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Chapter 8
Forecasting
Introduction
· Forecasting is a prelude to planning, an estimate of what conditions will exist over some future period.
Demand Management
· The prime purpose of an organization is to serve the customer. Marketing focuses on meeting customer needs, but operations, through materials management, must provide the resources. The coordination of plans by these two parties is demand management. Demand management is the function of recognizing and managing all demands for products, including forecasting, order processing, making delivery promises (available-to-promise), and interfacing between manufacturing planning and control and the marketplace.
Demand Forecasting
· Forecasts are made for the strategic business plan, the production plan, and the master production schedule.
Characteristics of Demand
· The difference between “demand” and “sales” is that sales implies what is actually sold whereas demand shows the need for the item. Sometimes demand cannot be satisfied, and sales will be less than demand.
· The pattern shows that actual demand varies from period to period. The four reasons for this are trend, seasonality, random variation, and cycle.
· The shape of demand patterns for some products or services change over time, others do not. Those that retain the same general shape are called stable and those that do not are called dynamic. The more stable the demand, the easier it is to forecast.
Principles of Forecasting
· Forecasts have four major characteristics or principles: (1) Forecasts are usually wrong. Errors are inevitable and must be expected. (2) Every forecast should include an estimate of error. (3) Forecasts are more accurate for families or groups. (4) Forecasts are more accurate for nearer time periods. Anything that can be done to reduce lead-time will improve forecast accuracy.
Forecasting Techniques
· Forecasting methods are classified into 3 categories: qualitative, extrinsic, and intrinsic.
Some Important Intrinsic Techniques
· Usually methods that average out history are better because they dampen out some effects of random variation. It is best to forecast the average demand rather than second-guess what the effect of random fluctuation will be. A forecast of average demand should be made, and the estimate of error applied to it.
· The point is that a moving average always lags a trend, and the more periods included in the average, the greater the lag will be. On the other hand, if there is no trend but actual demand fluctuates considerably due to random variation, a moving average based on a few periods reacts to the fluctuation rather than forecasts the average. Moving averages are best used for forecasting products with stable demand when there is little trend or seasonality.
· A common forecasting technique, called exponential smoothing, gives the same results as a moving average but without the need to retain as much data and with easier calculations. The forecast can be based on the prior old calculated forecast and the new data.
· The weight given to the latest actual demand is called a smoothing constant and is represented by the greek letter alpha ( ). It is always expressed as a decimal from 0 to 1.0. The formula is: New forecast = ( )(latest demand) + (1- )(previous forecast).
Seasonality
· Many products have a seasonal or periodic demand pattern. The seasonal index is an estimate of how much the demand during the season will be above or below the average demand for the product.
Seasonal Index = Period Average Demand / Average Demand for all Periods
Seasonal Index = Period Average Demand / Average Demand for all Periods
· The average demand for all periods is a value that averages out seasonality. This is called the deseasonalized demand.
Seasonal Index = Period Average Demand / Deseasonalized Demand
Seasonal Index = Period Average Demand / Deseasonalized Demand
Tracking the Forecast
· Tracking the forecast is the process of comparing actual demand with the forecast. Forecast error is the difference between actual and forecast demand. Error can occur in two ways: bias and random variation. Bias exists when the cumulative actual demand varies from the cumulative forecast. Bias is a systematic error in which the actual demand is consistently above or below the forecast demand.
· Forecast error must be measured before it can be used to revise the forecast or to help in planning. One way to measure the variability is to calculate the total error ignoring the plus and minus signs and take the average. This is called mean absolute deviation (MAD): mean implies an average, absolute means without reference to plus and minus, and deviation refers to the error.
MAD = Sum of Absolute Deviations / Number of Observations
MAD = Sum of Absolute Deviations / Number of Observations