Jan Northcutt is the owner of bike shop named after her. She has been owner this business for over 20 years. She started to notice that the quality of her bikes that she was purchasing for her to sale were declining while the prices she was paying was going up. The features that she preferred on the bikes were impossible obtain quickly. Instead of panicking or placing blame on somebody else she met the problem head on. Jan started to buy all the necessary parts needed for her bike and rented a separate garage assisted by two helpers. She ended selling the bike shop as demand grew and lease a fairly large facility to house her inventory. As the operation increase, she backward integrated more and processes which allowed for less than 50% of component value of the manufacture bikes. Below is a chart of the actual demand that was listed on page 195 in textbook.
For what I see from the chart above is that Demand for the bikes are seasonality. This mean the bikes seasonal or periodic demand pattern. Also, there is a trend to pattern in the chart. When a forecast is created both of these aspect should factor in the decision making. Multiple regression or MR (Y is forecast, X’s are period and base) MAD ≈ 45.096
Simple regression or SR (deseasonalize demand, seasonal forecast, X is period) MAD ≈ 32.403
Exponential Smoothing or ES (adjusted for trend and seasonality) MAD ≈ 13.258
Forecast for January – April 2012 Month | Mean Base | Period | MR | SR | ES | January | 0.61 | 49 | 825.27 | 745.12 | 720.56 | February | 0.88 | 50 | 1107.05 | 1082.68 | 1039.50 | March | 0.87 | 51 | 1105.66 | 1078.04 | 1027.69 | April | 1.05 | 52 | 1296.43 | 1310.32 | 1240.31 |
Exponential smoothing forecast will provide lowest MAD. The disadvantages with this method is it has to be updated frequently once a month. Jan’s
