IBM Decision Optimization ILOG Decision Optimization in Manufacturing, E&U, and others Optimizing...

IBM Decision Optimization

ILOG Decision Optimization in Manufacturing, E&U, and others

Optimizing fast and efficiently, optimizing under uncertainty, solving complex problems

© 2014 International Business Machines Corporation 2


Optimization – The Science of Better Decisions

What to build, where and when?

How to best allocate aircrafts and crews?

Risk vs. potential reward?

Inventory cost vs. customer satisfaction?

Cost vs. carbon emission?

Optimization helps businesses:• create the best possible plans• explore alternatives and understand trade-off • respond to changes in business operations

Hans Schlenker – IBM ILOG Optimization

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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)



Agenda

Optimization in Manufacturing

Optimization in E&U

Further Industries

Use Cases

Q&A



Key performance categories in manufacturing

Risk Management

Product Innovation

Operational Efficiency



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• 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)

Optimization opportunities to improve key category performance in manufacturing



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?



Agenda


Optimization in E&U

Optimization in Other Sectors

Use Cases

Q&A



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Hot Button Issues In Energy and Utilities

Climate change

• Renewable energy

• Energy efficiency

• Low emission generation

• Transportation

• Renewable energy

• Energy efficiency

• Low emission generation

• Transportation

Market restructuring

• Gas/electric market convergence

• Market design and bid optimization

• Resource adequacy

• Price spikes and gaming

• Gas/electric market convergence

• Market design and bid optimization

• Resource adequacy

• Price spikes and gaming

Enterprise IT

• From better information to better decisions

• Empowering business users

• Data collection lags data management

• From better information to better decisions

• Empowering business users

• Data collection lags data management

• New contract and pricing policies

• Performance penalties

• Increased maintenance expense

• Increased T&D investment

• New contract and pricing policies

• Performance penalties

• Increased maintenance expense

• Increased T&D investment

Gridutilization

and reliability

Intelligent utility network



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Energy and Utilities market forces are creating the need for an evolution in the energy and utility value chain

Solar

Wind

Solar

Wind

HydroelectricSolar

NuclearWind

Energy Storage

Energy Storage

Energy Storage

UTILITY

Plug-in Vehicle

ConsumerPower FlowPeriodic Information FlowContinuous Information Flow

Coal/Natural Gas

NuclearHydroelectric

UTILITY

TRADITIONAL ENERGY VALUE CHAIN

TRANSFORMED ENERGY VALUE CHAIN

Evolving Energy and Utility Value Chain

Coal/Natural Gas

Demand Response

Demand Response

Demand Response

Demand Response



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Smart Grid enabled by today’s technology

INTERCONNECTED INTELLIGENT

+

INSTRUMENTED

=+Smarter

Energy• 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 decision-making

• improves performance in key dimensions

• under limits on available resources

• while observing many complex business rules and policies

Manage energy more precisely through information technology



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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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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



ILOG Optimization solutions solve these challenges for Energy & Utilities clients

• 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?

E&U



Agenda


Optimization in E&U


Use Cases

Q&A

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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



Portfolio management example

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Portfolio Management Example

• Asset expected returns

• Asset std deviation of returns

• Asset return correlations

Minimize risk (asset std deviation, correlations) or CVaR

Subject to

Sum (asset expected returns) ≥ target

…

Asset allocation

Portfolio return

Optimised Decisions



ILOG ODM Enterprise Results



ILOG ODM Enterprise Results

• Deploy scenarios - batch runs• Use results to provide recommendations to clients



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



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



Agenda


Optimization in E&U


Use Cases

Q&A

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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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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



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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– Between 2006 and 2009, ILOG system used for booking over

2.1 billions pounds.– Always in use as of today - Improved sales-force productivity– Reduced drastically rework

Advertisement Scheduling



• Situation– Client seeks to improve market introduction planning

for new car models

• Some years before the introduction of a newmodel, initial production and introduction dates have to be planned

– Solution has to take into account

• Demands by

• 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

Market Introduction Planning

Markets e.g. US, Asia, Europe

Car type e.g. sedan, convertible, coupé

Category e.g. for trade show, press, dealers



Success Story – Unit Commitment at REE

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

Business Problem:

•Unit commitment application replacing their heuristics/approximation-based method

•Incorporating windfarms and weather forecast into the plan



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



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



Pumped Storage Optimization at a Mid-Western Utility

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

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



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 plantThe bottom line:

– Expected improvement opportunity of as much as $8M annually with an initial goal to achieve at least 10% of that opportunity



E.ON Ruhrgas optimizes purchasing and storage of natural gas

• 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.

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.



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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.



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.

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

Decisions: • Which campaign to run on which channel to which customer segment.

Outbound campaign planning and executionMaximizing profitability taking into account operational constraints



What if you could schedule events in a way that increased ticket sales?

“We have to address numerous constraints to create a 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 Fussball Liga GmbH

The Opportunity

A professional soccer league in Europe

What Makes it Smarter

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 to advertisers

– 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.

– 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.



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



Agenda


Optimization in E&U


Use Cases

Q&A



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IBM Decision Optimization ILOG Decision Optimization in Manufacturing, E&U, and others Optimizing...

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