Description
It explains qualitative and quantitative approaches to forecasting. It explains methods like time series method, liner regression, moving average and exponential method of forecasting.
Demand forecasting
Purpose of forecasting
• • • • • • Production scheduling Reducing cost of purchasing raw materials Determining suitable price policy Setting sales targets Planning advertising and promotion Forecasting financial requirements
Long term purposes
• Planning expansion of new unit • Planning long term financial requirements • Planning man power requirements
Forecasting Methods
• Qualitative Approaches • Quantitative Approaches
Qualitative Approaches
• Usually based on judgments about causal factors that underlie the demand of particular products or services • Do not require a demand history for the product or service, therefore are useful for new products/services • Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future events • The approach/method that is appropriate depends on a product’s life cycle stage
Qualitative Methods
• • • • • • • Educated guess intuitive hunches Executive committee consensus Delphi method Survey of sales force Survey of customers Historical analogy Market research scientifically conducted surveys
Qualitative Forecasting Applications
Small and Large Firms
Technique
Manager’s Opinion
Low Sales
(less than $100M)
High Sales
(more than $500M)
40.7% 40.7% 29.6% 27
39.6% 41.6% 35.4% 48
Executive’s Opinion
Sales Force Composite Number of Firms
Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp. 92-100. Note: More than one response was permitted.
Quantitative Forecasting Approaches
• Based on the assumption that the “forces” that generated the past demand will generate the future demand, i.e., history will tend to repeat itself • Analysis of the past demand pattern provides a good basis for forecasting future demand • Majority of quantitative approaches fall in the category of time series analysis
Quantitative Forecasting Applications
Small and Large Firms
Technique
Moving Average
Simple Linear Regression Naive Single Exponential Smoothing Multiple Regression
Low Sales
(less than $100M)
High Sales
(more than $500M)
29.6%
14.8% 18.5% 14.8% 22.2%
29.2
14.6 14.6 20.8 27.1
Simulation
Classical Decomposition Box-Jenkins Number of Firms
3.7%
3.7% 3.7% 27
10.4
8.3 6.3 48
Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp. 92-100. Note: More than one response was permitted.
Time Series Analysis
• A time series is a set of numbers where the order or sequence of the numbers is important, e.g., historical demand • Analysis of the time series identifies patterns • Once the patterns are identified, they can be used to develop a forecast
Components of Time Series
• Trends are noted by an upward or downward sloping line • Seasonality is a data pattern that repeats itself over the period of one year or less • Cycle is a data pattern that repeats itself... may take years • Irregular variations are jumps in the level of the series due to extraordinary events • Random fluctuation from random variation or unexplained causes
Quantitative Forecasting Approaches
• • • • Linear Regression Simple Moving Average Weighted Moving Average Exponential Smoothing (exponentially weighted moving average) • Exponential Smoothing with Trend (double exponential smoothing)
Long-Range Forecasts
• Time spans usually greater than one year • Necessary to support strategic decisions about planning products, processes, and facilities
Simple Linear Regression
• Linear regression analysis establishes a relationship between a dependent variable and one or more independent variables. • In simple linear regression analysis there is only one independent variable. • If the data is a time series, the independent variable is the time period. • The dependent variable is whatever we wish to forecast.
Simple Linear Regression
• Regression Equation This model is of the form: Y = a + bX
Y = dependent variable X = independent variable a = y-axis intercept b = slope of regression line
Simple Linear Regression
• Constants a and b The constants a and b are computed using the following equations: 2
a=
? x ? y-? x? xy n ? x -( ? x)
2 2
b=
n? xy- ? x? y n ? x 2 -( ? x)2
Simple Linear Regression
• Once the a and b values are computed, a future value of X can be entered into the regression equation and a corresponding value of Y (the forecast) can be calculated.
Simple Linear Regression
• Simple linear regression can also be used when the independent variable X represents a variable other than time. • In this case, linear regression is representative of a class of forecasting models called causal forecasting models.
Coefficient of Correlation (r)
• The coefficient of correlation, r, explains the relative importance of the relationship between x and y. • The sign of r shows the direction of the relationship. • The absolute value of r shows the strength of the relationship. • The sign of r is always the same as the sign of b. • r can take on any value between –1 and +1.
Coefficient of Correlation (r)
• Meanings of several values of r: -1 a perfect negative relationship (as x goes up, y goes down by one unit, and vice versa) +1 a perfect positive relationship (as x goes up, y goes up by one unit, and vice versa) 0 no relationship exists between x and y +0.3 a weak positive relationship -0.8 a strong negative relationship
Coefficient of Correlation (r)
• r is computed by:
r? ? n ? x 2 ? ( ? x )2 ? ? n ? y 2 ? ( ? y )2 ? ? ?? ? n? xy ? ? x ? y
Coefficient of Determination (r2)
• The coefficient of determination, r2, is the square of the coefficient of correlation. • The modification of r to r2 allows us to shift from subjective measures of relationship to a more specific measure. • r2 is determined by the ratio of explained variation to total variation:
Ranging Forecasts
• Forecasts for future periods are only estimates and are subject to error. • One way to deal with uncertainty is to develop best-estimate forecasts and the ranges within which the actual data are likely to fall. • The ranges of a forecast are defined by the upper and lower limits of a confidence interval.
Seasonalized Time Series Regression Analysis
• Select a representative historical data set. • Develop a seasonal index for each season. • Use the seasonal indexes to deseasonalize the data. • Perform linear regression analysis on the deseasonalized data. • Use the regression equation to compute the forecasts. • Use the seasonal indexes to reapply the seasonal patterns to the forecasts.
Short-Range Forecasts
• Time spans ranging from a few days to a few weeks • Cycles, seasonality, and trend may have little effect • Random fluctuation is main data component
Evaluating Forecast-Model Performance
Short-range forecasting models are evaluated on the basis of three characteristics: •Impulse response •Noise dampening •accuracy
– Impulse response – Noise-dampening ability – Accuracy
Monitoring Accuracy
• Accuracy of a forecasting approach needs to be monitored to assess the confidence you can have in its forecasts and changes in the market may require reevaluation of the approach • Accuracy can be measured in several ways
– Standard error of the forecast (covered earlier) – Mean absolute deviation (MAD) – Mean squared error (MSE)
Short-Range Forecasting Methods
• • • • (Simple) Moving Average Weighted Moving Average Exponential Smoothing Exponential Smoothing with Trend
Simple Moving Average
• An averaging period (AP) is given or selected • The forecast for the next period is the arithmetic average of the AP most recent actual demands • It is called a “simple” average because each period used to compute the average is equally weighted
Simple Moving Average
• It is called “moving” because as new demand data becomes available, the oldest data is not used • By increasing the AP, the forecast is less responsive to fluctuations in demand (low impulse response and high noise dampening) • By decreasing the AP, the forecast is more responsive to fluctuations in demand (high impulse response and low noise dampening)
Weighted Moving Average
• This is a variation on the simple moving average where the weights used to compute the average are not equal. • This allows more recent demand data to have a greater effect on the moving average, therefore the forecast. • . . . more
Weighted Moving Average
• The weights must add to 1.0 and generally decrease in value with the age of the data. • The distribution of the weights determine the impulse response of the forecast.
Exponential Smoothing
• The weights used to compute the forecast (moving average) are exponentially distributed. • The forecast is the sum of the old forecast and a portion (a) of the forecast error (A t-1 - Ft-1). Ft = Ft-1 + a(A t-1 - Ft-1) • . . . more
Exponential Smoothing
• The smoothing constant, a, must be between 0.0 and 1.0. • A large a provides a high impulse response forecast. • A small a provides a low impulse response forecast.
Exponential Smoothing with Trend
• As we move toward medium-range forecasts, trend becomes more important. • Incorporating a trend component into exponentially smoothed forecasts is called double exponential smoothing. • The estimate for the average and the estimate for the trend are both smoothed.
Criteria for Selecting a Forecasting Method
• • • • • • Cost Accuracy Data available Time span Nature of products and services Impulse response and noise dampening
Reasons for Ineffective Forecasting
• Not involving a broad cross section of people • Not recognizing that forecasting is integral to business planning • Not recognizing that forecasts will always be wrong • Not forecasting the right things • Not selecting an appropriate forecasting method • Not tracking the accuracy of the forecasting models
Computer Software for Forecasting
• Examples of computer software with forecasting capabilities
– Forecast Pro – Autobox – SmartForecasts for Windows – SAS – SPSS – SAP – POM Software Library
Primarily for forecasting Have Forecasting modules
Some Specific Forecasting Data
• • • • • • • • • Consumer Confidence Index Consumer Price Index (CPI) Gross Domestic Product (GDP) Housing Starts Index of Leading Economic Indicators Personal Income and Consumption Producer Price Index (PPI) Purchasing Manager’s Index Retail Sales
doc_633554882.pptx
It explains qualitative and quantitative approaches to forecasting. It explains methods like time series method, liner regression, moving average and exponential method of forecasting.
Demand forecasting
Purpose of forecasting
• • • • • • Production scheduling Reducing cost of purchasing raw materials Determining suitable price policy Setting sales targets Planning advertising and promotion Forecasting financial requirements
Long term purposes
• Planning expansion of new unit • Planning long term financial requirements • Planning man power requirements
Forecasting Methods
• Qualitative Approaches • Quantitative Approaches
Qualitative Approaches
• Usually based on judgments about causal factors that underlie the demand of particular products or services • Do not require a demand history for the product or service, therefore are useful for new products/services • Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches about future events • The approach/method that is appropriate depends on a product’s life cycle stage
Qualitative Methods
• • • • • • • Educated guess intuitive hunches Executive committee consensus Delphi method Survey of sales force Survey of customers Historical analogy Market research scientifically conducted surveys
Qualitative Forecasting Applications
Small and Large Firms
Technique
Manager’s Opinion
Low Sales
(less than $100M)
High Sales
(more than $500M)
40.7% 40.7% 29.6% 27
39.6% 41.6% 35.4% 48
Executive’s Opinion
Sales Force Composite Number of Firms
Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp. 92-100. Note: More than one response was permitted.
Quantitative Forecasting Approaches
• Based on the assumption that the “forces” that generated the past demand will generate the future demand, i.e., history will tend to repeat itself • Analysis of the past demand pattern provides a good basis for forecasting future demand • Majority of quantitative approaches fall in the category of time series analysis
Quantitative Forecasting Applications
Small and Large Firms
Technique
Moving Average
Simple Linear Regression Naive Single Exponential Smoothing Multiple Regression
Low Sales
(less than $100M)
High Sales
(more than $500M)
29.6%
14.8% 18.5% 14.8% 22.2%
29.2
14.6 14.6 20.8 27.1
Simulation
Classical Decomposition Box-Jenkins Number of Firms
3.7%
3.7% 3.7% 27
10.4
8.3 6.3 48
Source: Nada Sanders and Karl Mandrodt (1994) “Practitioners Continue to Rely on Judgmental Forecasting Methods Instead of Quantitative Methods,” Interfaces, vol. 24, no. 2, pp. 92-100. Note: More than one response was permitted.
Time Series Analysis
• A time series is a set of numbers where the order or sequence of the numbers is important, e.g., historical demand • Analysis of the time series identifies patterns • Once the patterns are identified, they can be used to develop a forecast
Components of Time Series
• Trends are noted by an upward or downward sloping line • Seasonality is a data pattern that repeats itself over the period of one year or less • Cycle is a data pattern that repeats itself... may take years • Irregular variations are jumps in the level of the series due to extraordinary events • Random fluctuation from random variation or unexplained causes
Quantitative Forecasting Approaches
• • • • Linear Regression Simple Moving Average Weighted Moving Average Exponential Smoothing (exponentially weighted moving average) • Exponential Smoothing with Trend (double exponential smoothing)
Long-Range Forecasts
• Time spans usually greater than one year • Necessary to support strategic decisions about planning products, processes, and facilities
Simple Linear Regression
• Linear regression analysis establishes a relationship between a dependent variable and one or more independent variables. • In simple linear regression analysis there is only one independent variable. • If the data is a time series, the independent variable is the time period. • The dependent variable is whatever we wish to forecast.
Simple Linear Regression
• Regression Equation This model is of the form: Y = a + bX
Y = dependent variable X = independent variable a = y-axis intercept b = slope of regression line
Simple Linear Regression
• Constants a and b The constants a and b are computed using the following equations: 2
a=
? x ? y-? x? xy n ? x -( ? x)
2 2
b=
n? xy- ? x? y n ? x 2 -( ? x)2
Simple Linear Regression
• Once the a and b values are computed, a future value of X can be entered into the regression equation and a corresponding value of Y (the forecast) can be calculated.
Simple Linear Regression
• Simple linear regression can also be used when the independent variable X represents a variable other than time. • In this case, linear regression is representative of a class of forecasting models called causal forecasting models.
Coefficient of Correlation (r)
• The coefficient of correlation, r, explains the relative importance of the relationship between x and y. • The sign of r shows the direction of the relationship. • The absolute value of r shows the strength of the relationship. • The sign of r is always the same as the sign of b. • r can take on any value between –1 and +1.
Coefficient of Correlation (r)
• Meanings of several values of r: -1 a perfect negative relationship (as x goes up, y goes down by one unit, and vice versa) +1 a perfect positive relationship (as x goes up, y goes up by one unit, and vice versa) 0 no relationship exists between x and y +0.3 a weak positive relationship -0.8 a strong negative relationship
Coefficient of Correlation (r)
• r is computed by:
r? ? n ? x 2 ? ( ? x )2 ? ? n ? y 2 ? ( ? y )2 ? ? ?? ? n? xy ? ? x ? y
Coefficient of Determination (r2)
• The coefficient of determination, r2, is the square of the coefficient of correlation. • The modification of r to r2 allows us to shift from subjective measures of relationship to a more specific measure. • r2 is determined by the ratio of explained variation to total variation:
Ranging Forecasts
• Forecasts for future periods are only estimates and are subject to error. • One way to deal with uncertainty is to develop best-estimate forecasts and the ranges within which the actual data are likely to fall. • The ranges of a forecast are defined by the upper and lower limits of a confidence interval.
Seasonalized Time Series Regression Analysis
• Select a representative historical data set. • Develop a seasonal index for each season. • Use the seasonal indexes to deseasonalize the data. • Perform linear regression analysis on the deseasonalized data. • Use the regression equation to compute the forecasts. • Use the seasonal indexes to reapply the seasonal patterns to the forecasts.
Short-Range Forecasts
• Time spans ranging from a few days to a few weeks • Cycles, seasonality, and trend may have little effect • Random fluctuation is main data component
Evaluating Forecast-Model Performance
Short-range forecasting models are evaluated on the basis of three characteristics: •Impulse response •Noise dampening •accuracy
– Impulse response – Noise-dampening ability – Accuracy
Monitoring Accuracy
• Accuracy of a forecasting approach needs to be monitored to assess the confidence you can have in its forecasts and changes in the market may require reevaluation of the approach • Accuracy can be measured in several ways
– Standard error of the forecast (covered earlier) – Mean absolute deviation (MAD) – Mean squared error (MSE)
Short-Range Forecasting Methods
• • • • (Simple) Moving Average Weighted Moving Average Exponential Smoothing Exponential Smoothing with Trend
Simple Moving Average
• An averaging period (AP) is given or selected • The forecast for the next period is the arithmetic average of the AP most recent actual demands • It is called a “simple” average because each period used to compute the average is equally weighted
Simple Moving Average
• It is called “moving” because as new demand data becomes available, the oldest data is not used • By increasing the AP, the forecast is less responsive to fluctuations in demand (low impulse response and high noise dampening) • By decreasing the AP, the forecast is more responsive to fluctuations in demand (high impulse response and low noise dampening)
Weighted Moving Average
• This is a variation on the simple moving average where the weights used to compute the average are not equal. • This allows more recent demand data to have a greater effect on the moving average, therefore the forecast. • . . . more
Weighted Moving Average
• The weights must add to 1.0 and generally decrease in value with the age of the data. • The distribution of the weights determine the impulse response of the forecast.
Exponential Smoothing
• The weights used to compute the forecast (moving average) are exponentially distributed. • The forecast is the sum of the old forecast and a portion (a) of the forecast error (A t-1 - Ft-1). Ft = Ft-1 + a(A t-1 - Ft-1) • . . . more
Exponential Smoothing
• The smoothing constant, a, must be between 0.0 and 1.0. • A large a provides a high impulse response forecast. • A small a provides a low impulse response forecast.
Exponential Smoothing with Trend
• As we move toward medium-range forecasts, trend becomes more important. • Incorporating a trend component into exponentially smoothed forecasts is called double exponential smoothing. • The estimate for the average and the estimate for the trend are both smoothed.
Criteria for Selecting a Forecasting Method
• • • • • • Cost Accuracy Data available Time span Nature of products and services Impulse response and noise dampening
Reasons for Ineffective Forecasting
• Not involving a broad cross section of people • Not recognizing that forecasting is integral to business planning • Not recognizing that forecasts will always be wrong • Not forecasting the right things • Not selecting an appropriate forecasting method • Not tracking the accuracy of the forecasting models
Computer Software for Forecasting
• Examples of computer software with forecasting capabilities
– Forecast Pro – Autobox – SmartForecasts for Windows – SAS – SPSS – SAP – POM Software Library
Primarily for forecasting Have Forecasting modules
Some Specific Forecasting Data
• • • • • • • • • Consumer Confidence Index Consumer Price Index (CPI) Gross Domestic Product (GDP) Housing Starts Index of Leading Economic Indicators Personal Income and Consumption Producer Price Index (PPI) Purchasing Manager’s Index Retail Sales
doc_633554882.pptx