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OTM 24
Forecasting Part 1
| Question | Answer |
|---|---|
| forecasting | 1. process of predicting a future event 2. underlying basis of many critical business decisions |
| critical business decisions | 1.production 2.inventory 3.personnel 4.facilities |
| SKU **NOTES** | stock keeping unit |
| forecast characteristics | 1.they are usually wrong 2.good forecast is more than a number 3.aggregate forecasts more accurate 4. longer the forecast, the less accurate it is 5. forecasts should not be used to the exclusion of known information like special events |
| the _________ the forecast horizon, the ____accurate the forecast | longer , less accurate |
| forecasting approaches | 1. qualitative 2. quantitative 3.judgmental 4.time series |
| qualitative forecasting | 1.used when situation is vague and little data exists for new products and technology 2.involves intuition and experience (example forecasting sales on internet) |
| quantitative forecasting | **NOTES** look at past times and find average 1.used when situation is stable and historical data exists for existing products and current technology 2. involves mathematical techniques (example-forecasting sales of color tvs) |
| judgmental methods of forecasting | 1.jury of executive opinion 2. Delphi method 3. sales force composite approach 4. market survey |
| jury of executive opinion for forecasting | **NOTES** group think can be a problem because there are people who dominate |
| Delphi method for forecasting | **Notes** combats dominant people 1.collect info from dominant individuals 2.keep them separate from the group 3.consolidate in an unbiased method |
| time series method for forecasting (see slide 6 for example chart) | 1.time series is a set of evenly spaced numerical data obtained over regular time periods 2.forecasts are based only on past values (assumes factors that influenced past and present will also influence future) example chart shows year: sales: |
| time series components | 1.trend 2.cyclicality 3.seasonality |
| trend component (see slide 8 for sample graph) | 1. persistent overall upward or downward pattern 2.due to population, technology, etc. 3.several years duration |
| seasonal component (see slide 9 for sample graph) | **Notes**1.tends to be tied closely to a particular product or market 2.regular pattern of up and down fluctuations 3.due to weather, customs etc 4.occurs within 1 year |
| cyclical component (see slide 10 for sample graph) | 1.repeating up and down movements 2.due to interaction of factors influencing economy 3.usually 2-10 years duration**NOTES**it does not repeat itself and if longer than 1 year it is tied to the general economy |
| MA (see slide 11 for formula) | simple moving average |
| WMA (see slide 11 for this formula also) | weighted moving average |
| time series methods | 1.simple moving average 2. weighted moving average 3. exponential smoothing |
| exponential smoothing advantages **NOTES** weight should add up to 1 | 1.little data storage required 2. more weight given to more recent data, but all past data incorporated 3. has proven to be as accurate as much more sophisticated methods |
| exponential smoothing formula (see slide 12) | see slide 12 |
Created by:
rainesv