Forecasting

1. Forecasting

2. Eight Steps to Forecasting Determine the use of the forecast What objective are we trying to obtain? Select the items or quantities that are to be forecasted. Determine the time horizon of the forecast. Short time horizon – 1 to 30 days Medium time horizon – 1 to 12 months Long time horizon – more than 1 year Select the forecasting model or models Gather the data to make the forecast. Validate the forecasting model Make the forecast Implement the results

3. Forecasting Models

4. Model Differences Qualitative – incorporates judgmental & subjective factors into forecast. Time-Series – attempts to predict the future by using historical data. Causal – incorporates factors that may influence the quantity being forecasted into the model Time Series – What will happen in the future is a function of what happened in the past. Causal – Predict sales of cola: temperature, season, day of week, humidity etc.Time Series – What will happen in the future is a function of what happened in the past. Causal – Predict sales of cola: temperature, season, day of week, humidity etc.

5. Qualitative Forecasting Models Delphi method Iterative group process allows experts to make forecasts Participants: decision makers: 5 -10 experts who make the forecast staff personnel: assist by preparing, distributing, collecting, and summarizing a series of questionnaires and survey results respondents: group with valued judgments who provide input to decision makers

6. Qualitative Forecasting Models (cont) Jury of executive opinion Opinions of a small group of high level managers, often in combination with statistical models. Result is a group estimate. Sales force composite Each salesperson estimates sales in his region. Forecasts are reviewed to ensure realistic. Combined at higher levels to reach an overall forecast. Consumer market survey. Solicits input from customers and potential customers regarding future purchases. Used for forecasts and product design & planning Budgets Sales quotas Financial pro-formas Inventory Budgets Sales quotas Financial pro-formas Inventory Other types of Models: Budgets Sales quotas Financial pro-forma’s Inventory Budgets Sales quotas Financial pro-formas Inventory Budgets Sales quotas Financial pro-formas Inventory Other types of Models: Budgets Sales quotas Financial pro-forma’s Inventory

7. Forecast Error Bias - The arithmetic sum of the errors Mean Square Error - Similar to simple sample variance Variance - Sample variance (adjusted for degrees of freedom) Standard Error - Standard deviation of the sampling distribution MAD - Mean Absolute Deviation MAPE – Mean Absolute Percentage Error Bias is difference between the actual value and the forecasted value.Bias is difference between the actual value and the forecasted value.

8. Quantitative Forecasting Models Time Series Method Naïve Whatever happened recently will happen again this time (same time period) The model is simple and flexible Provides a baseline to measure other models Attempts to capture seasonal factors at the expense of ignoring trend

9. Naïve Forecast

10. Naïve Forecast Graph

11. Quantitative Forecasting Models Time Series Method Moving Averages Assumes item forecasted will stay steady over time. Technique will smooth out short-term irregularities in the time series.

12. Moving Averages

13. Moving Averages Forecast

14. Moving Averages Graph

15. Quantitative Forecasting Models

16. Weighted Moving Average

17. Weighted Moving Average

18. Quantitative Forecasting Models Time Series Method Exponential Smoothing Moving average technique that requires little record keeping of past data. Uses a smoothing constant a with a value between 0 and 1. (Usual range 0.1 to 0.3) Both moving averages and weighted moving averages are effective in smoothing out sudden fluctuations in the demand pattern in order to provide stable estimates. Increasing the size of k (number of periods averaged) smoothes out fluctuations even better. This requires keeping extensive historical records.Both moving averages and weighted moving averages are effective in smoothing out sudden fluctuations in the demand pattern in order to provide stable estimates. Increasing the size of k (number of periods averaged) smoothes out fluctuations even better. This requires keeping extensive historical records.

19. Exponential Smoothing Data

20. Exponential Smoothing

21. Exponential Smoothing

22. Trend & Seasonality Trend analysis technique that fits a trend equation (or curve) to a series of historical data points. projects the curve into the future for medium and long term forecasts. Seasonality analysis adjustment to time series data due to variations at certain periods. adjust with seasonal index – ratio of average value of the item in a season to the overall annual average value. example: demand for coal & fuel oil in winter months.

23. Linear Trend Analysis Midwestern Manufacturing Sales

24. Least Squares for Linear Regression Midwestern Manufacturing

25. Least Squares Method

26. Linear Trend Data & Error Analysis

27. Least Squares Graph

28. Seasonality Analysis A seasonal index with value below 1 indicates demand below average that month, and an index above 1 indicates demand above average that month. Using these seasonal indices, the future demand for any future month can be adjusted. For example, if the average demand for answering machines in year three is expected to be 100 units, then the forecast for January’s demand is 100 X 0.957 = 96 units, which is below average. May’s forecast is 100 X 1.309 = 131 units, which is above average.A seasonal index with value below 1 indicates demand below average that month, and an index above 1 indicates demand above average that month. Using these seasonal indices, the future demand for any future month can be adjusted. For example, if the average demand for answering machines in year three is expected to be 100 units, then the forecast for January’s demand is 100 X 0.957 = 96 units, which is below average. May’s forecast is 100 X 1.309 = 131 units, which is above average.

29. Deseasonalized Data Going back to the conceptual model, solve for trend: Trend = Y / Season (96 units/ 0.957 = 100.31) This eliminates seasonal variation and isolates the trend Now use the Least Squares method to compute the Trend

30. Forecast Now that we have the Seasonal Indices and Trend, we can reseasonalize the data and generate the forecast Y = Trend x Seasonal Index