IBM Decision Optimization documented ROI

IBM Decision Optimization ROI and Use Cases: Utility Company

Use Case 1: A Utility Company in Canada

What if you could predict and counteract the impact of a solar storm by analyzing data from sensors across the grid?

A utility company in Canada uses predictive analytics and high-performance computing to solve the industry’s most pressing challenges, including grid reliability in a volatile environment and requirements to expand renewable energy.

The Opportunity

This utility company has the most extensive transmission network in North America, and it is one of the few utilities to maintain its own research institute. Receiving approximately CAD100 million each year, the research institute is charged with improving the safety and reliability of power generation and transmission while fueling innovation. That includes planning for disruptive events such as solar storms as well as making incremental improvements in transmission efficiency, incorporating new sources of renewable energy into the grid and analyzing growing volumes of data from an increasingly smart grid. The sweeping scope of these challenges, however, can sometimes make it impractical for organizations to tackle them alone. Hoping to broaden its perspective and accelerate innovation, the company decided to join a portion of its research efforts with other providers in the energy industry and IBM.

What Makes It Smarter

Dramatic changes are reshaping the energy and utilities industry. Smart grids. Renewable energy. Electric vehicles. Innovation is the name of the game. That’s why this utility company in Canada joined a visionary global research collaboration, applying advanced predictive analytics and high-performance computing to solve the most pressing energy challenges facing the industry. In one project, the company is investigating how to incorporate more renewable energy sources into the grid and meet government targets without sacrificing reliability. With sophisticated scenario planning, it will be possible to understand and compensate for the intermittent nature of wind power to balance supply and demand. In another project, the utility provider is finding ways to analyze streaming data from grid sensors in near-real time to understand why outages occur and spot patterns that may signal an impending failure. For example, using coupled predictive models, the company would be able to forecast the impact of a solar storm on the grid, days in advance, and take preventive steps to avoid an outage.

Solution implementation

The utility provider joined IBM Research in launching a new research institute, a first-of-its-kind collaboration intended to accelerate innovation across the global energy and utilities marketplace. It is an enormous milestone for the industry; it promises to uncover the insights that will define the next phase in smart grid transformation. Members of the institute will apply predictive analytics and high-performance computing to the most pressing challenges facing the energy and utilities industry. The collaboration represents a significant IBM investment in Smarter Energy, a way of both solving real-world utility challenges and creating a powerful, integrated analytics and optimization platform based on IBM technology.

Using this powerful combination of advanced technologies, the research institute will build on the industry’s collective knowledge in five areas of joint research:

  • Outage planning optimization: Reducing the amount of time customers go without power
  • Asset management optimization: Improving the allocation of capital and operational expenses for upgrades and maintenance
  • Integration of renewable and distributed energy resources (DER): Meeting regulatory requirements and industry targets for incorporating renewable energy and DER into the grid while ensuring stability
  • Wide-area situational awareness: Detecting anomalies across the grid in real time to prevent cascading failures
  • The participatory network: Transforming the relationship between provider and consumer by building a participatory engagement model

Each research institute member focuses on two areas of research. For this utility provider, the emphasis is on wide-area situational awareness (WASA) and the integration of renewable energy sources and DER.

Solution Components

  • IBM® Cognos® Business Intelligence V10
  • IBM Decision Optimization
  • IBM InfoSphere® Streams
  • IBM InfoSphere Data Architect
  • IBM Informix® TimeSeries – Real-Time Loader
  • IBM Intelligent Operations Center
  • IBM Netezza® Analytics
  • IBM Rational® software
  • IBM SPSS® Modeler
  • IBM WebSphere® Application Server
  • IBM STG Lab Services


By joining this research institute with IBM, the utility company aims to improve the safety and reliability of its power grid and transmission systems in an environment of constant change and innovation. Indeed, the WASA project should give the organization the tools to predict and prevent outages by detecting patterns in the transmission network that could signal an impending outage. In particular, by combining real-time monitoring with solar activity forecasts, the project should help avoid outages caused by solar storms. And its work on integrating renewable energy will help the company meet government targets to increase wind generation capacity while accommodating growing demand. The collaboration also gives the organization access to IBM expertise in simulation code optimization and modeling as well as a powerful technology stack. In addition, the company will have usage rights to all the algorithms, software, patents and other innovations created by the institute and its members. These advantages will enable the utility to accelerate its research program and realize the business benefits of a smarter grid sooner.

Instrumented – The research involves collecting and analyzing an enormous amount of data from grid sensors, or synchrophasors, which record and transmit the state of transmission lines in real time. This time-stamped data includes power flows, voltage, frequency and phase angle, recording the state of the grid at any given point in time and space.

Interconnected – Data from grid sensors is correlated with solar storm forecasts as well as information from the company’s GIS system and grid topology and asset database, providing a comprehensive view of grid operations.

Intelligent – The research collaboration is addressing several challenges associated with the adoption of a smart grid, including the integration of renewable energy sources and the ability to predict and prevent outages. Using what-if scenario planning, utility companies will be able to incorporate intermittent wind and solar energy into the grid while maintaining stability and accommodating rising demand. And, by monitoring the grid and analyzing data in near-real time, companies will be able to detect patterns that could signal an impending failure and take steps to prevent it.

Real Business Results

  • Expects to increase grid reliability by detecting signs of failure in the near-real time stream of data
  • Anticipates helping meet regulatory requirements for expanded use of wind and solar power
  • Lays the groundwork for a smarter energy grid, using advanced analytics to predict and prevent failures while adapting to rising demand and a shifting energy mix

Some Other IBM Decision Optimization Use Cases

Company Industry Business problem ROI Public Reference
UPS Logistics Air Network design $87M / 2 years + 10% fewer planes yes
Logistics Co. – US Logistics network design > $5M / year cost saving
Logistics Co. – US Logistics routing application $0.6M / year reducing cost
Dairy Co. – APAC Logistics Dairy distribution $15M / year cost saving
Brewing Company – US Transportation Manufacturing sourcing + distribution $150M / year transportation cost saving
Continental Airlines Transportation Crew rescheduling $40M in a year
Railway Co. – EU Transportation Scheduling / pricing $16M / year + Revenue 2% (of EUR 1.5B = EUR 30M) + lower OPEX
NS Reiziger (Dutch Railway) Transportation Timetabling / rolling stock optimization / crew scheduling $27M / year reducing operational cost + $54M / year increase of fare revenue
Hotel Group Hotel Hotel planning some customers reduced cost by more than 10% ($50M)
AT&T Telco network recovery 35% reduction of spare capacity yes
Media Co. – US media sales process planning In 1996-2000 increased revenue by $200M, reduced rework by 80%
South African Defense defense Force / Equipment planning $1.1B / year yes
Bank – US Finance Cash inventory management Reduced replenishment cost by 55% + reduced cross-shipping fee by 63% (daily cash dispersion is $200M) yes
Indeval (Mexico) Finance Security trade settlement reducing the amount of cash that banks must have on hand to cover trades by 52% and saved Mexican banks more than $240M in interest in 18 months yes
Investment Co. Finance Portfolio planning For 600 clients with $185B valued portfolio > $100M saving on transaction cost yes
Grant, Mayo van Oterloo Finance Portfolio optimization $4M / year yes
Energy Co. – US Energy Hydro-power generation Minimum $0.8M / year reducing cost
REE (Red Electrica) Energy Unit commitment $130k / day cost reduction yes
Motorola Manufacturing Procurement management $100-150M / year yes
Samsung Electronics Manufacturing Semiconductor manufacturing 50% reduction in cycle time yes
Mining SW Co. – APAC Manufacturing Mine production planning 5% (> $35M) cost saving
Mining SW Co. – APAC Manufacturing Mine operation planning >$20M value increase (2-3% increase of mine value)
Car manufacturer Co. Manufacturing planning of sourcing > $50M / 5 years cost saving + $40M upfront investment savings
Car manufacturer Co. Manufacturing Car manufacturing saved the cost of building a 3rd production line + investment payback in 3 days
Soft Drink Co. Manufacturing Production sourcing $6M inventory reduction + 2% fewer miles
Steel manufacturer Manufacturing Steel manufacturing reducing 30-40% stock ($10B/year revenue)
Brewing Company – US Manufacturing Production planning > $1M / year reducing cost
US Water Products Manufacturing Manufacturing Inventory optimization $6.2M working capital reduction
2 Chilean Forestry firms Manufacturing Timber harvesting $20M/year + 30% fewer trucks