1. TheManagement Science Approach Problem Definition

2. What is Management Science? • Scientific approach applied to decision making • “Mess management”-- Early developer of MS • “The use of logic and mathematics in such a way as to not to interfere with common sense” • “The results should look, feel and taste like common sense” -- Prominent MS Consultant • “The use of [mathematical and statistical] techniques, mathematical programming, modeling, and computer science [to solve complex operational and strategic issues]. -- US Army

3. Definition ofManagement Science • Art of mathematical modeling • Science of the solution techniques for solving mathematical models • Ability to communicate results

4. Management Science Objective • Given a limited amount of personnel, resources and material, how do we use them most effectively to: • Maximize -- Profit, Efficiency • Minimize -- Cost, Time • Management Science is about doing the best you can with what you’ve got -- OPTIMIZATION

5. Management Science Applications • Linear Programming Models Using of scare resources to achieve maximum profits when there are constant returns to scale. • Steelcase scheduling monthly production desks, cabinets, and other office furniture to maximize profit by assigning workers and utilizing the steel, wood, and other resources that are available. • Texaco blending various grades of raw crudes to maximize profits while meeting production targets. • Integer Linear Programming Models Determining integer quantities (such as people, machines, airplanes, etc.) that maximize profits. • American Airlines assigning planes, crews, and support personnel on a daily basis. • McDonald’s assigning workers throughout the day.

6. Management Science Applications • Network Models Using specialized linear models to determine routes of shortest distance, connections that tie points together of minimum length or finding a maximum flow (through a series of pipes) • UPS scheduling deliveries in a fleet of trucks. • United Van Lines determining the least costly route between a pickup and delivery point. • Project Scheduling Models Scheduling of the various tasks that make up a project in order to minimize the time or cost it takes to complete the entire project. • William Lyon Homes scheduling the construction of a new tract of homes in Orange County. • CalTrans supervising the reconstruction of the Golden State Freeway after the devastating earthquake in the 1990’s.

7. Management Science Applications • Decision Models Making decisions about the best course of action when the future is not known with certainty. • Fidelity Investments making mutual fund decisions given the uncertainty of the company performance, and the markets. • The International Olympic Committee making site decisions given uncertain weather patterns and changing international conditions. • Inventory Models Determining how much of a product to order and when to place the order to minimize overall total costs. • Macy’s making merchandising decisions for the season. • See’s Candies producing goods for their own stores.

8. Management Science Applications • Queuing Models Analyzing the behavior of customer waiting lines to determine optimal staffing policies. • Disneyland designing waiting lines and policies for rides at the amusement park. • United States Postal Service determining staffing levels and type of waiting line at different branch offices. • Simulation Models Analyzing a variety models whose forms do not meet the assumptions or are too complex to be solved by other specialized techniques. • United States Army evaluating tactical combat situations. • Conagra Foods evaluating “what-if” situations in their food production processes.

9. Management Science Team Approach • Most management science models, particularly in larger companies are developed by “teams” of professionals. • Expertise from various specialists is integrated into building a good mathematical model • Engineers, accountants, economists, marketing analysts, production personnel, etc. are just some of the specialists that can be utilized in the model building process.

10. Parts of a Management Science Study • Problem Definition • Building Mathematical Models • Solving/Refining Mathematical Models • Communication of Results

11. Types of Management Science Problem Definitions • How Do We Get Started? • Evaluation of new operations and/or procedures • Can We Do Better? • Ongoing operations may be performing well, but perhaps they could improve • Help! • Situations where the company is clearly in trouble – “mess management”

12. Problem Definition Approach • Observe Operations • Try to view problem from various points of view within the organization. • Ease into complexity • Do a lot of listening; ask simple questions; initially build a simple, common sense model that can be made more complex later. • Recognize political realities • Managers will not usually supply evidence showing his/her failures – there can be a “blame game” for failures. • Decide what is really wanted -- the goal/objective • Managers can have a fuzzy or a definitive idea as to the objective; this can be at odds with the global objective. • Identify constraints • With input from various sources seek the factors that will limit the firm’s ultimate objective; include only relevant factors. • Seek continuous feedback • The management science team must solve the “right” problem; seek, share and document frequent input with decision makers.

13. Updating The Problem Definition • Once the problem has been defined it is time for the modeling/solution phase. • But results from this phase may result in a re-evaluation of the problem definition. • The model may be “infeasible” • The model may not provide “good enough results” • The model may highlight heretofore unobserved or unanticipated constraints • The model may result in a set of optimal or at least “good” possible courses of action allowing the decision maker to look at secondary objectives.

14. Review • Management science seeks to do the best you can with what you’ve got. • It involves modeling, solution approaches, and communication. • The process consists of: • Problem definition • Mathematical modeling • Solving the mathematical model • Communication/implementation of results. • Approaches/pitfalls associated with the problem definition step.