Forecasting case analysis: Caf Amazon has a large domestic supply chain that must efficiently supply over 3,000 stores all over the country. Although the stores might appear to be very similar, they are actually quite different. Depending on the location of the store, its size, and the profile of the customers served, PTTOR management configures the store offerings to take maximum advantage of the space available and customer preferences. Caf Amazon actual distribution system is much more complex but for the purpose of our exercise lets focus on a single item that is currently distributed through five distribution centers in Thailand. Our item is a logo branded coffeemaker that is sold at some of the larger retail stores. The coffeemaker has been a steady seller over the years due to its reliability and rugged construction. PTTOR does not consider this a seasonal product, but there is some variability in demand. Demand for the product over the past 13 weeks is shown in the following table. The demand at the distribution centres (DCs) varies between about 40 units on average per week in Chiang Mai and 48 units in Surat Thani. The current quarters data are pretty close to the demand shown in the table. Management would like you to experiment with some forecasting models to determine what should be used in a new system to be implemented. The new system is programmed to use one of two forecasting models: Simple moving average or Exponential smoothing Week 1 2 3 4 5 6 7 8 9 10 11 12 13 Average 45 37 33 38 55 30 18 58 47 37 23 55 40 40 41 40 46 48 55 18 62 44 30 45 50 42 Chiang Mai Chachoengsao Ayutthaya Surat Thani 26 35 44 34 22 55 48 72 62 28 27 95 35 45 47 47 27 42 35 40 51 64 70 65 55 43 38 47 42 48 Khon Kaen 46 74 40 35 45 38 48 56 50 46 32 43 54 40 162 199 189 213 246 288 245 204 236 257 174 248 229 223 Total -5 Week 45 38 58 37 Chiang Mai Chachoengsao Ayutthaya 62 18 40 35 62 22 44 48 42 35 64 43 Surat Thani Khon Kaen 43 40 46 35 Total 254 153 244 252 198 Questions: 1) Consider using a simple moving average model. Experiment with models using five weeks and three weeks past data. The past data in each region are given below (Week -1 is the week before week 1 in the table, -2 is two weeks before week 1, etc.). Evaluate the forecasts that would have been made over the 13 weeks using the overall (At the end of the 13 weeks) Mean Absolute Deviation and Mean Absolute Percent Error as criteria. 2) Next, consider using a simple exponential smoothing model. In your analysis, test two alpha values, 0.2 and 0.4. Use the same criteria for evaluating the model as in Part 1. When using an alpha value of 0.2, assume that the forecast for week 1 is the past three-week average (The average demand for periods -3, -2, and -1). For the model using an alpha of 0.4, assume that the forecast for week 1 is the past five-week average. 3) PTTOR is considering simplifying the supply chain for their coffeemaker. Instead of stocking the coffeemaker in all five distribution centers, they are considering only supplying it from a single location. Evaluate this option by analyzing how accurate the forecast would be based on the demand aggregated across all regions. Use the model that you think is best from your analysis of Parts 1 and 2. Evaluate your new forecast using Mean Absolute Deviation and Mean Absolute Percent Error. 4) What are the advantages and disadvantages of aggregating demand from a forecasting view? Are there other things that should be considered when going from multiple DCs to a DC? -4 -3 -2 30 48 72 40 54 -1