Operations Research:
The Science of Better
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What we do
 Our accomplishments
 Our mission
 Our expertise
 Our scope
 Our approach
 Our services
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Our accomplishments
 [name of client/organization]
 [business problem]
 [O.R. solution]
 [value we delivered]
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Our mission
 Help you with the challenge of making
complex decisions by:
– Performing quantitative analysis that provides
insight
– Providing sensible options and recommending
courses of action
– Reducing risk
– Improving the quality of recurring decisions
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Our mission (con’t)
 Have a dramatic, positive impact on a
project’s or organization’s value
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Our expertise
 Business consulting
 Modeling & analysis
– Math modeling, simulation, decision support,
data mining, optimization, revenue
management, supply chain management,
logistics, strategical analysis
 Technical support, training
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Our scope
 Serving any organizational unit, any
function, any geographic area
 Assistance from strategy through execution
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Our approach
 Partner with management to frame and
prioritize the issues
 Focus on business impact and
implementation
 Establish a disciplined, consultative
approach through teamwork and
collaboration
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Our approach (con’t)
 Build an objective, quantitative structure
for analysis
 Transfer technology to your organization so
you can take over the project
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Our services
 Strategic planning
 Supply chain management
 Pricing and revenue management
 Logistics and site location
 Simulation
 Marketing research
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Our services (con’t)
 Scheduling
 Portfolio management
 Inventory analysis
 Forecasting
 Sales analysis
 Auctioning
 Risk analysis
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A few words about
operations research
 What is it?
 Conventional computing isn’t enough
 Combinatorial explosion
 The distinct nature of operations research
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O.R.: What is It?
Operations research (O.R.) is
the discipline of applying advanced
analytical methods to help make
better decisions
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Conventional computing
is not enough
 Cannot enumerate alternatives
 Combinatorial explosion of viable options
“Few”
alternatives
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Never got
outside the
box
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Combinatorial explosion
Example 1

Problem: Find shortest path through 10 points
– Assume a super-powered computer analyzes,
quantifies, and compares a million alternatives every
second
– Answer is found in less than one second

Now find the shortest path through 20 points
– Same computer would take over 39,000 years to solve
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Combinatorial explosion
Example 2

Construct a 5 stock portfolio out of 100 possible
stocks
– Evaluate for risk and return using same computer as
Example 1
– Answer found in 1.25 minutes

Now diversify to 9 stocks
– Answer found in 220 days

Now diversify to 10 stocks
– Answer found in 5.5 years
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Distinct nature of O.R.
Applies leading-edge analysis
 Can find the best among many choices, in
reasonable time
 Can consider and balance multiple objectives
 Can help measure, control, and reduce risk

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O.R. success stories
Representative cases from the annual
INFORMS Edelman Competition
2002: Continental Airlines Survives 9/11
2001: Merrill Lynch Integrated Choice
2001: NBC Optimizes Ad Sales
2000: Ford Motor Prototype Vehicle Testing
1996: Procter & Gamble Supply Chain
1991: American Airlines Revolutionizes Pricing
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Case 1: Continental Airlines
Survives 9/11
 The problem
– Long before September 11, 2001, Continental
asked how it could plan recovery from
potential disasters such as massive weather
delays
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Continental Airlines (con’t)
 Strategic objectives and requirements
–
–
–
–
–
1,400 daily flights
5,000 pilots
9,000 flight attendants
FAA regulations
Union contracts
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Continental Airlines (con’t)
 The O.R. solution
– Working together with an operations research
consulting firm
– Used an optimization model to generate
optimal assignments of pilots and crews
following a major disruption of service
– Solution offers a system-wide view of the
disrupted flight schedule and all available crew
information
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Continental Airlines (con’t)
 The value
– Millions of dollars and thousands of hours
saved for the airline and its passengers with
each major disruption
– After 9/11, Continental was the first airline to
resume normal operations
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Case 2: Merrill Lynch
Integrated Choice
 The problem
– Deal with the competitive threat of online
investment firms
– Without alienating financial advisors,
undervaluing services, or incurring substantial
revenue risk
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Merrill Lynch (con’t)
 Objectives and requirements
– Evaluate new products and pricing options
– Evaluate options of online vs. traditional
advisor-based services
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Merrill Lynch (con’t)
 The solution
– Merrill Lynch’s O.R. group simulated clientchoice behavior, allowing it to:
» Evaluate the total revenue at risk
» Assess the impact of various pricing schedules
» Analyze the bottom-line impact of introducing
different online and offline investment choices
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Merrill Lynch (con’t)
 The value
– Introduced two new products garnering $83
billion ($22 billion in new assets) and
produced $80 million in incremental revenue
– Helped management identify and mitigate
revenue risk of as much as $1 billion
– Reassured financial advisors
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Case 3: NBC Optimizes
Ad Sales
 The problem
– NBC sales staff had to manually develop sales
plans for advertisers, a long and laborious
process to balance the needs of NBC and its
clients
– The company also sought to improve the
pricing of its ad slots as a way of boosting
revenue
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NBC (con’t)
 Objectives and requirements
– Complete intricate sales plans
– Reduce labor cost
– Maximize income
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NBC (con’t)
 The O.R. solution
– Used optimization models to reduce labor time
and
– Used revenue management to improve pricing
of its ad spots, which were viewed as a
perishable commodity
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NBC (con’t)
 The value
– In their first four years, the systems
» increased revenues by over $200 million
» improved sales-force productivity
» improved customer satisfaction
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Case 4: Ford Motor Prototype
Vehicle Testing
 The problem
– Developing prototypes for new cars and
modified products is enormously expensive
– Sought to reduce costs on these unique, firstof-their-kind creations
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Ford (con’t)
 Objectives and requirements
– Needed to verify the designs of its vehicles and
perform all necessary tests
– Prototypes often sit idle awaiting various tests,
so increasing their usage would offer clear
benefits
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Ford (con’t)
 The solution
– Ford and an O.R. consulting firm developed a
Prototype Optimization Model (POM) to
reduce the number of prototype vehicles
– The model determined an optimal set of
vehicles that can be shared and used to satisfy
all testing needs
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Ford (con’t)
 The value
– Ford reduced annual prototype costs by $250
million
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Case 5: Procter & Gamble
Supply Chain
 The problem
– To ensure smart growth, P&G needed to
»
»
»
»
improve its supply chain
streamline work processes
drive out non-value-added costs
eliminate duplication
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Procter & Gamble (con’t)
 Objectives and requirements
– P&G recognized that there were potentially
millions of feasible options for its 30 product
strategy teams to consider
– Executives needed sound analytical support to
realize P&G’s goal within the tight, one-year
deadline
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Procter & Gamble (con’t)
 The solution
– The P&G O.R. department and an O.R.
consulting team created decision-making
models and software.
– Followed a modeling strategy of solving two
easier-to-handle sub-problems:
» Distribution/location
» Product sourcing
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Procter & Gamble (con’t)
 The value
– The overall effort saved $200 million a year
before tax
– Allowed P&G to write off $1 billion in assets
and transition costs
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Case 6: American Airlines
Revolutionizes Pricing
 The problem
– To compete effectively in a fierce market, the
company needed to “sell the right seats to the
right customers at the right prices”
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American Airlines (con’t)
 Objectives and requirements
– Airline seats are a perishable commodity whose
value varies
» At times of scarcity they’re worth a premium
» After flight departs, they’re worthless
– The new system had to develop an approach to
pricing while creating software that could
accommodate millions of bookings,
cancellations, and corrections
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American Airlines (con’t)

The solution
– The team developed yield management (a.k.a. revenue
management) and dynamic pricing
– The model broke down the problem into three subproblems:
» Overbooking
» Discount allocation
» Traffic management
– The forecasting and optimization models were
embedded in a large revenue management system that
interfaces with the reservation system
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American Airlines (con’t)
 The value
– In 1991, American Airlines estimated a benefit
of $1.4 billion over the previous three years
– Since then, yield management has been
adopted by other airlines, hotels, car rentals,
and cruises—resulting in billions
of dollars in added profits
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Some important points
 Differences between O.R. and IT
 Keys to success
 Conclusion
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Differences between
O.R. and IT

IT
– Focuses on data as a
corporate resource
– Stores, retrieves,
formats, displays data
– Understands business
processes and
transactions
Operations Research

O.R.
– Helps management select
the best decision or set of
decisions
– Applies analysis to convert
data into useful information
– Works with management
to help gain the deepest
insights from analytical
results
– Is typically embedded in an
information system to
provide recommended
decisions or actions
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Keys to success






Focuses on business impact and implementation
Improves decision processes while reducing risk
Helps operations become more efficient and effective
Establishes a disciplined, consultative approach
Transfers technology to your department so you can take
over the project
Uses the appropriate analytical tools
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Conclusion
Operations research is the discipline of applying
advanced analytical methods to help make better
decisions
 We collaborate easily with organizations like
yours to achieve major goals
 We can help you realize powerful benefits in
dollars, time, productivity, customer satisfaction,
marketshare, and more

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