Developing and implementing statistical process control tools in a Jordanian company. R.H. Fouad* and Salman D. Al-Shobaki

Transcription

1 Int. J. Manufacturing Technology and Management, Vol. 17, No. 4, Developing and implementing statistical process control tools in a Jordanian company R.H. Fouad* and Salman D. Al-Shobaki Department of Industrial Engineering, Hashemite University, P.O. Box , Zarka 13133, Jordan rhfouad@hu.edu.jo sshobaki@hu.edu.jo *Corresponding author Abstract: Statistical Quality Control (SQC) is a branch of Total Quality Management (TQM) that defines a quality philosophy and set the guiding principles that represent the foundation for a continuously improving organisation. Statistical Process Control (SPC) and Acceptance Sampling are the two major parts of SQC. SPC is an effective tool that aims to get and keep processes under control and ensure that the product is manufactured as designed and intended. SPC is comprised of seven tools: Pareto diagram, cause and effect diagram, check sheets, process flow diagram, scatter diagram, histogram and control charts. A case study has been carried out to monitor real life data in a Jordanian manufacturing company that specialises in producing fertilisers. Pareto diagram, histograms and control charts for variables were implemented to investigate the major causes of non-conformities and possible remedies were proposed. The full strength of the seven statistical control tools was implemented and other companies are encouraged to follow suite and implement them simultaneously. Keywords: SQC; statistical quality control; SPC; statistical process control; TQC; total quality control; basic tools of quality; Pareto diagram; histogram and control charts for variables. Reference to this paper should be made as follows: Fouad, R.H. and Al-Shobaki, S.D. (2009) Development and implementing statistical process control tools in a Jordanian company, Int. J. Manufacturing Technology and Management, Vol. 17, No. 4, pp Biographical notes: R.H. Fouad received his PhD from Bradford University of Bradford, UK in 1991, in Industrial Technology and Production Management, studied his MSc at Cranfield University, UK in 1988 in Industrial Engineering and Operations Management and his BSc at the University of Baghdad, Iraq in 1983 in Mechanical Engineering. Presently he works as an Associate Professor at the Industrial Engineering Department at the Hashemite University. His current research interests are operations management, maintenance management, statistical process control and lean manufacturing. Salman D. Al-Shobaki is a BSC graduate from Yarmouk University in Jordan, in 1994, an MSc from Brunel University in London, UK in 1995 and received his PhD from Imperial College of Science, Technology and Medicine, University of London, UK in Presently, he works as an Assistant Professor at the Department of Industrial Engineering, at Faculty of Copyright 2009 Inderscience Enterprises Ltd.

2 338 R.H. Fouad and S.D. Al-Shobaki Engineering, Hashemite University (HU), Zarka, Jordan. His current research interests lie in industrial management aspects such as quality control and management, quality and excellence models, ergonomics, time and motion study. 1 Introduction Statistical Process Control (SPC) is receiving increasing attention as a management tool to observe, assess and compare important characteristics of product to a set standard. The various procedures in quality control involve considerable use of statistical principles. It has become clear that an effective quality control programme enhances the quality of product being produced and increases profits (Besterfield, 2004). SPC is comprised of seven tools, Pareto chart, Histogram, process flow diagram, control charts, scatter diagram, check sheets and cause and effect diagram. SPC seeks to maximise profit by the following ways: improving product quality, improving productivity, reducing wastage, reducing defects and improving customer value. A Jordanian manufacturing company was chosen to apply three basic statistical tools of quality control; Pareto chart, histogram and control charts. 2 Company background Al-Qawafel Industrial Agriculture Establishment is a Jordanian manufacturing company that produces Fertilisers. The company has six various production lines. The quality control department in the company includes two sections: Test Laboratories and Research and Development (R&D). Suspension fertiliser production line is the pilot production line was chosen to implement the SPC tools for the purpose of this paper. The following main tests are applied to fertilisers produced in suspension fertiliser production line: density test, temperature test, PH concentration test, CL percentage test and sieve test. 3 Pareto s chart A Pareto chart is simply a frequency distribution (or Histogram) of attribute data arranged by category (Montgomery, 2005). Pareto chart is a helpful tool for problem analysis. Problems and their associated frequency or cost are arranged in descending order according to their relative importance in bar chart form, the chart is a visual method of identifying which problems are most significant (vital few), that usually accounts as 80% of the total results and the least significant problems (useful many) usually accounts as 20% of the total results. The graph has the advantage of providing a visual impact of those vital few characteristics that need attention. Pareto charts also have the advantage of limiting the tendency of people to focus on the most recent problems rather than on the most important ones. Figure 1 shows a constructed Pareto chart for the main tests performed on fertilisers at Al-Qawafel Industrial Agriculture. It reveals that the temperature and the PH

3 Developing and implementing statistical process 339 concentration are the vital few tests and represents around 84% of the total cumulative percentage. On the other hand, the useful many factors are the density, sieve and CL percentage and represent around 16% of the total cumulative percentage; moreover, the main reason of most rework is the temperature. Figure 1 Pareto chart for the tests performed on fertilisers at Al-Qawafel Industrial Agriculture 4 Histograms Histogram is a bar chart that shows the number of times (frequency) of each cells occurred where each cell contains a range of measured data. It is a pictorial display of the way the data is distributed over the various cells. Histogram analysis clarifies process capability, conformance to specifications, shape of population and discrepancies and gaps in data. Figure 2 shows a Histogram for temperature tests at Al-Qawafel Industrial Agriculture. It is noted that the measures of central tendency had an average = 24.14, the median = 12.4 and the mode = The measures of dispersion had a range of = 18.0 and a standard deviation = Also, the measures of shape had a Skewness (a 3 = 1.17 > 0) indicating that the data is skewed to the right and a Kurtosis (a 4 = 4.24 > 3) indicating that the data is more packed than normal. Figure 2 Histogram for temperature tests at Al-Qawafel Industrial Agriculture

4 340 R.H. Fouad and S.D. Al-Shobaki 5 Process capability and tolerances Process spread will be referred as the Process capability and is equal to 6σ. When design engineers establish product tolerance without regard to the spread of the process, three situations are possible (Besterfield, 2004): Case 1: (Most desirable case): where 6 σ < [USL LSL] [What is USL, LSL]. Case 2: (Satisfactory desirable case): where 6 σ = [USL LSL]. Case 3: (Undesirable case): where 6 σ > [USL LSL]. Where USL and LSL are the upper and lower specifications limits. The process capability is the region between the two boundaries (X-bar) ± 3S = Therefore, process capability is as follows: (13.49 C and C). The analysis of data shows that the process capability is greater than the tolerance, therefore, an undesirable situation exists and a non-conforming product is produced. This indicates that the company should strive to improve process capability. Considering Figure 3, the process capability analysis indicates that the range value is high because the factory temperature limits determined by quality department that has a wide dispersion. The values of the central tendency measures (average, median and mode) values are closely together, which means that the distribution is approximately normal. Also, the process capability is greater than tolerance, which cause high number of non-conformities as temperature reaches values that are greater than the upper specification limits. Figure 3 Process capability for controlling temperature tests (see online version for colours) 6 Control charts The control chart is a graphical record of the quality of a particular quality characteristic. In general, the control chart contains a centre line that represents the mean value for the in-control process. Two other horizontal lines, called the Upper Control Limit (UCL) and the Lower Control Limit (LCL) are also shown on the chart. The control chart enhances

5 Developing and implementing statistical process 341 the analysis of the process by showing how the process performs overtime and this will enable decisions-making concerning future production. It is used to locate any unusual trends and determine process centring and process variation. In general, the sources of variation and out of control points in control charts are classified as either Assignable causes (special cause) or Chance causes (common causes). When only chance causes are presenting a process, the process considered to be in a state of statistical control. When an assignable cause of variation is present the process is classified as out of control (Montgomery, 2005). Control charts provide information for quality improvement, to determine the process capability and for decisions concerning product specifications. Two types of variable control charts are used extensively when dealing with a quality characteristic that is variable; the sample average and range control chart and the sample average and standard deviation control chart. Mean and Range control charts are shown in Figure 4 and Standard deviation and Range control charts are shown in Figure 5 below. The first step is to post the preliminary data to the chart along with the control limits and central line. This has been accomplished and is shown in Figure 4. The next step is to adopt standard values for the centre lines or, more appropriately stated, the best estimate of the standard values with the available data. If an analysis of the preliminary data shows good control, then the control chart can consider, as representative of the process and these become the standard values. Most processes are not in control when first analysed. An analysis of Figure 5 shows that there is out of control points on the control chart that had assignable causes that need to be clarified. The subgroups with assignable causes are not considered part of the natural variation and are discarded from the date, and new values of centre line and control limits computed with remaining data. Thus the centre line and control limits must be revised after discarding the out of control points as shown in Figures 6 and 7. Figure 4 Mean and range control chart for temperature tests (see online version for colours)

6 342 R.H. Fouad and S.D. Al-Shobaki Figure 5 Standard deviation and range control chart for temperature tests (see online version for colours) The interpretation of control charts indicates some process characteristics and distinguishes between natural and unnatural variation. The unnatural variation results from assignable causes. From Figures 6 and 7, it was noted that the assignable causes can be deficiencies in the cooling system, lack of the chemical reactors maintenance and errors in the sampling process and specification of the correct sample size. Corrective actions were proposed as to replace the cooling system with a new one, implementing preventive maintenance, routine tests and inspections and choosing a representative sample size and frequency for the sampling procedure. As for the natural variations, which occur due to chance causes, are considered temporary and no corrective action is taken to modify them. The first priority is to eliminate all assignable causes then analysing the chance causes. Through in-depth study and analysis of the process, sources of chance causes were specified as follows: 1 human errors in calculations of statistical charts or in using test equipments and measuring equipments were out of calibration 2 poor maintenance plans 3 variation in incoming materials 4 gradual change in temperature and humidity 5 different workers taking samples and using the same chart 6 poor storage conditions.

7 Developing and implementing statistical process 343 Figure 6 Revised mean and range control chart for temperature tests (see online version for colours) Figure 7 Revised Standard deviation and range control chart for temperature tests (see online version for colours) 7 Conclusion From this study, several conclusions were developed. The Pareto diagram identifies that the temperature is the vital view characteristic that need attention. Histogram analysis shows that the process capability is greater than the tolerance, therefore, an undesirable

8 344 R.H. Fouad and S.D. Al-Shobaki situation exists and a non-conforming product is produced. The interpretation of control charts indicates sources of assignable causes were deficiencies in the cooling system, lack of the chemical reactors maintenance and sampling process and specifying the correct sample size. Also the sources of chance causes were human errors (in calculations of statistical charts or in using test equipments and measuring equipments were out of calibration), poor maintenance plans, variation in incoming materials, gradual change in temperature and humidity, different workers taking samples and using the same chart and poor storage conditions. Acknowledgement It is recommended that a further study should be carried out and brainstorming sessions should be formally held to further analyse the causes of the temperature control problem using Ishikawa diagram to represent a more meaningful relationship between bad effects and to take action and correct the problems causes. References Besterfield, D.H. (2004) Quality Control, 7th edition, Pearson-Prentice Hall. Montgomery, D. (2005) Introduction to Statistical Quality Control, 5th edition, John Wiley.