Script

MS APPROACH – Definition

Slide 1

  • Welcome back.
  • In this module we begin our discussion of the management science process.

Slide 2

  • We begin by defining the discipline of management science. If you picked up 50 different texts in the field of management science, which is sometimes called operations research, you would probably get 50 different definitions.
  • George Kimball, who published the first text in the field defined it as “ a scientific method of providing executive departments with a quantitative basis for decisions regarding operations under their control.
  • One early consultant and leader in the field, Russell Ackoff, described it as “mess management”as the field attempts to model situations into integrated and coherent parts.
  • Gene Woolsey, a prominent management science consultant for over 40 years, has pointed out that with all the mathematical sophistication in models that have or could be developed for a particular situation, the end result should not interfere with common sense.
  • He states it even more strongly – that the end recommendation should look, feel, and taste like common sense.
  • The U.S. Department of the Army, which continuously employs many management science consultants, has defined the field in one of its publications as the use of statistical and mathematical techniques, mathematical programming, modeling, and computer science to solve complex operational and strategic issues.

Slide 3

  • Whatever definition you would like to use, the management science process can basically be broken down into 3 parts.
  • It is the ART of mathematical modeling
  • The SCIENCE of the development and implementation of the solution techniques for solving mathematical models
  • And the ability to COMMUNICATE your results – its projected results and its possible ramifications.

Slide 4

  • In a sentence, management science can be thought of in the following way,
  • Given a limited amount of personnel, resources, and materials, how do you use them most effectively to
  • Maximize something – like profit or efficiency
  • Or minimize something – like Costs or time
  • Basically, it is about doing the best you can with what you’ve got in constrained situations – it is about optimization!

Slide 5

  • We now give just a brief glimpse at some of the thousands of applications of management science. Some are one-time studies while others are ongoing programs.
  • The first topic we will study is called linear programming.
  • Linear programming models assume that relationships are linear, that is, have constant returns to scale. They seek to allocate scarce resources so that overall, the project attains a maximum profit or minimum cost.
  • Steelcase has used linear programming to schedule production of desks, cabinets, and so forth given limits on manpower, resources and demand.
  • Texaco has used linear programming linear programming to determine how to blend various grades of raw crude to produce various petroleum products.
  • In integer programming models
  • The results must be integers – such as the number of planes built or workers assigned.
  • American Airlines has used integer models to optimally schedule aircraft and crews. It assists in making virtually instantaneous change recommendations such as when an airport is experiencing a delay or certain crew members phone in sick.
  • McDonald’s has used integer models to schedule personnel throughout the day.

Slide 6

  • Another type of management science models
  • Network models
  • Consist of efficient techniques for determining best transportation routes, routes of shortest distance, and connections of minimal total distance, and so forth.
  • UPS uses network models to schedule their fleet of trucks
  • United Van Lines uses techniques to determine the least costly route (which is not always the shortest route) between two destinations.
  • Project scheduling models
  • Seek optimal scheduling of tasks of a project to minimize the overall project completion time or its cost.
  • Local builder, William Lyon Homes, uses charts known as Gantt charts, to keep track of the building phases of its projects.
  • And Cal Trans used project scheduling models to supervise the rebuilding of freeways after a disastrous earthquake hit the region

Slide 7

  • Another type of a mathematical model,
  • Known as a decision model
  • Give the decision maker valuable information about the short term and long term consequences of making particular decisions.
  • At Fidelity Investments, decisions are made about investments in an ever changing and frequently volatile market,
  • The International Olympic Committee has used decision models in make site selections given many uncertainties which include weather conditions, availability of resources at potential sites, and not the least – security.
  • Inventory modeling is a key area in management science.
  • Inventory models assist the decision maker in determining how much of a product to produce or order and indicates an appropriate level of inventory at which the production should begin or the order placed.
  • Macy’s uses inventory models in ordering products from many vendors.
  • See’s Candies uses inventory models to schedule production of various kinds of candies.

Slide 8

  • Other management science models include
  • Queuing models
  • Which analyze waiting line behavior
  • Disneyland consistently evaluates results from queuing as it designs new programs to minimize customer dissatisfaction at waiting in long lines.
  • The United State Postal Service uses different queuing strategies at different locations given different arrival and service patterns and lobby conditions
  • Many models are simply too complex to derive an “optimal” solution by direct means. As a consequence, simulation models are used to simply evaluate “what-if” outcomes when certain conditions occur.
  • The United States Army uses simulation models extensively in evaluating tactical combat situations.
  • And Conagra Foods uses such “what-if’ models to evaluate possible changes in their food production processes.

Slide 9

  • Management science is not about one individual sitting down and figuring out what is best for an organization.
  • Most management science models, particularly in larger companies are developed by a team of professionals working closely together with a common objective on projects.
  • No one person or group of people typically has the expertise required to analyze a complex situation.
  • Typically large models might include experts from several fields including accounting, economics, mathematics, computer science, production managers, and so forth.

Slide 10

  • A management science study consists of several phases. Frequently these are condense into 6 or 7, but here we take an even broader approach, suggesting that in the most general sense, a management science study consists of 4 basic parts:
  • Defining a problem or situation
  • Modeling it
  • Solving and revising the model
  • Presenting the results

Slide 11

  • Basically management science is called in in three situations:
  • Helping a project or business get started
  • Where management science is used to evaluate new operations and procedures
  • Assisting an ongoing operation to perform better
  • This includes helping troubled organizations improve their lot or making healthy ones even more successful.
  • And coordinating what can only be called dysfunctional or at least troubled organizations think systematically and realistically to try to weather potential disaster.
  • This is part of this mess management function alluded to earlier.

Slide 12

  • In the problem definition and data gathering phase of a management science study, one must use good judgment to obtain the most reliable information.
  • It is most helpful if member of the team can see first-hand the operations themselves.
  • They should try to look at the situation from many points of view – management, the worker, the customer, the accountant, and so forth.
  • When they begin building the mathematical model they should begin with a very broad conceptual model that is easily understood by everyone.
  • They should do a lot of listening, ask basic questions, and try to learn the overall situation. They should strive to be looked at as part of the team, not a potential adversary. Once the basics are understood, more complex models can slowly be developed.
  • There is not an organization in this world that does not have politics in it.
  • You have been called in because someone, usually middle to upper management, wants to make improvements. By default this seems to suggest that someone or some part of the organization is not pulling its weight or at least not operating optimally. Few managers will supply information that will make him or her look bad. Recognize this and just work around it. It is a trick to perform your work without someone feeling scrutinized.
  • When you build your model, you must decide what the objective really is.
  • Sometimes managers have a fuzzy idea as to what the objective should be, other times they can be quite definitive. But in either case some give and take questioning is needed to be sure. It can be, for instance that a manager thinks he really wishes to improve his inventory service level (which is the percent of orders where he runs out of stock), when in fact the true objective is to increase profit – higher service levels can actually reduce profit by carrying much unneeded inventory just to cover demand for a few extra service cycles.
  • Identifying constraints …
  • Is, of course, essential to good model building. These constraints can come from many sources, and no one person or group may be able to supply the modelers with all the constraints. When the model is actually formulated, however, many constraints may be eliminated as being extraneous, or simply not that relevant. Although care should be taken about dropping potential constraints, the model should be made as simple as possible.
  • And during the course of contacts, model building, temporary solutions, revised model building and so forth, you should be in constant contact with direct supervisor or the person to whom you or your consulting group is responsible to. When additions or changes to the model are contemplated, these people should be informed to see if they are correct assumptions or changes within the context of your model.
  • There should be no surprises when a final report is given. You must always be certain you are solving the right problem. And with constant interaction this should always be the case.

Slide 13

  • The model can and should be continuously updated all the way up to the final recommendation.
  • When an initial formulation is complete the problem modeling and solution phases begin.
  • But results from the model may yield some surprises or unusual results.
  • The model may turn out to be infeasible
  • Or the results may not be as dramatic as you expected
  • And the model may highlight some unobserved or unanticipated results or constraints.
  • Or the model may yield good results which allow the decision maker to now begin looking at secondary objectives. In all these cases the problem may be sent back to the “definition” phase to update, change, append or delete constraints, after which, the problem is remodeled and re-solved. This process cycle may repeat several times before a final end product is produced.

Slide 14

  • Let’s review what we’ve discussed in this module.
  • We’ve defined management science as a discipline that uses mathematical models that seek to maximize some objective given a comprehensive set of constraints; or more simply, doing the best you can with what you’ve got.
  • It involves problem definition, modeling, solution approaches and communication
  • The management science process involves a team approach that first must
  • Define what needs to be solved and what constraints are limiting success
  • They build appropriate mathematical models that idealize a particular situation under study
  • And develop or employ known solution techniques to solve these models,
  • And after a lengthy give and take process of revising and re-solving the model, perhaps many times, and after they are relatively comfortable with the results, they must communicate the results to higher management with clarity in terms that are consistent with their understanding of both the problem and the mathematical details,
  • Finally we discussed some of the approaches and “curve balls” that can be thrown at you during the problem definition phase.

That’s it for this module. Do any assigned homework and I’ll be back to talk to you again next time.