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  1. INTRODUCTION • What is Management Science? • Why do we study this field? • Overview Dr. C. Lightner Fayetteville State University

  2. WHAT IS MANAGEMENT SCIENCE? Management Science (MS) is the study of mathematical or quantitative methods and techniques for solving common management problems. Dr. C. Lightner Fayetteville State University

  3. WHY DO WE STUDY MANAGEMENT SCIENCE? • Decision Making is more cost effective when MS techniques are employed. • Examples: • Clothing Store Managers must determine the appropriate inventory to stock. By analyzing data about past clothing sales, managers can make more informed decisions about future sales. • WALMART has distribution centers and stores located throughout the United States. Someone must decide Which distribution center will service each store and the cheapest mode of transportation for making deliveries to each location, while adhering to the limitations of each distribution center and the demands of each store. • The airline industry must determine the number of reservations to take for each flight. Although a plane only has the capacity to fly C passengers, they often overbook flights since customers are known to cancel their reservation. Their goal is to maximize the number of passengers on each flight, while minimizing the cost that the airline incurs when more than C passengers show up to fly. Dr. C. Lightner Fayetteville State University

  4. Decision Making • The Decision making process involves the following five steps: • Define the problem. • Identify alternatives. • Determine the criteria. • Evaluate the alternatives. • Choose an alternative. Dr. C. Lightner Fayetteville State University

  5. Defining the Problem. • We will learn to transform general problems into a well defined problem that can be approached using quantitative methods. • The well defined problem is often represented as a model. Dr. C. Lightner Fayetteville State University

  6. Model Development • Models are representatives of real objects or situations and can be presented in various forms. • There are 3 classifications of models: Iconic Models Analog Models Mathematical Models Anderson, Sweeny, and Williams Dr. C. Lightner Fayetteville State University

  7. Model Classifications • Iconic models are physical replicas of real objects. Examples: A model airplane or toy truck. • Analog models are physical in form, but they don’t have the same physical appearance as the object being modeled. Examples: Speedometer or thermometer • Mathematical models represent problems by a system of symbols and mathematical relationships or expressions. • We will focus on building mathematical models. Anderson, Sweeny, and Williams Dr. C. Lightner Fayetteville State University

  8. Overview • This course is divided into 3 Major Segments • Segment 1 Linear Programming (weeks 1-3) • Segment 2 Project Management (week 4) Decision Analysis (week 5) Forecasting (week 6) • Segment 3 Simulation (weeks 7-8) Dr. C. Lightner Fayetteville State University

  9. Overview • Linear Programming Linear Programming is a problem-solving approach developed for situations involving maximizing or minimizing a linear function subject to linear constraints that limit the degree to which the objective can be pursued. • Project Management: PERT/CPM In many situations managers are responsible for planning, scheduling and controlling projects that consist of numerous separate jobs or tasks performed by a variety of departments, individuals, and so forth. The PERT (Program Evaluation and Review Technique) and CPM (Critical Path Method) techniques help managers carry out their project scheduling responsibilities. • Decision Analysis decision analysis can be used to determine optimal strategies in situations involving several decision alternatives and an uncertain or risk-filled pattern of events. Anderson, Sweeny, and Williams Dr. C. Lightner Fayetteville State University

  10. Overview • Forecasting Forecasting methods are techniques that can be used to predict future aspects of a business operation. • Simulation Simulation is a technique used to model the operation of a system. This technique employs a computer program to model the operation and perform simulation computations. Anderson, Sweeny, and Williams Dr. C. Lightner Fayetteville State University