Bi-Objective Multiple Criteria Data Envelopment Analysis combined with the Overall Equipment Effectiveness: An application in an automotive company. (20th July 2017) - da Silva, Aneirson Francisco; Marins, Fernando Augusto Silva; Tamura, Patricia Miyuki; Dias, Erica Ximenes
Abstract: The mass production companies need to seek high efficiency in the use of equipment and human resources, as well as in the consumption of their inputs. One of the key methods to address these challenges is the adoption of Overall Equipment Effectiveness, derived from Total Productive Maintenance. This work aims to propose a new efficiency indicator, called Overall Machinery Effectiveness, to be applied in an automotive company in Brazil that adopted Overall Equipment Effectiveness indicator. The studied company made available production data from ten months, associated to two Press machines, generating twenty Decision Making Units for Data Envelopment Analysis and Bi-Objective Multiple Criteria Data Envelopment Analysis models application. As results, Press #2 was identified as being the most critical because, among the first ten DMUs in the efficiency ranking, seven are associated to Press #1. The targets values recommended by the new indicator were considered feasible to be implemented by the company, thus validating in practice the new proposed procedure for the management of machines effectiveness. Moreover, the identification of the relevant variables (input and output) for the Press #1, and Press #2, allowed the decision maker to act in the best way to increase their efficiency. Graphical abstract: This work aims to propose a new efficiency indicator, called Overall Machinery Effectiveness (OME), to be applied in an automotive company in Brazil that adopted Overall Equipment Effectiveness (OEE) indicator. Highlights: New indicator for measuring machine efficiency is proposed for an automotive company. The new indicator complements Overall Equipment Effectiveness. The new indicator finds out the most critical machines. The new indicator estimate targets for improving their machinery effectiveness.