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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.
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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.
FORECAST: • The art and science of predicting future (It may involve using statistics and mathematical model, or may be a subjective prediction). • Forecasting is used to make informed decisions. • Short-range (up to 1 Yr): planning purchasing, job scheduling, workforce levels, job assignment. • Medium-rang (3 Mth – 3 Yr): sales planning, production planning and budgeting. • Long-range (more than 3 Yr): planning for new products, facility location or expansion, and R&D.
Forecasts • Forecasts affect decisions and activities throughout an organization • Accounting, finance • Human resources • Marketing • MIS • Operations • Product / service design
I see that you willget an A this semester. Features of Forecasts • Assumes causal systempast ==> future • Forecasts rarely perfect because of randomness • Forecasts more accurate forgroups cf. (compared to) individuals • Forecast accuracy decreases as time horizon increases
Timely Meaningful Units Accurate Reliable Cost-effective Easy to use Be Written Elements of a Good Forecast
6 Steps in the Forecasting Process “The forecast” Step 6 Monitor the forecast (modify, revise) Step 5 Make the forecast Step 4 Obtain, clean and analyze data (eliminate outliers, incorrect data) Step 3 Select a forecasting technique (Moving AVG, Weighted AVG, etc) Step 2 Establish a time horizon (How long?) Step 1 Determine purpose of forecast (How/when it will be used?, Resources)
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
2 ( Actual forecast) MSE = n - 1 ( Actual forecast / Actual*100) MAPE = n MAD, MSE, and MAPE 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 (the errors are presented as percentage)
Types of Forecasts Qualitative method • 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 Quantitative method
Qualitative method (Judgmental forecast) • Executive opinions (long-range planning, new product development) • Sales force opinions (direct contact with customers; however, sales staff are overly influenced by recent experience) • Consumer surveys (specific information; but money and time-consuming)
Quantitative method • Naïve approach • Moving average • Exponential smoothing • Trend projection • Linear regression Time series models Associative model
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 • Random variations - caused by chance and unusual circumstances
Randomvariation Trend Time Cycles Time Forecast Variations Year 1 Year 2 Year 3 Seasonal variations Month
Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell.... Naive Forecasts The forecast for any period equals the previous period’s actual value.
Naïve 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 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 wnAt-n+ … wn-1At-2 + w1At-1 Ft = WMAn= n Moving 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.
At-n+ … At-2 + At-1 Ft = MAn= n Simple Moving Average Actual MA5 MA3
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.
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)
Actual .4 .1 Picking a Smoothing Constant
Parabolic Exponential Growth Common Nonlinear Trends Figure 3.5
Ft Ft = a + bt 0 1 2 3 4 5 t Linear Trend Equation • Ft = Forecast for period t • t = Specified number of time periods • a = Value of Ft at t = 0 • b = Slope of the line
n (ty) - t y b = 2 2 n t - ( t) y - b t a = n Calculating a and b
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 Linear Trend Calculation
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
Computedrelationship Linear Model Seems Reasonable 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
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
(Actual - forecast) Tracking signal = MAD Tracking Signal • 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 can • Improve forecast quality in the supply chain • Lower costs • Shorter lead times • Gazing at the Crystal Ball (reading in text)
