Statistical monitoring of supplier performance in a quality management system environment for the Iranian automotive industry

• Main Author: Darestani, Soroush Avakh
• Format: Thesis
• Language: English; English
• Published: 2010
• Online Access: http://psasir.upm.edu.my/id/eprint/26707/1/FK%202010%20106R.pdf; http://psasir.upm.edu.my/id/eprint/26707/

Quality and delivery are two of the crucial indicators in today’s automotive manufacturing industry. About 60% of prices of goods are allocated to raw material and purchased parts by suppliers in the automotive industry. The need for evaluation and monitoring of supplier’s performance has been emphasized by previous researches and also in Quality Management System of the automotive industry ISO/TS16949. Thus, it is important to evaluate and monitor suppliers in the automotive sector. The review of literature reveals the lack of a multi-variable monitoring system for supplier performance. Therefore, this study was carried out with the aim to develop a multi-variable supply chain performance monitoring model for the automotive industry that would allow companies to monitor their suppliers’ performance. Delivery Performance Monitoring Algorithm (DPMA) was developed for monitoring supplier’s on-time-delivery (OTD) based on the PDCA approach. In addition, control charts were also modelled for the OTD and Part per Million (PPM), while Binomial capability process (BCP) was done for measuring the PPM capability. Furthermore, the exploratory product audit method (PQAS) was developed based on normal distribution so as to quantify supplier’s quality. For this purpose, the capability process analysis, Johnson transformation, Anderson-Darling normality test, time series prediction techniques were employed. The main contribution of this research is that statistical process control could be used to help automotive companies to monitor their supplier’s performance. An investigation carried out on 344 consecutive deliveries performance of OEM’s suppliers, in which the mean of OTD was obtained by 79.10 (where standard deviation was 18.77) gave the indication of far from customers’ target by 90. Out of control signals were eliminated from the control charts. The capability study indicated that eliminating the out-of-control signals improved the supplier’s capability. Therefore, PQAS was performed and the supplier’s quality level was obtained by 77%, indicating the causes of reducing product quality accordingly. The results also indicated that eliminating the out-of-control signals could enhance the product quality scores at significant level 5%. As such, the suppliers’ quality rating PPM was quantified and monitored using the control chart and the results indicated that establishing the state of statistical control on the PPM could enhance the PPM capability in 6� of binomial distribution. Thus, the results from the hypotheses testing significantly met the objectives of the study and the model could be employed by automotive sector. Undoubtedly, the implementation of statistical monitoring could increase organizational performance for both buyer and supplier perspectives.