1. Department of Business Administration
FALL
Demand Forecasting by
Asst. Prof. Sami Fethi

2. What is meant by Forecasting and Why?
Forecasting is the process of estimating a variable, such as the sale of the firm at some future date.
Forecasting is important to business firm, government, and non-profit organization as a method of reducing the risk and uncertainty inherent in most managerial decisions.
A firm must decide how much of each product to produce, what price to charge, and how much to spend on advertising, and planning for the growth of the firm.

3. The aim of forecasting
The aim of forecasting is to reduce the risk or uncertainty that the firm faces in its short-term operational decision making and in planning for its long term growth.
Forecasting the demand and sales of the firm’s product usually begins with macroeconomic forecast of general level of economic activity for the economy as a whole or GNP.

4. The aim of forecasting
The firm uses the macro-forecasts of general economic activity as inputs for their micro-forecasts of the industry’s and firm’s demand and sales.
The firm’s demand and sales are usually forecasted on the basis of its historical market share and its planned marketing strategy (i.e., forecasting by product line and region).
The firm uses long-term forecasts for the economy and the industry to forecast expenditure on plant and equipment to meet its long-term growth plan and strategy.

5. Forecasting Process Map
Demand
History
Causal
Factors
Statistical Model
Marketing
Sales
Product
Management
& Finance
Executive
Production &
Inventory
Control
Consensus
Process
Consensus
Forecast

6. Forecasting Business Planning
Method(s)
Demand
Estimates
Sales
Management
Team
Inputs:
Market,
Economic,
Other
Business
Strategy
Production Resource
Forecasts

7. Forecasting Techniques
A wide variety of forecasting methods are available to management. These range from the most naïve methods that require little effort to highly complex approaches that are very costly in terms of time and effort such as econometric systems of simultaneous equations.
Mainly these techniques can break down into two parts: qualitative approaches and quantitative approaches.

8. Qualitative approaches & Quantitative approaches

9. Qualitative Forecasts
Survey Techniques
Some of the best-know surveys
Planned Plant and Equipment Spending
Expected Sales and Inventory Changes
Consumers’ Expenditure Plans
Opinion Polls
Business Executives
Sales Force
Consumer Intentions

10. What are qualitative forecast ?
Qualitative forecast estimate variables at some future date using the results of surveys and opinion polls of business and consumer spending intentions.
The rational is that many economic decisions are made well in advance of actual expenditures.
For example, businesses usually plan to add to plant and equipment long before expenditures are actually incurred.

11. Qualitative Forecasts
Surveys and opinion pools are often used to make short-term forecasts when quantitative data are not available
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

12. Qualitative Forecasts
Polls can also be very useful in supplementing quantitative forecasts, anticipating changes in consumer tastes or business expectations about future economic conditions, and forecasting the demand for a new product.
Firms conduct opinion polls for economic activities based on the results of published surveys of expenditure plans of businesses, consumers and governments.

13. Qualitative Forecasts Techniques
Survey Techniques– The rationale for forecasting based on surveys of economic intentions is that many economic decisions are made in advance of actual expenditures (Ex: Consumer’s decisions to purchase houses, automobiles, TV sets, furniture, vocation, education etc. are made months or years in advance of actual purchases)

14. Qualitative Forecasts Techniques
Opinion Polls– The firm’s sales are strongly dependent on the level of economic activity and sales for the industry as a whole, but also on the policies adopted by the firm. The firm can forecast its sales by pooling experts within and outside the firm.

15. Opinion Polls
Executive Polling- Firm can poll its top management from its sales, production, finance for the firm during the next quarter or year.
Bandwagon effect (opinions of some experts might be overshadowed by some dominant personality in their midst).
Delphi Method – experts are polled separately, and then feedback is provided without identifying the expert responsible for a particular opinion.

16. Opinion Polls
Consumers intentions polling- Firms selling automobiles, furniture, etc. can pool a sample of potential buyers on their purchasing intentions. By using results of the poll a firm can forecast its sales for different levels of consumer’s future income.

17. Opinion Polls
Sales force polling – Forecast of the firm’s sales in each region and for each product line, it is based on the opinion of the firm’s sales force in the field (people working closer to the market and their opinion about future sales can provide essential information to top management).

18. 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.

19. Time Series Analysis
A time series (naive forecasting) is a set of numbers where the order or sequence of the numbers is important, i.e., historical demand
Attempts to forecasts future values of the time series by examining past observations of the data only. The assumption is that the time series will continue to move as in the past
Analysis of the time series identifies patterns
Once the patterns are identified, they can be used to develop a forecast

20. Forecast Horizon Short term Medium term Long term Up to a year
One to five years
Long term
More than five years

21. Reasons for Fluctuations in Time Series Data
Secular Trend are noted by an upward or downward sloping line.
Cycle fluctuations is a data pattern that may cover several years before it repeats itself.
Seasonality is a data pattern that repeats itself over the period of one year or less.
Random influences (noise) results from random variation or unexplained causes.

22. Cyclical Fluctuations
Time-Series Analysis
Secular Trend
Long-Run Increase or Decrease in Data
Cyclical Fluctuations
Long-Run Cycles of Expansion and Contraction
Seasonal Variation
Regularly Occurring Fluctuations
Irregular or Random Influences

25. Trend Projection
The simplest form of time series is projecting the past trend by fitting a straight line to the data either visually or more precisely by regression analysis.

26. Trend Projection- 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.

27. Linear Trend: St = S0 + b t b = Growth per time period
Trend Projection
Linear Trend: St = S0 + b t b = Growth per time period
Constant Growth Rate St = S0 (1 + g)t g = Growth rate
Estimation of Growth Rate ln St = ln S0 + t ln (1 + g)

28. Trend Projection- Simple Linear Regression
Regression Equation
This model is of the form:
Y = a + bX
Y = dependent variable (the value of time series to be forecasted for period t)
X = independent variable ( time period in which the time series is to be forecasted)
a = y-axis intercept (estimated value of the time series, the constant of the regression)
b = slope of regression line (absolute amount of growth per period)

29. Trend Projection- Simple Linear Regression
Constants a and b
The constants a and b are computed using the equations given:
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.

30. Example for Trend Projection College Enrollment
Simple Linear Regression
At a small regional college enrollments have grown steadily over the past six years, as evidenced below. Use time series regression to forecast the student enrollments for the next three years.
Students Students
Year Enrolled (1000s) Year Enrolled (1000s)

31. Example for Trend Projection College Enrollment

32. Example for Trend Projection College Enrollment
Simple Linear Regression
Y7 = (7) = 3.65 or 3,650 students
Y8 = (8) = 3.83 or 3,830 students
Y9 = (9) = 4.01 or 4,010 students
Note: Enrollment is expected to increase by 180
students per year.

33. Example for Trend Projection- Electricity sales
Suppose we have the data show electricity sales in a city between and The data are shown in the following table. Use time series regression to forecast the electricity consumption (mn kilowatt) for the next four quarters.
Do not forget to use the formulae a and b

34. Formulae a and b

35. Example for Trend Projection

36. Example for Trend Projection
Y = X
Y17 = (17) = in the first quarter of 2001
Y18 = (18) = in the second quarter of 2001
Y19 = (19) = in the third quarter of 2001
Y20 = (20) = in the fourth quarter of 2001
Note: Electricity sales are expected to increase by mn kilowatt-hours per quarter.

37. Example for Trend Projection using St = S0 (1 + g)t
Running the regression above in the form of logarithms: ln St = ln S0 + t ln (1 + g) to construct the equation which has coefficients a and b.
Antilog of 2.49 is and Antilog of is
St = 12.06(1.026)t

38. Example for Trend Projection using St = S0 (1 + g)t
S17= 12.06(1.026)17 = in the first quarter of 2001
S18= 12.06(1.026)18 = in the second quarter of 2001
S19= 12.06(1.026)19 = in the third quarter of 2001
S20= 12.06(1.026)20= in the fourth quarter of 2001
These forecasts are similar to those obtained by fitting a linear trend

39. Seasonal Variation

40. Average of Ratios for Each Seasonal Period
Seasonal Variation
Ratio to Trend Method
Actual
Trend Forecast
Ratio =
Seasonal Adjustment =
Average of Ratios for Each Seasonal Period
Adjusted Forecast
Trend Forecast
Seasonal Adjustment =

41. Ratio to Trend Method: Example Calculation for Quarter 1
Seasonal Variation
Ratio to Trend Method: Example Calculation for Quarter 1
Trend Forecast for = (0.394)(17) = 18.60
Seasonally Adjusted Forecast for = (18.60)(0.887) = 16.50

42. Seasonal Variation
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 seas. indexes to reapply the seasonal patterns to the forecasts.

43. Example: Computer Products Corp.
Seasonalized Times Series Regression Analysis
An analyst at CPC wants to develop next year’s quarterly forecasts of sales revenue for CPC’s line of Epsilon Computers. The analyst believes that the most recent 8 quarters of sales (shown on the next slide) are representative of next year’s sales.

44. Example: Computer Products Corp.

45. Example: Computer Products Corp.

46. Example: Computer Products Corp.

47. Example: Computer Products Corp.

48. Example: Computer Products Corp.

49. Example: Computer Products Corp.

50. Smoothing Techniques
Smoothing techniques are useful when the time series exhibit little trend or seasonal variations.
(Simple) Moving Average
Weighted Moving Average
Exponential Smoothing
Exponential Smoothing with Trend

51. 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
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)

52. Moving Average Forecasts-Formula
Forecast is the average of data from w periods prior to the forecast data point.
It is called “moving” because as new demand data becomes available, the oldest data is not used

53. Example: Central Call Center
Moving Average
CCC wishes to forecast the number of incoming calls it receives in a day from the customers of one of its clients, BMI. CCC schedules the appropriate number of telephone operators based on projected call volumes.
CCC believes that the most recent 12 days of call volumes (shown on the next slide) are representative of the near future call volumes.

54. Example: Central Call Center

55. Example: Central Call Center
Moving Average
Use the moving average method with an AP = 3
days to develop a forecast of the call volume in Day 13
F13 = ( )/3 = calls

56. Evaluating Forecast-Model Performance
Accuracy
Accuracy is the typical criterion for judging the performance of a forecasting approach
Accuracy is how well the forecasted values match the actual values
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
Mean absolute deviation (MAD)
Mean squared error (MSE)

57. Monitoring Accuracy

58. Example: Central Call Center

59. Measures the Accuracy of a Forecasting Method
Monitoring Accuracy
Measures the Accuracy of a Forecasting Method

60. 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.
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.

61. Example: Central Call Center
Weighted Moving Average
Use the weighted moving average method with an AP = 3 days and weights of .1 (for oldest datum), .3, and .6 to develop a forecast of the call volume in Day 13.
F13 = .1(168) + .3(198) + .6(159) = calls
Note: The WMA forecast is lower than the MA forecast because Day 13’s relatively low call volume carries almost twice as much weight in the WMA (.60) as it does in the MA (.33).

62. Exponential Smoothing Forecasts
Forecast is the weighted average of the forecast and the actual value from the prior period.

63. Exponential Smoothing Forecasts
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).
The smoothing constant, , must be between 0.0 and 1.0.
A large  provides a high impulse response forecast.
A small  provides a low impulse response forecast.

64. Example: Central Call Center
Exponential Smoothing
If a smoothing constant value of .25 is used and the exponential smoothing forecast for Day 11 was calls, what is the exponential smoothing forecast for Day 13?
F12 = (198 – ) =
F13 = (159 – ) =

65. Example: Central Call Center
Forecast Accuracy - MAD
Which forecasting method (the AP = 3 moving average or the a = .25 exponential smoothing) is preferred, based on the MAD over the most recent 9 days? (Assume that the exponential smoothing forecast for Day 3 is the same as the actual call volume.)

66. Example: Central Call Center

67. Example-For Smoothing Techniques Electricity sales data from 2000
Example-For Smoothing Techniques Electricity sales data from to (t=12)

68. Example For Smoothing Techniques
RMSE for 3-qma=2.95
Sqroot of 78.33/9=2.95
RMSE for 5-qma=2.99
Sqroot of 62.48/7=2.98
Thus three-quarter moving average forecast is marginally better than the corresponding five- moving average forecast.

69. Example-Exponential Smoothing
(20+22)/2=21=F1
Example-Exponential Smoothing
F2= 0.3 (20)+(1-0.3) 21=20.7 with w=0.3
F2= 0.5 (20)+(1-0.5) 21=20.5 with w=0.5

70. Example-Exponential Smoothing
F2= 0.3 (20)+(1-0.3) 21=20.7 with w=0.3
F2= 0.5 (20)+(1-0.5) 21=20.5 with w=0.5
RMSE with w=0.3 is 2.70
RMSE with w=0.5 is 2.91
Both exponential forecasts are better than the previous techniques in terms of average values.

71. Barometric Methods
National Bureau of Economic Research
Department of Commerce
Leading Indicators
Lagging Indicators
Coincident Indicators
Composite Index
Diffusion Index

72. Barometric Methods
As conducted today, is primarily the result of the work conducted at the National Bureau of Economic Research (NBER) and the Conference Board.
Leading economic indicators – is used to forecast an increase in general business activity, and vice versa. (Ex: an increase in building permits can be used to forecast an increase in housing construction)
When some time series move in step or coincide with movements in general economic activity are called coincident indicators
Indicators which follow or lag movements in economic activity and are called lagging indicators

73. Economic Indicators

74. Leading indicators (10 series)
Average weekly hours, manufacturing
Initial claims for unemployment insurance, thousands
Manufacturers’ new orders, consumer goods and materials
Vendor performance, slower deliveries diffusion index
Manufacturers’ new orders, nondefense capital goods
Building permits, new private housing units
Stock prices, 500 common stocks
Money supply, M2
Interest rate spread, 10-year Treasury bonds less federal funds
Index of consumer expectations

75. Coincident indicators (4 series)
Employees on nonagricultural payrolls
Personal income less transfer payments
Industrial production
Manufacturing and trade sales
Lagging indicators (7 series)
Average duration of unemployment, weeks
Ratio, manufacturing and trade inventories to sales
Change in labor cost per unit of output, manufacturing
Average prime rate charged by banks
Commercial and industrial loans outstanding
Ratio, consumer installment credit to personal income
Change in consumer price index for services

76. Econometric Models
The characteristic that distinguishes econometric model from other forecasting methods is that they seek to identify and measure the relative importance (elasticity) of the various determinants of demand or other economic variables to be forecasted.
Econometric forecasting frequently incorporates or uses the best features of other forecasting techniques, such as trend and seasonal variations, smoothing techniques, and leading indicators

77. QX = a0 + a1PX + a2Y + a3N + a4PS + a5PC + a6A + e
Econometric Models
Single Equation Model of the Demand For Cereal (Good X)
QX = a0 + a1PX + a2Y + a3N + a4PS + a5PC + a6A + e
QX = Quantity of X
PX = Price of Good X
Y = Consumer Income
N = Size of Population
PS = Price of Muffins
PC = Price of Milk
A = Advertising
e = Random Error

78. Multiple Equation Model of GNP
Econometric Models
Multiple Equation Model of GNP
Reduced Form Equation

80. 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

81. Example-Econometric Models
Suppose we have the following equation and the estimated results for air travel between the USA and Europe from 1965 to 1978:
Q= ln Pt ln GNPt
Q is number of passengers per year traveling between the two continents.
Pt is the average yearly air fare
GNPt is U.S gross national product
Suppose the estimated Pt+1 and GNPt+1 in 1979 are $ 550 and $ respectively.
Forecast the number of passengers in 1979.

82. Example-Econometric Models
Qt+1= (antilog of 550) (antilog of 1480)
= (6.310) (7.300)
=8.775
The antilog of = 6,470,000 passengers for 1979
The accuracy of the forecast depends on the accuracy of estimated demand coefficients and the estimated values of both the independent and explanatory variables in the demand equation.

83. The End
Thanks