Operation management of Tom Pulling Toys - Essay Example

?Executive Summary Tom Pulling Toys has a manufacturing unit in China which is used to meet the global demand especially in US and Europe through retailers. Lately, the company had to face tough competition as well as quality issues forcing it to think about quality planning & improvement. There is a plan to use Statistical Process Control (SPC) for gauging the processes at a doll manufacturing line. The company needs to analyze the processes for consistent colouring and height of the dolls. Data has been recorded for the two aspects over a 30 day period. C-chart has been used for plotting colour defects while x bar and R charts have been used for height of the dolls. The results show the processes to be out of control and the production process to be faulty. Recommendations for improving the situation have also been provided. Table of Contents Executive Summary 1 1.Introduction & Problem Description 3 2.Methodology & Results 3 3.Discussion & Recommendations 6 4.Conclusion 7 5.References 7 1. Introduction & Problem Description Tom Pulling Toys is a global manufacturer of educational toys. Its toys are being manufactured in China and sold in the European and US markets. However, due to rising competition and increasing customer complaints, the company is in a spot of bother. To tackle the situation the company is planning to adopt Total Quality Management practices in its operations. As a starting step, the company has decided to use Statistical Process Control at one of its doll manufacturing lines. For this, data collection has been done over a 30 day period. The data collection has been done from the point of view of colouring defects and the height of dolls as these were the two areas the customers complained about. As an output of the analysis on the collected data, the company needs to know whether its processes are in control. The company also needs recommendations in case the processes are not in control. 2. Methodology & Results For the first set of data regarding the number of colour defectives, firstly the mean number of defectives is calculated. C-chart is the most suitable chart for this purpose since it is used when number of defects or errors is given and the size of sample (here 200) is constant. Using the mean the two 3 sigma control limits are established as: Lower Control Limit (LCL) = c bar – 3 * (c bar)^.5 Upper Control Limit (UCL) = c bar + 3 * (c bar)^.5 The minimum value of Lower Control Limit can be 0. Hence, a negative value for the same is replaced by 0. The mean number of defectives, LCL and UCL are obtained as 6.033, 0 and 13.402 respectively. C-chart is plotted using the number of defectives and control limits. The same is shown in Figure 2.1. Figure 1: c-chart for Number of defectives From the chart, it can be observed that one data point lies above the UCL indicating that the process is not in control. However, since only 1 out of 30 points lies outside, it can be removed by outlier analysis. For the second data set, x bar and R charts are appropriate. For R chart, the ranges are calculated for each of the 30 samples. Mean range or R bar is then computed as an average of these ranges. The 3 sigma control limits for R chart are then established as: Lower Control Limit (LCL) = R bar * D3 Upper Control Limit (UCL) = R bar * D4 The R bar, LCL and UCL are obtained as .5533, .254 and .853 respectively (Table of Control Chart Constants). R chart is plotted using the ranges, mean range and the two control limits. The same is shown in Figure 2.2. Figure 2.2: R chart for height of doll From the chart it can be observed that a large number of data points lies outside the two control limits. This shows that the process is out of control. For x bar chart, the mean height for each sample is calculated and then mean of mean heights (x bar bar) is calculated. The 3 sigma control limits are the established as: Lower Control Limit (LCL) = x bar bar – R bar * A2 Upper Control Limit (UCL) = x bar bar + R bar * A2 The x bar bar, LCL and UCL are obtained as 45.942, 45.857 and 46.027 respectively (Table of Control Chart Constants). X bar chart is plotted using the means, x bar bar and the two control limits. The same is shown in Figure 2.3. Figure 2.3: x bar chart for height of doll From the chart, it can be observed that, a large number of data points lie outside the two control limits. Moreover, the chart depicts trends in the data. 3. Discussion & Recommendations The company needs to focus on colour defects to prevent customer complaints. It is to be noted that even if the number of defects is 1, it is still a defect. The idea should be to aim for zero defects or achieve a six sigma level (3.14 defects per million). For this the DMAIC approach of Six Sigma must be used. However, from the point of view of heights of dolls, the process is totally out of control. There is a visible problem in the production process. Over the first few days, the heights are consistently below the LCL while over the last few days they are consistently above UCL. Linear downwards or upwards trends are noticed in both cases. Such errors are always due to flawed production process. It is recommended that the company carries out a root cause analysis of this problem. Several quality tools can be used to study this problem. Ishikawa diagram, 5 Whys approach and Pareto Charts can help in the same. Use of scatter diagrams and process maps is also suggested to support this analysis. (Introduction to Statistical Process Control ; Heizer & Render 2010). 4. Conclusion Statistical Process Control (SPC) has played its role in uncovering the problems in the production process. Now, it depends upon the management how to utilize this information. Ideally, the company should study the causes of the problem, rectify the same and again monitor through SPC. In fact, this has to be a continuous process. 5. References ‘Table of Control Chart Constants’, Available February 22, 2012 from ‘Introduction to Statistical Process Control’, Available February 22, 2012 from Heizer, Jay H. & Render, Barry 2010, Operations Management, Prentice Hall Publications Read More