Autor(es):
Silva, Aneirson Francisco da [UNESP] ; Silva Marins, Fernando Augusto [UNESP] ; Tamura, Patricia Miyuki [UNESP] ; Dias, Erica Ximenes [UNESP]
Data: 2018
Identificador Persistente: http://hdl.handle.net/11449/159627
Origem: Oasisbr
Assunto(s): Overall machinery effectiveness; Data envelopment analysis; Multiple criteria data envelopment analysis; BiO-MCDEA models; Overall equipment effectiveness; Overall machinery effectiveness; Overall machinery effectiveness; Data envelopment analysis; Data envelopment analysis; Multiple criteria data envelopment analysis; Multiple criteria data envelopment analysis; BiO-MCDEA models; BiO-MCDEA models; Overall equipment effectiveness; Overall equipment effectiveness
Descrição
Made available in DSpace on 2018-11-26T15:44:42Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-07-20
National Council for Scientific and Technological Development
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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. (C) 2017 Elsevier Ltd. All rights reserved.
Sao Paulo State Univ, Fac Engn, Campus Guaratingueta, BR-12516410 Guaratingueta, SP, Brazil
Sao Paulo State Univ, Fac Engn, Campus Guaratingueta, BR-12516410 Guaratingueta, SP, Brazil
National Council for Scientific and Technological Development: CNPq - 306214/2015-6
FAPESP: FAPESP - 2015/12711-4
FAPESP: FAPESP- 2015/24560-0
