F ORECAST A CCURACY By: Agung Utama. I NTRODUCTION A forecast is never completely accurate, there will be always deviation from the actual demand. This.

Slides:

Advertisements

Similar presentations

Decomposition Method.

Advertisements

4/16/ Ardavan Asef-Vaziri Variable of interest Time Series Analysis.

Forecasting Chapter 15.

T T18-03 Exponential Smoothing Forecast Purpose Allows the analyst to create and analyze the "Exponential Smoothing Average" forecast. The MAD.

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.

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

Forecasting IME 451, Lecture 2. Laws of Forecasting 1.Forecasts are always wrong! 2.Detailed forecasts are worse than aggregate forecasts! Dell forecasts.

CHAPTER 3 Forecasting.

T T18-04 Linear Trend Forecast Purpose Allows the analyst to create and analyze the "Linear Trend" forecast. The MAD and MSE for the forecast.

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.

T T18-05 Trend Adjusted Exponential Smoothing Forecast Purpose Allows the analyst to create and analyze the "Trend Adjusted Exponential Smoothing"

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.

15-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Forecasting Chapter 15.

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

Applied Business Forecasting and Planning

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

Relationships Among Variables

Homework Solution Weighted Averages - Exponential Smoothing - Trend Cool-Man Air Conditioners Manual ManualComputer-Based TM MGMT E-5070 Part B.

Forecasting Chapter 15.

Introduction to Forecasting COB 291 Spring Forecasting 4 A forecast is an estimate of future demand 4 Forecasts contain error 4 Forecasts can be.

The Importance of Forecasting in POM

Demand Management and Forecasting

Demand Management and Forecasting

Operations Management

Introduction to Management Science

3-1Forecasting William J. Stevenson Operations Management 8 th edition.

Forecasting MD707 Operations Management Professor Joy Field.

1 DSCI 3023 Forecasting Plays an important role in many industries –marketing –financial planning –production control Forecasts are not to be thought of.

Operations Research II Course,, September Part 6: Forecasting Operations Research II Dr. Aref Rashad.

1 Forecasting Formulas Symbols n Total number of periods, or number of data points. A Actual demand for the period (  Y). F Forecast demand for the period.

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

Lesson 5.1 Forecasting Moving Averages Exponential Smoothing.

Forecasting Chapter 9. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Define Forecast.

MNG221 - Management Science Forecasting. Lecture Outline Forecasting basics Moving average Exponential smoothing Linear trend line Forecast accuracy.

15-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Forecasting Chapter 15.

Forecasting Chapter 9. Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall9 - 2 Chapter Objectives Be able to:  Discuss the importance.

Business Processes Sales Order Management Aggregate Planning Master Scheduling Production Activity Control Quality Control Distribution Mngt. © 2001 Victor.

Chapter 4 Class 2.

15-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Forecasting Chapter 15.

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna Port of Baltimore Exponential Smoothing Example QtrActual Tonnage Unloaded.

Economics 173 Business Statistics Lecture 27 © Fall 2001, Professor J. Petry

Chapter 4 Forecasting. Ch. 4: What is covered? Moving AverageMoving Average Weighted Moving AverageWeighted Moving Average Exponential SmoothingExponential.

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.

Forecasting Production and Operations Management 3-1.

Demand Management and Forecasting Chapter 11 Portions Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.

Forecast 2 Linear trend Forecast error Seasonal demand.

CHAPTER: 12 FORECASTING (MSC 301) MD. TAMZIDUL ISLAM FACULTY, BBS.

3-1 McGraw-Hill/Irwin Operations Management, Seventh Edition, by William J. Stevenson Copyright © 2002 by The McGraw-Hill Companies, Inc. All rights reserved.

Forecasting. Model with indicator variables The choice of a forecasting technique depends on the components identified in the time series. The techniques.

Chapter 11 – With Woodruff Modications Demand Management and Forecasting Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin.

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.

Forecasting Quantitative Methods. READ FIRST Outline Define Forecasting The Three Time Frames of Forecasting Forms of Forecast Movement Forecasting Approaches.

T T18-02 Weighted Moving Average Forecast Purpose Allows the analyst to create and analyze the "Weighted Moving Average" forecast for up to 5.

Forecasting Chapter 9.

Operations Management Contemporary Concepts and Cases

Forecasting techniques

Forecasting Chapter 11.

Exponential Smoothing with Trend Adjustment - continued

Forecasting Chapter 15.

OUTLINE Questions? Quiz Results Quiz on Thursday Continue Forecasting

T18-08 Calculate MAD, MSE Purpose Allows the analyst to create and analyze the MAD and MSE for a forecast. A graphical representation of history and.

OUTLINE Questions? Quiz Go over homework Next homework Forecasting.

Demand Management and Forecasting

Presentation transcript:

F ORECAST A CCURACY By: Agung Utama

I NTRODUCTION A forecast is never completely accurate, there will be always deviation from the actual demand. This difference between the foracast and the actual is the forecast error. Although forecast eror is inevitable, the objective of forecasting is that it be as slight as possible. There are different measures of forecast error, including: Mean Absolute Deviation (MAD), Mean Absolute Percent Deviation (MAPD), Cummulative Error (CE), and Error Bias (E).

M EAN A BSOLUTE D EVIATION (MAD) MAD is an average of the difference between the forecast and actual demand, as computed by the following formula: MAD= Σ І Dt-Ft I n Where: t =The period number Dt= Demand in period t Ft = The forecast for period t n = The total number of periods I I = Absolute value The smaller the value of MAD, the more accurate the forecast, although viewed alone, MAD is difficult to assess.

C OMPUTATIONAL VALUES FOR MAD periodDemand (Dt) Forecast Ft (α = 0.30) Error (et) (Dt-Ft) I dt-Ft I

Using the data in the table, MAD is computed: MAD=ΣI Dt-Ft I n = 53l39 11 = 4.85

T HE M EAN A BSOLUTE P ERCENT D EVIATION Measures the absolute error as a percentage of demand rather than per period. As a result, it eliminates the problem of interpreting the measure of accuracy relative to the magnitude of the demand and forecast values, as MAD does. A lower percent deviation implies a more accurate forecast. MAPD = ΣI Dt-Ft I Σ Dt = = 0.10 or 10%

C UMULATIVE E RROR Cumulative error is computed simply by summing the forecast errors, as shown in the formula: E =Σ e t A large positive value indicates that the forecast is probably consistently lower than the actual demand, or is biased low. The comulative eror based on the previuos data is simply computed as: E =Σ e t = 49.31

A VERAGE E RROR (B IAS ) It is computed by averaging the comulative error over the number of time periods The comulative error is interpreted similarly to the comulative error. A positive value indicates low bias, and a negative value indicates high bias. A value close to zero implies a lack of bias. The formula is: Ḗ =Σ e t n = = 4.48

D ISCUSSION QUESTIONS Registration number for a marketing seminar over the past 10 weeks are shown below: a) Starting with week 2 and ending with week 11, forecast registrations using the naïve forecasting method. b) Starting with week 3 and ending with week 11, forecast registrations using a two-week moving average. Week Registrations

Demand for heart transplant surgery at RSCM has increased steadily in the past few years: The director of medical services predicted 6 years ago that demand in year 1 would be 41 surgeries. a) Use exponential smoothing, first with a smoothing constant of 0.6 and then with 1.9, to develop forecasts for years 2 through 6. b) Use MAD criterion, which of the two forecasting methods is best? Year Heart Transplant ?

Given the following data, use least squares regression to derive a trend equation. What is your estimate of the demand in period 7? In period 12? Period Number