Management Science Approach
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
• Art of mathematical modeling
• Science of the solution techniques
for solving mathematical models
• Ability 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 --
• 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
• 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.
• 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.
• 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
• 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.
• 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
• 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
• Engineers, accountants, economists, marketing
analysts, production personnel, etc. are just some
of the specialists that can be utilized in the model
Parts of a Management Science
• Problem Definition
• Building Mathematical Models
• Solving/Refining Mathematical Models
• Communication of Results
Types of Management Science
• How Do We Get Started?
– Evaluation of new operations and/or
• Can We Do Better?
– Ongoing operations may be performing well,
but perhaps they could improve
– 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
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
– 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
• 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.