Statistical Process Control in the Analysis of the Saudi Standards - Case Study Example

Name: Course: Tutor: Date: Introduction Statistical Process Control (SPC) is about competition among businesses regarding excellence, deliver, and costs issues. The future of most organizations depends on the quality and relevance of the services and products that they produce. Therefore, many organizations are in a constant endeavor to improve on the quality of their goods and services. Businesses do this not only to meet the customer’s needs, but to surpass the same so as to remain relevant in the market. In an effort to improve the aforementioned above, there is need to apply Statistical Process Control (SPC); which is a key central tool in an organizational production (Oakland, 2008, p. 3). Furthermore, the major tools used in SPC include control charts, fishbone, flowchart, histograms, scatter diagrams, and Pareto analysis. Control chart is the key tool in SPC, basically since it exhibits procedure behaviors and can be used to examine and control processes within the specified control limits. Using the available raw data, SPC is a practice with the aim of scheming, and supervising business-related progress, which consequently provides the capability to improve an organization’s overall excellence. SPC is employed to analyze, the given set of data, using statistical tools so that processes can be managed and improved using control charts (Stapenhurst, 2005, p. 1). This paper will discuss the application of SPC in the analysis of the Saudi Standards, Metrology and Quality Organisation (SASO), which was established in 1972. Background information about Saudi Standards, Metrology and Quality Organization (SASO) SASO is responsible for Saudi standards, implementing quality systems and doing many tests in its laboratories at the headquarters in Riyadh and five other branches. Among these tests there is examination for private cars that come to the Kingdom of Saudi Arabia without a certificate of conformity. The test is done to these cars during a specific time period. SASO made data of the last year 2010 for a period of 12 months. The number of cars which were examined each month and how long did it take to complete the examination in minutes can be observed from the data. Similarly, it can be observed from the data that the time to finish the examination was becoming longer in some months which affect customer time. Random samples of 15 cars examination per month for the period of January to December, in 2010 were recorded. Process Control Application Any organization has some process that need to be controlled in a manner to minimize variations beyond the six standard deviations, so that the services provided and goods produced may be optimally delivered (Corriou, 2004, p. 3). Control charts are monitored so that variations in the process can be located (Mason & Young, 2002, p. 119). The assignment will discuss some statistical process control applications at (SASO) based in Riyadh laboratory. Control charts: X-bar chart and R-chart will be used for studying waiting times for SASO’S customers to complete the examination. The above are used because the data collected is a continuous variable and not a discrete. Attempts will be made to determine all reasons that cause delays in the inspections by using the flowchart, fishbone techniques and Pareto analysis to discern the main causes of the delays. Control charts are tools used in statistics to differentiate between variations in a process that are resulting from common causes or variations resulting from special causes. It is a graphical representation of a process stability or instability. Variations, which appear in a process, can result by either causes which are within the process and are called common causes, or the variation can result from causes outside the process and are called special causes. Control charts are used mainly to determine process stability or instability. Process stability is defined as a situation characterized by the process displaying consistency in the past and is expected to continue in the same manner in future. Whereas process instability is a state in which the process deviates from the control limits which is usually three standard deviation or deviation from the control limits by plus or minus three. In general control charts are important because they help in monitoring the process over time. Additionally, they also help in differentiation between common and special causes; they show how a process performs over time. Control charts also evaluate the effectiveness of change to improve a process. R-chart will be used in order to detect the causes which increase process variability (Costa & Rahim, 2006, p. 81). Considering the data below, which is continuous, and the sample size is 15, thus the use of X-bar chart and R-chart; shown below. Advantages of R-chart: i. R-charts control the consistency of the process progression. ii. Establishes if the process is in control using statistical control tools. iii. Helps detects the possible patterns in the process (Charantimath, 2003, p. 82). Interpretation of R & X-bar chart: Figure 1: R- chart Examining the R-chart: All the plots are inside the required range and the process is stable and in-control due to the fact that, points are not over and above the upper/lower warning or action limits. Besides, there is no indication of increasing or decreasing trends. This implies that this process of issuance of certificates of conformity is meticulously in-control (Appendix 2). Figure 2: X- chart By looking into the X-bar chart, it can be elucidated that the ranges from January through to July, are stable and within control limits. Furthermore, there is no sign of rising or declining in the trend. Nevertheless, the plots between August and September are out of control; because both exceed the upper action limit. In interpreting the above charts the Six Standard deviation technique is applied (Taner, Sezen and Antony 2007, p. 331). A process is out of control if: i. A point falls outside the third standard deviation , ii. Nine successive points fall in the first standard deviation or beyond the central line, iii. Six uninterrupted points fall in the same direction; either all are declining or all are escalating, iv. If 14 points alternate up-wards and down wards, v. Two out of three consecutive points fall in the third standard deviation ; or beyond on the same side of the center line, vi. Four out of five consecutive points fall in the second standard deviation ; or beyond of the same side of the central line, vii. 15 points in a row fall in the first standard deviation , and lastly if, viii. 11 successive points fall in the second standard deviation or beyond; (Boslaugh & Watters, 2008 p. 334). An investigation provided the following reasons that may have brought the process to be out of control due to increasing waiting time. These include: i. Increased number of vacations that the employees had, ii. Increased number of customers and iii. Lack of enough parking spaces in the inspection facility. Looking at the above, R- chart indicates that the process above is under control, however considering the X-Bar chart, it signals that the process is out of control in the months of July, August and September. This is because from the graph above, the graph goes beyond/ above the Upper Control Limit (UCL). Notice that other authors refer to this as Upper Warning Limit (UWL) or Upper Action Limit (UAL). Similarly, there is (LCL), (LAL), or (LWL) depending on the author (Stapenhurst, 2009, p. 409). By calculating the charts again and in doing so excluding the above months that are out of control, the results come out as: Figure 4: X- chart Figure 3: R- chart The three months that are out of control are excluded so that the resulting control charts can be investigated, to see if it is still out of control without the three months or whether it will now be in control. For the process to be in control, the focus shifts to the three months and ways of putting the process back in control. Recommendations: i. The (SASO) can apply the use of various SPC tools that can help predict if the process is out of control or not. ii. There should be a continuous process-control monitoring and evaluation which can help predict if the process is out of control. This can be done with the application of the latest technology. iii. The management of (SASO) should help control the number of employees who are in vacation. iv. More parking spaces should be provided in the firm to reduce congestion. v. The analysis of the above control charts shows that all plots in the R-chart are within UCL and LCL. Furthermore, there is no indication of increasing or decreasing trend. This means that this process is under care and in-control. Advantages of X-bar chart and R- Charts According to Charantimath,( 2003, p. 82), the following are the advantages of X-bar and R- charts: i. Ascertain if the process is in statistical control. ii. Data is graphed as soon as it appears, thus enables the state of the process to be assessed quickly and action can be taken in case of variation. iii. Find trends in the process which help in the setting up of the process, changing the process and reorganizing of the process that shows whether the process is in control or out of control thus necessitates action to be taken. iv. X-bar charts are useful in providing guidelines for rearranging the process; R-chart helps in control standardization of the product. v. The charts help in determining if a process s in control; which is shown by both the average and range values being in within the control limits/lines. Diadvantages of X-bar Chart i. It requires a lot of training and practices in terms of statistical analysis. ii. It requires the top management to be dedicated to and convinced. Using Flowchart And Fishbone Technique. The control charts have revealed that the average waiting time is high at 42.006 working days. This calls for further analysis in improving the process of inspecting cars so that the owners can be issued with certificate of conformity. A flow chart is another tool in quality improvement process that can be applied to determine at what stage of the whole process, do potential problem spot lies. Flow charts show the process or procedures that take place in a given process. It maps the process from the initial stage through the complex steps that may be involved until the end (Speegle, 2009, p. 187). Advantages of a flowchart It makes the understanding of the whole process easy to comprehend. By having a glance at the process in a flowchart, one comprehends and understands the whole process easily. According to the Saudi Standards, Metrology and Quality Organization, the procedure for issuance of the certificate of conformity is reflected in the following flowchart. The flow chart shows what steps a customer follows, so that he/she gets a certificate of confirmation. With this in mind, fishbone analysis will help understand the possible causes of delays in the issuance of the certificate of conformity. Flowcharts help in the planning process by establishing a flow of events; which can be used for resource scheduling (Speegle, 2009, p. 187). They also help in effective analysis of the process to determine the possible causes of the problem. It helps in appropriate documentation of the process as well as communication of the whole process at a glance. Disadvantages of Flowcharts According to Escoe (2001, p. 46), the following are the disadvantages of flowcharts: i. Flowcharts take longer to develop. ii. Require additional software to develop in addition to the common office software. iii. The procedure which comes with the software may be not suitable for the company. iv. Employees may require further training for them to effectively use the software and may as well require more time to adjust to the use of the software. Flowchart of the process at the Saudi Standards, Metrology and Quality Organisation (SASO) Fishbone In analysis of the fish bone to determine the causes of the delays in the issuance of the certificate of conformity, there are several possible causes of the delay observed in the X-bar chart above. The problems can be alleviated using the following recommendations: i. Employing enough employees. ii. In-servicing of the employees on new developments so that they are constantly updated and thus, become more effective. iii. Potential customers should make appointments online so that the bulging number of customers in the inspection facility can be controlled. The management should determine the capacity of the inspection facility so that they only make appointments with the maximum numbers of the customers that the inspection facility can accommodate. iv. Employ the use of new technological development to automate the process, like computer programs which can be used to make the work easy. v. Ethical issues in the work place should be inculcated so that chances of corruption in the process are minimized. vi. The environment also need to be updated by putting up appropriate and directive signs so that customers can move within the quality assurance inspection facility with a lot of ease. In addition, more parking facilities can be constructed so that the problem of customers moving within the inspection facility is reduced or eradicated. vii. Short brochures should be given to the customers immediately they enter the quality assurance inspection facility. This should outline the major stages and directions as well as the requirements so that the customers can be familiar with the process to minimize the delays. viii. Due to increased number of customers, new facilities should be established or the huge number of the existing facilities should be revamped or up-graded and expanded in terms or parking spaces, so that the problem of increasing customers is alleviated. Advantages of Fishbone i. Fishbone helps in finding the problem besides; It also helps trace the problem down the process (Kreitner, 2008, p. XVII). ii. It is easy to use owing to it not requiring special training Disadvantages of Fishbone i. It needs a group of experts in the issue to brainstorm, which takes time and is ultimately difficult to synchronize. ii. It may lead to focus in one or more causes and ignore others. Parerto Diagrams Pareto charts are quality control tools that are used to tell where the problems are within a given process. After identifying the actual position of the problem within the process; the next focus is on maximising the effects of efforts geared towards solving the problem (Associates, 1995, p. 4). Pareto charts helps in identification of causes with more effects compared to other causes (Associates, 1995, p. 15). Looking at the above fishbone, various causes of the delays in the process of issuing of the certificate of conformity have varied effects as shown in the following table. Cause Percentage Increased number of customers 37% Lack of enough parking spaces. 22% Not using computers to easy the work 15% Lack of enough employees. 10% The process is done manually 7% Complicated and unnecessary stages 4% Customers not presenting the correct data. 3% Not enough certificates. 2% Use of old computers. 1% Pareto Analysis In analysing the pareto chart or diagram, one realises that about 80% of the problems are caused by about 20% of the causes (Shim & Siegel, 1999, p. 17). The above pareto diagram clearly indicates that the delay is majorly caused by the first three causes, which cause over 80% of the delay in the process. Recommendations. Efforts should be focused on solving the major causes of the delays so that cases of delays may be minimised. This can be done by considering the following: The major cause of the delay in the process is increased number of customers. Therefore, more planning and readjustment of the process is required. More facilities of inspection for example, should be put up. Using computerised programs will not only make the process faster but it will also make the process easy and more accurate so this is also advisable. There is also the need to reduce the number of vacations for the employees. Advantages Of Pareto Diagram According to Saxena, (2009, p. 301), the following are the advantages of pareto diagram: i. It embodies the various fundamentals of the predicament in a hierarchical order. ii. It represents the causes of the problem in a pictorial manner which enhances one’s understanding of the underlying causes of the problem at a quick glance. iii. The magnitude of the effects of each causes of the problem, in this case the delays, is seen in a comparative manner; this helps in choosing which among the causes need to be addressed to solve the greater part of the problem. iv. Pareto diagrams motivates the personnel to work as a team in identifying the various possible causes of the problems. v. It is simple and easily understandable and, vi. It can be used for an elongated period of time, without losing its relevance. In conclusion, the process of issuance of the certificate of conformity can be improved by observing the above recommendations and constantly making efforts to assess the situation and using various process control tools to detect variability and address it accordingly. The management should put in place proactive measures to early detect if the process is headed in the direction of out of control so that necessary measures be taken. References Associates, J. (1995). Pareto Charts: Plain & Simple. Madison: Oriel Incorporated. Boslaugh, S., & Watters, P. A. (2008). Statistics in a Nutshell. Sebastopol, Calif: O'Reilly Media. Charantimath. (2003). Total Quality Management. New Delhi: Pearson Education India. Corriou, J.-P. (2004). Process control: theory and applications. London: Springer. Costa, A., & Rahim, M. (2006). A Synthetic Control Chart for Monitoring The Process Mean And Variance [Electronic Version]. Journal of Quality in Maintenance Engineering, 12(1), 81-88. Escoe, A. (2001). The Practical Guide To People-Friendly Documentation. Milwaukee: AS Quality Press. Kreitner, R. (2008). Management. Boston: Houghton Mifflin Co. Mason, R. L., & Young, J. C. (2002). Multivariate statistical process control with industrial Applications. Philadelphia, Penns: Society for Industrial and Applied Mathematics (SIAM) Oakland, J. S. (2008). Statistical Process Control. Burlington: Butterworth-Heinemann. Saxena, J. P. (2009). Production And Operations Management. New Delhi: Tata McGraw-Hill Privated Ltd. Stapenhurst, T. (2005). Mastering Statistical Process Control. Oxford; Boston: Butterworth Heinemann. Stapenhurst, T. (2009). The Benchmarking Book: a How-To-Guide To Best Practice For Managers and Practitioners. Oxford: Butterworth-Heinemann. Shim, J. K., & Siegel, J. G. (1999). Operations Management. Hauppauge, NY: Barron's Educational Series. Speegle, M. (2009). Quality Concepts For The Process Industry. Clifton Park, NY: Delmar Cengage Learning. Taner, M., Sezen, B., & Antony, J. (2007). An Overview Of Six Sigma Applications In Healthcare Industry [Electronic Version]. International Journal of Health Care Quality Assurance, 20(4), 329-340. Appendix: Appendix 1 Table shows the number of cars that have been tested monthly for the year 2010 Month No Of examination for private cars at (SASO) Jan 197 Feb 187 March 181 Apr 192 May 185 Jun 190 July 193 Aug 133 Sep 138 Oct 143 Nov 195 Dec 180 Appendix 2: Month C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 Mean Range Jan 42 46 61 53 60 46 54 57 50 58 36 55 51.5 25 Feb 51 37 41 63 45 42 47 53 46 51 57 60 49.41667 26 March 52 51 48 66 59 51 47 46 45 51 46 65 52.25 21 Apr 41 47 53 56 66 55 66 52 63 52 41 62 54.5 25 May 54 51 57 53 49 50 51 67 45 51 47 50 52.08333 22 Jun 42 52 62 51 54 55 47 51 61 55 61 60 54.25 20 July 62 52 51 40 47 56 60 66 52 53 61 55 54.58333 26 Aug 57 56 67 52 72 52 70 66 65 51 61 64 61.08333 21 Sep 67 72 75 69 56 76 62 66 61 67 71 75 68.08333 20 Oct 61 55 56 57 65 66 58 73 55 57 69 55 60.58333 18 Nov 46 52 62 49 51 61 43 43 51 50 47 58 51.08333 19 Dec 47 47 51 49 44 61 50 67 60 52 49 45 51.83333 23 Mean 55.10417 22.16667 MEAN RANGE Upper Lower Action Lines X±A2(R-bar) Action Lines D.001(R-bar) D.999(R-bar) 55.104±0.27 (22.167) 1.87 x22.167 0.4 x22.167 Warning Lines X±2/3A2(R-bar) Warning Lines D.025(R-bar) D.975(R-bar) 55.104±018 (22.167) 1.51 X22.167 0.58 X22.167 UAL=55.104 + (0.27 X 22.167)=61.0892 UAL=1.87 x22.167= 41.4517 LAL= 55.104 - (0.27 X 22.167)=49.1192 LAL=0.4 x22.167= 8.8667 UWL= 55.104+ (0.18 X 22.167)= 59.0942 UWL=1.51 X22.167= 33.4717 LWL = 55.104 - (0.18 X 22.167)= 51.1142 LWL= 0.58 X22.167= 12.8567 Mean chart Table: Month X X-bar UAL UWL LWL LAL Jan 51.5 55.104 61.0892 59.0942 51.1142 49.1192 Feb 49.41667 55.104 61.0892 59.0942 51.1142 49.1192 March 52.25 55.104 61.0892 59.0942 51.1142 49.1192 Apr 54.5 55.104 61.0892 59.0942 51.1142 49.1192 May 52.08333 55.104 61.0892 59.0942 51.1142 49.1192 Jun 54.25 55.104 61.0892 59.0942 51.1142 49.1192 July 54.58333 55.104 61.0892 59.0942 51.1142 49.1192 Aug 61.08333 55.104 61.0892 59.0942 51.1142 49.1192 Sep 68.08333 55.104 61.0892 59.0942 51.1142 49.1192 Oct 60.58333 55.104 61.0892 59.0942 51.1142 49.1192 Nov 51.08333 55.104 61.0892 59.0942 51.1142 49.1192 Dec 51.83333 55.104 61.0892 59.0942 51.1142 49.1192 Range chart Table: Month R R-bar UAL UWL LWL LAL Jan 25 22.167 41.4517 33.4717 12.8567 8.8667 Feb 26 22.167 41.4517 33.4717 12.8567 8.8667 March 21 22.167 41.4517 33.4717 12.8567 8.8667 Apr 25 22.167 41.4517 33.4717 12.8567 8.8667 May 22 22.167 41.4517 33.4717 12.8567 8.8667 Jun 20 22.167 41.4517 33.4717 12.8567 8.8667 July 26 22.167 41.4517 33.4717 12.8567 8.8667 Aug 21 22.167 41.4517 33.4717 12.8567 8.8667 Sep 20 22.167 41.4517 33.4717 12.8567 8.8667 Oct 18 22.167 41.4517 33.4717 12.8567 8.8667 Nov 19 22.167 41.4517 33.4717 12.8567 8.8667 Dec 23 22.167 41.4517 33.4717 12.8567 8.8667 Appendix 3: R & X-bar chart after recalculation: Month C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 Mean Range Jan 42 46 61 53 60 46 54 57 50 58 36 55 51.5 25 Feb 51 37 41 63 45 42 47 53 46 51 57 60 49.4167 26 March 52 51 48 66 59 51 47 46 45 51 46 65 52.25 21 Apr 41 47 53 56 66 55 66 52 63 52 41 62 54.5 25 May 54 51 57 53 49 50 51 67 45 51 47 50 52.0833 22 Jun 42 52 62 51 54 55 47 51 61 55 61 60 54.25 20 July 62 52 51 40 47 56 60 66 52 53 61 55 54.5833 26 Nov 46 52 62 49 51 61 43 43 51 50 47 58 51.0833 19 Dec 47 47 51 49 44 61 50 67 60 52 49 45 51.8333 23 Mean 52.3889 23 MEAN RANGE UAL=52.389 + (0.27 X 23)= 58.599 UAL=1.87 x23= 43.01 LAL=52.389- (0.27 X23)= 46.179 LAL=0.4 x 23= 9.2 UWL= 52.389+ (0.18 X 23)= 56.529 UWL=1.51 X23= 34.73 LWL = 52.389 - (0.18 X 23)= 48.249 LWL= 0.58 X23= 13.34 Mean chart Table: Month X X-bar UAL UWL LWL LAL Jan 51.5 52.389 58.599 56.529 48.249 46.179 Feb 49.4167 52.389 58.599 56.529 48.249 46.179 March 52.25 52.389 58.599 56.529 48.249 46.179 Apr 54.5 52.389 58.599 56.529 48.249 46.179 May 52.0833 52.389 58.599 56.529 48.249 46.179 Jun 54.25 52.389 58.599 56.529 48.249 46.179 July 54.5833 52.389 58.599 56.529 48.249 46.179 Nov 51.0833 52.389 58.599 56.529 48.249 46.179 Dec 51.8333 52.389 58.599 56.529 48.249 46.179 Range chart Table: Month R R-bar UAL UWL LWL LAL Jan 25 23 43.01 34.73 13.34 9.2 Feb 26 23 43.01 34.73 13.34 9.2 March 21 23 43.01 34.73 13.34 9.2 Apr 25 23 43.01 34.73 13.34 9.2 May 22 23 43.01 34.73 13.34 9.2 Jun 20 23 43.01 34.73 13.34 9.2 July 26 23 43.01 34.73 13.34 9.2 Nov 19 23 43.01 34.73 13.34 9.2 Dec 23 23 43.01 34.73 13.34 9.2 Read More