Qualitative methods: These types of forecasting methods are based on judgments or opinions, and are subjective in nature. They do not rely on any mathematical computations.

Quantitative methods: These types of forecasting methods are based on quantitative models, and are objective in nature. They rely heavily on mathematical computations.

QUALITATIVE FORECASTING METHODS

Qualitative Methods

Executive OpinionMarket ResearchDelphi Method

Approach in which a group of managers meet and collectively develop a forecast.Approach that uses surveys and interviews to determine customer preferences and assess demand.Approach in which a forecast is the product of a consensus among a group of experts.

QUANTITATIVE FORECASTING METHODS

Quantitative forecasting methods can be divided into two categories: time series models and causal models.

Quantitative Methods

Time Series ModelsCausal Models

Time series models look at past patterns of data and attempt to predict the future based upon the underlying patterns contained within those data.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

ModelDescription

NaïveUses last period’s actual value as a forecast

Simple Mean (Average)Uses an average of all past data as a forecast Simple Moving AverageUses an average of a specified number of the most recent observations, with each observation receiving the same emphasis (weight) Weighted Moving AverageUses an average of a specified number of the most recent observations, with each observation receiving a different emphasis (weight) Exponential SmoothingA weighted average procedure with weights declining exponentially as data become older Trend Adjusted Exponential SmoothingAn exponential smoothing model with a mechanism for making adjustments when strong trend patterns are inherent in the data Seasonal IndexesA mechanism for adjusting the forecast to accommodate any seasonal patterns inherent in the data Linear Trend LineTechnique that uses the least squares method to fit a straight line to the data

PATTERNS THAT MAY BE PRESENT IN A TIME SERIES

Level or horizontal: Data are relatively constant over time, with no growth or decline.

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.

Random: Erratic and unpredictable variation in the data over time.

DATA SET TO DEMONSTRATE FORECASTING METHODS

The following data set represents a set of hypothetical demands that have occurred over several consecutive years. The data have been collected on a quarterly basis, and these quarterly values have been amalgamated into yearly totals.

For various illustrations that follow, we may make slightly different assumptions about starting points to get the process started for different models. In most cases we will assume that each year a forecast has been made for the subsequent year. Then, after a year has transpired we will have observed what the actual demand turned out to be (and we will surely see differences between what we had forecasted and what actually occurred, for, after all, the forecasts are merely educated guesses).

Finally, to keep the numbers at a manageable size, several zeros have been dropped off the numbers (i.e., these numbers represent demands in thousands of units).

YearQuarter 1Quarter 2Quarter 3Quarter 4Total Annual Demand 120283418100

2588610452300

340547234200

410414017482500

5116170210104600

6136198246120700

ILLUSTRATION OF THE NAÏVE METHOD

Naïve method: The forecast for next period (period t+1) will be...