Chapter 11 Solved Problems 1. Exhibit 11.2 Example Linear and Nonlinear Trend Patterns 2.

Slides:



Advertisements
Similar presentations
Forecasting OPS 370.
Advertisements

Guide to Using Excel 2007 For Basic Statistical Applications To Accompany Business Statistics: A Decision Making Approach, 8th Ed. Chapter 16: Analyzing.
1 Chapter 7 My interest is in the future because I am going to spend the rest of my life there.— Charles F. Kettering Forecasting.
Guide to Using Excel For Basic Statistical Applications To Accompany Business Statistics: A Decision Making Approach, 5th Ed. Chapter 15: Analyzing and.
4-1 Operations Management Forecasting Chapter 4 - Part 2.
1 BIS APPLICATION MANAGEMENT INFORMATION SYSTEM Advance forecasting Forecasting by identifying patterns in the past data Chapter outline: 1.Extrapolation.
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.
Analyzing and Forecasting Time Series Data
Chapter 12 - Forecasting Forecasting is important in the business decision-making process in which a current choice or decision has future implications:
1 Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Management Science, 3e by Cliff Ragsdale.
Session 10a. Decision Models -- Prof. Juran2 Overview Forecasting Methods Exponential Smoothing –Simple –Trend (Holt’s Method) –Seasonality (Winters’
Chapter 3 Forecasting McGraw-Hill/Irwin
FORECASTING. Types of Forecasts Qualitative Time Series Causal Relationships Simulation.
Quantitative Business Forecasting Introduction to Business Statistics, 5e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing.
Forecasting McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
T T18-06 Seasonal Relatives Purpose Allows the analyst to create and analyze the "Seasonal Relatives" for a time series. A graphical display of.
1 OM3 Chapter 11 Forecasting and Demand Planning © 2012 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a.
Slides 13b: Time-Series Models; Measuring Forecast Error
Chapter 2 Data Patterns and Choice of Forecasting Techniques
© 2003 Prentice-Hall, Inc.Chap 12-1 Business Statistics: A First Course (3 rd Edition) Chapter 12 Time-Series Forecasting.
1 OM2, Ch. 11 Forecasting and Demand Planning ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly.
FORECASTING AND DEMAND PLANNING
Forecasting using trend analysis
© 2002 Prentice-Hall, Inc.Chap 13-1 Statistics for Managers using Microsoft Excel 3 rd Edition Chapter 13 Time Series Analysis.
LSS Black Belt Training Forecasting. Forecasting Models Forecasting Techniques Qualitative Models Delphi Method Jury of Executive Opinion Sales Force.
Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall7-1 Chapter 7: Forecasting.
IES 371 Engineering Management Chapter 13: Forecasting
Time Series Analysis Introduction Averaging Trend Seasonality.
CHAPTER 3 FORECASTING.
Linear Trend Lines Y t = b 0 + b 1 X t Where Y t is the dependent variable being forecasted X t is the independent variable being used to explain Y. In.
Forecasting OPS 370.
1 Spreadsheet Modeling & Decision Analysis: A Practical Introduction to Management Science, 3e by Cliff Ragsdale.
3-1Forecasting. 3-2Forecasting FORECAST:  A statement about the future value of a variable of interest such as demand.  Forecasts affect decisions and.
Forecasting Professor Ahmadi.
Forecasting MD707 Operations Management Professor Joy Field.
DSc 3120 Generalized Modeling Techniques with Applications Part II. Forecasting.
1 DSCI 3023 Forecasting Plays an important role in many industries –marketing –financial planning –production control Forecasts are not to be thought of.
DAVIS AQUILANO CHASE PowerPoint Presentation by Charlie Cook F O U R T H E D I T I O N Forecasting © The McGraw-Hill Companies, Inc., 2003 chapter 9.
© 2000 Prentice-Hall, Inc. Chap The Least Squares Linear Trend Model Year Coded X Sales
Chapter 6 Business and Economic Forecasting Root-mean-squared Forecast Error zUsed to determine how reliable a forecasting technique is. zE = (Y i -
Irwin/McGraw-Hill  The McGraw-Hill Companies, Inc Forecasting Chapter 11.
Time-Series Forecasting Overview Moving Averages Exponential Smoothing Seasonality.
Trend Projection Model b0b0 b1b1 YiYi
1 1 Slide Forecasting Professor Ahmadi. 2 2 Slide Learning Objectives n Understand when to use various types of forecasting models and the time horizon.
Copyright ©2016 Cengage Learning. All Rights Reserved
4-1 Operations Management Forecasting Chapter 4 - Part 2.
© 1999 Prentice-Hall, Inc. Chap Chapter Topics Component Factors of the Time-Series Model Smoothing of Data Series  Moving Averages  Exponential.
Welcome to MM305 Unit 5 Seminar Prof Greg Forecasting.
Time Series Analysis and Forecasting. Introduction to Time Series Analysis A time-series is a set of observations on a quantitative variable collected.
©2003 Thomson/South-Western 1 Chapter 17 – Quantitative Business Forecasting Slides prepared by Jeff Heyl, Lincoln University ©2003 South-Western/Thomson.
Forecasting Demand. Forecasting Methods Qualitative – Judgmental, Executive Opinion - Internal Opinions - Delphi Method - Surveys Quantitative - Causal,
4 - 1 Course Title: Production and Operations Management Course Code: MGT 362 Course Book: Operations Management 10 th Edition. By Jay Heizer & Barry Render.
1 1 Chapter 6 Forecasting n Quantitative Approaches to Forecasting n The Components of a Time Series n Measures of Forecast Accuracy n Using Smoothing.
Forecasting is the art and science of predicting future events.
CHAPTER 12 FORECASTING. THE CONCEPTS A prediction of future events used for planning purpose Supply chain success, resources planning, scheduling, capacity.
Forecasting Demand. Problems with Forecasts Forecasts are Usually Wrong. Every Forecast Should Include an Estimate of Error. Forecasts are More Accurate.
McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 3 Forecasting.
Forecast 2 Linear trend Forecast error Seasonal demand.
1 Doing Statistics for Business Doing Statistics for Business Data, Inference, and Decision Making Marilyn K. Pelosi Theresa M. Sandifer Chapter 13 Time.
Forecasting. Model with indicator variables The choice of a forecasting technique depends on the components identified in the time series. The techniques.
3-1Forecasting Weighted Moving Average Formula w t = weight given to time period “t” occurrence (weights must add to one) The formula for the moving average.
Predicting Future. Two Approaches to Predition n Extrapolation: Use past experiences for predicting future. One looks for patterns over time. n Predictive.
Welcome to MM305 Unit 5 Seminar Dr. Bob Forecasting.
Welcome to MM305 Unit 5 Seminar Forecasting. What is forecasting? An attempt to predict the future using data. Generally an 8-step process 1.Why are you.
Chapter 9 Forecasting Techniques
Operations Management Contemporary Concepts and Cases
An Integrated Goods and Services Approach
Forecasting Chapter 11.
FORCASTING AND DEMAND PLANNING
Forecasting Elements of good forecast Accurate Timely Reliable
Presentation transcript:

Chapter 11 Solved Problems 1

Exhibit 11.2 Example Linear and Nonlinear Trend Patterns 2

Exhibit 11.3 Seasonal Pattern of Home Natural Gas Usage 3

Exhibit Extra Trend and Business Cycle Characteristics (each data point is 1 year apart) 4

Exhibit 11.4 Call Center Volume Example of a time series with trend and seasonal components: 5

Exhibit 11.5 Chart of Call Volume 6

Basic Concepts in Forecasting Forecast error is the difference between the observed value of the time series and the forecast, or A t – F t. Mean Square Error (MSE) Mean Absolute Deviation Error (MAD) Mean Absolute Percentage Error (MAPE) Σ ( A t – F t ) 2 MSE = [11.1] T ׀A t – F t ׀ MAD = [11.2] T Σ׀ ( A t – F t )/A t ׀ X 100 MAPE = [11.3] T 7

Exhibit 11.6 Forecast Error of Example Time Series Data 8

Solved Problem Develop three-period and four-period moving-average forecasts and single exponential smoothing forecasts with a = 0.5. Compute the MAD, MAPE, and MSE for each. Which method provides a better forecast? PeriodDemandPeriodDemand

Based on these error metrics (MAD, MSE, MAPE), the 3-month moving average is the best method among the three. Solved Problem 10

Exhibit 11.7 Summary of 3-Month Moving-Average Forecasts 11

Exhibit 11.8 Milk-Sales Forecast Error Analysis 12

Single Exponential Smoothing Single Exponential Smoothing (SES) is a forecasting technique that uses a weighted average of past time-series values to forecast the value of the time series in the next period. F t+1 =  A t + (1 –  )F t = F t +  (A t – F t ) [11.5] 13

Exhibit 11.9 Summary of Single Exponential Smoothing Milk-Sales Forecasts with α =

Exhibit Graph of Single Exponential Smoothing Milk-Sales Forecasts with α =

Regression as a Forecasting Approach Regression analysis is a method for building a statistical model that defines a relationship between a single dependent variable and one or more independent variables, all of which are numerical. Y t = a + bt(11.7)  Simple linear regression finds the best values of a and b using the method of least squares.  Excel provides a very simple tool to find the best-fitting regression model for a time series by selecting the Add Trendline option from the Chart menu. 16

Exhibit Factory Energy Costs 17

Exhibit Format Trendline Dialog Box 18

Exhibit Least-Squares Regression Model for Energy Cost Forecasting 19

Exhibit Gasoline Sales Data 20

Exhibit Chart of Sales versus Time 21

Exhibit Multiple Regression Results 22