Process Improvement Sudies in Appliance Manufacturing

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1 ABSTRACT Process Improvement Sudies in Appliance Manufacturing Dr. Hulya Yazici Arcelik A.S. The study illustrates the use of SAS/QC, STAT modules of SAS Institute for computerized process data analysis. SAS/QC and STAT modules enabled the monitoring and analysis of the on line data. A substantial variance reduction and process capability is achieved for the process parameters as well as quality variables. The improvement studies are deployed to other control points in the plant. Furthermore, automated data collection systems and SAS modules via links to the network system are planned to enhance process control and improvement throughout the company. INTRODUCTION The concepts and techniques for control, assurance and improvement of processes were developed to define, maintain and continuously improve organizational systems. Quality evolution beginned in 1930 continued with total quality management (TQM) principles in 1980 s, IS standardisation system, concurrent engineering approach, and finally process reengineering in 1990 s. Quality methodologies are designed to facilitate the development of quality organizations. Main characteristics of these methodologies can be cited as: - customer - driven - process oriented - continuous improvement Initiated in 1930 s by Fisher and Shewhart to respond to quality problems in agriculture and production, statistical quality control was later enriched by Deming s management philosophies, quality planning defined by Juran, Crosby s zero-defects culture, design for quality by Taguchi. Statistical quality control that was initially aimed to detect malfunctions, was redefined mainly by Deming and Juran to reduce variability in production processes. The use of statistical process control (SPC) techniques can lead to better understanding of process variables and thus a reduction of variation in the process. This often yields a more stable process and improves the ability to predict solutions to problems. Implementation of SPC requires continuous effort, time, and strong cooperation at all levels of an organization. From top management to line operator, complete participation and understanding is necessary. Lack of knowledge of statistical techniques is also a contributing factor to the lengthy implementation of statistical techniques. Statistical process control is not only a technical approach. It provides a means to share knowledge and communicate problems between managers and operators. A

2 thorough understanding of the process is key to successful SPC implementation and familiarization with total quality management principles. TQM is a management philosophy implemented to improve quality, operations, productivity, and customer satisfaction on a continuing basis (Attaran & Fitzgerald, 1995). TQM comprises a group of ideas and techniques for enhancing competitive performance by improving the quality of products and processes (Grant, R. M., Shani, R., Krishnan, R.) The business process and quality management principles is followed by ISO-9000 quality system and Business Reengineering, a popular and radical methodology of the 1990 s. A shared point of view of these methodologies is the process management concept that is defined as a systematic way of monitoring, analysing and improving the work performed (Reid, R. L., Ehresman, T. 1991). A thorough understanding of an underlying production process expands to all customer-supplier driven organizational processes. Input, output, customer, supplier relationships and performance measures are defined for each work process. Data are needed to communicate with the process. Data help to understand and analyze the characteristics of a process. Data are used to recognize trends, shifts, and deviations from expected performance. Then data are evaluated to detect process capability, the natural behaviour of a process after the unnatural or abnormal disturbances were eliminated (Robson, G. 1991). Following illustrates statistical analysis of manufacturing process data, control and capability studies using SAS QC and STAT modules (SAS Institute, 1994/95). A significant variance reduction and process capability are achieved with systematic data evaluation and process improvement. PROCESS IMPROVEMENT Arcelik A.S. is a leading home appliances manufacturer in Turkey with one billion US. dollars in sales revenues. The manufacturer with over five plants throughout the country produces and distributes a comprehensive range of home appliances ranging from washers, refrigerators, dishwashers to vacuum cleaners. Arcelik is also an exporter to the European and Asian markets. Quality improvement efforts beginned in the early 1980 s, continued with ISO-9001 certification in Total quality management principles were adopted as strongest indicators of a company s competitiveness. Process improvement projects throughout the organization were designed to implement the total quality management principles. As part of the quality improvement plan, the manufacturing processes of the washing machine are reviewed. Washing machine assembly process is a continuous batch manufacturing with a throughput of machines a day. Control and improvement of the processes prior to the assembly, such as mechanical cutting, painting and plastic injection molding are found critical for reducing the quality defects. The paint process with its high rework and scrap rate is selected as a pilot process improvement project. Figure 1 shows the sub assembly process flow. A data

3 collection and analysis methodology is applied for nine months, taking into consideration the seasonal fluctuations. MECHANICAL CUTTING INCOMING MATERIAL AND ROCESSING SURFACE PROCESS ING CATAPHORES FINAL PAINT QC ASSEMBLY PROCESS Data Collection Procedure Figure 1. Sub Assembly Process Flow Two critical measurable variables determine the quality of the paint. A product dispersion was detected from the initial plotting of these variables using SAS/QC (SAS Institute, 1994/95). Changes within the same day were noted. It was felt that the large dispersion in paint quality was due primarily to final coating process variation. It was also decided to refine the sampling procedure to eliminate it as a possible source of error. The new procedure called for close monitoring of 20 process parameters from the following stages: Analysis of incoming material Paint mixing Final coating The sampling procedure also called for five consecutive machine bodies to be marked after coming out the mechanical cutting. This procedure is repeated every 2 hours during the 8-hour day shift. Control studies did not include the night shift, because a second paint shop was in effect. Data recorded manually on the special data collection forms by the same technicians. Later on, written data is transferred to desktop computers and a SAS dataset is created with all the 20 independent process parameters and 18 dependent quality variables. XR charting of the quality as well as process variables is done periodically to monitor the process. Systematic as well as special causes of variation were detected such as runs, number of points on the same side of the average, and patterns can be seen in Figure 2. Figure 2A-2B. XR Control Charts for two Paint Quality Variables

4 Using SAS/STAT (SAS Institute, 1994/95), an analysis of variance is run on three months of data for the possible effects of process variables on paint quality. The analysis showed significant effect of three process variables on paint quality process parameters. Table 1 shows the GLM (General Linear Models) Procedure output of SAS/STAT(SAS Institute, 1994/95). Following the statistical analysis it was decided to control some process parameters either by keeping them constant or controlling them within a defined range. Table 1. GLM Output of Analysis of Variance for Paint Process Variables General Linear Models Procedure Class Level Information Class Levels Values ABOYADEB KATI VISKOZIB Number of observations in data set = 175 General Linear Models Procedure Dependent Variable: RENKHATA Source DF Sum of Squares Mean Square F Value Pr > F Model Error R-Square C.V. Root MSE RENKHATA Mean Source DF Type I SS Mean Square F Value Pr > F ABOYADEB KATI VISKOZIB Source DF Type III SS Mean Square F Value Pr > F ABOYADEB KATI VISKOZIB While controlling the dispersion for process variables, the analysis of variance indicated interrelations of uncontrollable variables such as ambiance temperature and humidity with the booth temperature. Optimization of some of the controllable variables such as paint blend time and quantity of solvent taking into consideration the influence of uncontrollable variables, an experimental design is simulated using again SAS/STAT (SAS Institute, 1994/95), Design Procedure. Process capability studies Nine months monitoring and improvement of the paint process resulted in significant variance reduction of paint quality as well as paint process variables. Percent of defects is improved about five times. Spread is reduced to one third of the initial state. Paint quality target values are set to mid point of the specification limits. Process capability analysis indicated the percent of observations lower than LSL

5 (lower specification limit) and percent upper than USL (upper specification limit). Two graphs show the process capability improvement, where the process performance is evaluated prior and following statistical control (Figures 3 and Figure 4). SAS/QC (SAS Institute, 1994/95), provides all Capability indices (Cp, Cpk, Cpm) including Cpm which is considered as an alternative measure considering process variation and proximity to the conformity target (Spiring, F.A.). All capability indices are found to be above 1.0 for paint quality variables. The capability analysis also shows that the process is not completely centered at the target value which is the mid point of the specification limits. Despite the debate among researchers (Tadikamalla, P.), Motorola s 6σ process capability is the industry benchmark especially for electronic products. This allows a 1.5σ deviation from the target value. In our case, this value was 2.3σ that indicated further need for process control and improvement. Figures 3 and 4. Process Capability Analysis of Paint Quality CONCLUSION Statistical analysis of process and quality variables of the paint process indicated the significance of variance reduction and process capability improvement of the paint process. SAS modules provided the comprehensive and timely evaluation of data collected. Systematic process control studies are now deployed to other sub assembly processes. Automated data collection systems and SAS modules via links to the network system are planned to enhance process control and improvement throughout the company. REFERENCES Attaran, M. and Fitzgerald, H. D. (1995) Implementing TQM in the Delivery of Government-Contracted Healtcare Industrial Management 37 (March/April): 9-14 Grant, R.M. and Shani, R. and Krihnan, R. (1994) TQM s Challenge to Management Theory and Practice Sloan Management Review, (Winter): Grant, E. & Leavenworth, R. S. Statistical Quality Control, Mc-Graw Hill, Reid, R. L. and Ehresman, T. (1991) A Structured Approach to Achieving Excellence an Integrated Process Management Methodology In Proceedings of the SME TQM 91. Robson, G. Continuous Process Improvement, The Free Press, SAS Institute, versions 6.09 and 6.10, Spiring, F. The Cpm Ýndex, Quality Progress, February 1991,

6 Tadikamalla, P. R. The Confusion over Six-Sigma Quality, Quality Progress, November 1994, 83-85