IBM401 Lecture 10
- 1. Quantitative Analysis for BusinessLecture 10September 13th, 2010http://www.slideshare.net/saark/ibm401-lecture-10
- 2. Example iData shown is average monthly production of a commodity for the year 1948 – 1958Construct a 5 year moving averageConstruct a 4 year centered moving average
- 3. Example iiMonthly sales of A4 copy paper have been recorded over 12 months (Year 1)Using F0 = 1700, which α do you recommend?α = 0.2α = 0.5From selected α, calculate Trend using β = 0.1 and T0 = 100
- 4. Example iiSupposed sales team has come back with additional data of Year 2 sales, find seasonal index using 4 period centered-moving-averageDeseasonalize the data
- 5. Example iiUsing the Forecast method obtained from part 1 of this question, calculate tracking signal of June of year 1.
- 6. solution
- 7. Example i5-year moving average MA5-year = (Ft-5 + Ft-4 + Ft-3 + Ft-2 + Ft-1)/54-year centered MACMA4-year = (0.5*(Ft-2 + Ft+2) + (Ft + Ft-1 + Ft-2))/4
- 8. Example i5-year moving average & 4-year centered MA
- 9. Example iiDetermining which α, choose the one with lowest MADNew forecast = Last period’s forecast + (Last period’s actual demand - Last period’s forecast)
- 10. Example ii
- 11. Example ii
- 12. Example iicalculate Trend using β = 0.1 and T0 = 100
- 13. Example ii
- 14. Example iiFind seasonal index using 4 period centered-moving-averageUse CMA formulaDeseasonalizethe dataFind seasonal index from CMAst = Actualt / CMAt
- 15. Example ii – Find CMA and seasonal index
- 16. Example ii – Find average seasonal index
- 17. Example ii - deseasonalize
- 18. Example iiUsing the Forecast method obtained from part 1 of this question, calculate tracking signal of June of year 1RSFE = Ratio of running sum of forecast errors = ∑(actual demand in period i - forecast demand in period i)
- 19. Example ii – Tracking signal
- 20. Example iiJune, Year 1 Tracking signal