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17/09/2016 03:18:43 PM

FORECASTING

1. Types of Forecasting

Forecasting can be classified into four basic types: qualitative, time series analysis, causal relationships, and simulation.

  • Qualitative techniquesare subjective or judgmental and are based on estimates and opinions
  • Time series analysis is based on the idea that data relating to past demand can be used to predict future demand. Past data may include several components, such as trend, seasonal, or cyclical influences, and are described in the following section.
  • Causal forecasting, which we discuss using the linear regression technique, assumes that demand is related to some underlying factor or factors in the environment.
  • Simulation modelsallow the forecaster to run through a range of assumptions about the condition of the forecast.

In this chapter we focus on qualitative and time series techniques since these are most often used in supply chain planning and control.

2. Components Of Demand

In most cases, demand for products or services can be broken down into six components: average demand for the period, a trend, seasonal element, cyclical elements, random variation, and autocorrelation. Exhibit 9.1 illustrates a demand over a four-year period, showing the average, trend, and seasonal components and randomness around the smoothed demand curve.

  • Trendsrepresent either an increase or a decline over the years and caused by factors such as population growth, changes in population, culture, income. A widely used forecasting method plots data and then searches for the curve pattern  (such as linear, S-curve, asymptotic, or exponential) that fits best
  • Trend linesare the usual starting point in developing a forecast. These trend lines are then adjusted for seasonal effects, cyclical elements, and any other expected events that may influence the final forecast.
  • Seasonality refers to fluctuations in output and sales related to the seasonal of the year. Obvious examples of products with highly seasonal demand include: Christmas cards, Valentine cards, Fireworks , Sun lotion, Overcoats, Swimwear, College textbooks, Holidays, Winter clothes, Summer clothes.
  • Autocorrelation denotes the persistence of occurrence. More specifically, the value expected at any point is highly correlated with its own past values. In waiting line theory, the length of a waiting line is highly autocorrelated. That is, if a line is relatively long at one time, then shortly after that time, we would expect the line still to be long. When demand is random, it may vary widely from one week to another. Where high autocorrelation exists, demand is not expected to change very much from one week to the next.
  • Cyclical factors are more difficult to determine because the time span may be unknown or the cause of the cycle may not be considered. Cyclical influence on demand may come from such occurrences as political elections, war, economic conditions, or sociological pressures.
  •  Random variations are caused by chance events. Statistically, when all the known causes for demand (average, trend, seasonal, cyclical, and autocorrelative) are subtracted from total demand, what remains is the unexplained portion of demand. If we cannot identify the cause of this remainder, it is assumed to be purely random chance.

REFERENCES

Robert Jacobs and Richard B. Chase, Operation and Supply Chain Management: The core, Chapter11: Demand Management and Forecasting, Page  304-353, McGraw-Hill, 2e, 2010.

Người đăng: Huỳnh Linh Lan

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