Production & Operations Management

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Forecast can help managers by reducing some of the uncertainty, thereby enabling them to develop more meaningful plans than they might otherwise.

A forecast is a statement about the future.

Features common to all forecasts
1.The same underlying causal system that existed in the past will continue to exist in the future. 2.Forecasts are rarely perfect; actual results usually differ from predicted values. 3.Forecasts for groups of items tend to be more accurate than forecasts for individual items. Forecast accuracy decreases as the time period covered by the forecast-the time horizon-increases.

Steps in the Forecasting Process
There are five basic steps in the forecasting process:
1.Determine the purpose of the forecast and when it will be needed. This will provide an indication of the level of detail required in the forecast, the amount of resources (manpower, computer time, dollars) that can be justified, and the level of accuracy necessary. 2.Establish a time horizon that the forecast must cover, keeping in mind that accuracy decreases as the time horizon increases. 3.Select a forecasting technique.

4.Gather and analyze the appropriate data, and then prepare the forecast. Identify any assumptions that are made in conjunction with preparing and using the forecast. 5.Monitor the forecast to see if it is performing in a satisfactory manner. If it is not, reexamine the method, assumptions, validity of data, and so on; modify as needed; and prepare a revised forecast.

There are two general approaches to forecasting:

Judgmental methods:
Consumer surveysQuestioning consumers on future plans.
Sales force composites Joint estimates obtained from salespeople. Executive opinionFinance, marketing, and manufacturing managers join to prepare forecast. Delphi technique Series of questionnaires answered anonymously by managers and staff; successive questionnaires are based on information obtained from previous surveys. COMPONENTS OF TIME SERIES DEMAND

1. trend: a gradual increase or decrease in the average over time 2. seasonal influence: predictable short-term cycling behaviour due to time of day, week, month, season, year, etc. 3. cyclical movement: unpredictable long-term cycling behaviour due to business cycle or product/service life cycle 4. random error: remaining variation that cannot be explained by the other four components

Cannot fully explain or predict behavior of people.
Time series:
Naive Next value in a series will equal the previous value. Moving averages Forecast is based on an average of recent values. Exponential smoothing Sophisticated form of averaging.
Associative models:
Simple regression Values of one variable are used to predict values of another variable. Multiple regressionTwo or more variables are used to predict values of another variable.

Moving Averages
A moving average forecast uses a number of the most recent actual data values in generating a forecast. The moving average forecast can be computed using the following equation:
MAn =
Wherei = „Age“ of the data (i = 1,2,3 …)
n Number of periods in the moving average
Ai Actual value with age i

Exponential Smoothing:
Exponential Smoothing is a sophisticated weighted averaging method that is still relatively easy to use and understand. Each new forecast is based on the previous forecast plus a percentage of the difference between that forecast and the actual value of the series at that point. That is:

New forecast = Old forecast + * (Actual – Old forecast)

Where α is a percentage and (Actual – Old forecast) represents the forecast error. More concisely,
Ft = Ft-1 + * ( At-1 - Ft-1 )
Ft = Forecast for period t
Ft-1 = Forecast for period t - 1
α = Smoothing constant
At-1 = Actual demand or sales for period t-1

Trend Equation:
A linear trend equation has the form...
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