Lecture 3 Forecasting CT – Chapter 3.

Slides:



Advertisements
Similar presentations
Technology Forecasting Learning Objectives
Advertisements

Agenda of Week V. Forecasting
Forecasting the Demand Those who do not remember the past are condemned to repeat it George Santayana ( ) a Spanish philosopher, essayist, poet.
Operations Management Forecasting Chapter 4
Inventory Models – Chapter 11
What is Forecasting? A forecast is an estimate of what is likely to happen in the future. Forecasts are concerned with determining what the future will.
T T18-03 Exponential Smoothing Forecast Purpose Allows the analyst to create and analyze the "Exponential Smoothing Average" forecast. The MAD.
1 Lecture 4 MGMT 650 Network Models – Shortest Path Project Scheduling Forecasting.
Forecasting 5 June Introduction What: Forecasting Techniques Where: Determine Trends Why: Make better decisions.
Forecasting.
1 Lecture 2 Decision Theory Chapter 5S. 2  Certainty - Environment in which relevant parameters have known values  Risk - Environment in which certain.
CHAPTER 3 Forecasting.
Forecasting To Accompany Russell and Taylor, Operations Management, 4th Edition,  2003 Prentice-Hall, Inc. All rights reserved.
Chapter 3 Forecasting McGraw-Hill/Irwin
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 3 Forecasting.
Chapter 13 Forecasting.
Operations Management Forecasting Chapter 4
Copyright 2006 John Wiley & Sons, Inc. Beni Asllani University of Tennessee at Chattanooga Forecasting Operations Chapter 12 Roberta Russell & Bernard.
08/08/02SJSU Bus David Bentley1 Course Part 2 Supply and Demand Management.
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 3 Forecasting Car buyer- Models & Option Does the dealer know!
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 3 Forecasting.
Chapter 2 Data Patterns and Choice of Forecasting Techniques
Chapter 3 Forecasting McGraw-Hill/Irwin
The Importance of Forecasting in POM
CHAPTER 3 FORECASTING.
Forecasting Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill.
Forecasting McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.
Chapter 3 Forecasting.
Forecasting.
Forecasting.
3-1 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved.
Operations Management
3-1Forecasting CHAPTER 3 Forecasting Homework Problems: # 2,3,4,8(a),22,23,25,27 on pp
3-1Forecasting William J. Stevenson Operations Management 8 th edition.
3-1Forecasting. 3-2Forecasting FORECAST:  A statement about the future value of a variable of interest such as demand.  Forecasts affect decisions and.
McGraw-Hill/Irwin Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. 3 Forecasting.
3-1 Forecasting I see that you will get an A this semester. 10 th ed.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 3 Forecasting.
Forecasting. 預測 (Forecasting) A Basis of Forecasting In business, forecasts are the basis for budgeting and planning for capacity, sales, production and.
McGraw-Hill/Irwin Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. 3 Forecasting.
Welcome to MM305 Unit 5 Seminar Prof Greg Forecasting.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 3 Forecasting.
15-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Forecasting Chapter 15.
OM3-1 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights.
BUAD306 Chapter 3 – Forecasting.
3-1Forecasting Ghana Institute of Management and Public Administration [GIMPA] McGraw-Hill/Irwin Operations Management, Eighth Edition, by William J. Stevenson.
Adeyl Khan, Faculty, BBA, NSU Car buyer- Models & Option Does the dealer know! Basic Managerial function- Planning.
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.
CHAPTER 12 FORECASTING. THE CONCEPTS A prediction of future events used for planning purpose Supply chain success, resources planning, scheduling, capacity.
3-1Forecasting CHAPTER 3 Forecasting McGraw-Hill/Irwin Operations Management, Eighth Edition, by William J. Stevenson Copyright © 2005 by The McGraw-Hill.
3-1Forecasting William J. Stevenson Operations Management 8 th edition.
Stevenson 3 Forecasting. 3-2 Learning Objectives  List the elements of a good forecast.  Outline the steps in the forecasting process.  Compare and.
Forecasting Production and Operations Management 3-1.
McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 3 Forecasting.
Forecas ting Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill.
Forecast 2 Linear trend Forecast error Seasonal demand.
3-1 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved.
Chapter 3 Lecture 4 Forecasting. Time Series is a sequence of measurements over time, usually obtained at equally spaced intervals – Daily – Monthly –
T T18-02 Weighted Moving Average Forecast Purpose Allows the analyst to create and analyze the "Weighted Moving Average" forecast for up to 5.
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.
Copyright © 2014 by McGraw-Hill Education (Asia). All rights reserved. 3 Forecasting.
1 By: Prof. Y. Peter Chiu 9 / 1 / / 1 / 2012 Chapter 2 -A Forecasting.
3-1 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved.
Forecasts.
RAJEEV GANDHI COLLEGE OF MANAGEMENT STUDIES
FORCASTING AND DEMAND PLANNING
Stevenson 3 Forecasting.
Forecasting Elements of good forecast Accurate Timely Reliable
Presentation transcript:

Lecture 3 Forecasting CT – Chapter 3

Forecast A statement about the future value of a variable of interest such as demand. Forecasts affect decisions and activities throughout an organization Accounting, finance Human resources Marketing Operations Product / service design

Uses of Forecasts Accounting Cost/profit estimates Finance Cash flow and funding Human Resources Hiring/recruiting/training Marketing Pricing, promotion, strategy Operations Schedules, MRP, workloads Product/service design New products and services

Elements of a Good Forecast Timely Accurate Reliable Meaningful Written Easy to use

Steps in the Forecasting Process Step 1 Determine purpose of forecast Step 2 Establish a time horizon Step 3 Select a forecasting technique Step 4 Gather and analyze data Step 5 Prepare the forecast Step 6 Monitor the forecast “The forecast”

Types of Forecasts Judgmental - uses subjective inputs Time series - uses historical data assuming the future will be like the past Associative models - uses explanatory variables to predict the future

Judgmental Forecasts Executive opinions Sales force opinions Consumer surveys Outside opinion Delphi method Opinions of managers and staff Achieves a consensus forecast

Time Series Forecasts Trend - long-term movement in data Seasonality - short-term regular variations in data Cycle – wavelike variations of more than one year’s duration Irregular variations - caused by unusual circumstances

Forecast Variations Figure 3.1 Trend Cycles Irregular variation 90 89 88 Seasonal variations

Smoothing/Averaging Methods Used in cases in which the time series is fairly stable and has no significant trend, seasonal, or cyclical effects Purpose of averaging - to smooth out the irregular components of the time series. Four common smoothing/averaging methods are: Moving averages Weighted moving averages Exponential smoothing

Example of Moving Average Sales of gasoline for the past 12 weeks at your local Chevron (in ‘000 gallons). If the dealer uses a 3-period moving average to forecast sales, what is the forecast for Week 13? Past Sales Week Sales Week Sales 1 17 7 20 2 21 8 18 3 19 9 22 4 23 10 20 5 18 11 15 6 16 12 22

Management Scientist Solutions MA(3) for period 4 = (17+21+19)/3 = 19 Forecast error for period 3 = Actual – Forecast = 23 – 19 = 4

MA(5) versus MA(3)

Review of last class Forecasting Smoothing/averaging method What is a forecast? Organizational functions that use forecasts Desirable characteristics of a forecast Types of forecasts Types of time series Smoothing/averaging method Moving averages Weighted moving averages Advantage

Exponential Smoothing Premise - The most recent observations might have the highest predictive value. Therefore, we should give more weight to the more recent time periods when forecasting.

Exponential Smoothing Ft+1 = Ft + (At - Ft) Weighted averaging method based on previous forecast plus a percentage of the forecast error A-F is the error term,  is the % feedback

Picking a Smoothing Constant  .1 .4 Actual

Suitable for time series data that exhibit a long term linear trend Linear Trend Equation Suitable for time series data that exhibit a long term linear trend Ft Ft = a + bt a Ft = Forecast for period t t = Specified number of time periods a = Value of Ft at t = 0 b = Slope of the line 0 1 2 3 4 5 t

Sale increases every time period @ 1.1 units Linear Trend Example Linear trend equation F11 = 20.4 + 1.1(11) = 32.5 Sale increases every time period @ 1.1 units

Actual/Forecasted sales Actual vs Forecast Linear Trend Example 35 30 25 Actual/Forecasted sales 20 Actual 15 Forecast 10 5 1 2 3 4 5 6 7 8 9 10 Week F(t) = 20.4 + 1.1t

Forecasting with Trends and Seasonal Components – An Example Business at Terry's Tie Shop can be viewed as falling into three distinct seasons: (1) Christmas (November-December); (2) Father's Day (late May - mid-June); and (3) all other times. Average weekly sales ($) during each of the three seasons during the past four years are known and given below. Determine a forecast for the average weekly sales in year 5 for each of the three seasons. Year Season 1 2 3 4 1 1856 1995 2241 2280 2 2012 2168 2306 2408 3 985 1072 1105 1120

Management Scientist Solutions

Interpretation of Seasonal Indices Seasonal index for season 2 (Father’s Day) = 1.236 Means that the sale value of ties during season 2 is 23.6% higher than the average sale value over the year Seasonal index for season 3 (all other times) = 0.586 Means that the sale value of ties during season 3 is 41.4% lower than the average sale value over the year

Forecast Accuracy Error - difference between actual value and predicted value Mean Absolute Deviation (MAD) Average absolute error Mean Squared Error (MSE) Average of squared error

MAD and MSE  Actual  forecast MAD = n MSE = Actual forecast)   n ( 2   n (

Measure of Forecast Accuracy MSE = Mean Squared Error

Forecasting Accuracy Estimates Example 10 of textbook

Sources of Forecast errors Model may be inadequate Irregular variations Incorrect use of forecasting technique

Characteristics of Forecasts They are usually wrong A good forecast is more than a single number Aggregate forecasts are more accurate The longer the forecast horizon, the less accurate the forecast will be Forecasts should not be used to the exclusion of known information

Choosing a Forecasting Technique No single technique works in every situation Two most important factors Cost Accuracy Other factors include the availability of: Historical data Computers Time needed to gather and analyze the data Forecast horizon