1. MBA.782.ForecastingCAJ9.11.1 Demand Management Qualitative Methods of Forecasting Quantitative Methods of Forecasting Causal Relationship Forecasting Focus Forecasting Development of a Forecasting System Operations Management Forecasting

2. MBA.782.ForecastingCAJ9.11.2 Forecasts are seldom __________ - find the best method Forecasting methods assume there is some underlying stability in the system _______________ product forecasts are more accurate than individual product forecasts Basis of long-run planning –budget planning and cost control Marketing - sale forecast Operations - capacity, scheduling, inventory Forecasting Forecasting in Business

3. MBA.782.ForecastingCAJ9.11.3 Forecasting Demand Management Independent demand –demand for item is independent of demand for _____ other item Dependent demand –demand for item is dependent upon the demand for ______ _______ item

4. MBA.782.ForecastingCAJ9.11.4 14 The greater the ability to react, the less accurate the forecast has to be A __________ between the cost of doing the forecast and the opportunity cost of proceeding with misleading numbers Factors: 1. Time horizon to forecast 2. Data availability 3. Accuracy required 4. Size of forecasting budget 5. Availability of qualified personnel Forecasting Choice of Forecasting Model

5. MBA.782.ForecastingCAJ9.11.5 Qualitative (Judgmental) Quantitative – Time Series Analysis > past data – Causal Relationships > related to some other factors – Simulation > test assumptions Forecasting Types of Forecasting

6. MBA.782.ForecastingCAJ9.11.6 1.Choose the participants - never meeting as a ________ 2.Through a questionnaire, obtain forecasts from all participants 3.Summarize the results and redistribute them to the participants along with appropriate new questions 4.Summarize again, refining forecasts and conditions, and develop new questions. 5.Repeat Step 4 if necessary. Distribute the final results to all participants. Qualitative Methods Delphi Method

7. MBA.782.ForecastingCAJ9.11.7 Components of Demand –Trend, Seasonal, Cyclic, Random Time Series Analysis Causal Relationships Simulation Forecasting Quantitative Methods

8. MBA.782.ForecastingCAJ9.11.8 _______________, overall upward or downward pattern Due to population, technology etc. Linear; S-curve; asymptotic; exponential Mo., Qtr., Yr. Response Components of Demand Trend Component

9. MBA.782.ForecastingCAJ9.11.9 Regular pattern of ____ & ________ fluctuations Due to weather, customs etc. Occurs within __ _______ Mo., Qtr. Response Summer Components of Demand Seasonal Component

10. MBA.782.ForecastingCAJ9.11.10 Repeating up & down movements Due to interactions of factors influencing economy Non-annual; __________ ; Mo., Qtr., Yr. Response Cycle Components of Demand Cyclical Component

11. MBA.782.ForecastingCAJ9.11.11 Erratic, unsystematic, ‘residual’ fluctuations Unexplained portion Components of Demand Random Component

12. MBA.782.ForecastingCAJ9.11.12 Set of ________ spaced numerical data –Obtained by observing response variable at regular time periods Forecast based only on _______ values –Assumes that factors influencing past, present, & future will continue Example Year:19931994199519961997 Sales:78.763.589.793.292.1 Quantitative Methods What is a Time Series?

13. MBA.782.ForecastingCAJ9.11.13 Used if demand is ____ growing nor declining rapidly Used often for smoothing –Remove ____________ fluctuations Equation where: F t = forecast for period t, A t = actual demand realized in period t, Time Series Analysis Simple Moving Average

14. MBA.782.ForecastingCAJ9.11.14 15 Let’s develop 3-week moving average forecasts for demand. Assume you only have 3 weeks of actual demand data for the respective forecasts Time Series Analysis Simple Moving Average

15. MBA.782.ForecastingCAJ9.11.15 Allows different ________ to be assigned to past observations –Older data usually ______ important Weights based on experience, trial-and-error Equation...... Time Series Analysis Weighted Moving Average

16. MBA.782.ForecastingCAJ9.11.16 20 Determine the 3-period weighted moving average forecast for period 4. Weights:t-10.5 t-20.3 t-30.2 Time Series Analysis Weighted Moving Average

17. MBA.782.ForecastingCAJ9.11.17 Increasing n makes forecast ______ sensitive to changes Do not forecast _______ well Require ______ historical data Time Series Analysis Disadvantages of M.A. Methods

18. MBA.782.ForecastingCAJ9.11.18 To ramp changes of demand Demand High weight n = 2 65 55 45 35 Ramp Shift -3 2 1 T +1 2 3 4 5 6 7 8 Low weight n = 6 Time Series Analysis Responsiveness of M.A. Methods Forecast _____ with increasing demand, and _______ with decreasing demand

19. MBA.782.ForecastingCAJ9.11.19 Premise--The most ________ observations might have the highest predictive value. Therefore, we should give _______ weight to the more recent time periods when forecasting Requires smoothing constant (  ) –Ranges from 0 to 1 –Subjectively chosen Involves _______ record keeping of past data Time Series Analysis Exponential Smoothing

20. MBA.782.ForecastingCAJ9.11.20 The equation used to compute the forecast is... F t = F t-1 +  ·(A t-1 - F t-1 ) where.... F t = forecast demand A t = actual demand realized  = smoothing constant Exponential because each increment in the past is decreased by (1 -  ): Time Series Analysis Exponential Smoothing

21. MBA.782.ForecastingCAJ9.11.21 Determine exponential smoothing forecasts for periods 2-10 using  =0.20 (Let F 1 =D 1 ) Time Series Analysis Exponential Smoothing

22. MBA.782.ForecastingCAJ9.11.22 3000 2500 2000 1500 1000 1 2 3 4 5 6 7 8 9 10 11 12 Actual demand alpha =.1 alpha =.5 alpha =.9 Exponential Smoothing Responsiveness to Different Values of 

23. MBA.782.ForecastingCAJ9.11.23 30 Attempts to __________ (somewhat) the lag in the exponential smoothing method Trend equation with a smoothing constant, ___ (delta) formulae…… FIT t = Forecast including trend FIT t = F t + T t F t = FIT t-1 +  (A t-1 - FIT t-1 ) T t = T t-1 +   (A t-1 - FIT t-1 ) Time Series Analysis Exponential Smoothing with Trend

24. MBA.782.ForecastingCAJ9.11.24 30 Error = Actual - Forecast E t = A t - F t RSFE = running sum of the forecast errors RSFE =  E t Bias = Average Error –occurs when a _______________ mistake is made Bias = RSFE / n Random errors –cannot be explained by the forecast model being used. Time Series Analysis Forecast Accuracy

25. MBA.782.ForecastingCAJ9.11.25 30 Mean Absolute Deviation is the sum of each error’s magnitude divided by the number of error--so we get the ___________ magnitude of the forecast error Time Series Analysis Forecast Errors If the errors are normally distributed, the standard deviation, _________________

26. MBA.782.ForecastingCAJ9.11.26 32 Time Series Analysis Forecast Errors

27. MBA.782.ForecastingCAJ9.11.27 TS measures the ________ of MADs that the forecast is above or below the actual value of the variable –Good tracking signal has _____ values In the usual statistical manner, if control limits were set at plus or minus 3 standard deviations (or + 3.75 MADs), then _____ percent of the points would fall within these limits. Measures how _____ the forecast is predicting actual values Time Series Analysis Tracking Signal

28. MBA.782.ForecastingCAJ9.11.28 33 Describe functional relationship between two or more correlated variables. Equation of the form: Y = a + bx –used to predict Y for some _________ value of x Useful for long-run decisions and aggregate planning Assumes a straight-line (linear) relationship Use in _____ _______ and _______ forecasting Time Series Analysis Linear Regression

29. MBA.782.ForecastingCAJ9.11.29 Seasonality ….. A seasonal factor (index) is the amount of the correction necessary to _________ for the season of the year Decomposition ….. To ___________ the basic components of trend and seasonality Forecasting Integrative Example

30. MBA.782.ForecastingCAJ9.11.30 Given three years of quarterly data: Determine the seasonal factors. Forecasting Integrative Example

31. MBA.782.ForecastingCAJ9.11.31 Deseasonalize the actual demand data by _________ by the appropriate seasonal factor: Forecasting Integrative Example

32. MBA.782.ForecastingCAJ9.11.32 The perform a linear regression, least squares approximation of the relationship between quarter (x) and ___ - seasonalized sales (y): y = a + b x Forecasting Integrative Example

33. MBA.782.ForecastingCAJ9.11.33 Project the ________ using the predictive equation for each quarter of year 4: Forecasting Integrative Example Quarter 13: F 13 = 10.44 + 0.1882 ( ___ ) = _____ Quarter 14: F 14 = 10.44 + 0.1882 ( ___ ) = _____ Quarter 15: F 15 = 10.44 + 0.1882 ( ___ ) = _____ Quarter 16: F 16 = 10.44 + 0.1882 ( ___ ) = _____

34. MBA.782.ForecastingCAJ9.11.34 Adjust for seasonality by multiplying by the seasonal factors for the appropriate quarters: Forecasting Integrative Example

35. MBA.782.ForecastingCAJ9.11.35 Forecasting Integrative Example

36. MBA.782.ForecastingCAJ9.11.36 One occurrence causes another If the causing element if _____ enough in advance, it can be used as a basis for forecasting The independent variable must be a _________ indicator Challenge is to find those occurrences that are ________ the causes Forecasting Causal Relationship Forecasting

37. MBA.782.ForecastingCAJ9.11.37 Uses simulation –____________ to test various forecasting models Pick the model that produces the smallest error Illustrate…. Forecasting Focus Forecasting

38. MBA.782.ForecastingCAJ9.11.38 Choice depends _________ on –the type of business, and who is using the forecast No pattern or direction in forecast error –Seen in plots of errors over time ____________ forecast error –Mean absolute deviation (MAD) Focus Forecasting –has merit –computer time is not an issue –component of many business systems Forecasting Developing a Forecasting System

39. MBA.782.ForecastingCAJ9.11.39 Forecasting

40. MBA.782.ForecastingCAJ9.11.40 Forecasting Chapter Wrap-Up Read Chapter 11 Concepts and Terminology Review Lecture Notes Recommended Problems