To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Forecasting.

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Presentation transcript:

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Forecasting Chapter 13

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Patterns of Demand Quantity Time (a) Horizontal: Data cluster about a horizontal line. Figure 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Patterns of Demand Quantity Time (b) Trend: Data consistently increase or decrease. Figure 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Patterns of Demand Quantity |||||||||||| JFMAMJJASOND Months (c) Seasonal: Data consistently show peaks and valleys. Figure 13.1 Year 1 Year 2

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Patterns of Demand Quantity |||||| Years (c) Cyclical: Data reveal gradual increases and decreases over extended periods. Figure 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Demand Forecast Applications TABLE 13.1DEMAND FORECAST APPLICATIONS Time Horizon Medium TermLong Term Short Term (3 months–(more than Application(0–3 months) 2 years) 2 years) Total sales Groups or families of products or services Staff planning Production planning Master production scheduling Purchasing Distribution Causal Judgment Forecast quantityIndividual products or services Decision areaInventory management Final assembly scheduling Workforce scheduling Master production scheduling ForecastingTime series techniqueCausal Judgment Total sales Facility location Capacity planning Process management Causal Judgment

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Dependent variable Independent variable XY Figure 13.2 Estimate of Y from regressionequation Regressionequation: Y = a + bX Actualvalue of Y Value of X used to estimate Y Deviation, or error {

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression SalesAdvertising Month(000 units)(000 $) Example 13.1 a = – b = X r = 0.98 r 2 = 0.96 s yx = 15.61

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Figure 13.3 SalesAdvertising Month(000 units)(000 $) a = – b = X r = 0.98 r 2 = 0.96 s yx = |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Sales (thousands of units)

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Figure 13.3 SalesAdvertising Month(000 units)(000 $) a = – b = X r = 0.98 r 2 = 0.96 s yx = |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Y = – X Sales (thousands of units)

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Figure 13.3 SalesAdvertising Month(000 units)(000 $) a = – b = X r = 0.98 r 2 = 0.96 s yx = |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Y = – X Sales (thousands of units)

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Figure 13.3 SalesAdvertising Month(000 units)(000 $) a = – b = X r = 0.98 r 2 = 0.96 s yx = |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Y = – X Sales (thousands of units) Forecast for Month 6 X = $1750, Y = – (1.75)

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Figure 13.3 SalesAdvertising Month(000 units)(000 $) a = – b = X r = 0.98 r 2 = 0.96 s yx = |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Y = – X Sales (thousands of units) Forecast for Month 6 X = $1750, Y = , or 183,015 units

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Figure 13.3 SalesAdvertising Month(000 units)(000 $) a = – b = X r = 0.98 r 2 = 0.96 s yx = |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Y = – X Sales (thousands of units)

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Figure 13.3 SalesAdvertising Month(000 units)(000 $) a = – b = X r = 0.98 r 2 = 0.96 s yx = |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Y = – X Sales (thousands of units) If current stock = 62,500 units, Production = 183,015 – 62,500 = 120,015 units

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Figure 13.4

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression SalesAdvertising Month(000 units)(000 $) Example 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression SalesAdvertising Month(000 units)(000 $) a = Y – b X b =b =b =b =  XY – n XY  X 2 – n X 2 Example 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Example 13.1 a = Y – b X b =b =b =b =  XY – n XY  X 2 – n X 2

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Total ,259 Y = 171X = 1.64 Causal Methods Linear Regression Example 13.1 a = Y – b X b =b =b =b =  XY – n XY  X 2 – n X 2

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression a = Y – b X b =b =b =b = – 5(1.64)(171) – 5(1.64) 2 Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Total ,259 Y = 171X = 1.64 Example 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression a = Y – b X b = Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Total ,259 Y = 171X = 1.64 Example 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression a = 171 – (1.64) b = Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Total ,259 Y = 171X = 1.64 Example 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression a = – b = Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Total ,259 Y = 171X = 1.64 Example 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression a = – b = Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Total ,259 Y = 171X = 1.64 Y = – (X) Example 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression a = b = Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Total ,259 Y = 171X = 1.64 Y = – (X) Figure 13.3 Advertising (thousands of dollars) |||| — 250 — 200 — 150 — 100 — 50 Sales (thousands of units)

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Figure 13.3 a = b = Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Total ,259 Y = 171X = 1.64 Y = – (X) |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Sales (thousands of units)

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression a = b = Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Total ,259 Y = 171X = 1.64 Y = – (X) Sales (thousands of units) |||| Advertising (thousands of dollars) 300 — 250 — 200 — 150 — 100 — 50 Figure 13.3

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Total ,259 Y = 171X = 1.64 Example 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Total ,259 Y = 171X = 1.64 n  XY –  X  Y [ n  X 2 – (  X) 2 ][ n  Y 2 – (  Y) 2 ] r =r =r =r = Example 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Total ,259 Y = 171X = 1.64 r = 0.98 Example 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Total ,259 Y = 171X = 1.64 r = 0.98 r 2 = 0.96  YX = Example 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Total ,259 Y = 171X = 1.64 r = 0.98 r 2 = 0.96  YX = Forecast for Month 6: Advertising expenditure = $1750 Y = (1.75) Y = (1.75) Example 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Causal Methods Linear Regression Sales, YAdvertising, X Month(000 units)(000 $)XYX 2 Y , , , , ,681 Total ,259 Y = 171X = 1.64 r = 0.98 r 2 = 0.96  YX = Forecast for Month 6: Advertising expenditure = $1750 Y = or 183,015 hinges Y = or 183,015 hinges Example 13.1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Simple Moving Averages Figure 13.5 Week — — — — — |||||| Patient arrivals Actual patient arrivals

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Simple Moving Averages Example 13.2 Actual patient arrivals — — — — — Week |||||| Patient arrivals

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Simple Moving Averages Example 13.2 Actual patient arrivals arrivals — — — — — Week |||||| Patient WeekArrivals Patient arrivals

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Simple Moving Averages Example 13.2 Actual patient arrivals arrivals — — — — — Week |||||| Patient WeekArrivals Patient arrivals

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Simple Moving Averages Actual patient arrivals Week — — — — — |||||| Patient WeekArrivals F4 =F4 =F4 =F4 = Example 13.2 Patient arrivals

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Simple Moving Averages Example 13.2 Actual patient arrivals — — — — — Week |||||| Patient WeekArrivals F 4 = Patient arrivals

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Simple Moving Averages Example 13.2 Actual patient arrivals — — — — — Week |||||| Patient WeekArrivals F 4 = Patient arrivals

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Simple Moving Averages Example 13.2 Actual patient arrivals Week — — — — — |||||| Patient WeekArrivals F5 =F5 =F5 =F5 = Patient arrivals

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Simple Moving Averages Example 13.2 Actual patient arrivals — — — — — Week |||||| Patient WeekArrivals F 5 = Patient arrivals

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Simple Moving Averages Figure 13.6 Week — — — — — |||||| Patient arrivals Actual patient arrivals 3-week MA forecast 6-week MA forecast

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Exponential Smoothing — — — — —Week |||||| Exponential Smoothing  = 0.10 F t +1 = F t +  (D t – F t ) Example 13.3 Patient arrivals

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Exponential Smoothing — — — — —Week |||||| Exponential Smoothing  = 0.10 F 4 = 0.10(411) (390) F 3 = ( )/2 F 3 = ( )/2 D 3 = 411 F t +1 = F t +  (D t – F t ) Example 13.3 Patient arrivals

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Exponential Smoothing Example — — — — —Week |||||| F 4 = Exponential Smoothing  = 0.10 F 3 = ( )/2 F 3 = ( )/2 D 3 = 411 F t +1 = F t +  (D t – F t ) Patient arrivals

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Exponential Smoothing Example 13.3 Week — — — — — |||||| F 4 = F 4 = D 4 = 415 Exponential Smoothing  = 0.10 F 4 = F 5 = F t +1 = F t +  (D t – F t ) Patient arrivals

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Exponential Smoothing Example 13.3 Week — — — — — |||||| Patient arrivals

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Exponential Smoothing — — — — — Patient arrivals Week |||||| Example 13.3 Exponential smoothing  = 0.10

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Exponential Smoothing Example — — — — — Patient arrivals Week |||||| week MA forecast 6-week MA forecast Exponential smoothing  = 0.10

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Trend-Adjusted Exponential Smoothing ||||||||||||||| — — — — — — Patient arrivals Week Example 13.4 Actual blood test requests

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Trend-Adjusted Exponential Smoothing ||||||||||||||| — — — — — — Patient arrivals Week Medanalysis, Inc. Demand for blood analysis A t =  D t + (1 –  )(A t-1 + T t-1 ) T t =  (A t – A t-1 ) + (1 –  )T t-1 Example 13.4

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Trend-Adjusted Exponential Smoothing ||||||||||||||| — — — — — — Patient arrivals Week A 1 = 0.2(27) (28 + 3) T 1 = 0.2( ) (3) Medanalysis, Inc. Demand for blood analysis A 0 = 28 patients T 0 = 3 patients  = 0.20  = 0.20 A t =  D t + (1 –  )(A t-1 + T t-1 ) T t =  (A t – A t-1 ) + (1 –  )T t-1 Example 13.4

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Trend-Adjusted Exponential Smoothing ||||||||||||||| — — — — — — Patient arrivals Week A 1 = 30.2 T 1 = 2.8 Medanalysis, Inc. Demand for blood analysis A 0 = 28 patients T 0 = 3 patients  = 0.20  = 0.20 A t =  D t + (1 –  )(A t-1 + T t-1 ) T t =  (A t – A t-1 ) + (1 –  )T t-1 Forecast 2 = = 33 Example 13.4

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Trend-Adjusted Exponential Smoothing ||||||||||||||| — — — — — — Patient arrivals Week Medanalysis, Inc. Demand for blood analysis A 2 = 30.2 D 2 = 44 T 1 = 2.8  = 0.20  = 0.20 A t =  D t + (1 –  )(A t-1 + T t-1 ) T t =  (A t – A t-1 ) + (1 –  )T t-1 A 2 = 0.2(44) ( ) T 2 = 0.2( ) (2.8) Example 13.4

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Trend-Adjusted Exponential Smoothing ||||||||||||||| — — — — — — Patient arrivals Week Medanalysis, Inc. Demand for blood analysis A 2 = 30.2 D 2 = 44 T 1 = 2.8  = 0.20  = 0.20 A t =  D t + (1 –  )(A t-1 + T t-1 ) T t =  (A t – A t-1 ) + (1 –  )T t-1 A 2 = 35.2 T 2 = 3.2 Forecast = = 38.4 Example 13.4

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Trend-Adjusted Exponential Smoothing Figure 13.7 ||||||||||||||| — — — — — — Patient arrivals Week Actual blood test requests Trend-adjusted forecast

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Trend-Adjusted Exponential Smoothing Figure 13.7 ||||||||||||||| — — — — — — Patient arrivals Week Trend-adjusted forecast Actual blood test requests Number of time periods15.00 Demand smoothing coefficient (  )0.20 Initial demand value28.00 Trend-smoothing coefficient (  )0.20 Estimate of trend3.00

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Trend-Adjusted Exponential Smoothing ||||||||||||||| — — — — — — Patient arrivals Week Trend-adjusted forecast Actual blood test requests – – – – – – – – – – TABLE 13.2FORECASTS FOR MEDANALYSIS SmoothedTrendForecast WeekArrivalsAverageAverageForecastError

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Trend-Adjusted Exponential Smoothing Figure 13.7 ||||||||||||||| — 70 — 60 — 50 — 40 — 30 — Patient arrivals Week Trend-adjusted forecast Actual blood test requests SmoothedTrendForecast WeekArrivalsAverageAverageForecastError – – – – – – – – – – SUMMARY Average demand49.80 Mean square error76.13 Mean absolute deviation7.35 Forecast for week Forecast for week Forecast for week

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. QuarterYear 1Year 2Year 3Year Total Total Time-Series Methods Seasonal Influences Example 13.5

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Seasonal Influences Figure 13.8(a)

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Time-Series Methods Seasonal Influences Figure 13.8(b)

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Seasonal Patterns Figure 13.9 Period Demand |||||||||||||||| (a) Multiplicative pattern

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Seasonal Patterns Figure 13.9 Period |||||||||||||||| Demand (b) Additive pattern

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Choosing a Method Forecast Error Measures of Forecast Error E t = D t – F t  |E t | n Et2Et2nnEt2Et2nnn CFE =  E t  = MSE = MAD = MAPE =  [ |E t | (100) ] / D t n  (E t – E ) 2 n – 1

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) % – – – Total– % Choosing a Method Forecast Error Example 13.6

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) – % – – – Total– % Measures of Error Example 13.6

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) – % – – – Total– % CFE = – 15 Measures of Error Example 13.6

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) – % – – – Total– % CFE = – 15 Measures of Error E = = – – 15 8 Example 13.6

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) – % – – – Total– % MSE = = CFE = – 15 Measures of Error E = = – – 15 8 Example 13.6

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) – % – – – Total– % MSE = = CFE = – 15 Measures of Error E = = – – 15 8  = 27.4 Example 13.6

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) – % – – – Total– % MSE = = CFE = – 15 Measures of Error MAD = = E = = – – 15 8  = 27.4 Example 13.6

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) – % – – – Total– % MSE = = CFE = – 15 Measures of Error MAD = = MAPE = = 10.2% 81.3% 8 E = = – – 15 8  = 27.4 Example 13.6

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Choosing a Method Forecast Error Absolute Error AbsolutePercent Month,Demand,Forecast,Error,Squared,Error,Error, tD t F t E t E t 2 |E t |(|E t |/D t )(100) – % – – – Total– % MSE = = CFE = – 15 Measures of Error MAD = = MAPE = = 10.2% 81.3% 8 E = = – – 15 8  = 27.4 Example 13.6

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Choosing a Method Tracking Signals TABLE 13.3PERCENTAGE OF THE AREA OF THE NORMAL PROBABILITY DISTRIBUTION WITHIN THE CONTROL LIMITS OF THE TRACKING SIGNAL Control Limit SpreadEquivalentPercentage of Area (number of MAD)Number of  2 within Control Limits ± 0.80 ± 1.20 ± 1.60 ± 2.00 ± 2.40 ± 2.80 ± 3.20 ± 1.0 ± 1.5 ± 2.0 ± 2.5 ± 3.0 ± 3.5 ± 4.0

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Choosing a Method Tracking Signals Tracking signal = CFEMAD — — — — 0 0 — –0.5 –0.5 — –1.0 –1.0 — –1.5 –1.5 — ||||| Observation number Observation number Tracking signal Control limit Figure Out of control

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Choosing a Method Forecast Error Figure 13.11(a)

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Choosing a Method Forecast Error Figure 13.11(b)

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Choosing a Method Forecast Error Figure 13.11(b)

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Choosing a Method Forecast Error Figure 13.11(b)

To Accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Seventh Edition © 2004 Prentice Hall, Inc. All rights reserved. Air-Quality – Discussion Question – – – – – – – – – –0 |||||||||||||| Year 2 Year 1 JulyAugust Date Visibility rating Figure 13.12