KNITWEAR PRODUCT QUALITY ANALYSIS AND DEVELOPMENT OF SPC IMPLEMENTATION PLAN Tajammal Hussain
Institute of Quality and Technology Management
University of the Punjab, Lahore
Department of Mathematical Sciences
COMSATS Institute of Information Technology
Defence Road, Off-Raiwind Road, Lahore.
Statistical process control (SPC) is a powerful technique which knitwear industry can use in its pursuit of continuous effort to achieve sustainable and compatible garments quality at optimum cost reducing the magnitude of nonconforming garments in continuous mode of production. In this paper scientific investigation highlights the need for implementation of SPC to knitwear industry by exploring the nature of defects, which cause cost of quality to raise an intolerable level taking away the competitive edge of industry from its competitors in international market. It is scientifically observed that these defects occurring in garments manufacturing, in some extent, are overlapping and these overlapping are highlighted in three major factors. Further, it illustrates some prerequisites to successful implementation of SPC and a comprehensive framework for the introduction and application of SPC program in knitwear industry, to control the over all process.
Key words. Statistical process control, quality, Factor Analysis, inspection and prevention models
Pakistan Knitwear industry is renowned and poised for victory and rapid progress in the ever-hot competition international market, which is conceived and appreciated by the statistical facts and figures. Although, Being one of the largest cotton producer and very economical and easily available workforce, Pakistan has competitive advantage over rest of its rivals in international market that can lead it to excel the opportunities which are exposed by the concept of free boarder trade among the world, Pakistan is facing a severe challenge from its big market rivals like China, India and Bangladesh. The major problems experienced by the industry are lack of quality adoptability and strive to achieve the stable and customer oriented quality product without incurring too much appraisal and failure costs. These big threats to industry are just because of quality variation controlling and prevention methods that are causing a rejection rate to rise from 5 % to 10%. Statistical process control (SPC) is an integral part of monitoring, managing, maintaining and improving the performance of a process (either manufacturing or service) through the effective use of statistical methods. In many organizations today, SPC initiatives fail to perform adequately due to the lack of understanding of the technique and its applicability within the organization. It is recognized that failure to operate SPC effectively may result in increased Product recalls, product rework, scrap rate, customer complaints, and warranty costs and decreased product margin, productivity, market share, etc. (Little, 2001). The lack of SPC success in some companies may be related to the adoption of a wrong methodology (Ribeiro and Cabral, 1999). Too often organizations look at "the control chart" as the only approach to handle issues and this will not work (Xie and Goh, 1999).The successful application of SPC rather requires a blend of planning skills, engineering skills, management skills, statistical skills and communication skills (Antony, 2000).
2. Inspection-based quality control vs. prevention-based quality control The traditional approach to manufacturing is to rely on production to make the product and on quality control to inspect the product and screen out items not meeting specifications. This involves a strategy of detection or Inspection. Inspection is an activity, which is often expensive, unreliable and provides very little information as to...
References: (i) Antony, J. (2000), "Ten key ingredients for making SPC successful in organizations", Measuring Business Excellence, Vol. 4 No. 4, pp. 7-11.
(ii) Antony, J. and Kaye, M. (1999), Experimental Quality: A Strategic Approach to Achieve and Improve Quality,
(iii) Bird, R
(iv) Does, R.J.M.M. et al., (1997), "A framework for implementation of statistical process control International Journal of Quality Science, Vol. 2 No. 3, pp. 181-98.
(v) Does, R.J.M.M. et al. (1999), Statistical Process Control in Industry, Kluwer Academic Publishers, Norwall, MA.
(vi) Gaafar, L.K. and Keats, J.B. (1992), "Statistical process control: a guide for implementation", International Journal of Quality & Reliability Management, Vol. 9 No. 4, pp. 9-20.
(vii) Goh, T.N. and Xie, M. (1998), "Prioritizing processes in initial implementation of SPC", IEEE Transactions on Engineering Management, Vol. 45 No. 1, pp. 66-71.
(viii) Little, T.A. (2001), "10 requirements for effective process control: a case study", Quality Progressive. 34 No. 2, pp. 46-52.
(ix) Mason, B. and Antony, J. (2000), "Statistical process control: an essential ingredient for improving service and manufacturing quality", Managing Service Quality, Vol. 10 No. 4,pp. 233-8.
(x) Montgomery, D.C. (1991), Introduction to Statistical Quality Control, John Wiley & Sons, NewYork, NY.
(xi) Oakland, J. (1999), Statistical Process Control, Butterworth-Heinemann, Oxford...
(xii) Xie, M. and Goh, T.N. (1999), "Statistical techniques for quality", The TQM Magazine, Vol. 11 No. 4, pp. 238-41
Comparison of Originations Defect Categories
Please join StudyMode to read the full document