Chap 3: Forecasting Flashcards
It is a statement about the future value of a variable such as demand.
forecast
It is the art and science of predicting future events.
Forecasting
Elements of Good Forecast
Should be timely
Should be accurate
Should be reliable
Should be expressed in meaningful units
Should be in writing
Should be simple to understand and use
Should be cost effective
Steps in the Forecasting Process
- Determine the purpose of the forecast.
- Establish a time horizon
- Select a forecasting technique.
- Obtain, clean, and analyze appropriate data.
- Make the forecast.
- Monitor the forecast.
APPROACHES TO FORECASTING
-QUALITATIVE APPROACHES
-QUANTITATIVE APPROACHES
OVERVIEW OF QUALITATIVE METHODS
- Jury of executive opinion.
- Delphi Method.
- Sales force composite.
- Consumer market survey.
OVERVIEW OF QUANTITATIVE METHODS
I. Time-Series Models
II. Associative Model
Decomposition of a Time Series
- Trend
- Seasonality
- Cycles
- Random variations
Random Variations includes…
a. Naïve approach
b. Moving averages
c. Weighted Moving Average
d. Exponential Smoothing
MEASURING FORECAST ERROR
-MEAN ABSOLUTE DEVIATION (MAD)
-MEAN SQUARED ERROR (MSE)
-MEAN ABSOLUTE PERCENTAGE ERROR (MAPE)
Associative Model includes…
a. Trend projection
b. Associative Forecasting Methods: Regression and Correlational Analysis
Trend Projection includes…
-SEASONAL VARIATIONS IN DATA
-CYCLICAL VARIATIONS IN DATA
Under this method, the opinions of a group of high level experts or managers, often in combination with statistical models, are pooled to arrive at a group estimate of demand.
Jury of executive opinion
Three different types of Delphi Method patrticipants
-Decision Makers
-Staff Personnel
-Respondents
usually consists of a group of 5 to 10 experts who will be making the actual
forecast
Decision makers
assist decision makers by preparing, distributing, collecting, and summarizing a series of questionnaires and survey results.
Staff personnel
are a group of people, often located in different places, whose
judgments are valued.
Respondents
In this approach, each salesperson estimates what sales will be in his or her region. These forecasts are then reviewed to ensure that they are realistic. Then they are combined at the district and national levels to reach an overall forecast.
Sales force composite
This method solicits input from customers or potential customers regarding future purchasing plans. It can help not only in preparing a forecast but also in improving product design and planning for new products.
Consumer market survey
It is based on a sequence of evenly spaced (weekly, monthly,
quarterly, and so on) data points.
Time-Series Models
It is the gradual upward or downward movement of the data over time.
Changes in income, population, age distribution, or cultural views may
account for movement in trend.
Trend
It is a data pattern that repeats itself after a period of days, weeks, months, or quarters. There are six common seasonality patterns
Seasonality
Patterns in the data that occur every several years. They are usually tied into the business cycle and
Cycles
Are “blips” in the data caused by chance and unusual situations. They follow no discernible pattern, so they cannot be predicted.
Random variations
The simplest way to forecast is to assume that demand in the next period will be equal to demand in the most recent period.
Naïve approach
Uses a number of historical actual data values to generate a forecast. It is useful if we can assume that market demands will stay fairly steady over time.
Moving averages
When a detectable trend or pattern is
present, weights can be used to place more emphasis on recent values. This practice makes forecasting techniques more responsive to changes because more recent periods may be more heavily weighted.
Weighted Moving Average –
It is a sophisticated weighted-moving average forecasting method that is still fairly easy to use. It involves very little
record keeping of past data.
Exponential Smoothing
This value is computed by taking
the sum of the absolute values of the individual forecast errors (deviations) and
dividing by the number of periods of data (n)
MEAN ABSOLUTE DEVIATION (MAD)
The average of the squared differences
between the forecasts and observed values
MEAN SQUARED ERROR (MSE)
This is computed as the average of the absolute difference between the forecasted and actual values,
expressed as percentage of the actual values.
MEAN ABSOLUTE PERCENTAGE error (MAPE)
This technique fits a trend line to a series
of historical data points and then projects the line into the future for medium to long-range forecasts.
Trend Projection
Are regular up and down movements in a time series that relate to recurring
events such as weather of holidays. Demand for coal and fuel oil, for
example, peaks during cold winter months. Demand for golf clubs or
sunscreen may be highest in summer.
SEASONAL VARIATIONS IN DATA
Are like seasonal variations in data but occur every several years, not weeks, months or quarters.
CYCLICAL VARIATIONS IN DATA
Unlike time-series forecasting, this models usually consider several variables that are related to the quantity being predicted. Once these related variables have been found, a statistical model is built and used to forecast the item of interest. T
ASSOCIATIVE FORECASTING METHODS
