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

Slides:

Advertisements

Similar presentations

Homework Solution Weighted Averages - Exponential Smoothing - Trend

Advertisements

© 2003 Anita Lee-Post Forecasting Part 3 By Anita Lee-Post By.

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

Bina Nusantara Model Ramalan Pertemuan 14: Mata kuliah: K0194-Pemodelan Matematika Terapan Tahun: 2008.

Forecasting Demand for Services

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

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

Forecasting Ross L. Fink.

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:

Chapter 13 Forecasting.

FORECASTING. Types of Forecasts Qualitative Time Series Causal Relationships Simulation.

Demand Management and Forecasting. Types of Forecasts Qualitative Time Series Causal Relationships Simulation.

Operations Management R. Dan Reid & Nada R. Sanders

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

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

Homework Solution Tracking Signals Turner Industries MGMT E-5070 Manual Computer-Based.

Solution MGMT E st Quarter Forecast for the Fourth Year.

Forecasting Chapter 15.

Winter’s Exponential smoothing

Chapter 4 Forecasting Mike Dohan BUSI Forecasting What is forecasting? Why is it important? In what areas can forecasting be applied?

CHAPTER 3 FORECASTING.

Forecasting OPS 370.

Demand Management and Forecasting

1 What Is Forecasting? Sales will be $200 Million!

Time-Series Forecast Models EXAMPLE Monthly Sales ( in units ) Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Data Point or (observation) MGMT E-5070.

Time-Series Forecast Models EXAMPLE Monthly Sales ( in units ) Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Data Point or (observation) MGMT E-5070.

Forecasting Professor Ahmadi.

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

© 2000 Prentice-Hall, Inc. Chap The Least Squares Linear Trend Model Year Coded X Sales

Exponential Smoothing All statistical fitting methods involve some type of smoothing e.g., in regression, a trend line is fitter to the data points Consider.

1 Given the following data, calculate forecasts for months April through June using a three- month moving average and an exponential smoothing forecast.

Time-Series Forecasting Overview Moving Averages Exponential Smoothing Seasonality.

CH.8 Forecasting Learning objectives: After completing this chapter, you should be able to: 1.Explain the importance of forecasting in organization. 2.Describe.

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.

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.

Lesson 5.1 Forecasting Moving Averages Exponential Smoothing.

Forecasting. 預測 (Forecasting) A Basis of Forecasting In business, forecasts are the basis for budgeting and planning for capacity, sales, production and.

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

© 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.

Review Use data table from Quiz #4 to forecast sales using exponential smoothing, α = 0.2 What is α called? We are weighting the error associated with.

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

©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,

Quantitative Forecasting Methods (Non-Naive)

Forecasting Demand for Services. Learning Objectives l Recommend the appropriate forecasting model for a given situation. l Conduct a Delphi forecasting.

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

4 - 1 Course Title: Production and Operations Management Course Code: MGT 362 Course Book: Operations Management 10 th Edition. By Jay Heizer & Barry Render.

Module: Forecasting Operations Management as a Competitive Weapon.

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.

1 Forecasting. 2 Should you carry an umbrella today? Part of the answer for you most likely depends on how much you care about getting wet! Assuming you.

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.

Manual Computer-Based Homework Assignment Tracking Signals Turner Industries Due on, or before February 19th.

Assignable variation Deviations with a specific cause or source. forecast bias or assignable variation or MSE? Click here for Hint.

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.

Operations Management Demand Forecasting. Session Break Up Conceptual framework Software Demonstration Case Discussion.

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 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.

1 By: Prof. Y. Peter Chiu 9 / 1 / / 1 / 2012 Chapter 2 -A Forecasting.

Lecture 9 Forecasting. Introduction to Forecasting * * * * * * * * o o o o o o o o Model 1Model 2 Which model performs better? There are many forecasting.

DSCI 346 Yamasaki Lecture 7 Forecasting.

Forecasting Chapter 9.

Forecasting Elements of good forecast Accurate Timely Reliable

OUTLINE Questions? Quiz Go over homework Next homework Forecasting.

Presentation transcript:

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

Cool-Man Air Conditioners REQUIREMENT: 1.What effect did the smoothing coefficient have on the forecast for Cool-Man air conditioners? 2.Which smoothing coefficient gives the most accurate forecast?

Cool-Man Air Conditioners YearActual Sales α = 0.30 Forecast Absolute Deviation ? Total ( Σ )372.8

Cool-Man Air Conditioners YearActual Sales α = 0.60 Forecast Absolute Deviation ? Total ( Σ )259.0

Cool-Man Air Conditioners YearActual Sales α = 0.90 Forecast Absolute Deviation ? Total ( Σ )190.5

Cool-Man Air Conditioners MAD α = 0.30 = / 5 = MAD α = 0.60 = / 5 = MAD α = 0.90 = / 5 = Because it has the lowest MAD, the smoothing coefficient α = 0.90 gives the most accurate forecast.

WE SELECT THE “FORECASTING” MODULE

WE WANT TO DEVELOP A NEW PROGRAM FOR MOVING AVERAGES & EXPONENTIAL SMOOTHING, WE SELECT “TIME SERIES ANALYSIS”

THE DATA CREATION SCREEN - Insert the number of past years - We can label those months or years any way we want - We can also insert a title for the problem

“ The Data Table ” Appears We insert the annual sales We can also still change the periods titles, i.e. 1 st Year, 1 st Month, etc. “Naïve Method” is the default method

We first forecast via Exponential smoothing ( a = 0.30 )

The 6 th Year Forecast ( units ) MAD Forecast Error ( units )

We now forecast via Exponential smoothing ( a = 0.60 )

The 6 th Year Forecast ( units ) The MAD Forecast Error ( 51.8 units )

We now forecast via Exponential Smoothing ( a = 0.90 )

The 6 th Year Forecast ( units ) MAD Forecast Error ( 38.1 units )

To forecast via 3-Year Moving Average each period is weighted evenly, ( “1” )

The 6 th Year Forecast ( 555 units ) The MAD Forecast Error ( 67 units )

We now forecast via The Trend Projection Method

The 6 th Year Forecast ( units ) Y = (33.6)(6) = The Regression Line Y = X

Cool-Man Air Conditioners REQUIREMENT: 1.Would you use exponential smoothing with a smoothing coefficient of a = 0.30, a three (3) year moving average, or a trend to predict the sales of Cool-Man air conditioners?

Cool-Man Air Conditioners THREE YEAR MOVING AVERAGE Year Actual Sales Forecast Absolute Deviation ? Total ( Σ )134.0

Cool-Man Air Conditioners TIME SERIES FORECAST Year Actual Sales Forecast Absolute Deviation ? Total ( Σ ) 28.0

Cool-Man Air Conditioners MethodMADCalculations Exponential Smoothing ( a =.30 ) / 5 = Three-Year Moving Average / 2 = 67.0 Regression ( Trend Line ) / 5 = 5.6 Regression ( trend line ) is the preferred method because of its low MAD ( mean absolute deviation )

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