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

Forecasting 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 – to 30 days  Medium time horizon – to 12 months  Long time horizon – more than year       Select the forecasting model or models Gather the data to make the forecast Validate the forecasting model Make the forecast Implement the results Forecasting Models Forecasting Techniques Qualitative Models Time Series Methods Delphi Method Jury of Executive Opinion Sales Force Composite Consumer Market Survey Naive Moving Average Weighted Moving Average Exponential Smoothing Trend Analysis Causal Methods Simple Regression Analysis Multiple Regression Analysis Seasonality Analysis Multiplicative Decomposition 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  Qualitative Forecasting Models  Delphi method  Iterative group process allows experts to make forecasts  Participants:    decision makers: -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 Qualitative Forecasting Models (cont)  Jury of executive opinion    Sales force composite     Opinions of a small group of high level managers, often in combination with statistical models Result is a group estimate 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 Forecast Error       Forecast Error  At  Ft T Bias - The arithmetic sum of MSE  | forecast error | /T the errors t 1 Mean Square Error - Similar to T simple sample variance  ( At  Ft ) / T t 1 Variance - Sample variance (adjusted for degrees of freedom) Standard Error - Standard deviation of the sampling distribution T T MAD - Mean Absolute MAD  | forecast error | /T  |At  Ft | / T Deviation t 1 t 1 MAPE – Mean Absolute T Percentage Error MAPE 100 [|At  Ft | / At ] / T t 1 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 Ft Yt  Ft Yt  : Quarterly data Ft Yt  12 : Monthly data Naïve Forecast Naïve Forecast Graph Weighted Moving Average Weighted Moving Average Quantitative Forecasting Models  Time Series Method  Exponential Smoothing Moving average technique that requires little record keeping of past data  Uses a smoothing constant α with a value between and (Usual range 0.1 to 0.3)  Forecast for period t  forecast for period t -   (actual value in period t - - forecast for period t - 1) Exponential Smoothing Data Exponential Smoothing Exponential Smoothing 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 Linear Trend Analysis Midwestern Manufacturing Sales Least Squares for Linear Regression Midwestern Manufacturing Least Squares Method ^ Y a  bX Where ^ Y = predicted value of the dependent variable (demand) X = value of the independent variable (time) _ _ a = Y-axis intercept b = slope of the regression line b= [ XY - n X Y ] _  2 X n X     Linear Trend Data & Error Analysis Least Squares Graph Seasonality Analysis Ratio = demand / average demand Seasonal Index – ratio of the average value of the item in a season to the overall average annual value Example: average of year January ratio to year January ratio (0.851 + 1.064)/2 = 0.957 If Year average monthly demand is expected to be 100 units Forecast demand Year January: 100 X 0.957 = 96 units Forecast demand Year May: 100 X 1.309 = 131 units 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  Forecast  Now that we have the Seasonal Indices and Trend, we can reseasonalize the data and generate the forecast  Y = Trend x Seasonal Index ...     Select the forecasting model or models Gather the data to make the forecast Validate the forecasting model Make the forecast Implement the results Forecasting Models Forecasting Techniques...Eight Steps to Forecasting  Determine the use of the forecast    What objective are we trying to obtain? Select... incorporates factors that may influence the quantity being forecasted into the model  Qualitative Forecasting Models  Delphi method  Iterative group process allows experts to make forecasts 