Forecasting

Forecast… is a statement about the future value of a variable of interest (such as demand). affects decisions and activities throughout an organization. reduces uncertainty (replaces data).

General statements about forecasting Assumes causal system past ==> future Forecasts rarely perfect because of randomness (Be ready to react). Forecasts more accurate for groups vs. individuals Forecast accuracy decreases as time horizon increases

Possible targets of forecasting Factors outside the organization, that are hard to change eg. weather, inflation, unemployment Factors within the organization with a greater possibility to change. eg. age structure, labor turnover, scrap ratio

The good forecasting system… Timely Accurate Reliable Meaningful Written Easy to use + cost effectiveness

Steps in the forecasting process Step 1 Determine purpose of forecast Step 2 Establish a time horizon Step 3 Select a forecasting technique Step 4 Gather and analyze data Step 5 Prepare the forecast Step 6 Monitor the forecast “The forecast”

Forecast accuracy (measures of goodness) Forecast error: et = At – Ft Mean absolute deviation: MAD Mean squared error: MSE Mean absoulute percent error: MAPE MAPE 7

Types of forecast Judgmental - uses subjective inputs Executive opinions, salesforce opinions, consumer surveys etc. Time series (time ordered sequence of observations) - uses historical data assuming the future will be like the past Associative models - uses explanatory variables to predict the future

Time series forecasts Trend - long-term movement in data Seasonality - short-term regular variations in data Cycle – wavelike variations of more than one year’s duration Irregular variations - caused by unusual circumstances Random variations - caused by chance

Forecast Variations Trend Cycles Irregular variation 90 89 88 Seasonal variations

Naive forecasts The forecast for any period equals the previous period’s actual value. Advantages: Simple to use Virtually no cost Quick and easy to prepare Data analysis is nonexistent Easily understandable Cannot provide high accuracy Can be a standard for accuracy

3 uses of naive forecasts Stable time series data F(t) = A(t-1) Seasonal variations F(t) = A(t-n) Data with trends F(t) = A(t-1) + (A(t-1) – A(t-2))

Techniques of averaging (smoothing variations in the data) (Simple) Avarage Moving average Weighted moving average Exponential smoothing

Average Variable Time 15

Ft = (At-n+…+At-2+At-1)/n Moving average Ft = (At-n+…+At-2+At-1)/n Example (3 yrs): € 42 € 40 € 43 € 41 €? New data: 6. € 38 7. ? New data: 7. € 37 8. ? What would be the forecast with 5 years moving average?

If the trend is permanent 17

Mottó: egy biztos – minden bizonytalan Changing trend Mottó: egy biztos – minden bizonytalan Az előrejelzés a jövőbeni események megjósolásának tudománya és művészete Miért tudomány? Miért művészet? 18

Weighted moving average Data: Aug. 95 Sept. 100 Oct. 110 Nov. ? Előrejelzés Súlyok: Weights: Time Present -1 -2 Weight 3 2 1 Forecast: 20

Exponential smoothing New forecast = forecast for the previous period + α*error Where the error is = actual data for the last period – forecasted data for the last period α: smoothing constant (usually 0.05<α<0.5) 21

Linear trends Ft = a + bt where And where n = number of periods, y = value of the time series

Nonlinear trends

Associative Forecasting Predictor variables - used to predict values of variable interest Regression - technique for fitting a line to a set of points Least squares line - minimizes sum of squared deviations around the line

Controlling the Forecast Control chart A visual tool for monitoring forecast errors Used to detect non-randomness in errors Forecasting errors are in control if All errors are within the control limits No patterns, such as trends or cycles, are present

Sources of Forecast errors Model may be inadequate Irregular variations Incorrect use of forecasting technique

Choosing a Forecasting Technique No single technique works in every situation Two most important factors Cost Accuracy Other factors include the availability of: Historical data Computers Time needed to gather and analyze the data Forecast horizon