Operation management of Tom Pulling Toys Essay Example | Topics and Well Written Essays - 1250 words

Operation management of Tom Pulling Toys - Essay Example The intention of this study is Tom Pulling Toys as 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. For the first set of data regarding the number of colour defecti ves, 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 F igure 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