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4 Forecasting Demand PowerPoint presentation to accompany
Heizer and Render Operations Management, 10e, Global Edition Principles of Operations Management, 8e, Global Edition PowerPoint slides by Jeff Heyl © 2011 Pearson Education
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?? What is Forecasting? Process of predicting a future event
Underlying basis of all business decisions Production Inventory Personnel Facilities ?? © 2011 Pearson Education
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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
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Distinguishing Differences
Medium/long range forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants and processes Short-term forecasting usually employs different methodologies than longer-term forecasting Short-term forecasts tend to be more accurate than longer-term forecasts © 2011 Pearson Education
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Influence of Product Life Cycle
Introduction – Growth – Maturity – Decline Introduction and growth require longer forecasts than maturity and decline As product passes through life cycle, forecasts are useful in projecting Staffing levels Inventory levels Factory capacity © 2011 Pearson Education
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Product Life Cycle Introduction Growth Maturity Decline
Company Strategy/Issues Best period to increase market share R&D engineering is critical Practical to change price or quality image Strengthen niche Poor time to change image, price, or quality Competitive costs become critical Defend market position Cost control critical Internet search engines Sales Drive-through restaurants CD-ROMs Analog TVs iPods Boeing 787 LCD & plasma TVs Twitter Avatars Xbox 360 Figure 2.5 © 2011 Pearson Education
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Product Life Cycle Introduction Growth Maturity Decline
OM Strategy/Issues Product design and development critical Frequent product and process design changes Short production runs High production costs Limited models Attention to quality Forecasting critical Product and process reliability Competitive product improvements and options Increase capacity Shift toward product focus Enhance distribution Standardization Fewer product changes, more minor changes Optimum capacity Increasing stability of process Long production runs Product improvement and cost cutting Little product differentiation Cost minimization Overcapacity in the industry Prune line to eliminate items not returning good margin Reduce capacity Figure 2.5 © 2011 Pearson Education
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Types of Forecasts Economic forecasts Technological forecasts
Address business cycle – 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
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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
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Seven Steps in Forecasting
Determine the use of the forecast Select the items to be forecasted Determine the time horizon of the forecast Select the forecasting model(s) Gather the data Make the forecast Validate and implement results © 2011 Pearson Education
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The Realities! Forecasts are seldom perfect
Most techniques assume an underlying stability in the system © 2011 Pearson Education
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Forecasting Approaches
Qualitative Methods Used when situation is vague and little data exist New products New technology Involves intuition, experience e.g., forecasting sales on Internet © 2011 Pearson Education
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Forecasting Approaches
Quantitative Methods Used when situation is ‘stable’ and historical data exist Existing products Current technology Involves mathematical techniques e.g., forecasting sales of color televisions © 2011 Pearson Education
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Overview of Qualitative Methods
Jury of executive opinion Pool opinions of high-level experts, sometimes augment by statistical models Delphi method Panel of experts, queried iteratively © 2011 Pearson Education
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Overview of Qualitative Methods
Sales force composite Estimates from individual salespersons are reviewed for reasonableness, then aggregated Consumer Market Survey Ask the customer © 2011 Pearson Education
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Jury of Executive Opinion
Involves small group of high-level experts and managers Group estimates demand by working together Combines managerial experience with statistical models Relatively quick ‘Group-think’ disadvantage © 2011 Pearson Education
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Sales Force Composite Each salesperson projects his or her sales
Combined at district and national levels Sales reps know customers’ wants Tends to be overly optimistic © 2011 Pearson Education
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Delphi Method Iterative group process, continues until consensus is reached 3 types of participants Decision makers Staff Respondents Decision Makers (Evaluate responses and make decisions) Staff (Administering survey) Respondents (People who can make valuable judgments) © 2011 Pearson Education
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Consumer Market Survey
Ask customers about purchasing plans What consumers say, and what they actually do are often different Sometimes difficult to answer © 2011 Pearson Education
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Overview of Quantitative Approaches
Naive approach Moving averages Exponential smoothing Trend projection Linear regression time-series models associative model © 2011 Pearson Education
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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
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Time Series Components
Trend Cyclical Seasonal Random © 2011 Pearson Education
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Average demand over 4 years
Components of Demand Trend component Demand for product or service | | | | Time (years) Seasonal peaks Actual demand line Average demand over 4 years Random variation Figure 4.1 © 2011 Pearson Education
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Trend Component Persistent, overall upward or downward pattern
Changes due to population, technology, age, culture, etc. Typically several years duration © 2011 Pearson Education
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Seasonal Component Regular pattern of up and down fluctuations
Due to weather, customs, etc. Occurs within a single year Number of Period Length Seasons Week Day 7 Month Week 4-4.5 Month Day 28-31 Year Quarter 4 Year Month 12 Year Week 52 © 2011 Pearson Education
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Cyclical Component Repeating up and down movements
Affected by business cycle, political, and economic factors Multiple years duration Often causal or associative relationships © 2011 Pearson Education
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Random Component Erratic, unsystematic, ‘residual’ fluctuations
Due to random variation or unforeseen events Short duration and nonrepeating M T W T F © 2011 Pearson Education
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Naive Approach Assumes demand in next period is the same as demand in most recent period e.g., If January sales were 68, then February sales will be 68 Sometimes cost effective and efficient Can be good starting point © 2011 Pearson Education
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∑ demand in previous n periods
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 Moving average = ∑ demand in previous n periods n © 2011 Pearson Education
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Moving Average Example
January 10 February 12 March 13 April 16 May 19 June 23 July 26 Actual 3-Month Month Shed Sales Moving Average 10 12 13 ( )/3 = 11 2/3 ( )/3 = 13 2/3 ( )/3 = 16 ( )/3 = 19 1/3 © 2011 Pearson Education
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Graph of Moving Average
Moving Average Forecast | | | | | | | | | | | | J F M A M J J A S O N D Shed Sales 30 – 28 – 26 – 24 – 22 – 20 – 18 – 16 – 14 – 12 – 10 – Actual Sales © 2011 Pearson Education
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Weighted Moving Average
Used when some trend might be present Older data usually less important Weights based on experience and intuition Weighted moving average = ∑ (weight for period n) x (demand in period n) ∑ weights © 2011 Pearson Education
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Weighted Moving Average
Weights Applied Period 3 Last month 2 Two months ago 1 Three months ago 6 Sum of weights Weighted Moving Average January 10 February 12 March 13 April 16 May 19 June 23 July 26 Actual 3-Month Weighted Month Shed Sales Moving Average 10 12 13 [(3 x 13) + (2 x 12) + (10)]/6 = 121/6 [(3 x 16) + (2 x 13) + (12)]/6 = 141/3 [(3 x 19) + (2 x 16) + (13)]/6 = 17 [(3 x 23) + (2 x 19) + (16)]/6 = 201/2 © 2011 Pearson Education
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Potential Problem With Moving Average
Require extensive historical data © 2011 Pearson Education
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Moving Average And Weighted Moving Average
30 – 25 – 20 – 15 – 10 – 5 – Sales demand | | | | | | | | | | | | J F M A M J J A S O N D Actual sales Moving average Figure 4.2 © 2011 Pearson Education
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Exponential Smoothing
Form of weighted moving average Weights decline exponentially Most recent data weighted most Requires smoothing constant () Ranges from 0 to 1 Subjectively chosen Involves little record keeping of past data © 2011 Pearson Education
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Exponential Smoothing
New forecast = Last period’s forecast + a (Last period’s actual demand – Last period’s forecast) Ft = Ft – 1 + a(At – 1 - Ft – 1) where Ft = new forecast Ft – 1 = previous forecast a = smoothing (or weighting) constant (0 ≤ a ≤ 1) © 2011 Pearson Education
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Exponential Smoothing Example
Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20 © 2011 Pearson Education
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Exponential Smoothing Example
Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20 New forecast = (153 – 142) © 2011 Pearson Education
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Exponential Smoothing Example
Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20 New forecast = (153 – 142) = = ≈ 144 cars © 2011 Pearson Education
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Choosing The objective is to obtain the most accurate forecast no matter the technique We generally do this by selecting the model that gives us the lowest forecast error Forecast error = Actual demand - Forecast value = At - Ft © 2011 Pearson Education
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Common Measures of Error
Mean Absolute Deviation (MAD) MAD = ∑ |Actual - Forecast| n Mean Squared Error (MSE) MSE = ∑ (Forecast Errors)2 n © 2011 Pearson Education
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Common Measures of Error
Mean Absolute Percent Error (MAPE) MAPE = ∑100|Actuali - Forecasti|/Actuali n i = 1 © 2011 Pearson Education
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Comparison of Forecast Error
Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded a = .10 a = .10 a = .50 a = .50 © 2011 Pearson Education
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Comparison of Forecast Error
MAD = ∑ |deviations| n Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded a = .10 a = .10 a = .50 a = .50 = 82.45/8 = 10.31 For a = .10 = 98.62/8 = 12.33 For a = .50 © 2011 Pearson Education
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Comparison of Forecast Error
MSE = ∑ (forecast errors)2 n Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded a = .10 a = .10 a = .50 a = .50 = 1,526.54/8 = For a = .10 MAD = 1,561.91/8 = For a = .50 © 2011 Pearson Education
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Comparison of Forecast Error
MAPE = ∑100|deviationi|/actuali n i = 1 Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded a = .10 a = .10 a = .50 a = .50 = 44.75/8 = 5.59% For a = .10 MAD MSE = 54.05/8 = 6.76% For a = .50 © 2011 Pearson Education
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Comparison of Forecast Error
Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded a = .10 a = .10 a = .50 a = .50 MAD MSE MAPE 5.59% 6.76% © 2011 Pearson Education
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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
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Least Squares Method Equations to calculate the regression variables
y = a + bx ^ b = Sxy - nxy Sx2 - nx2 a = y - bx © 2011 Pearson Education
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Least Squares Example Time Electrical Power
Year Period (x) Demand x2 xy ∑x = 28 ∑y = 692 ∑x2 = 140 ∑xy = 3,063 x = 4 y = 98.86 b = = = 10.54 ∑xy - nxy ∑x2 - nx2 3,063 - (7)(4)(98.86) 140 - (7)(42) a = y - bx = (4) = 56.70 © 2011 Pearson Education
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Least Squares Example The trend line is y = 56.70 + 10.54x ^
Time Electrical Power Year Period (x) Demand x2 xy ∑x = 28 ∑y = 692 ∑x2 = 140 ∑xy = 3,063 x = 4 y = 98.86 The trend line is y = x ^ b = = = 10.54 ∑xy - nxy ∑x2 - nx2 3,063 - (7)(4)(98.86) 140 - (7)(42) a = y - bx = (4) = 56.70 © 2011 Pearson Education
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Least Squares Example Trend line, y = 56.70 + 10.54x ^ 160 – 150 –
| | | | | | | | | 160 – 150 – 140 – 130 – 120 – 110 – 100 – 90 – 80 – 70 – 60 – 50 – Year Power demand © 2011 Pearson Education
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Seasonal Variations In Data
The multiplicative seasonal model can adjust trend data for seasonal variations in demand © 2011 Pearson Education
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Seasonal Variations In Data
Steps in the process: Find average historical demand for each season Compute the average demand over all seasons Compute a seasonal index for each season Estimate next year’s total demand Divide this estimate of total demand by the number of seasons, then multiply it by the seasonal index for that season © 2011 Pearson Education
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Seasonal Index Example
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Demand Average Average Seasonal Month Monthly Index © 2011 Pearson Education
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Seasonal Index Example
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Demand Average Average Seasonal Month Monthly Index 0.957 Seasonal index = Average monthly demand Average monthly demand = 90/94 = .957 © 2011 Pearson Education
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Seasonal Index Example
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Demand Average Average Seasonal Month Monthly Index © 2011 Pearson Education
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Seasonal Index Example
Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Demand Average Average Seasonal Month Monthly Index Forecast for 2010 Expected annual demand = 1,200 Jan x .957 = 96 1,200 12 Feb x .851 = 85 1,200 12 © 2011 Pearson Education
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Seasonal Index Example
2010 Forecast 2009 Demand 2008 Demand 2007 Demand 140 – 130 – 120 – 110 – 100 – 90 – 80 – 70 – | | | | | | | | | | | | J F M A M J J A S O N D Time Demand © 2011 Pearson Education
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Associative Forecasting
Used when changes in one or more independent variables can be used to predict the changes in the dependent variable Most common technique is linear regression analysis We apply this technique just as we did in the time series example © 2011 Pearson Education
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Associative Forecasting
Forecasting an outcome based on predictor variables 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 though to predict the value of the dependent variable ^ © 2011 Pearson Education
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Associative Forecasting Example
Sales Area Payroll ($ millions), y ($ billions), x 2.0 1 3.0 3 2.5 4 2.0 2 3.5 7 4.0 – 3.0 – 2.0 – 1.0 – | | | | | | | Sales Area payroll © 2011 Pearson Education
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Associative Forecasting Example
Sales, y Payroll, x x2 xy ∑y = 15.0 ∑x = 18 ∑x2 = 80 ∑xy = 51.5 b = = = .25 ∑xy - nxy ∑x2 - nx2 (6)(3)(2.5) 80 - (6)(32) x = ∑x/6 = 18/6 = 3 y = ∑y/6 = 15/6 = 2.5 a = y - bx = (.25)(3) = 1.75 © 2011 Pearson Education
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Associative Forecasting Example
y = x ^ Sales = (payroll) If payroll next year is estimated to be $6 billion, then: 4.0 – 3.0 – 2.0 – 1.0 – | | | | | | | Nodel’s sales Area payroll 3.25 Sales = (6) Sales = $3,250,000 © 2011 Pearson Education
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Correlation How strong is the linear relationship between the variables? Correlation does not necessarily imply causality! Coefficient of correlation, r, measures degree of association Values range from -1 to +1 © 2011 Pearson Education
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Correlation Coefficient
nSxy - SxSy [nSx2 - (Sx)2][nSy2 - (Sy)2] © 2011 Pearson Education
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Correlation Coefficient
y x (a) Perfect positive correlation: r = +1 y x (b) Positive correlation: 0 < r < 1 Correlation Coefficient r = nSxy - SxSy [nSx2 - (Sx)2][nSy2 - (Sy)2] y x (c) No correlation: r = 0 y x (d) Perfect negative correlation: r = -1 © 2011 Pearson Education
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For the Nodel Construction example:
Correlation Coefficient of Determination, r2, measures the percent of change in y predicted by the change in x Values range from 0 to 1 Easy to interpret For the Nodel Construction example: r = .901 r2 = .81 © 2011 Pearson Education
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Adaptive Forecasting It’s possible to use the computer to continually monitor forecast error and adjust the values of the a and b coefficients used in exponential smoothing to continually minimize forecast error This technique is called adaptive smoothing © 2011 Pearson Education
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Focus Forecasting Developed at American Hardware Supply, based on two principles: Sophisticated forecasting models are not always better than simple ones There is no single technique that should be used for all products or services This approach uses historical data to test multiple forecasting models for individual items The forecasting model with the lowest error is then used to forecast the next demand © 2011 Pearson Education
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Forecasting in the Service Sector
Presents unusual challenges Special need for short term records Needs differ greatly as function of industry and product Holidays and other calendar events Unusual events © 2011 Pearson Education
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Fast Food Restaurant Forecast
20% – 15% – 10% – 5% – (Lunchtime) (Dinnertime) Hour of day Percentage of sales Figure 4.12 © 2011 Pearson Education
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FedEx Call Center Forecast
12% – 10% – 8% – 6% – 4% – 2% – 0% – Hour of day A.M. P.M. 2 4 6 8 10 12 Figure 4.12 © 2011 Pearson Education
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Printed in the United States of America.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America. © 2011 Pearson Education
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