Chapter 5: Demand Forecasting

Chapter 5: Demand Forecasting
Department of Business Administration FALL I see that you will get an A this semester. Chapter 5: Demand Forecasting

Outline: What You Will Learn . . .
List the elements of a good forecast. Outline the steps in the forecasting process. Describe at least three qualitative forecasting techniques and the advantages and disadvantages of each. Compare and contrast qualitative and quantitative approaches to forecasting. Briefly describe averaging techniques, trend and seasonal techniques, and regression analysis, and solve typical problems. Describe two measures of forecast accuracy. Describe two ways of evaluating and controlling forecasts. Identify the major factors to consider when choosing a forecasting technique

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.

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.

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.

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

Uses of Forecasts Accounting Cost/profit estimates Finance
Cash flow and funding Human Resources Hiring/recruiting/training Marketing Pricing, promotion, strategy MIS IT/IS systems, services Operations Schedules, MRP, workloads Product/service design New products and services

Features of Forecasts Assumes causal system past ==> future
Forecasts rarely perfect because of randomness Forecasts more accurate for groups vs. individuals Forecast accuracy decreases as time horizon increases I see that you will get an A this semester.

Elements of a Good Forecast
Timely Accurate Reliable Meaningful Written Easy to use

Steps in the Forecasting Process
Step 1 Determine purpose of forecast Step 2 Establish a time horizon Step 3 Select a forecasting technique Step 4 Obtain, clean and analyze data Step 5 Make the forecast Step 6 Monitor the forecast “The forecast”

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 three parts: Qualitative approaches (Judgmental forecasts) and Quantitative approaches (Time-series forecasts) and Associative model forecasts).

Forecasting Techniques
Judgmental - uses subjective inputs such as opinion from consumer surveys, sales staff etc.. Time series - uses historical data assuming the future will be like the past Associative models - uses explanatory variables to predict the future

Qualitative Forecasts or Judgmental 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

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.

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.

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.

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) 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.

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.

Consumers intentions polling-
Qualitative Forecasts or Judgmental Forecasts 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. 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).

Quantitative Forecasting Approaches
Based on the assumption, 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.

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

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

Reasons for Fluctuations in Time Series Data
Secular Trend are noted by an upward or downward sloping line- long-term movement in data (e.g. Population shift, changing income and cultural changes). Cycle fluctuations is a data pattern that may cover several years before it repeats itself- wavelike variations of more than one year’s duration (e.g. Economic, political and agricultural conditions). Seasonality is a data pattern that repeats itself over the period of one year or less- short-term regular variations in data (e.g. Weekly or daily restaurant and supermarket experiences). Irregular variations caused by unusual circumstances (e.g. Severe weather conditions, strikes or major changes in a product or service). Random influences (noise) or variations results from random variation or unexplained causes. (e.g. residuals)

Forecast Variations Trend Cycles Irregular variation 90 89 88
Seasonal variations

Uses for Naïve Forecasts
Stable time series data F(t) = A(t-1) Seasonal variations F(t) = A(t-n) Data with trends F(t) = A(t-1) + (A(t-1) – A(t-2))

Techniques for Averaging
Moving average Weighted moving average Exponential smoothing

At-n + … At-2 + At-1 Ft = MAn= n n Moving Averages
Moving average – A technique that averages a number of recent actual values, updated as new values become available. At-n + … At-2 + At-1 Ft = MAn= n Weighted moving average – More recent values in a series are given more weight in computing the forecast. wnAt-n + … wn-1At-2 + w1At-1 Ft = WMAn= n n=total amount of number of weights

Simple Moving Average Actual MA5 MA3 At-n + … At-2 + At-1 Ft = MAn= n

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)

Exponential Smoothing
Ft = Ft-1 + (At-1 - Ft-1) Ft = forecast for period t Ft-1 = forecast for the previous period = smoothing constant At-1 = actual data for the previous period Premise--The most recent observations might have the highest predictive value. Therefore, we should give more weight to the more recent time periods when forecasting. Weighted averaging method based on previous forecast plus a percentage of the forecast error A-F is the error term,  is the % feedback

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.

Example-Moving Average
Central Call Center (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.

Example-Moving Average
Use the moving average method with an AP = 3 days to develop a forecast of the call volume in Day 13 (The 3 most recent demands) compute a three-period average forecast given scenario above: F13 = ( )/3 = calls

Example-Weighted Moving Average
Weighted Moving Average (Central Call Center ) 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. compute a weighted average forecast given scenario above: 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). 1

Example-Exponential Smoothing
Exponential Smoothing (Central Call Center) Suppose 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 – ) = Ft = Ft-1 + (At-1 - Ft-1)

Example 2-Exponential Smoothing
Exponential Smoothing (Actual Demand forecasting ) Suppose a smoothing constant value of .10 is used and the exponential smoothing forecast for the previous period was 42 units (actual demand was 40 units). what is the exponential smoothing forecast for the next periods? F3 = (40 – 42) = 41.8 F4 = (43 – 41.8) = 41.92

Example 2-Exponential Smoothing Graphical presentation
 .1 .4 Actual

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. 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.

Linear Trend Equation Ft = a + bt Ft = Forecast for period t
t = Specified number of time periods a = Value of Ft at t = 0 b = Slope of the line

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

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)

Trend Projection- Calculating a and b
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.

Trend Projection- Calculating a and b
Or If formula b is used first, it may be used formula a in the following format:

Example 1 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

Example1 for Trend Projection

Example1 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.

Example 2 for Trend Projection
Estimate a trend line using regression analysis Year Time Period (t) Sales (y) 2003 2004 2005 2006 2007 2008 1 2 3 4 5 6 20 40 30 50 70 65 Use time (t) as the independent variable:

(continued) The linear trend model is: Year Time Period (t) Sales (y) 2003 2004 2005 2006 2007 2008 1 2 3 4 5 6 20 40 30 50 70 65

(continued) Forecast for time period 7: Year Time Period (t) Sales (y) 2003 2004 2005 2006 2007 2008 2009 1 2 3 4 5 6 7 20 40 30 50 70 65 ??

Example for Trend Projection using-Non linear form 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

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

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 (SEF) Mean absolute deviation (MAD) Mean squared error (MSE) Mean absolute percent error (MAPE) Root mean squared error (RMSE)

Forecast Accuracy Error - difference between actual value and predicted value Mean Absolute Deviation (MAD) Average absolute error Mean Squared Error (MSE) Average of squared error Mean Absolute Percent Error (MAPE) Average absolute percent error Root Mean Squared Error (RMSE) Root Average of squared error

MAD, MSE, and MAPE  Actual  forecast MAD = n  ( Actual  forecast)
2  ( Actual  forecast) MSE = n - 1 MAPE = Actual forecast  n / Actual)*100) (

MAD, MSE and MAPE MAD Easy to compute Weights errors linearly MSE
Squares error More weight to large errors MAPE Puts errors in perspective RMSE Root of Squares error

Example-MAD, MSE, and MAPE Compute MAD, MSE and MAP for the following data showing actual and the predicted numbers of account serviced. 22/8=2.75 76/8-1=10.86 10.26/8=1.28 %

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.)

Example: Central Call Center-Forecast Accuracy - MAD
E AP4 = =26.3 EEXP4 = =25.0 Example: Central Call Center-Forecast Accuracy - MAD MADMA = 20.5/9 = 2.27 F4MA = ( )/3 = calls F4EXP = (186 – 186) = calls MADEXP = 18.0/9= 2.0

Example-For MA Techniques Electricity sales data from 2000. 1 to 2002
Example-For MA Techniques Electricity sales data from to (t=12)-Forecast Accuracy - RMSE AP = 3 moving average AP = 5 moving average

Example-For MA Techniques Electricity sales data from 2000. 1 to 2002
Example-For MA Techniques Electricity sales data from to (t=12)-Forecast Accuracy - RMSE 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.

Example-Exponential Smoothing Forecast Accuracy - RMSE
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

Example-Exponential Smoothing Forecast Accuracy - RMSE
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.

Seasonal Variation

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 =

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 Deseasonalize data=actual sales/seasonal relative (index)

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 seasonal indexes to reapply the seasonal patterns to the forecasts.

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. Calculate the seasonal indexes

Example: Computer Products Corp.

Unseasonalized vs. Seasonalized
Quarter Seasonalized Sales Seasonal Index Deseasonalized Sales 1 2 3 4 5 6 7 8 9 10 11 … 23 40 25 27 32 48 33 37 50 0.825 1.310 0.920 0.945 27.88 30.53 27.17 28.57 38.79 36.64 35.87 39.15 44.85 38.17 43.48

Deflating a Time Series
Observed values can be adjusted to base year equivalent Allows uniform comparison over time Deflation formula: where = adjusted time series value at time t yt = value of the time series at time t It = index value at time t

Deflating a Time Series: Example
Which movie made more money (in real terms)? Year Movie Title Total Gross $ 1939 Gone With the Wind 199 1977 Star Wars 461 1997 Titanic 601 (Total Gross $ = Total domestic gross ticket receipts in $millions)

Gross adjusted to 1984 dollars
Deflating a Time Series: Example Year Movie Title Total Gross CPI (base year = 1984) Gross adjusted to 1984 dollars 1939 Gone With the Wind 199 13.9 1431.7 1977 Star Wars 461 60.6 760.7 1997 Titanic 601 160.5 374.5 GWTW made about twice as much as Star Wars, and about 4 times as much as Titanic when measured in equivalent dollars

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

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

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

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

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

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

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

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.

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.

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Chapter 5: Demand Forecasting

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