Analytics Exercise-Forecasting Supply Chain Demand Starbucks
Analytics Exercise-Forecasting Supply Chain Demand Starbucks
Forecasting is classified into four basic types: qualitative, time series analysis, causal relationships, and simulation. The decision on which to use is dependent upon the nature of the operation. Walmart has set the standard in the retail industry by having one of the world’s largest cloud-based data warehouses in existence today. However, even with large amounts of data and POS, or point of sale, data tracking, forecasting is often impacted by economic, technological, natural disasters, suppliers, and labor issues. These issues must be identified and solutions developed to maintain the flow within the supply chain.
Before you begin, be sure to review the following resources:
Read the Forecasting Supply Chain Demand – Starbucks Corporation case in your text on page 526.
Instructions
Once you have read the Starbucks Corporation case above, address the 4 questions below that are associated with the Starbucks case. You must use Excel to complete this assignment. Submit both your Excel spreadsheet and a Word document, or you can insert your answers to the questions inside the Excel file.
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 + 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, mean absolute percent error, and tracking signal as criteria.
Simple Moving Avg Model
WEEK –5 –4 –3 –2 –1
Atlanta 45 38 30 58 37
Boston 62 18 48 40 35
Chicago 62 22 72 44 48
Dallas 42 35 40 64 43
LA 43 40 54 46 35
Total 254 153 244 252 198
Next, consider using a simple exponential smoothing model. In your analysis, test two alpha values, programmed to use one of two forecasting models: simple moving average or exponential smoothing. .2 and .4. Use the same criteria for evaluating the model as in part 1. When using an alpha value of .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 .4, assume that the forecast for week 1 is the past five-week average.
Starbucks is considering simplifying the supply chain for its coffeemaker. Instead of stocking the coffeemaker in all five distribution centers, Starbucks is 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, mean absolute percent error, and the tracking signal.
What are some advantages and disadvantages of aggregating demand from a forecasting view? What else should be considered when going from multiple Distribution Centers (DC) to one DC?
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