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Demand Planning: Part 2

Demand Planning: Part 2. Collaboration requires shared information. Objectives. Hands on experience using smoothing procedures Enhanced trend and seasonal smoothing models Forecasting into the future Parameter & initialization estimation considerations Error measures. Smoothing Models.

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Demand Planning: Part 2

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  1. Demand Planning: Part 2 Collaboration requires shared information

  2. Objectives • Hands on experience using smoothing procedures • Enhanced trend and seasonal smoothing models • Forecasting into the future • Parameter & initialization estimation considerations • Error measures

  3. Smoothing Models Moving Average model Ft+1 = Lt = (Dt + Dt-1 + ….+ Dt-n )/ n Simple Exponential Smoothing Ft+1 = Lt = α Dt + ( 1- α )Lt-1 Dt = sales in t Lt = average in t Ft = forecast in t

  4. Forecasting Tools • Spreadsheets • Example: Excel • install the Data Analysis Toolpack (Tools/Add-Inns/Analysis Toolpack) • open the file containing the data • click on: Tools - Data Analysis (different options are available) • Other Add-ins: e.g., KADD and StatTools • Forecasting application software (2 types) • statistical packages • forecasting packages specifically designed for forecasting applications

  5. Hands on Exercise • Hot Pizza exercise, problem 2, page 214 • Use moving average (4 period) and simple exponential smoothing (alpha = .2 & .4) models with data, forecast weeks 13 to 16 into the future. • Northwestern Parts, (in class exercise for seasonal and tend enhanced models)

  6. Components of demand • Trend component: growth or decline over an extended period of time • Cyclical component: wavelike fluctuation around the trend • Seasonal component: pattern of change that repeats itself year after year • Random component: after removal of other components

  7. Pattern Issues • Which patterns are present in data? • Things are not constant over time • Need a process to identify change • Need a procedure to update quickly Enhancing Smoothing Procedures

  8. Grow in Sales

  9. Expo with Trend - Update Equations and Forecasting Model Basic Exponential Smoothing: Ft+1 = Lt = α Dt + ( 1- α )Lt-1 Update Equations with Trend: Level: Lt = α ( Dt ) + ( 1- α ) ( Lt- 1+Tt-1 ) Trend: Tt = β (Lt - Lt-1 ) + ( 1- β ) Tt-1 Forecast Equation for ‘n’ period in the future: Ft+n = Lt + n Tt Dt = sales in t Lt = average in t Tt = trend in t Ft = forecast in t

  10. Update smoothed average for recent trend Update Trend Factor Difference of two period “Average” Weighted combination of Past trend factor Current Forecast of trend factor Trend the forecast Trend Adjustment

  11. Seasonal Sale Pattern

  12. Expo with Season - Update Equations and Forecasting Model Basic Exponential Smoothing: Ft+1 = Lt = α Dt + ( 1- α )Lt-1 Update Equations: Level: Lt = α ( Dt / St ) + ( 1- α ) ( Lt- 1) Season: St+p = γ ( Dt / Lt ) + ( 1- γ ) St Forecast Equation for ‘n’ period in the future: Ft+n = (Lt ) St+n Dt = sales in t Lt = average in t St = season in t Ft = forecast in t p = season

  13. Deseasonalize recent sales data Calculate smoothed average Update Seasonal Factor Ratio of Actual to “Average” Weighted combination of Past deseasonalized seasonal factor Current Forecast of seasonal factor Seasonalize the forecast Seasonality Adjustment

  14. Trend & Seasonality Common

  15. Trend & Season (Winter’s) Update Equations and Forecasting Model Update Equations: Level: Lt = α ( Dt / St ) + ( 1- α ) ( Lt- 1+Tt-1 ) Trend: Tt = β (Lt - Lt-1 ) + ( 1- β ) Tt-1 Season: St+p = γ ( Dt / Lt ) + ( 1- γ ) St Forecast Equation for ‘n’ period in the future: Ft+n = (Lt + n Tt ) St+n Dt = sales in t Lt = average in t Tt = trend in t St = season in t Ft = forecast in t p = season

  16. Forecast Error • Building a Forecast • Fit to historical data • Project future data • Forecast Error • How well does model fit historical data? • Do we need to tune or refine the model? • Can we offer confidence intervals about our predictions?

  17. Measuring Forecast Error • MAD or MAE • average of the absolute errors • Bias (tendency measurement) • Sum of all errors (plus & minus) • MAPE (mean absolute percentage error) • Average absolute ratio of error to actual • MSE (mean square error) • Square of all errors divided by ‘n’

  18. = - e d f t t t n = S - MAD d f n t t = 1 t - d f n t t = S MAPE ( 100 ) n d = 1 t t ( ) n = S - 2 MSE d f n t t = 1 t Evaluating Forecast Models with Different Measures • Error in period t • Mean Absolute Deviation • Mean Absolute Percentage Error • Mean Squared Error

  19. Mabert Web Page • Prepare: Specialty Packaging Corp. (A), pp. 216-217. Develop forecasts for each quarter of 2007 for Clear and Black Plastic containers. Seasonal time series. Try using KADD analysis tool vs. provided Excel workbook. • Quiz: There will be a short quiz covering the fundamentals of demand planning and smoothing forecast models. Open book and notes.

  20. Mabert Web Page • URL address with useful files: • http://kelley.iu.edu/mabert/class-e730.html

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