Forecast: A prediction, projection, or estimate of some future activity, event, or occurrence.
Types of Forecasts
Predict a variety of economic indicators, like money supply, inflation rates, interest rates, etc. *
Predict rates of technological progress and innovation. *
Predict the future demand for a company’s products or services.
Since virtually all the operations management decisions (in both the strategic category and the tactical category) require as input a good estimate of future demand, this is the type of forecasting that is emphasized in our textbook and in this course.
TYPES OF FORECASTING METHODS
Qualitative methods: These types of forecasting methods are based on judgments, opinions, intuition, emotions, or personal experiences and are subjective in nature. They do not rely on any rigorous mathematical computations.
Quantitative methods: These types of forecasting methods are based on mathematical (quantitative) models, and are objective in nature. They rely heavily on mathematical computations.
QUALITATIVE FORECASTING METHODS
Approach in which consensus agreement is reached among a group of experts
Sales Force Composite
Approach in which each salesperson estimates sales in his or her region Executive
Approach in which a group of managers meet and collectively develop a forecast Market
Approach that uses interviews and surveys to judge preferences of customer and to assess demand
QUANTITATIVE FORECASTING METHODS
Time series models look at past patterns of data and attempt to predict the future based upon the underlying patterns contained within those data. Associative Models
Associative models (often called causal models) assume that the variable being forecasted is related to other variables in the environment. They try to project based upon those associations.
TIME SERIES MODELS
Uses last period’s actual value as a forecast
Simple Mean (Average)
Uses an average of all past data as a forecast
Simple Moving Average
Uses an average of a specified number of the most recent observations, with each observation receiving the same emphasis (weight)
Weighted Moving Average
Uses an average of a specified number of the most recent observations, with each observation receiving a different emphasis (weight)
A weighted average procedure with weights declining exponentially as data become older
Technique that uses the least squares method to fit a straight line to the data
A mechanism for adjusting the forecast to accommodate any seasonal patterns inherent in the data
DECOMPOSITION OF A TIME SERIES
Patterns that may be present in a time series
Trend: Data exhibit a steady growth or decline over time.
Seasonality: Data exhibit upward and downward swings in a short to intermediate time frame (most notably during a year).
Cycles: Data exhibit upward and downward swings in over a very long time frame.
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