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This comprehensive guide covers elements, processes, qualitative and quantitative approaches to forecasting. Includes averaging techniques, measures of accuracy, evaluation methods, factors for technique selection, and types of forecasts with examples. Learn about judgmental, time series, and associative models, along with trend, seasonality, and variations. Dive into forecasting variations such as irregularity, cycles, and random variations. Explore common techniques like moving averages, weighted moving averages, and exponential smoothing. Discover linear trend equation calculations and techniques for seasonality like centered moving average. Understand associative forecasting with predictor variables and regression analysis for informed decision-making.
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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.
Learning 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.
What is 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
Uses of Forecasts Forecasts affect decisions and activities throughout an organization
Features of Forecasts I see that you willget an A this semester. • Assumes causal systempast ==> future • Forecasts rarely perfect because of randomness • Forecasts more accurate forgroups vs. individuals • Forecast accuracy decreases as time horizon increases
Elements of a Good Forecast Timely Accurate Reliable Easy to use Written Meaningful
Steps in the Forecasting Process “The forecast” Step 6 Monitor the forecast Step 5 Make the forecast Step 4 Obtain, clean and analyze data Step 3 Select a forecasting technique Step 2 Establish a time horizon Step 1 Determine purpose of forecast
Types 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
Judgmental Forecasts • Executive opinions • Sales force opinions • Consumer surveys • Outside opinion • Delphi method • An iterative process • Opinions of managers and staff • Achieves a consensus forecast
Time 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
Forecast Variations Irregularvariation Trend Cycles 90 89 88 Seasonal variations Time
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.
Naive Forecasts • 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
Uses for Naive 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
Moving Averages At-n+ … At-2 + At-1 Ft = MAn= n wnAt-n+ … wn-1At-2 + w1At-1 Ft = WMAn= wn+wn-1+…+w1 • Moving average – A technique that averages a number of recent actual values, updated as new values become available. • Weighted moving average – More recent actual values in a series are given more weight in computing the forecast.
Simple Moving Average At-n+ … At-2 + At-1 Ft = MAn= n Actual MA5 MA3
Exponential 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)
Exponential Smoothing • 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)
Picking a Smoothing Constant Actual .4 .1
Linear Trend Equation Ft Ft = a + bt 0 1 2 3 4 5 t • Ft = Forecast for period t • t = Specified number of time periods • a = Value of Ft at t = 0 • b = Slope of the line
Calculating a and b n (ty) - t y b = 2 2 n t - ( t) y - b t a = n
Linear Trend Calculation 5 (2499) - 15(812) 12495 - 12180 b = = = 6.3 5(55) - 225 275 - 225 812 - 6.3(15) a = = 143.5 5 y = 143.5 + 6.3t
Common Nonlinear Trends Parabolic Exponential Growth
Techniques for Seasonality • Seasonal variations • Regularly repeating movements in series values that can be tied to recurring events. • Seasonal relative • Percentage of average or trend • Centered moving average • A moving average positioned at the center of the data that were used to compute it.
Associative Forecasting • Predictor variables - used to predict values of variable interest • Regression - technique for fitting a line to a set of points • Least squares line - minimizes sum of squared deviations around the line
Linear Model Seems Reasonable Computedrelationship A straight line is fitted to a set of sample points.
Linear Regression Assumptions • Variations around the line are random • Deviations around the line normally distributed • Predictions are being made only within the range of observed values • For best results: • Always plot the data to verify linearity • Check for data being time-dependent • Small correlation may imply that other variables are important
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
MAD, MSE, and MAPE 2 ( Actual forecast) MSE = n - 1 ( Actual forecast / Actual*100) MAPE = n Actual forecast MAD = n
MAD, MSE and MAPE • MAD • Easy to compute • Weights errors linearly • MSE • Squares error • More weight to large errors • MAPE • Puts errors in perspective
Controlling the Forecast • Control chart • A visual tool for monitoring forecast errors • Used to detect non-randomness in errors • Forecasting errors are in control if • All errors are within the control limits • No patterns, such as trends or cycles, are present
Sources of Forecast errors • Model may be inadequate • Irregular variations • Incorrect use of forecasting technique
Tracking Signal (Actual - forecast) Tracking signal = MAD • Tracking signal • Ratio of cumulative error to MAD Bias – Persistent tendency for forecasts to be Greater or less than actual values.
Choosing a Forecasting Technique • No single technique works in every situation • Two most important factors • Cost • Accuracy • Other factors include the availability of: • Historical data • Computers • Time needed to gather and analyze the data • Forecast horizon
Operations Strategy • Forecasts are the basis for many decisions • Work to improve short-term forecasts • Accurate short-term forecasts improve • Profits • Lower inventory levels • Reduce inventory shortages • Improve customer service levels • Enhance forecasting credibility
Supply Chain Forecasts • Sharing forecasts with supply chain can • Improve forecast quality in the supply chain • Lower costs • Shorter lead times • Gazing at the Crystal Ball, Mini Tab, SPSS
