1 Management Science. 2 Chapter Topics The Management Science Approach to Problem Solving Model Building: Break-Even Analysis Computer Solution Management.

Slides:



Advertisements
Similar presentations
Introduction to Management Science, Modeling, and Excel Spreadsheets
Advertisements

10-1 Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Nonlinear Programming Chapter 10.
Cost-Volume-Profit Analysis Managerial Accounting Prepared by Diane Tanner University of North Florida Chapter 7.
INTRODUCTION TO MODELING
2-1 Linear Programming: Model Formulation and Graphical Solution Chapter 2 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
© 2008 Prentice-Hall, Inc. Chapter 1 To accompany Quantitative Analysis for Management, Tenth Edition, by Render, Stair, and Hanna Power Point slides created.
Introduction to Management Science
Introduction to Management Science
1 Copyright © 2008 Cengage Learning South-Western. Heitger/Mowen/Hansen Cost-Volume-Profit Analysis: A Managerial Planning Tool Chapter Three Fundamental.
Introduction to Management Science
Operations Management
INTRODUCTION TO MANAGERIAL DECISION MODELING
Materi 2 (Chapter 2) ntroduction to Quantitative Analysis
BUS 2420 Management Science
Introduction to Management Science
6s-1Linear Programming CHAPTER 6s Linear Programming.
INTRODUCTION TO MANAGERIAL DECISION MODELING
Introduction to Management Science
Introduction to Management Science
To accompany Quantitative Analysis for Management, 9e by Render/Stair/Hanna 1-1 © 2006 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter 1 Introduction.
Introduction to Modelling
Linear Programming: Computer Solution and Sensitivity Analysis
3-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Linear Programming: Computer Solution and Sensitivity Analysis Chapter 3.
Introduction to Management Science
Management Science Chapter 1
Introduction to Management Science
Introduction to Management Science
Linear Programming: Model Formulation and Graphical Solution
Management Science Chapter 1
Linear programming. Linear programming… …is a quantitative management tool to obtain optimal solutions to problems that involve restrictions and limitations.
Chapter Four Cost-Volume-Profit Analysis: A Managerial Planning Tool
Stevenson and Ozgur First Edition Introduction to Management Science with Spreadsheets McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies,
Management Science Chapter 1
9-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Multicriteria Decision Making Chapter 9.
Multicriteria Decision Making
Introduction to Management Science
Modeling.
Chapter Topics Computer Solution Sensitivity Analysis
Introduction to Management Science
1-1 Management Science Chapter 1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. 6S Linear Programming.
Nonlinear Programming
1 Linear Programming: Model Formulation and Graphical Solution.
Chapter 9 - Multicriteria Decision Making 1 Chapter 9 Multicriteria Decision Making Introduction to Management Science 8th Edition by Bernard W. Taylor.
Cost-Volume-Profit Analysis: A Managerial Planning Tool Management Accounting: The Cornerstone for Business Decisions Copyright ©2006 by South-Western,
3-1 Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Linear Programming: Computer Solution and Sensitivity Analysis Chapter 3.
Introduction. MS / OR Definition: Management Science (MS) or Operations Research (OR) is the scientific discipline devoted to the analysis and solution.
Chapter 6 Cost-Volume-Profit Analysis and Relevant Costing.
CDAE Class 12 Oct. 4 Last class: 2. Review of economic and business concepts Today: 3. Linear programming and applications Quiz 3 (sections 2.5 and.
Department of Business Administration SPRING Management Science by Asst. Prof. Sami Fethi © 2007 Pearson Education.
Department of Business Administration SPRING Management Science by Asst. Prof. Sami Fethi © 2007 Pearson Education.
6s-1Linear Programming William J. Stevenson Operations Management 8 th edition.
Chapter 1: Introduction to Managerial Decision Modeling.
Supply Chain Management By Dr. Asif Mahmood Chapter 9: Aggregate Planning.
Management Science Chapter 1
Prepared by John Swearingen
Operations Research Chapter one.
Management Science Chapter 1
Decision Support Systems
MID-TERM EXAM/REVISION
Goal programming.
Management Science Chapter 1
Management Science Chapter 1
Management Science Chapter 1
Chapter 1 Introduction to Quantitative Analysis
Research Operation / Management science
Decision Science Chapter 1 Intoduction.
Chapter 1.
Presentation transcript:

1 Management Science

2 Chapter Topics The Management Science Approach to Problem Solving Model Building: Break-Even Analysis Computer Solution Management Science Modeling Techniques Business Use of Management Science Techniques Management Science Models in Decision Support Systems

3 Management Science Approach Management science uses a scientific approach to solving management problems. It is used in a variety of organizations to solve many different types of problems. It encompasses a logical mathematical approach to problem solving.

4 Figure 1.1 The Management Science Process Management Science Approach

5 Steps in the Management Science Process Observation - Identification of a problem that exists in the system or organization. Definition of the Problem - problem must be clearly and consistently defined showing its boundaries and interaction with the objectives of the organization. Model Construction - Development of the functional mathematical relationships that describe the decision variables, objective function and constraints of the problem. Model Solution - Models solved using management science techniques. Model Implementation - Actual use of the model or its solution.

6 Information and Data: Business firm makes and sells a steel product Product costs $5 to produce Product sells for $20 Product requires 4 pounds of steel to make Firm has 100 pounds of steel Business Problem: Determine the number of units to produce to make the most profit given the limited amount of steel available. Problem Definition Example of Model Construction (1 of 2)

7 Variables: X = number of units (decision variable) Z = total profit Model: Z = $20X - $5X (objective function) 4X = 100 lb of steel (resource constraint) Parameters: $20, $5, 4 lbs, 100 lbs (known values) Formal Specification of Model: maximize Z = $20X - $5X subject to 4X = 100 Problem Definition Example of Model Construction (2 of 2)

8 Model Building Break-Even Analysis (1 of 7) Used to determine the number of units of a product to sell or produce (i.e. volume) that will equate total revenue with total cost. The volume at which total revenue equals total cost is called the break-even point. Profit at break-even point is zero.

9 Model Components Fixed Costs (c f ) - costs that remain constant regardless of number of units produced. Variable Cost (c v ) - unit cost of product. Total variable cost (vc v ) - function of volume (v) and variable per-unit cost. Total Cost (TC) - total fixed cost plus total variable cost. Profit (Z) - difference between total revenue vp (p = price) and total cost. Z = vp - c f - vc v Model Building Break-Even Analysis (2 of 7)

10 Model Building Break-Even Analysis (3 of 7) Computing the Break-Even Point The break-even point is that volume at which total revenue equals total cost and profit is zero: V = c f /(p - c v ) Example: Western Clothing Company c f = $10000 c v = $8 per pair p = $23 per pair V = pairs, break-even point

11 Model Building Break-Even Analysis (4 of 7) Graphical Solution Figure 1.2 Break-Even Model

12 Model Building Break-Even Analysis (5 of 7) Figure 1.3 Sensitivity Analysis - Break-even Model with a Change in Price

13 Figure 1.4 Sensitivity Analysis - Break-Even Model with a Change in Variable Cost Model Building Break-Even Analysis (6 of 7)

14 Figure 1.5 Sensitivity Analysis - Break-Even Model with a Change in Fixed Cost Model Building Break-Even Analysis (7 of 7)

15 Break-Even Analysis Excel Computer Solution (1 of 5) Exhibit 1.1

16 Exhibit 1.2 Break-Even Analysis Excel QM Computer Solution (2 of 5)

17 Break-Even Analysis Excel QM Computer Solution (3 of 5) Exhibit 1.3

18 Break-Even Analysis QM for Windows Computer Solution (4 of 5) Exhibit 1.4

19 Break-Even Analysis QM for Windows Computer Solution (5 of 5) Exhibit 1.5

20 Figure 1.6 Modeling Techniques Management Science Modeling Techniques

21 Linear Mathematical Programming - clear objective; restrictions on resources and requirements; parameters known with certainty. Probabilistic Techniques - results contain uncertainty. Network Techniques - model often formulated as diagram; deterministic or probabilistic. Forecasting and Inventory Analysis Techniques - probabilistic and deterministic methods in demand forecasting and inventory control. Other Techniques - variety of deterministic and probabilistic methods for specific types of problems. Characteristics of Modeling Techniques

22 Some application areas: - Project Planning - Capital Budgeting - Inventory Analysis - Production Planning - Scheduling Interfaces - Applications journal published by Institute for Operations Research and Management Sciences Business Use of Management Science

23 A decision support system (DSS) is a computer-based system that helps decision makers address complex problems that cut across different parts of an organization and operations. A DSS is normally interactive, combining various databases and different management science models and solution techniques with a user interface that enables the decision maker to ask questions and receive answers. Online analytical processing system (OLAP), the analytical hierarchy process (AHP), and enterprise resource planning (ERP) are types of decision support systems. Decision support systems are most useful in answering “what-if?” questions and performing sensitivity analysis. Management Science Models Decision Support Systems (1 of 2)

24 Figure 1.7 A Decision Support System Management Science Models Decision Support Systems (2 of 2)