TYPES OF FORECASTING METHODS
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 Opinion
Market Research
Delphi 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 Models
Causal 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
Model
Description
Naïve
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) Exponential Smoothing
A weighted average procedure with weights declining exponentially as data become older Trend Adjusted Exponential Smoothing
An exponential smoothing model with a mechanism for making adjustments when strong trend patterns are inherent in the data Seasonal Indexes
A mechanism for adjusting the forecast to accommodate any seasonal patterns inherent in the data Linear Trend Line
Technique 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).
Year
Quarter 1
Quarter 2
Quarter 3
Quarter 4
Total Annual Demand 1
20
28
34
18
100
2
58
86
104
52
300
3
40
54
72
34
200
4
104
140
174
82
500
5
116
170
210
104
600
6
136
198
246
120
700
ILLUSTRATION OF THE NAÏVE METHOD
Naïve method: The forecast for next period (period t+1) will be...
Topic: Forecasting, Moving average, Exponential smoothing
Pages: 18 (3899 words)