Production Operation Analysis Case [PDF]

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Andrew Nicola Nagata Radisic 2101697736 GSLC 1



Known Data Period 16-17 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Questions 1. Plot the data into graph 2. Forecast using regression. Is it good? 3. Find seasonal index 4. Adjust your forecast. Is it better? 5. What is your forecast for Jan 2018?



Demand 51 67 65 129 225 272 238 172 143 131 125 103 112 137 191 250 416 487 421 285 235 222 192 165



Answer 1. Data graph on period Janaruary 16 – December 17



2. Using Regression Model, gathered level and trend used for searching forecast score.



Gained from the method Data Analysis from Microsoft Excel, with demand and period score. Estimated data calculated has a value less calculated by the number of requests, the error value is also classified as large. So, the calculation of the estimate is not very good using the TrendCorrected Exponential Smoothing method.



Period 16-17 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24



Demand 51 67 65 129 225 272 238 172 143 131 125 103 112 137 191 250 416 487 421 285 235 222 192 165



Level 101.33 104.29 106.00 113.12 129.45 150.76 168.96 180.12 187.10 191.30 193.28 191.49 189.01 187.74 190.98 199.80 225.34 259.24 287.70 302.39 310.26 314.53 313.53 307.50



Trend 7.10 6.27 5.36 5.71 7.83 10.53 12.06 11.88 10.90 9.56 8.05 6.08 4.37 3.24 3.24 4.36 8.59 13.65 16.62 16.23 14.56 12.50 9.80 6.63



Forecast 106.93 108.43 110.56 111.36 118.84 137.29 161.29 181.02 192.00 198.00 200.87 201.32 197.57 193.38 190.98 194.22 204.16 233.93 272.89 304.32 318.62 324.81 327.03 323.33



3. Seasonal Index gained from Dt Bar (Deseasonalized) divided by Demand. Period 16-17 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24



Demand



Dt Bar 51 67 65 129 225 272 238 172 143 131 125 103 112 137 191 250 416 487 421 285 235 222 192 165



106.93 115.14 123.36 131.58 139.79 148.01 156.23 164.44 172.66 180.88 189.09 197.31 205.52 213.74 221.96 230.17 238.39 246.61 254.82 263.04 271.26 279.47 287.69 295.91



Seasonal Index 0.477 0.582 0.527 0.980 1.610 1.838 1.523 1.046 0.828 0.724 0.661 0.522 0.545 0.641 0.861 1.086 1.745 1.975 1.652 1.083 0.866 0.794 0.667 0.558



4. From forecast data, we get a value that is getting closer to the value of demand. After adjusting from the previous data, the results are better. Seasonal Index 0.48 0.58 0.53 0.98 1.61 1.84 1.52 1.05 0.83 0.72 0.66 0.52 0.54 0.64 0.86 1.09 1.75 1.97 1.65 1.08 0.87 0.79 0.67 0.56



Adjusted Adjusted Adjusted Level Trend Forecast 98.71 106.93 109.10 111.84 113.38 120.93 138.36 160.78 179.36 190.07 196.29 199.69 200.92 198.37 195.42 194.08 197.82 207.58 235.20 271.09 300.19 313.88 320.28 323.10 320.59



8.22 8.22 7.01 6.15 5.23 5.70 8.04 10.92 12.45 12.10 10.93 9.42 7.78 5.72 3.98 2.92 3.08 4.42 9.06 14.42 17.36 16.63 14.58 12.23 9.28



51.00 67.00 61.18 115.68 190.91 232.70 223.03 179.59 158.86 146.42 136.98 109.16 113.73 130.81 171.59 213.97 350.58 418.65 403.55 309.35 275.11 262.54 223.48 186.98



5. From the picture above it can be seen that the demand for the 25th period (January 2018) is predicted to be worth 314.13.



Untuk melihat data tersebut dan file Excel jika dibutuhkan, dapat dilihat pada link berikut



Case Study Top-Slice Drivers 1. Develop a quantitative forecast model for Jacob. Which modeling technique did you choose, and why? What are the assumptions behind your model? 2. According to your model, when will Top-Slice need to have the expanded work cell up and running? What are the implications for when Jacob should start the expansion effort? 3. Now suppose that over lunch the marketing vice president says to Jacob: “We’re feeling a lot of heat from Chinese manufacturers who are offering very similar clubs to ours, but at significantly lower prices. The legal department is working on a patent infringement case, but if we can’t block these clubs from entering the market, I expect to see our sales flatten, and maybe even fall, over the rest of the year.” What questions should Jacob ask? How would the answers to these questions affect the forecast? Does it still make sense to use quantitative forecasting under these circumstances? Why? Answer: 1. The modeling technique that I chose for this case is the Holt model. I chose the Holt model to have a more accurate approach. The actual data provided shows there is an upward trend so that each period of demand continues to grow. The use of the Holt model in my opinion is more suitable because of the role of trends that affect demand..



Top Slice Driver Demand 2000 1800 1600 1400 1200



Bomber



1000



Hook King Sir Slice-A-Lot



800 600 400 200 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24



3000 2500 2000 1500 1000 500 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Demand Total Forecast



2. Based on Holt's workmanship model using alpha = 0.1 and beta = 0.2, it is estimated that the total demand reaches above 2700 drivers in August 2012. Therefore, preparation for the expanded work cell must be done 3 months before that time, namely May 2012 as explained in Jacob's staff statement in the matter. The involvement of these conditions will affect the production limit per period so that the company can produce more products for the period afterwards.



3. In my opinion, Jacob must ask how Chinese producers can imitate the product, sell it cheaper, and lack information about the plagiarism of the product. Companies must immediately patent a product from Top-Slice Company. This certainly affects the forecast (forecast) product demand. Product demand can decrease because of similar products from competing companies. The use of quantitative forecasting in this situation makes little sense. Quantitative forecasting so far depends on the actual data of previous periods and upward trends. The existence of competing products has the opportunity to reduce demand so that if you continue to use the actual data of the previous period, forecasting will eventually be far from the actual data going forward.