Review Test 2 BSAD 30 Dave Novak Source: Anderson et al., 2015 Quantitative Methods for Business 13 th edition – some slides are directly from J. Loucks.

Slides:

Advertisements

Similar presentations

Using Solver to solve a minimization LP + interpretation of output BSAD 30 Dave Novak Source: Anderson et al., 2013 Quantitative Methods for Business 12.

Advertisements

©The McGraw-Hill Companies, Inc. 2008McGraw-Hill/Irwin Lesson 12.

LINEAR PROGRAMMING SENSITIVITY ANALYSIS

Chapter 5 Sensitivity Analysis: An Applied Approach

Copyright (c) 2003 Brooks/Cole, a division of Thomson Learning, Inc. 1 Chapter 5 Sensitivity Analysis: An Applied Approach to accompany Introduction to.

Linear Programming Problem

Lesson 11 Multicriteria Decisions within LP Framework.

Decision analysis: part 2

BA 452 Lesson A.2 Solving Linear Programs 1 1ReadingsReadings Chapter 2 An Introduction to Linear Programming.

Optimization Models Module 9. MODEL OUTPUT EXTERNAL INPUTS DECISION INPUTS Optimization models answer the question, “What decision values give the best.

19- 1 Chapter Nineteen McGraw-Hill/Irwin © 2005 The McGraw-Hill Companies, Inc., All Rights Reserved.

1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.

Sensitivity analysis BSAD 30 Dave Novak

Operations Management

Chapter 3 Forecasting McGraw-Hill/Irwin

1 1 Slide LINEAR PROGRAMMING Introduction to Sensitivity Analysis Professor Ahmadi.

Forecasting McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.

Linear Programming ISQA 459/559. Getting Started with LP Game problem Terms Algebraic & Graphical Illustration LP with Excel.

Jon Curwin and Roger Slater, QUANTITATIVE METHODS: A SHORT COURSE ISBN © Cengage Chapter 3: Using Graphs.

1 1 Slide © 2009 South-Western, a part of Cengage Learning Chapter 6 Forecasting n Quantitative Approaches to Forecasting n Components of a Time Series.

Slides by John Loucks St. Edward’s University.

Operations and Supply Chain Management

1 1 Slide © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole.

Chapter 19 Linear Programming McGraw-Hill/Irwin

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

PowerPoint presentation to accompany Operations Management, 6E (Heizer & Render) © 2001 by Prentice Hall, Inc., Upper Saddle River, N.J B-1 Operations.

Linear Programming Topics General optimization model LP model and assumptions Manufacturing example Characteristics of solutions Sensitivity analysis Excel.

STDM - Linear Programming 1 By Isuru Manawadu B.Sc in Accounting Sp. (USJP), ACA, AFM

Announcement-exam rules

Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education.

Transparency Masters to accompany Heizer/Render – Principles of Operations Management, 5e, and Operations Management, 7e © 2004 by Prentice Hall, Inc.,

Introduction to Linear Programming BSAD 141 Dave Novak.

Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Time Series Forecasting Chapter 16.

McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. Time Series Forecasting Chapter 13.

Time-Series Forecasting Learning Objectives 1.Describe What Forecasting Is 2. Forecasting Methods 3.Explain Time Series & Components 4.Smooth a Data.

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.

1 Systems Analysis Methods Dr. Jerrell T. Stracener, SAE Fellow SMU EMIS 5300/7300 NTU SY-521-N NTU SY-521-N SMU EMIS 5300/7300 Statistical Analysis Other.

Time Series Analysis and Forecasting

To Accompany Ritzman & Krajewski, Foundations of Operations Management © 2003 Prentice-Hall, Inc. All rights reserved. Chapter 9 Demand Forecasting.

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

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

1 The Dual in Linear Programming In LP the solution for the profit- maximizing combination of outputs automatically determines the input amounts that must.

1 1 Slide © 2008 Thomson South-Western. All Rights Reserved © 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or.

Chapter 6 Simplex-Based Sensitivity Analysis and Duality

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

Jon Curwin and Roger Slater, QUANTITATIVE METHODS: A SHORT COURSE ISBN © Thomson Learning 2004 Jon Curwin and Roger Slater, QUANTITATIVE.

Sensitivity analysis continued… BSAD 30 Dave Novak Source: Anderson et al., 2013 Quantitative Methods for Business 12 th edition – some slides are directly.

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

3-1Forecasting CHAPTER 3 Forecasting McGraw-Hill/Irwin Operations Management, Eighth Edition, by William J. Stevenson Copyright © 2005 by The McGraw-Hill.

Operations Research By: Saeed Yaghoubi 1 Graphical Analysis 2.

Linear Programming Wyndor Glass Co. 3 plants 2 new products –Product 1: glass door with aluminum framing –Product 2: 4x6 foot wood frame window.

MBF1413 | Quantitative Methods Prepared by Dr Khairul Anuar 8: Time Series Analysis & Forecasting – Part 1

13 – 1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall. Forecasting 13 For Operations Management, 9e by Krajewski/Ritzman/Malhotra.

Chapter 9 Forecasting. 1. Define Forecast. Forecasting  Forecast – An estimate of the future level of some variable.  Why Forecast?  Assess long-term.

To accompany Krajewski & Ritzman Operations Management: Strategy and Analysis, Fourth Edition  1996 Addison-Wesley Publishing Company, Inc. All rights.

Times Series Forecasting and Index Numbers Chapter 16 Copyright © 2014 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin.

Managerial Decision Modeling 6 th edition Cliff T. Ragsdale.

Forecas ting Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill.

Chapter 15 Forecasting. Forecasting Methods n Forecasting methods can be classified as qualitative or quantitative. n Such methods are appropriate when.

Chapter 3 Lecture 4 Forecasting. Time Series is a sequence of measurements over time, usually obtained at equally spaced intervals – Daily – Monthly –

PERT / CPM – Time-Cost Tradeoffs BSAD 30 Dave Novak Source: Anderson et al., 2015 Quantitative Methods for Business 13 th edition – some slides are directly.

Forecasting Chapter 9.

Chapter Nineteen McGraw-Hill/Irwin

Introduction to Linear Programs

Introduction to linear programming (LP): Minimization

Forecasting is an Integral Part of Business Planning

Optimization Models Module 9.

Chapter Nineteen McGraw-Hill/Irwin

Forecasting - Introduction

Presentation transcript:

Review Test 2 BSAD 30 Dave Novak Source: Anderson et al., 2015 Quantitative Methods for Business 13 th edition – some slides are directly from J. Loucks © 2013 Cengage Learning 1

Chapter 6 You should be able to calculate all four basic measures of forecast accuracy (MFE, MAE, MSE, and MAPE) You should be able to make a forecast for any time period using any of the five forecasting methods we discussed (naïve, moving average, weighted moving average, exponential smoothing, linear trend 2

Using linear trend Seasonal pattern with trend The time series (the data we are looking at) includes both a seasonal effect AND a positive or negative trend over time We will have seasonal variables AND a time variable in our regression model 3

TV sales example 4 Assume that we have collected quarterly data on the number of TVs sold by a particular retailer over the past four years

TV sales example 5 Average sales for each quarter for the past four years

TV sales example 6 Year 1Year 2Year 3Year 4

Seasonal pattern with trend We have a positive trend in sales over time We have seasonal variation in sales that appears to repeat every year Requires the use of (k – 1) dummy variables 7

Seasonal pattern with trend General form of equation: 8 We can estimate the regression function coefficients b 0, b 1, b 2, b 3, b 4 using linear regression

Seasonal pattern with trend 9 Dummy variables capture seasonal effects Quarter #2 of year #4 = Time period 14 Quarter #3 of year #2 = Time period 7

Seasonal pattern with trend 10

Seasonal pattern with trend How much of the sales variability is explained by the linear regression model? 11

Seasonal pattern with trend In this model, Qtr4 is our baseline for comparison (we have no Qtr4 dummy). How many more / fewer TVs do you expect to sell in Qtr2 compared to Qtr4? 12

Seasonal pattern with trend How many more / fewer TVs do we expect to sell each quarter? 13

Seasonal pattern with trend Using the linear trend, forecast sales for time period 11 (Year 3, Quarter 3) 14

Seasonal pattern with trend Using the linear trend, forecast sales for time period 18 (Year 5, Quarter 2) 15

Chapter 7 and 8 You should be able to formulate an LP from a word problem You should be able to solve an LP graphically You should be able to calculate slack and surplus You should be able to interpret the Answer and Sensitivity Reports 16

CategoryProduct 1Product 2Product 3 Profit/unit$31$60$16 Machine 1 time/unit Machine 2 time/unit Better Products, Inc., manufactures three products on two machines. In a typical week, 40 hours are available on each machine. The profit contribution and production time in hours per unit are as follows: Two operators are required for machine 1; thus, 2 hours of labor must be scheduled for each hour of machine 1 time. Only one operator is required for machine 2. A maximum of 120 labor-hours is available for assignment to the machines during the coming week. Other production requirements are that product 1 cannot account for more than 55% of the units produced and that product 3 must account for at least 25% of the units produced.

Formulate the problem

Write the Standard Form of the LP

Answer Report

Solve for S1 and S4

Sensitivity Report

How many units of each product should be produced to maximize the total profit contribution? If required, round your answers to the nearest integer. What is the projected weekly profit associated with your solution?

How many hours of production time will be scheduled on each machine? If required, round your answers to two decimal places. What is the value of one additional hour of labor? If required, round your answers to two decimal places.

Assume that labor capacity can be increased to 130 hours. Develop the optimal product mix, assuming that the extra hours are made available? If required, round your answers to the nearest integer. Would you be interested in using the 10 additional hours of labor?

If labor hours decrease by 10, what is the impact on the optimal solution mix and the total profit?

If the profit you receive per unit of product #1 were to increase by $10, what is the optimal solution mix and profit maximizing solution? If the profit you receive per unit of product #1 were to decrease by $5, what is the optimal solution mix and profit maximizing solution?

Interpret the Shadow Price associated with constraint #1