An AHP parsimonious based approach to handle manufacturing errors in production processes

Abstract

This paper introduces a new hybrid tool to classify manufacturing errors in production processes. Our tool is a hybrid procedure based on the combination of the parsimonious AHP method with a clustering method. Parsimonious AHP permits to use a much smaller number of pairwise comparisons with respect to classical-AHP. At the same time, the use of a clustering method, allows for the assignment of the alternatives on the basis of their closeness to each other and not on the basis of some reference profiles as happens in several other methods. Furthermore, some veto systems help the decision maker to better define how the errors belong to different classes in a participative way. The approach has been tested in a largest automotive plants in Italy. This approach has provided the company with a participative and robust theoretically funded tool that supports the understanding of the seriousness of the manufacturing errors. From a methodological point of view, the hybrid tools, for the first time, combine the newly developed parsimonious AHP with a clustering method. From a practical point of view, this paper has introduced a new tool to classify and improve the production quality of the processes to an extremely competitive sector.

Keywords:

• Automotive
• manufacturing
• parsimonious AHP
• veto systems
• clustering

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Gerarda Fattoruso

Gerarda Fattoruso is senior researcher at University of Foggia (Italy) and Lecturer at NEOMA Business School, Rouen (France). She has been visiting in several universities in France, England and Spain. Gerarda deals with the analysis of decision-making processes and the construction of multi-criteria mathematical models to support the control functions in enterprises; she collaborates with a large company in automotive sector from 2018. On these topics she has obtained results presented at several conferences and published in international journals and has already received national and international awards as: best paper from the Object Group of Operations & Systems Management, University of Portsmouth (UK); Best paper in the DySES 2022 international conference; AMASES Award 2022 for the best paper from Ph.D. thesis; Anassilaos Awards and International Guido Dorso Award obtained from the Senate of the Republic in Italy.

Maria Barbati

Maria Barbati is a senior researcher at Ca’ Foscari University of Venice. She has previously been a senior lecturer in the Operations and Systems Management Group at the University of Portsmouth, where she holds a visiting fellow position. She completed her Ph.D. studies in facility location problems with equality considerations at the University of Naples Federico II. She regularly presents her work at international conferences. Her research is in multicriteria decision-aiding and combinatorial optimisation problems, focusing on facility location models and portfolio selection problems.

Alessio Ishizaka

Alessio Ishizaka is Distinguished professor at Neoma Business school. He was Head of the Information Systems, Supply Chain and Decision-Making department. He was also research lead and Deputy Director of the Centre of Operational Research and Logistics at the University of Portsmouth. Alessio received his PhD from the University of Basel (Switzerland). He worked successively for the University of Exeter (UK), University of York (UK) and Audencia Grande Ecole de Management Nantes (France). He has been visiting professor in several universities in Italy, France and Germany. His research is in the area of decision analysis, where he has published more than 160 papers. He is regularly involved in large European funded projects. He has been the chair, co-organiser and guest speaker of several conferences on this topic. He wrote the indispensible textbooks Multicriteria Decision Analysis: methods and software and Multi-Criteria Decision-Making Sorting Methods