Presentation on theme: "Technology Forecasting Learning Objectives"— Presentation transcript:
1 Technology Forecasting 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.
2 Learning ObjectivesBriefly 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.
3 FORECAST:A statement about the future value of a variable of interest such as demand.Forecasting is used to make informed decisions.Long-rangeShort-range
4 ForecastsForecasts affect decisions and activities throughout an organizationAccounting, financeHuman resourcesMarketingMISOperationsProduct / service design
5 Uses of Forecasts Accounting Cost/profit estimates Finance Cash flow and fundingHuman ResourcesHiring/recruiting/trainingMarketingPricing, promotion, strategyMISIT/IS systems, servicesOperationsSchedules, MRP, workloadsProduct/service designNew products and services
6 Features of Forecasts Assumes causal system past ==> future Forecasts rarely perfect because of randomnessForecasts more accurate for groups vs. individualsForecast accuracy decreases as time horizon increasesI see that you will get an A this semester.
7 Elements of a Good Forecast TimelyAccurateReliableMeaningfulWrittenEasy to use
8 Steps in the Forecasting Process Step 1 Determine purpose of forecastStep 2 Establish a time horizonStep 3 Select a forecasting techniqueStep 4 Obtain, clean and analyze dataStep 5 Make the forecastStep 6 Monitor the forecast“The forecast”
9 Types of Forecasts Judgmental - uses subjective inputs Time series - uses historical data assuming the future will be like the pastAssociative models - uses explanatory variables to predict the future
10 Judgmental Forecasts Executive opinions Sales force opinions Consumer surveysOutside opinionDelphi methodOpinions of managers and staffAchieves a consensus forecast
11 Time Series Forecasts Trend - long-term movement in data Seasonality - short-term regular variations in dataCycle – wavelike variations of more than one year’s durationIrregular variations - caused by unusual circumstancesRandom variations - caused by chance
13 Naive Forecasts Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell....The forecast for any period equals the previous period’s actual value.
14 Naïve Forecasts Simple to use Virtually no cost Quick and easy to prepareData analysis is nonexistentEasily understandableCannot provide high accuracyCan be a standard for accuracy
15 Uses for Naïve Forecasts Stable time series dataF(t) = A(t-1)Seasonal variationsF(t) = A(t-n)Data with trendsF(t) = A(t-1) + (A(t-1) – A(t-2))
16 Techniques for Averaging Moving averageWeighted moving averageExponential smoothing
17 Moving Averages At-n + … At-2 + At-1 Ft = MAn= n 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=nAt-n + … At-2 + At-1Ft = WMAn=nwnAt-n + … wn-1At-2 + w1At-1
19 Exponential Smoothing Ft = Ft-1 + (At-1 - Ft-1)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.
20 Exponential Smoothing Ft = Ft-1 + (At-1 - Ft-1)Weighted averaging method based on previous forecast plus a percentage of the forecast errorA-F is the error term, is the % feedback
27 Linear Trend Calculation y = ta=812-6.3(15)5b5 (2499)15(812)5(55)22512495121802756.3143.5
28 Techniques for Seasonality Seasonal variationsRegularly repeating movements in series values that can be tied to recurring events.Seasonal relativePercentage of average or trendCentered moving averageA moving average positioned at the center of the data that were used to compute it.
29 Associative Forecasting Predictor variables - used to predict values of variable interestRegression - technique for fitting a line to a set of pointsLeast squares line - minimizes sum of squared deviations around the line
30 Linear Model Seems Reasonable Computed relationshipA straight line is fitted to a set of sample points.
31 Linear Regression Assumptions Variations around the line are randomDeviations around the line normally distributedPredictions are being made only within the range of observed valuesFor best results:Always plot the data to verify linearityCheck for data being time-dependentSmall correlation may imply that other variables are important
32 Forecast AccuracyError - difference between actual value and predicted valueMean Absolute Deviation (MAD)Average absolute errorMean Squared Error (MSE)Average of squared errorMean Absolute Percent Error (MAPE)Average absolute percent error
33 MAD, MSE, and MAPE Actual forecast MAD = n MSE = Actual forecast) -12n(MAPE=Actualforecastn/ Actual*100)(
34 MAD, MSE and MAPE MAD MSE MAPE Easy to compute Weights errors linearly Squares errorMore weight to large errorsMAPEPuts errors in perspective
36 Controlling the Forecast Control chartA visual tool for monitoring forecast errorsUsed to detect non-randomness in errorsForecasting errors are in control ifAll errors are within the control limitsNo patterns, such as trends or cycles, are present
37 Sources of Forecast errors Model may be inadequateIrregular variationsIncorrect use of forecasting technique
38 Tracking Signal Tracking signal = (Actual - forecast) MAD Ratio of cumulative error to MADTracking signal=(Actual-forecast)MADBias – Persistent tendency for forecasts to beGreater or less than actual values.
39 Choosing a Forecasting Technique No single technique works in every situationTwo most important factorsCostAccuracyOther factors include the availability of:Historical dataComputersTime needed to gather and analyze the dataForecast horizon
40 Operations Strategy Forecasts are the basis for many decisions Work to improve short-term forecastsAccurate short-term forecasts improveProfitsLower inventory levelsReduce inventory shortagesImprove customer service levelsEnhance forecasting credibility
41 Supply Chain Forecasts Sharing forecasts with supply canImprove forecast quality in the supply chainLower costsShorter lead timesGazing at the Crystal Ball (reading in text)