Modified Time Series Decomposition Model

1. Modified Time Series Decomposition Model The Weathermen: Rob Harrison RD Trinidad Niall Needham

2. Presentation Overview • Company Background • The Problem • Methods of Attack • The Model • Results • Managerial Recommendations

3. SkinCeuticals’ History • Founded in 1994 • Originally manufactured and sold 1 product • Now manufacture and distribute around 40 products to more to all 50 states and 30 countries worldwide

4. The Problem • Cash is being poured into stocking a large inventory to prevent backorders and potential lost sales • SkinCeuticals needs an accurate demand forecast for each individual product • Lead times

5. Methods of Attack • Associative Time Series? • Product clustering • Moving Averages • Forecasting Software • Time Series Decomposition • Modified Time Series Decomposition

6. Time Series Decomposition Model • Breaks forecast into four components • Trend • Seasonal • Cyclical • Irregular

8. The Components • Trend • b = (t × Yt – (t × Yt)/n)  (t2 – (t)2/n) • a = Ŷ – b × ť • Cyclical • C = CMA  Tc • Seasonal and Irregular • S × I = (T × C × S × I)  (T × C) • Forecast • Ft = Tt × St × Ct

9. Modified Time Series Decomposition • Monthly Moving Error Forecast • Sales information for last year of sales deleted • Each of these months are forecasted • Forecast compared to actual sales data • Percent error is recorded and used in the final forecast

10. Yearly Results for Six Products

11. Monthly Results

12. Forecast Comparison

13. Forecast Comparison Cont.

14. Managerial Recommendations • The Modified Time Series Decomposition Model will increase have greater forecasting accuracy with time • In the meantime, the Modified Time Series Decomposition Model forecast should be used along with associative forecasting techniques to create a more accurate demand forecast.