Abstract
The management of a leading automobile-parts manufacturing company, specializingin piston rings, was facing a serious quality problem due to high scrap rates in mostof their products. No systematic effort was undertaken in the past to identify andtackle the quality characteristics and the processes which were responsible for thesehigh scrap rates. This article documents a systematic step-by-step approach that wasadopted for solving this quality management problem using various statistical qualityand process control tools. First, the most delicate and costly ring was selected forthis case study. The most problematic quality characteristic of this ring was thenidentified through a Pareto analysis. The process flow diagram involving all themanufacturing processes was studied, and the critical ones affecting the problematicquality characteristic were identified. Next it was checked whether these criticalprocesses were in statistical control or not by means of control charts. Two suchprocesses were identified which were not in statistical control. A simple recommendationsolved the problem with one of the processes. For the other process, which wasnot in statistical control, an in-depth study led to an Ishikawa Diagram diagnosingthe various factors affecting the key quality characteristic. These factors were thenclassified into control and noise factors and a full factorial experiment with replicationwas designed and carried out to analyse the variances of the averages andsignal-to-noise ratios of the experimental results. These ANOVAs and multiplecomparison t-tests suggested certain optimum levels for the control factors. Aconfirmatory experiment with these optimal settings yielded highly improved processcapability indices. The underlying assumptions of all the applied statistical techniqueshave been appropriately validated. This research helped the organization drasticallyreduce its scrap rates, resulting in a dramatic increase in profit.
Published Version
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