3-1 Technology Forecasting Technology Forecasting Learning Objectives Learning Objectives 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.
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3-1 Technology Forecasting Learning Objectives List the elements of a good forecast. Outline the steps in the forecasting process. Describe at least three.
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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.
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Learning ObjectivesLearning Objectives
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.
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FORECAST: A statement about the future value of a
variable of interest such as demand. Forecasting is used to make informed
decisions. Long-range Short-range
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ForecastsForecasts
Forecasts affect decisions and activities throughout an organization Accounting, finance Human resources Marketing MIS Operations Product / service design
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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
Uses of ForecastsUses of Forecasts
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Assumes causal systempast ==> future
Forecasts rarely perfect because of randomness
Forecasts more accurate forgroups vs. individuals
Forecast accuracy decreases as time horizon increases
I see that you willget an A this semester.
Features of ForecastsFeatures of Forecasts
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Elements of a Good ForecastElements of a Good Forecast
Timely
AccurateReliable
Mea
ningfu
l
Written
Easy
to u
se
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Steps in the Forecasting ProcessSteps 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”
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Types of ForecastsTypes of Forecasts
Judgmental - uses subjective inputs
Time series - uses historical data assuming the future will be like the past
Associative models - uses explanatory variables to predict the future
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Judgmental ForecastsJudgmental Forecasts
Executive opinions
Sales force opinions
Consumer surveys
Outside opinion Delphi method
Opinions of managers and staff
Achieves a consensus forecast
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Time Series ForecastsTime Series Forecasts
Trend - long-term movement in data Seasonality - short-term regular
variations in data Cycle – wavelike variations of more than
one year’s duration Irregular variations - caused by unusual
circumstances Random variations - caused by chance
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Forecast VariationsForecast Variations
Trend
Irregularvariation
Seasonal variations
908988
Figure 3.1
Cycles
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Naive ForecastsNaive Forecasts
Uh, give me a minute.... We sold 250 wheels lastweek.... Now, next week we should sell....
The forecast for any period equals the previous period’s actual value.
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Simple to use Virtually no cost Quick and easy to prepare Data analysis is nonexistent Easily understandable Cannot provide high accuracy Can be a standard for accuracy
Naïve ForecastsNaïve Forecasts
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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))
Uses for Naïve ForecastsUses for Naïve Forecasts
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Techniques for AveragingTechniques for Averaging
Moving average
Weighted moving average
Exponential smoothing
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Moving AveragesMoving Averages
Moving average – A technique that averages a number of recent actual values, updated as new values become available.
Weighted moving average – More recent values in a series are given more weight in computing the forecast.
Ft = MAn= n
At-n + … At-2 + At-1
Ft = WMAn= n
wnAt-n + … wn-1At-2 + w1At-1
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Simple Moving AverageSimple Moving Average
35
37
39
41
43
45
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1 2 3 4 5 6 7 8 9 10 11 12
Actual
MA3
MA5
Ft = MAn= n
At-n + … At-2 + At-1
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Exponential SmoothingExponential Smoothing
• 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.
Ft = Ft-1 + (At-1 - Ft-1)
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Exponential SmoothingExponential Smoothing
Weighted averaging method based on previous forecast plus a percentage of the forecast error