I see that you will get an A this semester. Department of Business Administration. Chapter 3: Forecasting. FALL 20 10 - 2011. Outline: What You Will Learn. List the elements of a good forecast. Outline the steps in the forecasting process. - PowerPoint PPT Presentation
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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
What is meant by Forecasting and Why?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 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 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.
A wide variety of forecasting methods are available to management. These range from the most naïve methodsnaïve methods that require little effort to highly complex approacheshighly 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: QQualitative approachesualitative approaches (Judgmental (Judgmental forecasts)forecasts) and QQuantitative approachesuantitative approaches (Time- (Time-series forecasts) and Associative model forecasts)series forecasts) and Associative model forecasts)..
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.
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.
Qualitative ForecastsQualitative Forecasts or or Judgmental ForecastsJudgmental Forecasts
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.
Qualitative ForecastsQualitative Forecasts or or Judgmental ForecastsJudgmental Forecasts
Consumers intentions polling-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 –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).
Qualitative ForecastsQualitative Forecasts or or Judgmental ForecastsJudgmental Forecasts
Time Series AnalysisA 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 patternsOnce the patterns are identified, they can be used to
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)
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
Ft = Ft-1 + (At-1 - Ft-1)Ft = forecast for period tFt-1 = forecast for the previous period smoothing constant At-1 = actual data for the previous period
Example-Moving AverageExample-Moving AverageCentral 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-Weighted Moving AverageExample-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 compute a weighted average forecast given scenario above:above:
F13 = .1(168) + .3(198) + .6(159) = 171.6 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).
Exponential Smoothing Exponential Smoothing ((Central Call Center) SupposeSuppose a smoothing constant value of .25 is used and a smoothing constant value of .25 is used and
the exponential smoothing forecast for Day 11 was the exponential smoothing forecast for Day 11 was 180.76 calls180.76 calls..
what is the exponential smoothing forecast for Day 13?what is the exponential smoothing forecast for Day 13?
Exponential Smoothing Exponential Smoothing (Actual (Actual Demand forecasting ) SupposeSuppose a smoothing constant value of . a smoothing constant value of .1010 is used and the exponential is used and the exponential
smoothing forecast for smoothing forecast for the previous periodthe previous period was was 42 units (actual demand 42 units (actual demand was 40 units).was 40 units).
what is the exponential smoothing forecast for what is the exponential smoothing forecast for the nextthe next periodsperiods?? FF33 = = 4242 + . + .1010((4040 – – 4242) = ) = 4141..88 FF44 = = 41.841.8 + . + .1010((4343 – – 41.841.8) = ) = 41.9241.92
Once the a a and b 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.
Suppose we have the data show electricity sales in a city between 1997.1 and 2000.4. The data are shown in the following table. Use time series regression to forecast the electricity consumption (mn kilowatt) for the next four next four quarters.quarters.
Y17 = 11.90 + 0.394(17) = 18.60 in the first quarter of 2001Y17 = 11.90 + 0.394(17) = 18.60 in the first quarter of 2001Y18 = 11.90 + 0.394(18) = 18.99 in the second quarter of 2001Y18 = 11.90 + 0.394(18) = 18.99 in the second quarter of 2001Y19 = 11.90 + 0.394(19) = 19.39 in the third quarter of 2001Y19 = 11.90 + 0.394(19) = 19.39 in the third quarter of 2001Y20 = 11.90 + 0.394(20) = 19.78 in the fourth quarter of 2001Y20 = 11.90 + 0.394(20) = 19.78 in the fourth quarter of 2001
Note:Note: Electricity sales are expected to increase Electricity sales are expected to increase by 0.394 mn kilowatt-hours per quarter.by 0.394 mn kilowatt-hours per quarter.
Example for Trend Projection using St = S0 (1 + g)t
S17= 12.06(1.026)17 = 18.66 in the first quarter of 2001 S18= 12.06(1.026)18 = 19.14 in the second quarter of 2001 S19= 12.06(1.026)19 = 19.64 in the third quarter of 2001 S20= 12.06(1.026)20= 20.15 in the fourth quarter of 2001
These forecasts are similar to those obtained by fitting a linear trend
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 waysStandard error of the forecast (SEF)Mean absolute deviation (MAD)Mean squared error (MSE) Mean absolute percent error (MAPE)Root mean squared error (RMSE)
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.)
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
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