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Agenda of Week V. Forecasting Forecasting control POM strategy Introduction of forecasting Forecasting methods Review of week 4 132 Qualitative methods.

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Presentation on theme: "Agenda of Week V. Forecasting Forecasting control POM strategy Introduction of forecasting Forecasting methods Review of week 4 132 Qualitative methods."— Presentation transcript:

1 Agenda of Week V. Forecasting Forecasting control POM strategy Introduction of forecasting Forecasting methods Review of week 4 132 Qualitative methods Quantitative methods MAD MSE MAPE Purpose : Understanding the whole process of forecasting

2 Review of Week IV. POM Strategy and Forecasting Purpose : Finishing the POM strategy Understanding the introduction of forecasting Forecasting POM Strategy 12 Competitiveness Marketing vs. operations Strategic hierarchy Strategy formulation Definition Important aspects Common features Good forecasting

3 Steps in the Forecasting Process 1. Determine the purpose of the forecast 2. Establish a time horizon 3. Select a forecasting technique 4. Obtain, clean, and analyze appropriate data 5. Make the forecast 6. Monitor the forecast

4 Forecast Accuracy and Control o Forecasters want to minimize forecast errors It is nearly impossible to correctly forecast real-world variable values on a regular basis So, it is important to provide an indication of the extent to which the forecast might deviate from the value of the variable that actually occurs o Forecast accuracy should be an important forecasting technique selection criterion

5 Forecast Accuracy and Control (contd.) o Forecast errors should be monitored Error = Actual – Forecast If errors fall beyond acceptable bounds, corrective action may be necessary

6 Forecast Accuracy Metrics MAD weights all errors evenly MSE weights errors according to their squared values MAPE weights errors according to relative error

7 Forecast Error Calculation Period Actual (A) Forecast (F) (A-F) Error |Error|Error 2 [|Error|/Actual]x100 1107110-3392.80% 212512144163.20% 31151123392.61% 4118120-2241.69% 51081091110.93% Sum133911.23% n = 5n-1 = 4n = 5 MADMSEMAPE = 2.6= 9.75= 2.25%

8 Forecasting Approaches o Qualitative Forecasting Qualitative techniques permit the inclusion of soft information such as: Human factors Personal opinions Hunches These factors are difficult, or impossible, to quantify o Quantitative Forecasting Quantitative techniques involve either the projection of historical data or the development of associative methods that attempt to use causal variables to make a forecast These techniques rely on hard data

9 Judgmental Forecasts o Forecasts that use submective inputs such as opinions from consumer surveys, sales staff, managers, executives, and experts Executive opinions Salesforce opinions Consumer surveys Delphi method

10 Time-Series Forecasts o Forecasts that project patterns identified in recent time-series observations Time-series - a time-ordered sequence of observations taken at regular time intervals o Assume that future values of the time-series can be estimated from past values of the time-series

11 Time-Series Behaviors o Trend o Seasonality o Cycles o Irregular variations o Random variation

12 Time-Series Forecasting - Naïve Forecast o Naïve Forecast Uses a single previous value of a time series as the basis for a forecast The forecast for a time period is equal to the previous time periods value Can be used when The time series is stable There is a trend There is seasonality

13 Time-Series Forecasting - Averaging o These Techniques work best when a series tends to vary about an average Averaging techniques smooth variations in the data They can handle step changes or gradual changes in the level of a series Techniques Moving average Weighted moving average Exponential smoothing

14 Moving Average o Technique that averages a number of the most recent actual values in generating a forecast

15 Moving Average o As new data become available, the forecast is updated by adding the newest value and dropping the oldest and then recomputing the the average o The number of data points included in the average determines the models sensitivity Fewer data points used-- more responsive More data points used-- less responsive

16 Weighted Moving Average o The most recent values in a time series are given more weight in computing a forecast The choice of weights, w, is somewhat arbitrary and involves some trial and error

17 Exponential Smoothing o A weighted averaging method that is based on the previous forecast plus a percentage of the forecast error

18 Associative Forecasting Techniques Home values may be related to such factors as home and property size, location, number of bedrooms, and number of bathrooms Associative techniques are based on the development of an equation that summarizes the effects of predictor variables Predictor variables - variables that can be used to predict values of the variable of interest

19 Simple Linear Regression o Regression - a technique for fitting a line to a set of data points Simple linear regression - the simplest form of regression that involves a linear relationship between two variables The object of simple linear regression is to obtain an equation of a straight line that minimizes the sum of squared vertical deviations from the line (i.e., the least squares criterion)

20 Least Squares Line


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