Effectiveness improvement in manufacturing industry; trilogy study and open innovation dynamics

Artikel

The purpose of this investigation is to compute overall equipment effectiveness (OEE) in the small-scale industry. The novel approach is introduced to detect bottlenecks by which OEE can be improved. This study attempts to help small-medium enterprises in analyzing performance in a better way. The automotive industry was chosen for conducting the research. The present study is comprised of three phases. In the first phase, OEE was computed and compared with world-class manufacturing. The second phase included three-level of Pareto analysis followed by making fishbone diagram to mitigate the losses. The third phase conducted improved OEE in the industry. There are seven major losses present in the industry that adversely affect the effectiveness of machine in any industry. This approach can reduce these losses and improve the quality, asset utilization (AU), OEE, total effective equipment performance (TEEP) and productivity of the machine. The study exposes that Pareto analysis uncovers all the losses and works on the principle of 80/20 rule. The major losses were thoroughly explored with the help of the fishbone diagram and solutions were implemented at the shop floor. As a result, availability, performance, quality, OEE, AU, and TEPP show improvements by 4.6%, 8.06%, 6.66%, 16.23%, 4.16%, and 14.58%, respectively. The approach offers a good opportunity for both researchers and small-medium enterprises around the world to analyze the indicators of production losses, performance, and productivity in the manufacturing industry.

Objekt anzeigen

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Journal of Open Innovation: Technology, Market, and Complexity ; ISSN: 2199-8531 ; Volume: 7 ; Year: 2021 ; Issue: 1 ; Pages: 1-21 ; Basel: MDPI

Klassifikation und Themen

• Klassifikation: Management
• Thema: asset utilization ; fishbone diagram ; manufacturing industry ; overall equipment effectiveness ; pareto analysis

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Tayal, Ashwani ; Kalsi, Nirmal Singh ; Gupta, Munish Kumar ; Pimenov, Danil Yurievich ; Sarikaya, Murat ; Pruncu, Catalin I.
• Ereignis: Veröffentlichung
• (wer): MDPI
• (wo): Basel
• (wann): 2021

Weitere Informationen

• DOI: doi:10.3390/joitmc7010007
• Handle: http://hdl.handle.net/10419/241592