DEA with double frontiers

Abstract

Selection of advanced manufacturing technologies (AMTs) is an important yet complex decision which requires careful consideration of various performance criteria. This paper proposes the use of data envelopment analysis (DEA) with double frontiers for the selection of AMTs, which considers not only the best (optimistic), but also the worst (pessimistic) relative efficiencies of each AMT. Compared with the traditional DEA, the DEA approach with double frontiers can identify the best AMT correctly and easily without the need to impose any weight restriction or the need to calculate the cross-efficiency matrix, which requires a large number of computations and may also result in inconsistent conclusions by aggressive and benevolent cross-efficiency models. Four numerical examples are examined using the DEA approach with double frontiers to illustrate its simplicity and usefulness in AMT selection and justification.

Keywords:

• advanced manufacturing technology
• AMT selection and justification
• data envelopment analysis
• DEA with double frontiers
• optimistic and pessimistic efficiencies
• overall performance

Acknowledgements

We thank the two anonymous reviewers for their valuable comments and suggestions that have helped improve the paper. The work described in this paper is supported by the National Natural Science Foundation of China (NSFC) under Grant No. 70771027 and also supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China, under Grant No. CityU-1203/04E.