Operations Management Assignment 1, 20% Marks, Due date: 10 July 2017 at 5:00pm Melbourne time. Please submit just one Microsoft Word file. You may copy and paste from Excel into your Word file 1. ZenoFex Case Study (total 10 marks for this case study) The ZenoFex is a manufacturer of electronic parts used in telecommunication industry. The demand for a special semiconductor produced in this firm in last 11 periods is given in Table 1. Table 1 Semiconductor Demand in last 11 periods Period 1 2 3 4 5 6 Semiconducto r 48 55 59 61 66 70 Period 7 8 9 10 11 Semiconducto r 73 78 86 89 93 Based on the above information answer the following questions: Among the following two forecasting methods, which method should be used to forecast the demand for this product? Why? Simple moving average n = 3 Exponential Smoothing with alpha = 0.4 2. FORTech Inc. Case Study, (total 10 marks for this case study) FORTech Inc, was about to launch a new product. Production Manager Jane Richardson asked her assistant, Scott, to check the capability of the oven used in the process. Scott obtained 18 random samples of 20 pieces each. The results of those samples are shown in the following table. After he analyzed the data, he concluded that the process was not capable based on a specification width of 1.44 cm. Jane was quite disappointed when she heard this. She had hoped that with the introduction of the new product her operation could run close to full capacity and regain some of its lost luster. The company had a freeze on capital expenditures of more than $10,000, and a replacement oven would cost many times that amount. Scott worked with the oven crew to see if perhaps different settings could produce the desired results, but they were unable to achieve any meaningful improvements. 1|Page Operations Management Assignment 1, 20% Marks, Due date: 10 July 2017 at 5:00pm Melbourne time. Sample 1 2 3 4 5 6 7 8 9 Mean 45.01 44.99 45.02 45.00 45.04 44.98 44.91 45.04 45.00 Range 0.85 0.89 0.86 0.91 0.87 0.90 0.86 0.89 0.85 Sample 10 11 12 13 14 15 16 17 18 Mean 44.97 45.11 44.96 45.00 44.92 45.06 44.94 45.00 45.03 Range 0.91 0.84 0.87 0.86 0.89 0.87 0.86 0.85 0.88 (values in centimetre) Still not ready to concede, Jane contacted one of her former professors and explained the problem. The professor suggested obtaining another set of samples, this time using a smaller sample size and taking more samples. Jane then conferred with Scott and they agreed that he would take 27 samples of five observations each. The results are shown in the following table. Sample 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Mean 44.96 44.98 44.96 44.97 45.02 45.03 45.04 45.02 45.08 45.12 45.07 45.02 45.01 44.98 Range 0.42 0.39 0.41 0.37 0.39 0.40 0.39 0.42 0.38 0.40 0.41 0.38 0.41 0.40 Sample 15 16 17 18 19 20 21 22 23 24 25 26 27 Mean 45.00 44.95 44.94 44.94 44.87 44.95 44.93 44.96 44.99 45.00 45.03 45.04 45.03 Range 0.39 0.41 0.43 0.40 0.38 0.41 0.39 0.41 0.40 0.44 0.42 0.38 0.40 Questions (each questions 2.5 marks, total 10 marks for this case study) Consider the following questions, and then write a brief report to Jane summarizing your findings. 1. How did Scott conclude that the process was not capable based on his first set of samples? (Hint: Estimate the process standard deviation, 3 , using A 2 R n ) 2. Does the second set of samples show anything that the first set didn't? Explain what and why. 3. Assuming the problem can be found and corrected, what impact do you think this would have on the capability of the process? Compute the potential process capability using the second data set. 2|Page Operations Management Assignment 1, 20% Marks, Due date: 10 July 2017 at 5:00pm Melbourne time. 4. If small samples can reveal something that large samples might not, why not just take small samples in every situation? 3|Page