IBM Decision Optimization
ILOG Decision Optimization in
Manufacturing, E&U, and others
Optimizing fast and efficiently, optimizing under
uncertainty, solving complex problems
IBM Decision Optimization
Optimization – The Science of Better Decisions
How to best allocate
aircrafts and crews?
Inventory cost vs.
customer satisfaction?
What to build,
where and when?
Optimization helps businesses:
• create the best possible plans
• explore alternatives and understand trade-off
• respond to changes in business operations
Risk vs. potential
reward?
© 2014 International Business Machines Corporation
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Cost vs. carbon
emission?
Hans Schlenker – IBM ILOG Optimization
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IBM Decision Optimization
Keys for Success in Complex Problem Solving
• Right Technology
– ILOG is leader in optimization and performance - IBM ILOG
CPLEX is the gold standard in optimization
– In market for over 30 years, first commercial company who
introduced mathematical modeling
• Right People
– Strong industry / logistics background
– In-depth knowledge of ILOG technology suite, with core focus
on supply chain planning problems
– Provide subject-matter expertise to other consulting firms on
their engagements
• Right Approach
– Model Tuning
– Decompositioning if needed
– Custom algorithms (through Java)
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Agenda
Optimization in Manufacturing
Optimization in E&U
Further Industries
Use Cases
Q&A
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Key performance categories in manufacturing
Risk
Management
Operational
Efficiency
Product
Innovation
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Optimization opportunities to improve key category
performance in manufacturing
• Sales & Operations Planning
(S&OP)
• Production Scheduling / Load
Balancing
• Plant Configuration and Asset
Management
• Supply Chain Optimization
– Inventory & Flow Path Optimization
– Network & Sourcing Optimization
– Strategic Route Planning
• Market Introduction Planning
• Packaging Configuration (eg, spare
parts)
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© 2014 International Business Machines Corporation
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IBM Decision Optimization
ILOG Optimization answers manufacturing industry
client questions about how to…
• Capture, analyze, and manage production requirements from
conception to end of life?
• Collaboratively reconcile sales and demand forecasts with supply
chain and production plans?
• Improve operational efficiency through complex project scheduling
to reduce manufacturing and logistics costs?
• Maximize resource utilization and efficiency while ensuring that
customer schedules are met?
• Make the most effective trade-offs in complex and dynamic supply
chain environments?
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Agenda
Optimization in Manufacturing
Optimization in E&U
Optimization in Other Sectors
Use Cases
Q&A
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Hot Button Issues In Energy and Utilities
Climate
change
• Renewable
energy
• Energy
efficiency
• Low emission
generation
• Transportation
Grid
Market
Enterprise
utilization restructuring
IT
and
reliability
• From better
• Gas/electric
information to
market
better decisions
convergence
• Market design • Empowering
business users
and bid
• Data collection
optimization
lags data
• Resource
management
adequacy
• Price spikes
and gaming
• New contract
and pricing
policies
• Performance
penalties
• Increased
maintenance
expense
• Increased T&D
investment
Intelligent utility network
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© 2014 International Business Machines Corporation
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IBM Decision Optimization
Energy and Utilities market forces are creating the need for an evolution
in the energy and utility value chain
Evolving Energy and Utility Value Chain
TRANSFORMED
ENERGY VALUE CHAIN
TRADITIONAL
ENERGY VALUE CHAIN
Coal/Natural Gas
Solar
UTILITY
Hydroelectric
Energy Storage
Demand
Response
Nuclear
Demand
Response
UTILITY
Wind
Coal/Natural Gas
Hydroelectric
Solar
Energy Storage
Nuclear
Wind
Solar
Energy Storage
Demand Response
Demand
Response
Plug-in Vehicle
Wind
Consumer
Power Flow
Periodic Information Flow
Continuous Information Flow
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© 2014 International Business Machines Corporation
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IBM Decision Optimization
Smart Grid enabled by today’s technology
Manage energy more precisely through information technology
INSTRUMENTED
INTELLIGENT
INTERCONNECTED
+
+
=
Smarter
• Sensors gather detailed
information about the
state of the power
system in real time
• Automated control of
the power flows at a fine
level of geographic
resolution
• Coordinate control
functions across many
domains in the utility
organization
Automatic decisionmaking
Energy
• improves
performance in key
dimensions
• under limits on
available resources
• while observing many
complex business
rules and policies
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© 2014 International Business Machines Corporation
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IBM Decision Optimization
What’s a Smart Grid?
Using information to substantially improve performance and lower
cost of electric service
 Real-time pricing
 Demand response
 Automated metering
 Intermittent generation
 Distributed generation
 Energy storage
 Fault and failure anticipation
 Sensors
 Dynamic switching
 Information flows
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© 2014 International Business Machines Corporation
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IBM Decision Optimization
Optimization Problems in the Energy and Utilities Industries
Classic Applications
• Generation/Resource Planning
• Unit Commitment/ Economic Dispatch with Wind
• Hydro/Thermal Scheduling
• Optimal Power Flow/ Security Constrained Dispatch
Novel Applications
• Contract and Risk Management
• Power Market Simulation
• Nuclear Power Outage Scheduling
• Distributed Generation Planning
• Demand Response
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© 2014 International Business Machines Corporation
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IBM Decision Optimization
ILOG Optimization solutions solve these challenges
for Energy & Utilities clients
E&U
• Show me how I can:
– Achieve compliance with emissions regulations?
– Improve bidding and provide faster, more flexible responses to changes in the
marketplace?
– Improve power generation by designing the optimal generation mix including
renewables and demand-side programs?
– Improve network utilization to reduce outage times?
– Improve distribution system reliability and performance through adoption of
smart grid technologies?
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Agenda
Optimization in Manufacturing
Optimization in E&U
Optimization in Other Sectors
Use Cases
Q&A
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© 2014 International Business Machines Corporation
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IBM Decision Optimization
Optimization Problems in the Financial Industries
Classic Applications
• Portfolio Optimization
• Trade Matching and Timing
• Asset-Liability Management
• Cash Management
Novel Applications
• Loan Configuration and Lending
• Derivatives Pricing
• Workforce scheduling/dispatch
• Ad scheduling
• Targeted Marketing
• Collateral management
• Trade Settlement - Netting
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IBM Decision Optimization
Portfolio management example
Portfolio Management Example
Portfolio return
Optimised
Decisions
• Asset expected returns
• Asset std deviation of returns
• Asset return correlations
Asset allocation
Minimize risk (asset std deviation, correlations)
or CVaR
Subject to
Sum (asset expected returns) ≥ target
…
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© 2014 International Business Machines Corporation
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IBM Decision Optimization
ILOG ODM Enterprise Results
© 2014 International Business Machines Corporation
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IBM Decision Optimization
ILOG ODM Enterprise Results
• Deploy scenarios - batch runs
• Use results to provide recommendations to clients
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Sample Applications in Airlines (1)
• Crew Scheduling
– Determine rotations of crews
• Maximize utilization
• Route crews through network
• Obey time constraints
• Fleet Assignment
–
–
–
–
–
Determine assignments of types of planes to flight
Minimize total fleet cost
Meet passenger demand
Obey connection time constraints
Schedule maintenance
• Ground Staff Scheduling
– Determine schedules of shifts
• Obey labor requirements
• Minimize number of needed personnel
– Determine rosters
• Assign people with right skills to right jobs
• Handle variability of staff availability
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Sample Applications in Airlines (2)
• Gate Allocation
– Allocate flights to gates
– Maximize utilization
– Obey schedules
• Revenue Optimization/Yield Management
– Determine number of seats to offer at each price
– Maximize Revenue
– Allocate different seats on same flight to different connections
• Irregular Operations due to schedule interruptions (e.g., weather,
9/11)
– Crew Rescheduling
– Fleet Reassignment
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Agenda
Optimization in Manufacturing
Optimization in E&U
Optimization in Other Sectors
Use Cases
Q&A
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© 2014 International Business Machines Corporation
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IBM Decision Optimization
Cash Management
• Customer
– A major provider of financial services technology
solutions
– Compliance, automated clearing house, electronic
billing and payment, investment services
• Problem
–
–
–
–
Manage stocking of ATMs
Reduce cash inventory carrying costs
Reduce delivery costs
Reduce cross-shipping penalties at FRB
• Solution
– IBM ILOG CPLEX used to solve a MILP model
• Benefits
– Reduce cash inventories by 35% (optimization + better
forecasting + better management)
– Reduce replenishment costs by 55%
– Decrease cross-shipping fees about 63%
– Project rated “Highly Successful” by client’s internal Six
Sigma Unit
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© 2014 International Business Machines Corporation
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IBM Decision Optimization
Rolling Stock Allocation at Netherlands Railways
• Situation
– Precisely matching trains and their cars to expected user traffic is crucial for a railway to keep
costs down and service on time.
– Netherlands Railways transports more than 1 million passengers a day in its own country, works
with partners in Germany, Belgium and France, and a subsidiary in Great Britain that carries
more than 300,000 passengers daily.
– Netherlands Railways’ more than 5,000 trains get passengers where they want to go in the
Netherlands through a network of 390 stations and 2,800 kilometers of track.
• Solution
– TIM, or Tool Inzet Materieel (Tool for Allocation of Rolling Stock) fully models the company’s
operations, including rail networks, stations and trains, and address constraints that included
passenger preferences, seasonal variations in traffic and transportation regulations.
– IBM ILOG OPL Development Studio proved the right tool for modeling the railway’s operations,
and IBM ILOG CPLEX the matching mathematical programming (MP) engine for deriving
optimal solutions from the models.
• Benefits
– The improvement in operating efficiency has been between 5 and 10 percent, netting the railway
cost savings of over €40 million annually.
– End users are able to make explicit choices between costs and customer satisfaction
– Faster planning means shorter lead time for scheduling and rescheduling
– Computer-generated plans contain fewer mistakes than manually built ones
– Planners can focus on exceptional events, and eventually fewer planners may be needed to
operate the railway
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Advertisement Scheduling Situation
– Find the “best” allocation of ad units to TV show episodes
where te main goal is maximizing impressions on the
targeted demographics:
• Problem
– Each TV show is assigned a rating that indicates the
audience for which it is suitable.
– Typical allowed unit lengths in seconds are 15, 30 and 60.
Advertisers cannot purchase units of arbitrary length.
– Business rules such as one type of advertisement per
each break, a campaign cannot have two spots in two
consecutive breaks, a spot should not appear more than
n times in breaks surrounded by a given program.
– Automate the system to handle huge amount of data and
make the schedule good at first – ready for acceptance
– Optimally serve fairly all customers, either large or small,
very important
• Solution
– ILOG Optimization-based sales system to improve
revenues, fairness and productivity
– Most profitable use of the broadcaster’s limited inventory
of advertising slots
– Usage of inventory has been improved by 4 – 5% - hence
increase of income
Benefits
© 2014 International Business Machines Corporation
– Between 2006 and 2009, ILOG system used for booking
over 2.1 billions pounds.
– Always
in use as of today - Improved sales-force
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productivity
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– Reduced drastically rework
IBM Decision Optimization
Market Introduction Planning
• Situation
– Client seeks to improve market introduction planning
for new car models
• Some years before the introduction of a new
model, initial production and introduction dates
have to be planned
– Solution has to take into account
• Demands by
Markets
e.g. US, Asia, Europe
Car type
e.g. sedan, convertible, coupé
Category
e.g. for trade show, press, dealers
• Different market introduction dates
• Category specific production sequences
• Type and market specific sales margins
– Planning goals
• Find earliest market introduction dates
• Balance production costs / delivery costs / market specific profit
• Maximize retail volume to produce after market introduction
– No standard package exists that provides all required functionality
• Solution: Customzed planning tool based on ILOG ODM Enterprise
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Success Story – Unit Commitment at REE
Business Problem:
•Unit commitment application replacing their heuristics/approximationbased method
•Incorporating windfarms and weather forecast into the plan
The methodology applied until now … was
an interactive methodology, which did not
guarantee an optimum solution. There
were many difficulties in the smaller
systems and it was hard to find the most
viable solution. Thanks to the new
methodology, we have resolved this
type of problem.
- Mr. Mustafa Pezic, REE Project Director
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Automotive Sales & Operations Planning
• Solution
– Solution based on IBM ILOG ODME
optimization platform
– Supports many collaborating planners
– Optimization for efficient supply-demand
balancing
• Benefits
– Increased agility: saved 1 month planning time
– Reduced planning effort: 75% less planning figures
– Better planning accuracy: 50% less plan changes
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Clearance Pricing Optimization for a Fast-Fashion Retailer
• Background
– Average item at store collects 85% of its full price - clearance sales still more than a billion euros
– Large number of articles for which markdown decisions must be made
– Legacy Clearance Pricing Decision Process
• Step1: A committee determime categories and associated markdown for Spain. Then, for other
countries translated with a conversion table after a systematic review of the unsold inventory and
sales performance
• Step2: Each country manager, typically on a weekly basis, in consultation with one or several
members of the pricing committee. mainly based on sales speed in 1st week and on-hand inventory
 Solution
– A data collection module, a demand prediction model and a price optimization model
– Some business rules/contstraint considered:
• All clearance prices must be chosen within a discrete feasible price set (e.g. 9.99, 14.99)
• Clearance sales price for any cluster decreases over time.
• Clusters that were priced together, remain together.
• The minimum inventory per category and the maximum number of distinct prices in a given period
• On-hand inventory
 Results
– Clearances sales revenue increased by about 6%,
– Cultural changes
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Pumped Storage Optimization at a Mid-Western Utility
Business Problem – Maximize market impact of the utility’s pumped
storage plant by optimizing its operating schedule to Independent
System Operator’s (ISO) market signals
Constraints
– Market Price forecast
– Reservoir capacity
– Unit generation and pumping
capacity
– Generation and pumping efficiency
– Reversible turbines cannot start in
pumping mode above certain
reservoir level
– Limit on pumping sessions: only
once a day
– Unit availability
– Unit startup interval & ramp rate
– Initial and final reservoir levels for the
period of analysis
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Benefits
• Standardized business procedure providing mathematically validated
schedule
– Model finds opportunities which may not be obvious
• Helps an operator to value the water in the pond and make a decision to
deviate from the schedule in real-time
– When asked to deviate from the original schedule, gives analysis of opportunity lost so
operator knows cost of deviation
• Increased utilization of the plant
The bottom line:
– Expected improvement opportunity of as much as $8M annually with an initial goal to
achieve at least 10% of that opportunity
© 2014 International Business Machines Corporation
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IBM Decision Optimization
E.ON Ruhrgas optimizes purchasing and storage of natural gas
Business Problem – Strengthen its entire natural gas supply chain from
the wellhead to the burner, as it expands beyond its home market to the
rest of Europe. Minimizing costs by optimizing activities for purchase
contracts and storage facilities became key to the company’s business
operations.
• Using IBM® ILOG CPLEX, E.ON Ruhrgas developed
an optimization solution that identifies the margins for
the quantities of purchase contracts and performs
sensitivity analysis to identify risks.
• CPLEX solves very large, real-world optimization
problems, while providing the speed required for
interactive applications.
• The system addresses problems ranging in size from
11,000-140,000 decision variables, 500-80,000
constraints and 50,000-1,300,000 data elements.
• E.ON Ruhrgas applies the results in managing
purchase contracts and storage facilities, determining
pipeline capacities and negotiating purchase costs.
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Benefits
Using CPLEX, E.ON Ruhrgas’ pricing and storage optimization system offer:
• Better planning and decision making.
• Greater competitiveness, as it allows E.ON Ruhrgas to react quickly to
market changes.
• Ability to analyze a large number of scenarios in trying to find the best
solution for optimizing activities.
CPLEX provides the fastest, most reliable implementation of the fundamental
algorithms for solving mathematical optimization problems. This gives E.ON
Ruhrgas a true competitive advantage, as the company can respond rapidly
to changes in the gas market.
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© 2014 International Business Machines Corporation
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IBM Decision Optimization
Outbound campaign planning and execution
Maximizing profitability taking into account operational constraints
Description: Maximize revenue given a budget, a set of possible campaigns for a product portfolio,
the propensity to buy and estimated revenue for each customer segment and the possible sales
channel
Data:
• Customer segments with propensity to buy and expected revenues.
• Channels with fixed and variable costs.
Decisions:
• Which campaign to run on which channel to which customer segment.
Objectives:
• Maximize expected overall benefits.
• Maximize expected revenue.
• Minimize campaign execution cost.
Constraints:
• Budget
• Min/max number of product per customer segment
• Channel capacities
• Incompatibilities between profiles, channels, product types
© 2014 International Business Machines Corporation
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IBM Decision Optimization
A professional soccer league in Europe
What if you could schedule events in a way that increased ticket sales?
The Opportunity
– This professional soccer league
association in Europe struggled to
manually schedule over 600 games per
year for more than three dozen soccer
teams.
– It needed to take into account a long list of
constraints and conditions.
– With the existing method, it also struggled
to accommodate unanticipated changes or
conflicts, resulting in cancelled games.
What Makes it Smarter
– Planning had to take into account many complex dependencies including
stadium occupancy, time of day, day of the week, holidays, seasonal
breaks, days teams cannot play, competitive fairness, trades-offs and
other match schedules.
– With the solution in place, the league staff was able to apply more than
150 constraints and an extensive list of conditions to generate scheduling
solutions for the league -accounting into the model, goals, binding
constraints, trade-offs, sensitivities and business options.
– By varying input data and parameters, several feasible schedules were
produced. The staff quickly evaluated and chose the most optimal
schedule from the variations within hours versus a process that took
nearly two months with the manual process.
Real Business Results
– Reduced schedule processing time from 2 months to 2 hours (99 percent
improvement)
– Increased game attendance due to more competitive fair matches and
fan-friendly schedules
– Increased revenue for primary stakeholders such as stadiums with sales
of tickets and concessions, and broadcaster with sale of commercial spots
“We have to address numerous constraints to create a
to advertisers
© 2014 International Business Machines Corporation
schedule that is acceptable to all the clubs. And now we can
quickly and easily determine the one that best meets all our
requirements.”
--Holger Hieronymus, Managing Director, Deutsche
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Fussball Liga GmbH
IBM Decision Optimization
Mine Planning at a Large Diversified Mining Company
 Situation
 Large diversified mining company with 700 digs sites (or pods) producing metals, petroleum
and other natural resources
 Needed to determine where and when to dig to obtain a desired mixture of mineral content
and ore grades
 While matching projected annual demand
 Considering a multitude of factors including: inventories, set-up costs, royalty
obligations, equipment availability, etc.
 Benefits
 Cost savings of 5%, or more than $35 million
 Plans created in days, instead of months
 Long-term planning capabilities with ability to explore infinite what-if production scenarios
© 2014 International Business Machines Corporation
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IBM Decision Optimization
Agenda
Optimization in Manufacturing
Optimization in E&U
Optimization in Other Sectors
Use Cases
Q&A
© 2014 International Business Machines Corporation
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IBM Decision Optimization
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