Publication: Prediction of the process capability for compression rubber part forming in the automotive supply chain
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Issued Date
2024-06-01
Resource Type
ISSN
17348412
eISSN
2300892X
Scopus ID
2-s2.0-85206939423
Journal Title
Journal of Achievements in Materials and Manufacturing Engineering
Volume
124
Issue
2
Start Page
78
End Page
85
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Achievements in Materials and Manufacturing Engineering Vol.124 No.2 (2024) , 78-85
Suggested Citation
Butdee S., Tangchaidee K. Prediction of the process capability for compression rubber part forming in the automotive supply chain. Journal of Achievements in Materials and Manufacturing Engineering Vol.124 No.2 (2024) , 78-85. 85. doi:10.5604/01.3001.0054.7761 Retrieved from: https://hdl.handle.net/20.500.14740/20319
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Abstract
Purpose: The paper proposes predicting production process capability for the compression rubber part in automotive supply chain management. Delivery of parts to tier 1 and OEM on time is the most important part of supply chain management, together with the delivery of on-quality and on-cost control to maintain the competitiveness of the supply chain. There are many suppliers to produce many automotive parts for tier 1. Therefore, the simulation approach properly predicts and prevents the process from getting into trouble during the actual production time. Production process quality control is critical to ensure that the good quality of the parts purchased can be delivered on time. Rubber parts are used widely in automotive, motorcycles, trucks, and other types of vehicles, with small sizes but in huge quantities to support general OEM brands and specific parts. The rubber part manufacturing process is complex and uncertain with compression moulding and rubber curing conditions. Therefore, good conditions can predict the production process's capability to commission and deliver on schedule. Design/methodology/approach: A neuro-fuzzy system is adopted and developed to deal with the uncertain process capability under multi-criteria decision-making. Findings: The methodology development can be used in the actual rubber part manufacturing supply chain environment and can predict uncertain problems that might occur in the subcontractor factories. Research limitations/implications: The prediction of the production process capability of the rubber part supply chain might be more effective on the real-time monitoring control system and can be tracking location part progress for further planning both success or rescheduling. Practical implications: The platform can be applied to audit and test the actual industrial supply chain, and problem and research questions are brought about from the real supply chain in the local country. Originality/value: The methodology development was originally created for the particular supply chain in rubber automotive parts that can replace the existing system to obtain a more efficient performance evaluation process.