4 Forecasting SCM 352 © 2011 Pearson Education, Inc. publishing as Prentice Hall Outline • • • • Global Company Profile: Disney World What is Forecasting? Types of Forecasts Forecasting Approaches – Overview of Qualitative & Quantitative Methods • Time-Series Forecasting • Monitoring and Controlling Forecasts Famous Forecasting Quotes "Those who have knowledge, don't predict. Those who predict, don't have knowledge. " -- Lao Tzu, 6th Century BC Chinese Poet "It is often said there are two types of forecasts ... lucky or wrong!!!! " -- "Control" magazine (Inst. of Ops. Mgmt.) (http://www.met.rdg.ac.uk/cag/forecasting/quotes.html) Forecasting at Disney World • • • • • • Global portfolio includes parks in Hong Kong, Paris, Tokyo, Orlando, and Anaheim Revenues are derived from people – how many visitors and how they spend their money Daily management report contains only the forecast and actual attendance at each park Disney generates daily, weekly, monthly, annual, and 5year forecasts Forecast used by labor management, maintenance, operations, finance, and park scheduling Forecast used to adjust opening times, rides, shows, staffing levels, and guests admitted © 2011 Pearson Education, Inc. publishing as Prentice Hall Forecasting at Disney World • • • • • • 20% of customers come from outside the USA Economic model includes gross domestic product, cross-exchange rates, arrivals into the USA A staff of 35 analysts and 70 field people survey 1 million park guests, employees, and travel professionals each year Inputs to the forecasting model include airline specials, Federal Reserve policies, Wall Street trends, vacation/holiday schedules for 3,000 school districts around the world Average forecast error for the 5-year forecast is 5% Average forecast error for annual forecasts is between 0% and 3% © 2011 Pearson Education, Inc. publishing as Prentice Hall What is Forecasting? • Process of predicting a future event • Underlying basis of all business decisions – – – – Production Inventory Personnel Facilities © 2011 Pearson Education, Inc. publishing as Prentice Hall Sales will be $200 Million! Forecasting Time Horizons • Short-range forecast • Up to 1 year, generally less than 3 months • Purchasing, job scheduling, workforce levels, job assignments, production levels • Medium-range forecast • 3 months to 3 years • Sales and production planning, budgeting • Long-range forecast • 3+ years • New product planning, facility location, research and development © 2011 Pearson Education, Inc. publishing as Prentice Hall Types of Forecasts • Economic forecasts – Address business cycle, e.g., inflation rate, money supply, housing starts, etc. • Technological forecasts – Predict rate of technological progress – Impacts development of new products • Demand forecasts – Predict sales of existing products and services © 2011 Pearson Education, Inc. publishing as Prentice Hall Strategic Importance of Forecasting • Human Resources – Hiring, training, laying off • • workers Capacity – Capacity shortages can result in undependable delivery, loss of customers, loss of market share Supply Chain Management – Good supplier relations and price advantages © 2011 Pearson Education, Inc. publishing as Prentice Hall Forecasting Approaches Qualitative Methods Quantitative Methods Used when situation is vague & little data exist Used when situation is stable & historical data exist New products New technology Existing products Current technology Involves intuition, experience e.g., forecasting sales on Internet © 2011 Pearson Education, Inc. publishing as Prentice Hall Involves mathematical techniques e.g., forecasting sales of color televisions Overview of Qualitative Methods • Jury of executive opinion – Pool opinions of high-level executives, sometimes augment by statistical models – ‘Group-think’ disadvantage • Sales force composite – Estimates from individual salespersons are reviewed for reasonableness, then aggregated – Sales reps know customers’ wants • Delphi method – Panel of experts, queried iteratively • Consumer market survey – Ask the customer © 2011 Pearson Education, Inc. publishing as Prentice Hall Quantitative Approaches 1. Naive approach 2. Moving averages 3. Exponential smoothing Time-Series Models 4. Trend projection 5. Linear regression © 2011 Pearson Education, Inc. publishing as Prentice Hall Associative Model Time Series Forecasting • Set of evenly spaced numerical data • Obtained by observing response variable at regular time periods • Forecast based only on past values, no other variables important • Assumes that factors influencing past and present will continue influence in future © 2011 Pearson Education, Inc. publishing as Prentice Hall Time Series Forecasting Trend Cyclical Seasonal Random © 2011 Pearson Education, Inc. publishing as Prentice Hall Components of Demand Demand for product or service Trend component Seasonal peaks Actual demand line Average demand over 4 years Random variation | 1 | 2 | 3 Time (years) © 2011 Pearson Education, Inc. publishing as Prentice Hall | 4 Naive Approach • Assumes demand in next period is the same as demand in most recent period – If May sales were 48, then June sales will be 48 • Sometimes can be cost effective and efficient • Can be good starting point © 2011 Pearson Education, Inc. publishing as Prentice Hall Moving Average Method • MA is a series of arithmetic means • Used if little or no trend • Used often for smoothing – Provides overall impression of data over time • Equation ∑ demand in previous n periods Moving average = n © 2011 Pearson Education, Inc. publishing as Prentice Hall Potential Problems With MA • Increasing n smooths the forecast but makes it • • less sensitive to changes Do not forecast trends well Require extensive historical data © 2011 Pearson Education, Inc. publishing as Prentice Hall Moving Average Example Month Actual Shed Sales January February March April May June July 10 12 14 16 18 23 26 © 2011 Pearson Education, Inc. publishing as Prentice Hall 3-Month Moving Average (10 + 12 + 14)/3 = 12 (12 + 14 + 16)/3 = 14 (14 + 16 + 18)/3 = 16 (16 + 18 + 23)/3 = 19 Weighted Moving Average Method • Used when trend is present – Older data usually less important • Weights based on intuition – Ranges between 0 & 1, & sum to 1.0 • Equation Σ(Weight for period n) (Demand in period n) WMA = ΣWeights © 2011 Pearson Education, Inc. publishing as Prentice Hall Weighted Moving Average Example Month Actual Shed Sales January February March April May June July 10 12 14 16 18 23 26 3-Month Moving Average (10*0.2 + 12*0.3 + 14*0.5) = 12.6 (12*0.2 + 14*0.3 + 16*0.5) = 14.6 (14*0.2 + 16*0.3 + 18*0.5) = 16.6 (16*0.2 + 18*0.3 + 23*0.5) = 20.1 Weights: heaviest weights applied to most recent month – 0.5, 0.3, 0.2 © 2011 Pearson Education, Inc. publishing as Prentice Hall Exponential Smoothing Method • Form of weighted moving average – Weights decline exponentially – Most recent data weighted most • Requires smoothing constant (α) – Ranges from 0 to 1 – Select the value of α that gives us the lowest forecast error (MAD or MSE) • Involves little record keeping of past data © 2011 Pearson Education, Inc. publishing as Prentice Hall Exponential Smoothing Equations • Ft = Ft-1 + α(At-1 - Ft-1) – Use for computing forecast • Ft = αAt-1 + α(1-α)At-2 + α(1- α)2·At-3 + α(1- α)3At-4 + ... + α(1- α)t-1·A0 – Ft = Forecast value – At = Actual value – α = Smoothing constant • What happens when α = 1? © 2011 Pearson Education, Inc. publishing as Prentice Hall Problem 4.6, Page 140 (b) What is the forecast for January? [iv] Exponential smoothing, α = 0.3 FSep = 18 FOct = 18 + 0.3(20-18) = 18.6 FNov = 18.6 + 0.3(20-18.6) = 19.02 FDec = 19.02 + 0.3(21-19.02) = 19.6 FJan = 19.6 + 0.3(23-19.6) = 20.62 © 2011 Pearson Education, Inc. publishing as Prentice Hall Month Sales January 20 February 21 March 15 April 14 May 13 June 16 July 17 August 18 September 20 October 20 November 21 December 23 Trend Projections Fitting a trend line to historical data points to project into the medium-to-long-range Linear trends can be found using the least squares technique y^ = a + bx ^ where y = computed value of the variable to be predicted (dependent variable) a = y-axis intercept b = slope of the regression line x = the independent variable © 2011 Pearson Education, Inc. publishing as Prentice Hall Least Squares Method Equations to calculate the regression variables y^ = a + bx Σxy - nxy b= Σx2 - nx2 a = y - bx © 2011 Pearson Education, Inc. publishing as Prentice Hall Interpretation of Coefficients ^ & advertising (x) • Example: Sales (y) • Slope (b) ^ changes by b for each 1 unit – Estimated y increase in x ^ is expected to increase • If b = 2, then sales (y) by 2 for each 1 unit increase in advertising (x) • Y-intercept (a) – Average value of y^ when x = 0 ^ is expected to • If a = 4, then average sales (y) be 4 when advertising (x) is 0 © 2011 Pearson Education, Inc. publishing as Prentice Hall Selecting a Forecasting Model • You want to achieve: – No pattern or direction in forecast error • Error = (At - Ft) = (Actual - Forecast) • Seen in plots of errors over time – Smallest forecast error • Mean square error (MSE) • Mean absolute deviation (MAD) © 2011 Pearson Education, Inc. publishing as Prentice Hall Measuring Forecast Error Mean Absolute Deviation (MAD) ∑ |actual - forecast| MAD = n Mean Squared Error (MSE) MSE = ∑ (forecast error)2 n © 2011 Pearson Education, Inc. publishing as Prentice Hall Comparison of Forecast Error Quarter Actual Tonnage Unloaded Rounded Forecast using Model A 1 2 3 4 180 168 159 175 179 167 160 184 © 2011 Pearson Education, Inc. publishing as Prentice Hall Absolute Deviation for Model A Rounded Forecast using Model B 177 171 156 172 Absolute Deviation for Model B Forecast Error - MAD MAD = Quarter Rounded ∑ |deviation| Actual Forecast Tonnage n with Unloaded Model A For1Model A 180 2 3 4 179 168 167 =159 (1+1+1+9)/4 160 =175 12/4 = 3 184 For Model B Absolute Deviation for Model A Rounded Forecast with Model B Absolute Deviation for Model B 1 1 1 9 12 177 171 156 172 3 3 3 3 12 = (3+3+3+3)/4 = 12/4 = 3 Model A and Model B have the same MAD values. © 2011 Pearson Education, Inc. publishing as Prentice Hall Forecast Error - MSE 2 Rounded ∑ (forecast error) Actual Forecast MSE = Tonnage n with Quarter Unloaded Model A For 1Model A180 2 3 4 179 168 167 (1+1+1+81)/4 159 160 175= 21 184 84/4 = = For Model B Absolute Deviation for Model A Rounded Forecast with Model B Absolute Deviation for Model B 1 1 1 9 12 177 171 156 172 3 3 3 3 12 = (9+9+9+9)/4 = 36/4 = 9 Model B has a smaller MSE (=9) than Model A (=21) © 2011 Pearson Education, Inc. publishing as Prentice Hall Monitoring & Controlling Forecasts • • • Tracking signal Measures how well the forecast is predicting actual values Ratio of running sum of forecast errors (RSFE) to mean absolute deviation (MAD) • Good tracking signal has low values • If forecasts are continually high or low, the forecast has a bias error © 2011 Pearson Education, Inc. publishing as Prentice Hall Monitoring & Controlling Forecasts Tracking RSFE signal = MAD ∑(actual demand in period i forecast demand in period i) Tracking = signal (∑|actual - forecast|/n) What’s the interpretation of a positive or negative RSFE? © 2011 Pearson Education, Inc. publishing as Prentice Hall Tracking Signal Signal exceeding limit Tracking signal + Upper control limit Acceptable range 0 MADs – Lower control limit Time © 2011 Pearson Education, Inc. publishing as Prentice Hall Thank You Questions? ?

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