96. Saving $140,000 Per Day:
How Companies are Achieving
Breakthrough Improvements in Bottom-
Line Performance Using Optimization
Dr. Jeremy Bloom
Product Marketing Manager, ILOG
Optimization
May, 2010
97. The Story In Brief
Better decisions faster
• IBM ILOG Optimization Products are Helping Many
Businesses Run More Efficiently
• IBM ILOG Optimization Uses Sophisticated Technology to
Solve Hard Business Problems
• IBM ILOG Optimization Products and Services Can Help Your
Business Run More Efficiently
• IBM ILOG Optimization Can Generate Hard Benefits to Your
Bottom Line
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98. What Can Optimization Do?
increased productivity at Europe’s most efficient car production
Automobile Manufacturer
facility by 30%
• South American country’s
two largest forest-products reduced their truck fleets by 30% and saved $20 million annually
companies
• Major Electronics
cut wafer-processing cycle time in half, to just 30 days
Manufacturer
responded to unexpected delays with efficient crew rescheduling,
International airline
saving $40 million in one year
cut package delivery costs by $87 million over 2 years and reduced
Package delivery company
its aircraft fleet by 10%
Television network increased annual advertising revenue by $50 million
Investment firm cut transaction costs by $100 million
Consumer packaged goods dramatically increased the direct loading of trucks off its packaging
manufacturer lines
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99. What Can Optimization Do?
• Whether the problem is large or
small, straightforward or
complex,
optimization supports effective
decision-making across a wide
range of issues.
• Firms in many industries use
optimization software to solve
business problems ranging from
long-term planning to real-time
scheduling and rescheduling.
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101. Benefits of Optimization
• Calculable ROIs, with paybacks within months, sometimes even
weeks
– Capital expense avoidance or deferral
– Operating expense reductions
– Total revenue, revenue mix, and margin improvements
• Improved customer satisfaction
– Provide better and more customized customer service
• Improved employee satisfaction
– Satisfy schedule preferences while improving productivity
– Better planning and scheduling processes
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102. Sophisticated Optimization Technology Solves Hard
Business Problems
• IBM ILOG Optimization helps businesses maximize resource
efficiency
– by helping companies make Choices
– to reach Targets
– while observing Limits
– driven by analyzing Data
• Using powerful, robust, scalable, and diversified optimization
technology and services
– Optimization has most value when there are many choices with
complex relationships that force trade-offs
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104. Case Study
Cash Management:
Restocking Automatic Teller
Machines
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105. Restocking Automatic Teller Machines
The Customer
• Provides financial electronic commerce services and
products to financial institutions worldwide
• Provides systems processing more than two-thirds of 14
billion annual automated clearing house transactions in the
US
• Provides reconciliation, financial messaging, workflow and
compliance products and services to more than 600 banks
and businesses
• Its clients manage more than 2.6 million portfolios totaling
about US $1.8 trillion in assets
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106. Restocking Automatic Teller Machines
The Business Problem
Schedule restocking taking into account customer withdrawal
habits and government cash management regulations
• Too much cash some times – carrying costs
• Too little cash at other times – angry customers
• Forecast errors – volatility
• Data errors – static, dirty, missing, wrong!
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107. Restocking Automatic Teller Machines
Vaults as Distribution Centers
• Services: counting, verifying, sorting, packaging, shipping
• Federal Reserve Regulations
– Cross-shipping penalties
– Custodial Inventory: De Minimis Exemptions, Fitness Issues, etc.
• Banks Organize Vaults Geographically by FRB zone
– 33 Zones in US
– From 2 to 12 Vaults per Zone
• High Service Levels
– Due to nature of product (cash) and customer (ATM’s and bank
branches)
– Substantial business case for optimization solution
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109. Restocking Automatic Teller Machines
Business Case Synopsis: Top-10 Bank Client
• Daily Retail Cash Dispensed
– $ 200 million (+20,000 retail outlets - Branches & ATM’s)
• Total Cash in System (before optimization)
– $ 7 billion
• Optimization Development Goals
– No change of current replenishment schedules
– Reduce cash inventory levels (i.e. carrying costs)
– Reduce replenishment costs (i.e. deliveries)
– Reduce cross-shipping costs (penalties at Fed)
– Improve reporting capability (information)
– “Piggybacking” fixed-charge denomination shipments
– Must solve overnight for implementation next day 14
110. Restocking Automatic Teller Machines
The Bottom Line: Results After 6 Months
• 58 Vault Pilot
• Reduced cash inventories by 35%*
• Reduced replenishment costs by 55%
• Cross-shipping fees decreased about 63%
• CPLEX runtimes within overnight window
• Project rated “Highly Successful” by client’s internal Six Sigma
Unit
• Rolled-out to entire enterprise in 2008
* Attributable to the optimization model and other factors including better forecasting, better
operations, better people, and better measurement.
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111. Restocking Automatic Teller Machines
What the Customer Says:
• “Our OPL model solved by CPLEX has proven to be a
powerful platform from which advanced uses of MILP can be
studied, showcased, and advanced. Several successful
efforts have been accomplished thus far with respect to
speed improvements, always the challenge for us.”
• “We like IBM ILOG’s people, and the reason we like them is
we could call people up and talk to intelligent, well-versed,
experienced people who either could answer our questions
directly or could point us to a resource that could answer our
questions.”
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112. Case Study
Transportation
Scheduling:
Train Timetabling
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113. Train Timetabling
The Customer
• Netherlands Railways
• Operates the busiest national railway network in Europe
• Manages more than 4,800 trains per day
• Has 2,100 km of track and 279 stations
• Between 1970 and 2006, traffic has nearly doubled
from 8 billion passenger km in to 15.8 billion
• During the same period, freight transport increased by
285 percent
In 2006,
• 9 million different passengers traveled 15.8 billion
passenger km
• Operating revenues of €1.5 billion and operating
income of €200 million 18