Presentation is loading. Please wait.

Presentation is loading. Please wait.

Forecasting MD707 Operations Management Professor Joy Field.

Published byCecilia Foster Modified over 9 years ago

Similar presentations

Presentation on theme: "Forecasting MD707 Operations Management Professor Joy Field."— Presentation transcript:

1 Forecasting MD707 Operations Management Professor Joy Field

2 Components of the Forecast 2

3 Forecasting using Judgment Methods Sales force estimates Executive opinion Market research Delphi method 3

4 Forecasting using Time Series Methods Naïve forecasts Moving averages Weighted moving averages Exponential smoothing Trend-adjusted exponential smoothing Multiplicative seasonal method 4

5 Moving Average Method Use a 3-month moving average, what is the forecast for month 5? If the actual demand for month 5 is 805 customers, what is the forecast for month 6? 5 MonthCustomers 1 800 2 740 3 810 4 790

6 Comparison of Three-Week and Six-Week Moving Average Forecasts 6

7 Weighted Moving Average Method Let Calculate the forecast for Month 5. If the actual number of customers in month 5 is 805, what is the forecast for month 6? 7 MonthCustomers 1 800 2 740 3 810 4 790

8 Exponential Smoothing Suppose What is the forecast for Month 5? If the actual number of customers in month 5 is 805, what is the forecast for month 6? 8 MonthCustomers 1 800 2 740 3 810 4 790

9 Trend-Adjusted Exponential Smoothing Using months 1-4, an initial estimate of the trend for Month 5 is 2 [(4-2+4)/3 = 2]. The starting forecast for month 5 is 54+2 = 56. Using and forecast the number of customers in month 6. 9 Month Customers 1 48 2 52 3 50 4 54 5 55

10 Trend-Adjusted Exponential Smoothing (cont.) If the actual number of customers in month 6 is 58, what is the forecast for month 7? 10

11 Multiplicative Seasonal Method Procedure  Calculate the trend line based on the available data using regression.  Calculate the centered moving average, with the number of periods equal to the number of seasons.  Calculate the seasonal relative for a period by dividing the actual demand for the period by the corresponding centered moving average.  Calculate the overall estimated seasonal relative by averaging the seasonal relatives from the same periods over the cycle.  Calculate the trend values for each of the periods to be forecast based on the trend line determined in Step 1.  To get a forecast for a given period in a future cycle, multiply the seasonal factor by the trend values. 11

12 Multiplicative Seasonal Method Example QuarterDemandCMA (4 seasons)MA (2 periods) Seasonal Relatives Normalized S.R. Year 1, Q1 100 Year 1, Q2 400 250 Year 1, Q3 300261.51.151.16 273 Year 1, Q4 2002740.730.75 275 Year 2, Q1 192285.50.670.69 296 Year 2, Q2 4082981.371.40 300 Year 2, Q3 384 Total3.924 Year 2, Q4 216 Year 3, Q1331(trend value*)227(forecast) Year 3, Q2344(trend value*)480(forecast) Year 3, Q3356(trend value*)417(forecast) Year 3, Q4369(trend value*)275(forecast) 12 * Using regression, the trend line is 218.6 + 12.48t.

13 Linear Regression where  y = dependent (predicted) variable  x = independent (predictor) variable  a = y-intercept of the line (i.e., value of y when x = 0)  b = slope of the line 13 y = a + bx

14 Linear Regression Line Relative to Actual Data 14

15 Regression Analysis Example Week x (Price) y (Appetizers) 1$2.70760 23.50510 32.00980 44.20250 53.10320 64.05480 15 An analyst for a chain of seafood restaurants is interested in forecasting the number of crab cake appetizers sold each week. He believes that the number sold has a linear relationship to the price and uses linear regression to determine if this is the case.

16 Regression Analysis Example (cont.) Regression Statistics Multiple R0.843 R-Square0.711 Adjusted R-Square0.639 Standard Error165.257 Observations6 ANOVA dfSSMSFSignificance F Regression1269160 9.8560.035 Residual410923927309 Total5378400 CoefficientsStandard Errort StatP-value Intercept1454.6295.94.920.008 Price ($)-277.688.4-3.190.035 16

17 Least Squares Regression Line Appetizer Example 17

18 Interpretation of the Regression Intercept 18

19 Another Regression Analysis Example HoursScore 3.090 2.195 5.865 3.880 4.295 3.260 5.385 4.670 19 A professor is interested in determining whether average study hours per week is a good predictor of test scores. The results of her study are: A student says: "Professor, what can I do to get a B or better on the next test. The professor asks, "On average, how many hours do you spend studying for this course per week?" The student responds, "About 2 hours." Use linear regression to forecast the student's test score.

20 Another Regression Analysis Example (cont.) 20 Regression Statistics Multiple R0.391 R-Square0.153 Adjusted R-Square0.0121 Standard Error13.544 Observations8 ANOVA dfSSMSFSignificance F Regression1199.2 1.090.3375 Residual61100.8183.5 Total71300 CoefficientsStandard Errort StatP-value Intercept97.317.35.60.0013 Study hours-4.34.20.3375

21 Forecast Error Measures Bias  Average error Variability  Mean squared error (MSE)  Standard deviation (s)  Mean absolute error (MAD)  Mean percent absolute error (MAPE) Relative bias  Tracking signal (TS) 21

22 Summarizing Forecast Accuracy PeriodActual (A)Forecast (F)Error (E=A-F)Abs ErrorError Sq [(Abs E)/A] x 100 11139518 32415.93 2858055255.88 396103-77497.29 486119-3333108938.37 512111744163.31 6100125-252562525.00 71426775 562552.82 89296-44164.35 972116-4444193661.11 Total-112159705214.06 MAD =23.9 MSE =1213.1 s =34.8 MAPE =23.8% 22

23 Tracking and Analyzing Forecast Errors PeriodActual (A)Forecast (F)Error (E=A-F)Assessing bias: 10102130-28Cumulative forecast error (periods 1-9) =-11 111071025MAD (periods 1-9) =23.9 121128923Tracking signal (periods 1-9) =-0.46 131189721 1489115-26Cumulative forecast error (periods 1-18) =-7 151428260MAD (periods 1-18) =26.28 16100130-30Tracking signal (periods 1-18) =-0.27 1794137-43 181118922Assessing error variability/size: Total4 Standard deviation (periods 1-9) = 2s control limits for errors: 0 +/- 2(34.8) = 34.8 0 +/- 69.6 23 2s Control Chart for Errors UCL = 69.6 LCL = -69.6

24 Forecast Performance of Various Forecasting Methods for a Medical Clinic Method Cumulative Sum of Forecast Errors (CFE – bias) Mean Absolute Deviation (MAD - variability) Simple moving average Three-week (n = 3)23.117.1 Six-week (n = 6)69.815.5 3-period weighted moving average w = 0.70, 0.20, 0.1014.018.4 Exponential smoothing = 0.165.614.8 = 0.241.015.3 24

Download ppt "Forecasting MD707 Operations Management Professor Joy Field."

Similar presentations

Forecasting OPS 370.

Forecasting OPS 370.
Forecasting the Demand Those who do not remember the past are condemned to repeat it George Santayana (1863-1952) a Spanish philosopher, essayist, poet.

Forecasting the Demand Those who do not remember the past are condemned to repeat it George Santayana ( ) a Spanish philosopher, essayist, poet.
4-1 Operations Management Forecasting Chapter 4 - Part 2.

4-1 Operations Management Forecasting Chapter 4 - Part 2.
Forecasting 5 June 2001. Introduction What: Forecasting Techniques Where: Determine Trends Why: Make better decisions.

Forecasting 5 June Introduction What: Forecasting Techniques Where: Determine Trends Why: Make better decisions.
Qualitative Forecasting Methods

Qualitative Forecasting Methods
Class 20: Chapter 12S: Tools Class Agenda –Answer questions about the exam News of Note –Elections Results—Time to come together –Giants prove that nice.

Class 20: Chapter 12S: Tools Class Agenda –Answer questions about the exam News of Note –Elections Results—Time to come together –Giants prove that nice.
Forecasting Ross L. Fink.

Forecasting Ross L. Fink.
Chapter 12 - Forecasting Forecasting is important in the business decision-making process in which a current choice or decision has future implications:

Chapter 12 - Forecasting Forecasting is important in the business decision-making process in which a current choice or decision has future implications:
Forecasting.

Forecasting.
CHAPTER 3 Forecasting.

CHAPTER 3 Forecasting.
Chapter 3 Forecasting McGraw-Hill/Irwin

Chapter 3 Forecasting McGraw-Hill/Irwin
Chapter 13 Forecasting.

Chapter 13 Forecasting.
Forecasting & Time Series Minggu 6. Learning Objectives Understand the three categories of forecasting techniques available. Become aware of the four.

Forecasting & Time Series Minggu 6. Learning Objectives Understand the three categories of forecasting techniques available. Become aware of the four.
Demand Management and Forecasting. Types of Forecasts Qualitative Time Series Causal Relationships Simulation.

Demand Management and Forecasting. Types of Forecasts Qualitative Time Series Causal Relationships Simulation.
Forecasting McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.

Forecasting McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
Chapter 11 Solved Problems 1. Exhibit 11.2 Example Linear and Nonlinear Trend Patterns 2.

Chapter 11 Solved Problems 1. Exhibit 11.2 Example Linear and Nonlinear Trend Patterns 2.
08/08/02SJSU Bus 140 - David Bentley1 Course Part 2 Supply and Demand Management.

08/08/02SJSU Bus David Bentley1 Course Part 2 Supply and Demand Management.
T18-06 - 1 T18-06 Seasonal Relatives Purpose Allows the analyst to create and analyze the Seasonal Relatives for a time series. A graphical display of.

T T18-06 Seasonal Relatives Purpose Allows the analyst to create and analyze the "Seasonal Relatives" for a time series. A graphical display of.
FORECASTING Operations Management Dr. Ron Lembke.

FORECASTING Operations Management Dr. Ron Lembke.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 3 Forecasting.

McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 3 Forecasting.

Similar presentations

Ads by Google