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- 1. © 2011 Pearson 4 - 1 4 Forecasting PowerPoint presentation to accompanyPowerPoint presentation to accompany Heizer and RenderHeizer and Render Operations Management, 10eOperations Management, 10e Principles of Operations Management, 8ePrinciples of Operations Management, 8e PowerPoint slides by Jeff Heyl
- 2. © 2011 Pearson 4 - 2 OutlineOutline Global Company Profile: Disney World What Is Forecasting? Forecasting Time Horizons The Influence of Product Life Cycle Types Of Forecasts
- 3. © 2011 Pearson 4 - 3 Outline – ContinuedOutline – Continued The Strategic Importance of Forecasting Human Resources Capacity Supply Chain Management Seven Steps in the Forecasting System
- 4. © 2011 Pearson 4 - 4 Outline – ContinuedOutline – Continued Forecasting Approaches Overview of Qualitative Methods Overview of Quantitative Methods Time-Series Forecasting Decomposition of a Time Series Naive Approach
- 5. © 2011 Pearson 4 - 5 Outline – ContinuedOutline – Continued Time-Series Forecasting (cont.) Moving Averages Exponential Smoothing Exponential Smoothing with Trend Adjustment Trend Projections Seasonal Variations in Data Cyclical Variations in Data
- 6. © 2011 Pearson 4 - 6 Outline – ContinuedOutline – Continued Associative Forecasting Methods: Regression and Correlation Analysis Using Regression Analysis for Forecasting Standard Error of the Estimate Correlation Coefficients for Regression Lines Multiple-Regression Analysis
- 7. © 2011 Pearson 4 - 7 Outline – ContinuedOutline – Continued Monitoring and Controlling Forecasts Adaptive Smoothing Focus Forecasting Forecasting in the Service Sector
- 8. © 2011 Pearson 4 - 8 Learning ObjectivesLearning Objectives When you complete this chapter youWhen you complete this chapter you should be able to :should be able to : 1. Understand the three time horizons and which models apply for each use 2. Explain when to use each of the four qualitative models 3. Apply the naive, moving average, exponential smoothing, and trend methods
- 9. © 2011 Pearson 4 - 9 Learning ObjectivesLearning Objectives When you complete this chapter youWhen you complete this chapter you should be able to :should be able to : 4. Compute three measures of forecast accuracy 5. Develop seasonal indexes 6. Conduct a regression and correlation analysis 7. Use a tracking signal
- 10. © 2011 Pearson 4 - 10 Forecasting at Disney WorldForecasting 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
- 11. © 2011 Pearson 4 - 11 Forecasting at Disney WorldForecasting at Disney World Disney generates daily, weekly, monthly, annual, and 5-year 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
- 12. © 2011 Pearson 4 - 12 Forecasting at Disney WorldForecasting 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
- 13. © 2011 Pearson 4 - 13 Forecasting at Disney WorldForecasting at Disney World 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%
- 14. © 2011 Pearson 4 - 14 What is Forecasting?What is Forecasting? Process of predicting a future event Underlying basis of all business decisions Production Inventory Personnel Facilities ??
- 15. © 2011 Pearson 4 - 15 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 Forecasting Time HorizonsForecasting Time Horizons
- 16. © 2011 Pearson 4 - 16 Distinguishing DifferencesDistinguishing Differences Medium/long rangeMedium/long range forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants and processes Short-termShort-term forecasting usually employs different methodologies than longer-term forecasting Short-termShort-term forecasts tend to be more accurate than longer-term forecasts
- 17. © 2011 Pearson 4 - 17 Influence of Product LifeInfluence of Product Life CycleCycle 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 Introduction – Growth – Maturity – DeclineIntroduction – Growth – Maturity – Decline
- 18. © 2011 Pearson 4 - 18 Product Life CycleProduct Life Cycle 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 Introduction Growth Maturity Decline CompanyStrategy/Issues Figure 2.5 Internet search engines Sales Drive-through restaurants CD-ROMs Analog TVs iPods Boeing 787 LCD & plasma TVs Twitter Avatars Xbox 360
- 19. © 2011 Pearson 4 - 19 Product Life CycleProduct Life Cycle Product design and development critical Frequent product and process design changes Short production runs High production costs Limited models Attention to quality Introduction Growth Maturity Decline OMStrategy/Issues 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
- 20. © 2011 Pearson 4 - 20 Types of ForecastsTypes of Forecasts Economic 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
- 21. © 2011 Pearson 4 - 21 Strategic Importance ofStrategic Importance of ForecastingForecasting 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
- 22. © 2011 Pearson 4 - 22 Seven Steps in ForecastingSeven Steps in Forecasting 1. Determine the use of the forecast 2. Select the items to be forecasted 3. Determine the time horizon of the forecast 4. Select the forecasting model(s) 5. Gather the data 6. Make the forecast 7. Validate and implement results
- 23. © 2011 Pearson 4 - 23 The Realities!The Realities! Forecasts are seldom perfect Most techniques assume an underlying stability in the system Product family and aggregated forecasts are more accurate than individual product forecasts
- 24. © 2011 Pearson 4 - 24 Forecasting ApproachesForecasting Approaches Used when situation is vague and little data exist New products New technology Involves intuition, experience e.g., forecasting sales on Internet Qualitative MethodsQualitative Methods
- 25. © 2011 Pearson 4 - 25 Forecasting ApproachesForecasting Approaches Used when situation is ‘stable’ and historical data exist Existing products Current technology Involves mathematical techniques e.g., forecasting sales of color televisions Quantitative MethodsQuantitative Methods
- 26. © 2011 Pearson 4 - 26 Overview of QualitativeOverview of Qualitative MethodsMethods 1. Jury of executive opinion Pool opinions of high-level experts, sometimes augment by statistical models 2. Delphi method Panel of experts, queried iteratively
- 27. © 2011 Pearson 4 - 27 Overview of QualitativeOverview of Qualitative MethodsMethods 3. Sales force composite Estimates from individual salespersons are reviewed for reasonableness, then aggregated 4. Consumer Market Survey Ask the customer
- 28. © 2011 Pearson 4 - 28 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 Jury of Executive OpinionJury of Executive Opinion
- 29. © 2011 Pearson 4 - 29 Sales Force CompositeSales 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
- 30. © 2011 Pearson 4 - 30 Delphi MethodDelphi Method Iterative group process, continues until consensus is reached 3 types of participants Decision makers Staff Respondents Staff (Administering survey) Decision Makers (Evaluate responses and make decisions) Respondents (People who can make valuable judgments)
- 31. © 2011 Pearson 4 - 31 Consumer Market SurveyConsumer Market Survey Ask customers about purchasing plans What consumers say, and what they actually do are often different Sometimes difficult to answer
- 32. © 2011 Pearson 4 - 32 Overview of QuantitativeOverview of Quantitative ApproachesApproaches 1. Naive approach 2. Moving averages 3. Exponential smoothing 4. Trend projection 5. Linear regression time-series models associative model
- 33. © 2011 Pearson 4 - 33 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 Time Series ForecastingTime Series Forecasting
- 34. © 2011 Pearson 4 - 34 Trend Seasonal Cyclical Random Time Series ComponentsTime Series Components
- 35. © 2011 Pearson 4 - 35 Components of DemandComponents of DemandDemandforproductorservice | | | | 1 2 3 4 Time (years) Average demand over 4 years Trend component Actual demand line Random variation Figure 4.1 Seasonal peaks
- 36. © 2011 Pearson 4 - 36 Persistent, overall upward or downward pattern Changes due to population, technology, age, culture, etc. Typically several years duration Trend ComponentTrend Component
- 37. © 2011 Pearson 4 - 37 Regular pattern of up and down fluctuations Due to weather, customs, etc. Occurs within a single year Seasonal ComponentSeasonal Component 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
- 38. © 2011 Pearson 4 - 38 Repeating up and down movements Affected by business cycle, political, and economic factors Multiple years duration Often causal or associative relationships Cyclical ComponentCyclical Component 0 5 10 15 20
- 39. © 2011 Pearson 4 - 39 Erratic, unsystematic, ‘residual’ fluctuations Due to random variation or unforeseen events Short duration and nonrepeating Random ComponentRandom Component M T W T F
- 40. © 2011 Pearson 4 - 40 Naive ApproachNaive 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
- 41. © 2011 Pearson 4 - 41 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 MethodMoving Average Method Moving average = ∑ demand in previous n periods n
- 42. © 2011 Pearson 4 - 42 January 10 February 12 March 13 April 16 May 19 June 23 July 26 Actual 3-Month Month Shed Sales Moving Average (12 + 13 + 16)/3 = 13 2 /3 (13 + 16 + 19)/3 = 16 (16 + 19 + 23)/3 = 19 1 /3 Moving Average ExampleMoving Average Example 1010 1212 1313 (1010 + 1212 + 1313)/3 = 11 2 /3
- 43. © 2011 Pearson 4 - 43 Graph of Moving AverageGraph of Moving Average | | | | | | | | | | | | J F M A M J J A S O N D ShedSales 30 – 28 – 26 – 24 – 22 – 20 – 18 – 16 – 14 – 12 – 10 – Actual Sales Moving Average Forecast
- 44. © 2011 Pearson 4 - 44 Used when some trend might be present Older data usually less important Weights based on experience and intuition Weighted Moving AverageWeighted Moving Average Weighted moving average = ∑ (weight for period n) x (demand in period n) ∑ weights
- 45. © 2011 Pearson 4 - 45 January 10 February 12 March 13 April 16 May 19 June 23 July 26 Actual 3-Month Weighted Month Shed Sales Moving Average [(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 Weighted Moving AverageWeighted Moving Average 1010 1212 1313 [(3 x 1313) + (2 x 1212) + (1010)]/6 = 121 /6 Weights Applied Period 33 Last month 22 Two months ago 11 Three months ago 6 Sum of weights
- 46. © 2011 Pearson 4 - 46 Increasing n smooths the forecast but makes it less sensitive to changes Do not forecast trends well Require extensive historical data Potential Problems WithPotential Problems With Moving AverageMoving Average
- 47. © 2011 Pearson 4 - 47 Moving Average AndMoving Average And Weighted Moving AverageWeighted Moving Average 30 – 25 – 20 – 15 – 10 – 5 – Salesdemand | | | | | | | | | | | | J F M A M J J A S O N D Actual sales Moving average Weighted moving average Figure 4.2
- 48. © 2011 Pearson 4 - 48 Form of weighted moving average Weights decline exponentially Most recent data weighted most Requires smoothing constant (α) Ranges from 0 to 1 Subjectively chosen Involves little record keeping of past data Exponential SmoothingExponential Smoothing
- 49. © 2011 Pearson 4 - 49 Exponential SmoothingExponential Smoothing t = Last period’s forecast + α (Last period’s actual demand – Last period’s forecast) Ft = Ft – 1 + α(At – 1 - Ft – 1) where Ft = new forecast Ft – 1 = previous forecast α = smoothing (or weighting) constant (0 ≤ α ≤ 1)
- 50. © 2011 Pearson 4 - 50 Exponential SmoothingExponential Smoothing ExampleExample Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant α = .20
- 51. © 2011 Pearson 4 - 51 Exponential SmoothingExponential Smoothing ExampleExample Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant α = .20 New forecast = 142 + .2(153 – 142)
- 52. © 2011 Pearson 4 - 52 Exponential SmoothingExponential Smoothing ExampleExample Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant α = .20 New forecast = 142 + .2(153 – 142) = 142 + 2.2 = 144.2 ≈ 144 cars
- 53. © 2011 Pearson 4 - 53 Effect ofEffect of Smoothing ConstantsSmoothing Constants Weight Assigned to Most 2nd Most 3rd Most 4th Most 5th Most Recent Recent Recent Recent Recent Smoothing Period Period Period Period Period Constant (α) α(1 - α) α(1 - α)2 α(1 - α)3 α(1 - α)4 α = .1 .1 .09 .081 .073 .066 α = .5 .5 .25 .125 .063 .031
- 54. © 2011 Pearson 4 - 54 Impact of DifferentImpact of Different αα 225 – 200 – 175 – 150 – | | | | | | | | | 1 2 3 4 5 6 7 8 9 Quarter Demand α = .1 Actual demand α = .5
- 55. © 2011 Pearson 4 - 55 Impact of DifferentImpact of Different αα 225 – 200 – 175 – 150 – | | | | | | | | | 1 2 3 4 5 6 7 8 9 Quarter Demand α = .1 Actual demand α = .5 Chose high values of α when underlying average is likely to change Choose low values of α when underlying average is stable
- 56. © 2011 Pearson 4 - 56 ChoosingChoosing αα The objective is to obtain the most accurate forecast no matter the technique We generally do this by selecting theWe generally do this by selecting the model that gives us the lowest forecastmodel that gives us the lowest forecast errorerror Forecast error = Actual demand - Forecast value = At - Ft
- 57. © 2011 Pearson 4 - 57 Common Measures of ErrorCommon Measures of Error Mean Absolute Deviation (MAD) MAD = ∑ |Actual - Forecast| n Mean Squared Error (MSE) MSE = ∑ (Forecast Errors)2 n
- 58. © 2011 Pearson 4 - 58 Common Measures of ErrorCommon Measures of Error Mean Absolute Percent Error (MAPE) MAPE = ∑100|Actuali - Forecasti|/Actuali n n i = 1
- 59. © 2011 Pearson 4 - 59 Comparison of ForecastComparison of Forecast ErrorError Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded α = .10 α = .10 α = .50 α = .50 1 180 175 5.00 175 5.00 2 168 175.5 7.50 177.50 9.50 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.30 82.45 98.62
- 60. © 2011 Pearson 4 - 60 Comparison of ForecastComparison of Forecast ErrorError Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded α = .10 α = .10 α = .50 α = .50 1 180 175 5.00 175 5.00 2 168 175.5 7.50 177.50 9.50 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.30 82.45 98.62 MAD = ∑ |deviations| n = 82.45/8 = 10.31 For α = .10 = 98.62/8 = 12.33 For α = .50
- 61. © 2011 Pearson 4 - 61 Comparison of ForecastComparison of Forecast ErrorError Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded α = .10 α = .10 α = .50 α = .50 1 180 175 5.00 175 5.00 2 168 175.5 7.50 177.50 9.50 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.30 82.45 98.62 MAD 10.31 12.33 = 1,526.54/8 = 190.82 For α = .10 = 1,561.91/8 = 195.24 For α = .50 MSE = ∑ (forecast errors)2 n
- 62. © 2011 Pearson 4 - 62 Comparison of ForecastComparison of Forecast ErrorError Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded α = .10 α = .10 α = .50 α = .50 1 180 175 5.00 175 5.00 2 168 175.5 7.50 177.50 9.50 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.30 82.45 98.62 MAD 10.31 12.33 MSE 190.82 195.24 = 44.75/8 = 5.59% For α = .10 = 54.05/8 = 6.76% For α = .50 MAPE = ∑100|deviationi|/actuali n n i = 1
- 63. © 2011 Pearson 4 - 63 Comparison of ForecastComparison of Forecast ErrorError Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded α = .10 α = .10 α = .50 α = .50 1 180 175 5.00 175 5.00 2 168 175.5 7.50 177.50 9.50 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.30 82.45 98.62 MAD 10.31 12.33 MSE 190.82 195.24 MAPE 5.59% 6.76%
- 64. © 2011 Pearson 4 - 64 Exponential Smoothing withExponential Smoothing with Trend AdjustmentTrend Adjustment When a trend is present, exponential smoothing must be modified Forecast including (FITt) = trend Exponentially Exponentially smoothed (Ft) + smoothed (Tt) forecast trend
- 65. © 2011 Pearson 4 - 65 Exponential Smoothing withExponential Smoothing with Trend AdjustmentTrend Adjustment Ft = α(At - 1) + (1 - α)(Ft - 1 + Tt - 1) Tt = β(Ft - Ft - 1) + (1 - β)Tt - 1 Step 1: Compute Ft Step 2: Compute Tt Step 3: Calculate the forecast FITt = Ft + Tt
- 66. © 2011 Pearson 4 - 66 Exponential Smoothing withExponential Smoothing with Trend Adjustment ExampleTrend Adjustment Example Forecast Actual Smoothed Smoothed Including Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt 1 12 11 2 13.00 2 17 3 20 4 19 5 24 6 21 7 31 8 28 9 36 10 Table 4.1
- 67. © 2011 Pearson 4 - 67 Exponential Smoothing withExponential Smoothing with Trend Adjustment ExampleTrend Adjustment Example Forecast Actual Smoothed Smoothed Including Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt 1 12 11 2 13.00 2 17 3 20 4 19 5 24 6 21 7 31 8 28 9 36 10 Table 4.1 F2 = αA1 + (1 - α)(F1 + T1) F2 = (.2)(12) + (1 - .2)(11 + 2) = 2.4 + 10.4 = 12.8 units Step 1: Forecast for Month 2
- 68. © 2011 Pearson 4 - 68 Exponential Smoothing withExponential Smoothing with Trend Adjustment ExampleTrend Adjustment Example Forecast Actual Smoothed Smoothed Including Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt 1 12 11 2 13.00 2 17 12.80 3 20 4 19 5 24 6 21 7 31 8 28 9 36 10 Table 4.1 T2 = β(F2 - F1) + (1 - β)T1 T2 = (.4)(12.8 - 11) + (1 - .4)(2) = .72 + 1.2 = 1.92 units Step 2: Trend for Month 2
- 69. © 2011 Pearson 4 - 69 Exponential Smoothing withExponential Smoothing with Trend Adjustment ExampleTrend Adjustment Example Forecast Actual Smoothed Smoothed Including Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt 1 12 11 2 13.00 2 17 12.80 1.92 3 20 4 19 5 24 6 21 7 31 8 28 9 36 10 Table 4.1 FIT2 = F2 + T2 FIT2 = 12.8 + 1.92 = 14.72 units Step 3: Calculate FIT for Month 2
- 70. © 2011 Pearson 4 - 70 Exponential Smoothing withExponential Smoothing with Trend Adjustment ExampleTrend Adjustment Example Forecast Actual Smoothed Smoothed Including Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt 1 12 11 2 13.00 2 17 12.80 1.92 14.72 3 20 4 19 5 24 6 21 7 31 8 28 9 36 10 Table 4.1 15.18 2.10 17.28 17.82 2.32 20.14 19.91 2.23 22.14 22.51 2.38 24.89 24.11 2.07 26.18 27.14 2.45 29.59 29.28 2.32 31.60 32.48 2.68 35.16
- 71. © 2011 Pearson 4 - 71 Exponential Smoothing withExponential Smoothing with Trend Adjustment ExampleTrend Adjustment Example Figure 4.3 | | | | | | | | | 1 2 3 4 5 6 7 8 9 Time (month) Productdemand 35 – 30 – 25 – 20 – 15 – 10 – 5 – 0 – Actual demand (At) Forecast including trend (FITt) with α = .2 and β = .4
- 72. © 2011 Pearson 4 - 72 Trend ProjectionsTrend 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 ^
- 73. © 2011 Pearson 4 - 73 Least Squares MethodLeast Squares Method Time period ValuesofDependentVariable Figure 4.4 Deviation1 (error) Deviation5 Deviation7 Deviation2 Deviation6 Deviation4 Deviation3 Actual observation (y-value) Trend line, y = a + bx^
- 74. © 2011 Pearson 4 - 74 Least Squares MethodLeast Squares Method Time period ValuesofDependentVariable Figure 4.4 Deviation1 (error) Deviation5 Deviation7 Deviation2 Deviation6 Deviation4 Deviation3 Actual observation (y-value) Trend line, y = a + bx^ Least squares method minimizes the sum of the squared errors (deviations)
- 75. © 2011 Pearson 4 - 75 Least Squares MethodLeast Squares Method Equations to calculate the regression variables b = Σxy - nxy Σx2 - nx2 y = a + bx^ a = y - bx
- 76. © 2011 Pearson 4 - 76 Least Squares ExampleLeast Squares Example b = = = 10.54 ∑xy - nxy ∑x2 - nx2 3,063 - (7)(4)(98.86) 140 - (7)(42 ) a = y - bx = 98.86 - 10.54(4) = 56.70 Time Electrical Power Year Period (x) Demand x2 xy 2003 1 74 1 74 2004 2 79 4 158 2005 3 80 9 240 2006 4 90 16 360 2007 5 105 25 525 2008 6 142 36 852 2009 7 122 49 854 ∑x = 28 ∑y = 692 ∑x2 = 140 ∑xy = 3,063 x = 4 y = 98.86
- 77. © 2011 Pearson 4 - 77 b = = = 10.54 ∑xy - nxy ∑x2 - nx2 3,063 - (7)(4)(98.86) 140 - (7)(42 ) a = y - bx = 98.86 - 10.54(4) = 56.70 Time Electrical Power Year Period (x) Demand x2 xy 2003 1 74 1 74 2004 2 79 4 158 2005 3 80 9 240 2006 4 90 16 360 2007 5 105 25 525 2008 6 142 36 852 2009 7 122 49 854 ∑x = 28 ∑y = 692 ∑x2 = 140 ∑xy = 3,063 x = 4 y = 98.86 Least Squares ExampleLeast Squares Example The trend line is y = 56.70 + 10.54x^
- 78. © 2011 Pearson 4 - 78 Least Squares ExampleLeast Squares Example | | | | | | | | | 2003 2004 2005 2006 2007 2008 2009 2010 2011 160 – 150 – 140 – 130 – 120 – 110 – 100 – 90 – 80 – 70 – 60 – 50 – Year Powerdemand Trend line, y = 56.70 + 10.54x^
- 79. © 2011 Pearson 4 - 80 Seasonal Variations In DataSeasonal Variations In Data The multiplicative seasonal model can adjust trend data for seasonal variations in demand
- 80. © 2011 Pearson 4 - 81 Seasonal Variations In DataSeasonal Variations In Data 1. Find average historical demand for each season 2. Compute the average demand over all seasons 3. Compute a seasonal index for each season 4. Estimate next year’s total demand 5. Divide this estimate of total demand by the number of seasons, then multiply it by the seasonal index for that season Steps in the process:Steps in the process:
- 81. © 2011 Pearson 4 - 82 Seasonal Index ExampleSeasonal Index Example Jan 80 85 105 90 94 Feb 70 85 85 80 94 Mar 80 93 82 85 94 Apr 90 95 115 100 94 May 113 125 131 123 94 Jun 110 115 120 115 94 Jul 100 102 113 105 94 Aug 88 102 110 100 94 Sept 85 90 95 90 94 Oct 77 78 85 80 94 Nov 75 72 83 80 94 Dec 82 78 80 80 94 Demand Average Average Seasonal Month 2007 2008 2009 2007-2009 Monthly Index
- 82. © 2011 Pearson 4 - 83 Seasonal Index ExampleSeasonal Index Example Jan 80 85 105 90 94 Feb 70 85 85 80 94 Mar 80 93 82 85 94 Apr 90 95 115 100 94 May 113 125 131 123 94 Jun 110 115 120 115 94 Jul 100 102 113 105 94 Aug 88 102 110 100 94 Sept 85 90 95 90 94 Oct 77 78 85 80 94 Nov 75 72 83 80 94 Dec 82 78 80 80 94 Demand Average Average Seasonal Month 2007 2008 2009 2007-2009 Monthly Index 0.957 Seasonal index = Average 2007-2009 monthly demand Average monthly demand = 90/94 = .957
- 83. © 2011 Pearson 4 - 84 Seasonal Index ExampleSeasonal Index Example Jan 80 85 105 90 94 0.957 Feb 70 85 85 80 94 0.851 Mar 80 93 82 85 94 0.904 Apr 90 95 115 100 94 1.064 May 113 125 131 123 94 1.309 Jun 110 115 120 115 94 1.223 Jul 100 102 113 105 94 1.117 Aug 88 102 110 100 94 1.064 Sept 85 90 95 90 94 0.957 Oct 77 78 85 80 94 0.851 Nov 75 72 83 80 94 0.851 Dec 82 78 80 80 94 0.851 Demand Average Average Seasonal Month 2007 2008 2009 2007-2009 Monthly Index
- 84. © 2011 Pearson 4 - 85 Seasonal Index ExampleSeasonal Index Example Jan 80 85 105 90 94 0.957 Feb 70 85 85 80 94 0.851 Mar 80 93 82 85 94 0.904 Apr 90 95 115 100 94 1.064 May 113 125 131 123 94 1.309 Jun 110 115 120 115 94 1.223 Jul 100 102 113 105 94 1.117 Aug 88 102 110 100 94 1.064 Sept 85 90 95 90 94 0.957 Oct 77 78 85 80 94 0.851 Nov 75 72 83 80 94 0.851 Dec 82 78 80 80 94 0.851 Demand Average Average Seasonal Month 2007 2008 2009 2007-2009 Monthly Index Expected annual demand = 1,200 Jan x .957 = 96 1,200 12 Feb x .851 = 85 1,200 12 Forecast for 2010
- 85. © 2011 Pearson 4 - 86 Seasonal Index ExampleSeasonal Index Example 140 – 130 – 120 – 110 – 100 – 90 – 80 – 70 – | | | | | | | | | | | | J F M A M J J A S O N D Time Demand 2010 Forecast 2009 Demand 2008 Demand 2007 Demand
- 86. © 2011 Pearson 4 - 87 San Diego HospitalSan Diego Hospital 10,200 – 10,000 – 9,800 – 9,600 – 9,400 – 9,200 – 9,000 – | | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 67 68 69 70 71 72 73 74 75 76 77 78 Month InpatientDays 9530 9551 9573 9594 9616 9637 9659 9680 9702 9724 9745 9766 Figure 4.6 Trend Data
- 87. © 2011 Pearson 4 - 88 San Diego HospitalSan Diego Hospital 1.06 – 1.04 – 1.02 – 1.00 – 0.98 – 0.96 – 0.94 – 0.92 – | | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 67 68 69 70 71 72 73 74 75 76 77 78 Month IndexforInpatientDays 1.04 1.02 1.01 0.99 1.03 1.04 1.00 0.98 0.97 0.99 0.97 0.96 Figure 4.7 Seasonal Indices
- 88. © 2011 Pearson 4 - 89 San Diego HospitalSan Diego Hospital 10,200 – 10,000 – 9,800 – 9,600 – 9,400 – 9,200 – 9,000 – | | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 67 68 69 70 71 72 73 74 75 76 77 78 Month InpatientDays Figure 4.8 9911 9265 9764 9520 9691 9411 9949 9724 9542 9355 10068 9572 Combined Trend and Seasonal Forecast
- 89. © 2011 Pearson 4 - 90 Associative ForecastingAssociative 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 didWe apply this technique just as we did in the time series examplein the time series example
- 90. © 2011 Pearson 4 - 91 Associative ForecastingAssociative 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 ^
- 91. © 2011 Pearson 4 - 92 Associative ForecastingAssociative Forecasting ExampleExample Sales Area Payroll ($ millions), y ($ billions), x 2.0 1 3.0 3 2.5 4 2.0 2 2.0 1 3.5 7 4.0 – 3.0 – 2.0 – 1.0 – | | | | | | | 0 1 2 3 4 5 6 7 Sales Area payroll
- 92. © 2011 Pearson 4 - 93 Associative ForecastingAssociative Forecasting ExampleExample Sales, y Payroll, x x2 xy 2.0 1 1 2.0 3.0 3 9 9.0 2.5 4 16 10.0 2.0 2 4 4.0 2.0 1 1 2.0 3.5 7 49 24.5 ∑y = 15.0 ∑x = 18 ∑x2 = 80 ∑xy = 51.5 x = ∑x/6 = 18/6 = 3 y = ∑y/6 = 15/6 = 2.5 b = = = .25 ∑xy - nxy ∑x2 - nx2 51.5 - (6)(3)(2.5) 80 - (6)(32 ) a = y - bx = 2.5 - (.25)(3) = 1.75
- 93. © 2011 Pearson 4 - 94 Associative ForecastingAssociative Forecasting ExampleExample y = 1.75 + .25x^ Sales = 1.75 + .25(payroll) If payroll next year is estimated to be $6 billion, then: Sales = 1.75 + .25(6) Sales = $3,250,000 4.0 – 3.0 – 2.0 – 1.0 – | | | | | | | 0 1 2 3 4 5 6 7 Nodel’ssales Area payroll 3.25
- 94. © 2011 Pearson 4 - 95 Standard Error of theStandard Error of the EstimateEstimate A forecast is just a point estimate of a future value This point is actually the mean of a probability distribution Figure 4.9 4.0 – 3.0 – 2.0 – 1.0 – | | | | | | | 0 1 2 3 4 5 6 7 Nodel’ssales Area payroll 3.25
- 95. © 2011 Pearson 4 - 96 Standard Error of theStandard Error of the EstimateEstimate where y = y-value of each data point yc = computed value of the dependent variable, from the regression equation n = number of data points Sy,x = ∑(y - yc)2 n - 2
- 96. © 2011 Pearson 4 - 97 Standard Error of theStandard Error of the EstimateEstimate Computationally, this equation is considerably easier to use We use the standard error to set up prediction intervals around the point estimate Sy,x = ∑y2 - a∑y - b∑xy n - 2
- 97. © 2011 Pearson 4 - 98 Standard Error of theStandard Error of the EstimateEstimate 4.0 – 3.0 – 2.0 – 1.0 – | | | | | | | 0 1 2 3 4 5 6 7 Nodel’ssales Area payroll 3.25 Sy,x = =∑y2 - a∑y - b∑xy n - 2 39.5 - 1.75(15) - .25(51.5) 6 - 2 Sy,x = .306 The standard error of the estimate is $306,000 in sales
- 98. © 2011 Pearson 4 - 99 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 CorrelationCorrelation
- 99. © 2011 Pearson 4 - 100 Correlation CoefficientCorrelation Coefficient r = nΣxy - ΣxΣy [nΣx2 - (Σx)2 ][nΣy2 - (Σy)2 ]
- 100. © 2011 Pearson 4 - 101 Correlation CoefficientCorrelation Coefficient r = nΣxy - ΣxΣy [nΣx2 - (Σx)2 ][nΣy2 - (Σy)2 ] y x(a) Perfect positive correlation: r = +1 y x(b) Positive correlation: 0 < r < 1 y x(c) No correlation: r = 0 y x(d) Perfect negative correlation: r = -1
- 101. © 2011 Pearson 4 - 102 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 CorrelationCorrelation For the Nodel Construction example: r = .901 r2 = .81
- 102. © 2011 Pearson 4 - 103 Multiple RegressionMultiple Regression AnalysisAnalysis If more than one independent variable is to be used in the model, linear regression can be extended to multiple regression to accommodate several independent variables y = a + b1x1 + b2x2 …^ Computationally, this is quiteComputationally, this is quite complex and generally done on thecomplex and generally done on the computercomputer
- 103. © 2011 Pearson 4 - 104 Multiple RegressionMultiple Regression AnalysisAnalysis y = 1.80 + .30x1 - 5.0x2 ^ In the Nodel example, including interest rates in the model gives the new equation: An improved correlation coefficient of r = .96 means this model does a better job of predicting the change in construction sales Sales = 1.80 + .30(6) - 5.0(.12) = 3.00 Sales = $3,000,000
- 104. © 2011 Pearson 4 - 105 Measures how well the forecast is predicting actual values Ratio of cumulative forecast errors to mean absolute deviation (MAD) Good tracking signal has low values If forecasts are continually high or low, the forecast has a bias error Monitoring and ControllingMonitoring and Controlling ForecastsForecasts Tracking SignalTracking Signal
- 105. © 2011 Pearson 4 - 106 Monitoring and ControllingMonitoring and Controlling ForecastsForecasts Tracking signal Cumulative error MAD = Tracking signal = ∑(Actual demand in period i - Forecast demand in period i) (∑|Actual - Forecast|/n)
- 106. © 2011 Pearson 4 - 107 Tracking SignalTracking Signal Tracking signal + 0 MADs – Upper control limit Lower control limit Time Signal exceeding limit Acceptable range
- 107. © 2011 Pearson 4 - 108 Tracking Signal ExampleTracking Signal Example Cumulative Absolute Absolute Actual Forecast Cumm Forecast Forecast Qtr Demand Demand Error Error Error Error MAD 1 90 100 -10 -10 10 10 10.0 2 95 100 -5 -15 5 15 7.5 3 115 100 +15 0 15 30 10.0 4 100 110 -10 -10 10 40 10.0 5 125 110 +15 +5 15 55 11.0 6 140 110 +30 +35 30 85 14.2
- 108. © 2011 Pearson 4 - 109 Cumulative Absolute Absolute Actual Forecast Cumm Forecast Forecast Qtr Demand Demand Error Error Error Error MAD 1 90 100 -10 -10 10 10 10.0 2 95 100 -5 -15 5 15 7.5 3 115 100 +15 0 15 30 10.0 4 100 110 -10 -10 10 40 10.0 5 125 110 +15 +5 15 55 11.0 6 140 110 +30 +35 30 85 14.2 Tracking Signal ExampleTracking Signal Example Tracking Signal (Cumm Error/MAD) -10/10 = -1 -15/7.5 = -2 0/10 = 0 -10/10 = -1 +5/11 = +0.5 +35/14.2 = +2.5 The variation of the tracking signal between -2.0 and +2.5 is within acceptable limits
- 109. © 2011 Pearson 4 - 110 Adaptive ForecastingAdaptive Forecasting It’s possible to use the computer to continually monitor forecast error and adjust the values of the α and β coefficients used in exponential smoothing to continually minimize forecast error This technique is called adaptive smoothing
- 110. © 2011 Pearson 4 - 111 Focus ForecastingFocus Forecasting Developed at American Hardware Supply, based on two principles: 1. Sophisticated forecasting models are not always better than simple ones 2. 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
- 111. © 2011 Pearson 4 - 112 Forecasting in the ServiceForecasting in the Service SectorSector 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
- 112. © 2011 Pearson 4 - 113 Fast Food RestaurantFast Food Restaurant ForecastForecast 20% – 15% – 10% – 5% – 11-12 1-2 3-4 5-6 7-8 9-10 12-1 2-3 4-5 6-7 8-9 10-11 (Lunchtime) (Dinnertime) Hour of day Percentageofsales Figure 4.12
- 113. © 2011 Pearson 4 - 114 FedEx Call Center ForecastFedEx Call Center Forecast Figure 4.12 12% – 10% – 8% – 6% – 4% – 2% – 0% – Hour of day A.M. P.M. 2 4 6 8 10 12 2 4 6 8 10 12
- 114. © 2011 Pearson 4 - 115 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.