Chapter 4: Forecasting

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Department
Food Agricultural and Resource Economics
Course
FARE 3310
Professor
Getu Hailu
Semester
Fall

Description
Chapter 4: Forecasting  Forecast is one of most important business functions because all other business decisions are based on a forecast of future  Make decisions like which market to pursue, products to produce, mow much inventory to carry etc.  Poor forecasting results in incorrect business decisions which leave company unprepared to meet future demands o Costly consequences (like loss of sales) What is Forecasting?  Process of PREDICTING a future event o Rarely perfect o Most techniques assume underlying stability in system o Product family/aggregated forecasts more accurate than individual product forecasts Forecasting Time Horizons  Short-range forecast o Up to 1 year (usually less than 3 months) o Purchasing, job scheduling, workforce levels, job assignments, production levels o More accurate than long-term forecasts  Medium-range forecasts o 3 months-3 years o Sales and production planning, budgeting o Deal with more comprehensive issues/support management decisions regarding planning/products, plants and processes  Long-range format o 3+ years o New product planning Types of Forecasts  Economic forecasts o Address business cycle inflation rate, money supply, housing starts  Technological forecasts o Predict rate of technological progress o Impacts development of new products  Demand forecasts o Predict sales of existing products and services Strategic Importance of Forecasting  Human resources o Hiring, training, laying off workers  Capacity o Capacity shortages can result in undependable delivery, loss of customers, loss of market share  Supply chain management o Good supplier relations and price advantages Steps in Forecasting 1. Determine use of forecast 2. Select items to be forecasted 3. Determine time horizon of forecast 4. Select forecasting model(s) 5. Gather data 6. Make forecast 7. Validate and implement results Forecasting Approaches  Qualitative Methods o Based on human judgment, opinions, subjective & non-mathematical o Can incorporate latest changes in environment and “inside info” o Can BIAS forecast a reduce forecast accuracy o Involves intuition, experience  Quantitative Methods o Used when situation is stable/historical data exist (e.g. existing products) o Involves mathematical techniques, quantitative in nature o Consistent and objective, consider large amount of data at one time o Often quantifiable data not available, only good as data based on Overview of Qualitative Approaches  Jury of executive opinion o Pool opinions of high-level experts (use statistical models) group of managers meet and come up with a forecast o Good for strategic/new product forecasting o One persons opinion can dominate forecast  Delphi method o Panel of experts, queried iteratively (seeks to develop consensus) o Excellent for forecasting long-term product development o Time-consuming to develop  Sales force composite o Estimates from individual salespersons are reviewed for reasonableness then aggregates  Consumer market survey o Ask customer use survey/interviews to identify customer preferences o Good determinant of consumer preferences o Can be difficult to develop a good questionnaire Overview of Quantitative Approaches  Naïve approach o Uses last period’s actual value as forecast o Simple and easy to use o Only good if data change little from period to period  Simple moving averages o Method is which only n of the most recent observations are averaged o Only good for level pattern o Important to select proper moving average  Weighted moving average o Method where n of the most recent observations and past observations have different weights o Good for level patters, allow placing different weights on past values o Selection of weights requires good judgment  Exponential smoothing o Weighted average procedure with weights declining exponentially as data gets older o Excellent for short/medium-term length forecasts o Choice of smoothing parameter a is crucial  Exponential smoothing with trend adjustment o Method with separate equations for forecasting the level and trend o Provides good results for trend data o Should only be used for data with trend  Trend projection o Technique uses the least-squares method to fit a strait lint to past data over time o Easy to use and u
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