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Forecasting Part 2 (1) - Forecasting Part2 Time Series...

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Forecasting Part 2
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A first step…  Plotting the data Time Series Definition: The pattern formed by repeated observations of demand for a product or service in their order of occurrence. Five basic patterns: Trend Seasonality Cycles Irregular variations Random variations
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Examples:
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Designing the Forecasting System Before using forecasting techniques, managers must make 3 decisions: 1. What to forecast. 2. What type of forecasting method to use. 3. What type of computer hardware or software (or both) to use.
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Deciding What to Forecast Selecting the correct unit of measurement for forecasting may be as important as choosing the best method. Using aggregation can produce more accurate forecasts. Many companies make forecasts for families of goods or services first, then derive forecasts for individual items. The most useful forecasts are those based on product or service units, rather than dollars.
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Choosing the Type of Forecasting Technique The forecaster’s objective : To develop a useful forecast from the information at hand with the techniques appropriate for the different characteristics of demand. This choice is often a trade-off between accuracy and costs. Qualitative methods Judgment methods Quantitative methods Causal methods (regression) Time-series methods
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Quantitative Methods Time-Series Methods A statistical approach that relies heavily on historical demand data to project the future size of demand and recognizes seasonal patterns and trends. Causal Methods Use historical data on independent variables to predict demand
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Demand Forecast Applications Application Short Term (0-3 months) Medium Term (3 months-2 years) Long Term (more than 2 years) Forecast quantity Individual products or services Total sales Groups of families of products or services Total sales Decision area Inventory management Final assembly scheduling Workforce scheduling Master production scheduling Staff planning Production planning Master production scheduling Purchasing Distribution Facility location Capacity planning Process management Forecasting technique Time series Causal Judgment Causal Judgment Causal Judgment
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Time-Series Methods Use historical information regarding only the dependent variable. Based on the assumption that the dependent variable’s past pattern will continue in the future. Several methods: Naïve forecast Simple moving averages Weighted moving averages Exponential smoothing
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Na ï ve Forecast The forecast for the next period equals the demand for the current period.
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