The Management Science Approach Problem Definition

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

Definition of Management Science ArtArt of mathematical modeling ScienceScience of the solution techniques for solving mathematical models communicateAbility to communicate results

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

Management Science Applications Linear Programming ModelsLinear 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 ModelsInteger 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.

Management Science Applications Network ModelsNetwork 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 ModelsProject 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.

Management Science Applications Decision ModelsDecision 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 ModelsInventory 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.

Management Science Applications Queuing ModelsQueuing 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 ModelsSimulation 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.

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.

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

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”

Problem Definition Approach 1.Observe Operations Try to view problem from various points of view within the organization. 2.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. 3.Recognize political realities Managers will not usually supply evidence showing his/her failures – there can be a “blame game” for failures. 4.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. 5.Identify constraints With input from various sources seek the factors that will limit the firm’s ultimate objective; include only relevant factors. 6.Seek continuous feedback The management science team must solve the “right” problem; seek, share and document frequent input with decision makers.

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.

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.