Forecasting

Forecasting

1. Time-Series Forecasting

   • Short-range forecast
     • Up to 1 year but is generally less than 3 months. Purchasing, job scheduling, workforce levels, job assignments, and production levelsU
   • Medium-range forecast
     • 3 months to 3 years. Sales planning, production planning and budgeting
   • Long-range forecast
     • 3 years or more. New products, capital expenditures, facility location or expansion, and research and development.

2. Quantitative Methods

   • Time-series models
     • Naive approach
     • Moving averages
     • Exponential smoothing
   • Associative model
     • Trend projection
     • Linear regression

3. What is Forecasting

   • Procces of predicting a future event. It is the basis of all business decisions. Production, Inventory, Personnel and Fcilities.

4. Seven Steps in the Forecasting System.

   • Determine the use of the forecast.
   • Select the items to be forecasted.
   • Determine the time horizon of the forecast.
   • Select the forecasting model(s).
   • Gather the data needed to make the forecast.
   • Make the forecast.
   • Validate and implement the results.

5. Qualitative Methods

   • Jury of executive opinion
     • Pool opinions of high-level experts, sometimes augment by statistical models.
   • Delphi method
     • Panel of experts, queried iteratively
   • Sales force composite
     • Estimates from individual salespersons are reviewed for reasonableness, then aggregated.
   • Market survey
     • Ask the costumer.

6. Time-Series Forecasting

   • Set of evenly spaced numerical data. Obtained by observing response variable at regular time periods.
   • Forecast based only on past values, no other variables important. Assumes that factors influencing past and present will continue influence in future.

7. Time Series Component

   • Trend
     • Is the gradual upward or downward movement of the data over time. Changes in income, population, age distribution, or cultural views may account for movement in trend.
   • Cycle
     • Are patterns in the data that occur every several years. They are usually tied into the business cycle.
   • Seasonal
     • is a data pattern that repeats itself after a period of days, weeks, months, or quarters.
   • Random
     • Are “blips” in the data caused by chance and unusual situations. They follow no discernible pattern, so they cannot be predicted.

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