Vice President - Data & Analytics, GE Transportation
Data to Decisions
At GE Transportation, we move the world and improve the world. We are a global technology leader and supplier of equipment, services and digital solutions to the rail, mining, marine, stationary power and drilling industries. Our innovations help customers deliver goods and services with greater speed and savings using our advanced manufacturing techniques and connected machines. GE Transportation is headquartered in Chicago, IL, and employs approximately 9,000 employees worldwide.
The transportation industry is facing unprecedented change, fueled by fierce competition, shifting demand and emerging technology. More tactically, operators are challenged by rising fuel costs and fleet maintenance, requiring innovative solutions for productivity and profitability. GE Transportation’s objective is to make rail a more competitive mode of transportation by helping our customers maximize operations and drive better service levels out of their fleet.
In this context, GE Transportation (GET) has created pillar applications, centralizing processes and data sets for core programs from product design through service. But the data collected within these applications and processes was not being utilized. Instead, GET relied on legacy data stores and legacy methods for retrieving and analyzing data. Leadership accessed dated KPI’s built by data experts who messaged the data sets to tell a very specific, targeted story that answered what leadership was looking for, and nothing more. GET needed advanced analytics infrastructure and organizational support to leverage insights drawn from the rich, connected data sets at its fingertips.
Recognizing that there was value in the data that was being collected but not utilized to the fullest, we established a multi-year roadmap that set the foundation to deliver immediate value & position for growth. The tenants of the strategy included:
- Leverage data as an asset to build value through analytics products which deliver bottom line benefit to GET
- Establish a data lake ecosystem which delivers speed & flexibility meeting today’s needs and anticipating tomorrow’s
- Create an organization with in-house intellectual property of our data, technology & solutions
A global team across 3 countries (fully insourced) aligned into 3 Agile DevOps teams and built the first AWS-based GPDB instance, which provided speed & flexibility necessary to meet the growing demands. The products delivering immediate value were recognized by leadership who could see the costs of long-term service agreements decline as a result of the analytic products.
The value of data as an asset was recognized at the end of 2017 when the organization centralized a business function under a single leader to bring together analytics product experts to drive value across the organization, with $80M in productivity recognized to date. The organization continued to invest in analytics products and technology, growing our ROI from an initial state of 2:1 to 15:1. The products facilitated an organizational change: teams relied on analytics to make key financial decisions, including the scope of how to remanufacture an engine and what work to perform on our locomotive assets to optimize cost & time in shop. The team became part of the annual financial planning cycle where the value delivered via analytics was embedded into the operational plans, making value growth essential to meeting business commitments.
The specific business impacts vary across the products however a few highlights of operational changes include:
- Enabled virtual software testing to predict software defects that would result in locomotive failures
- Prevention of 100’s of product failures
- Increased margin on part sales through advanced algorithms extracting characteristic with optical character recognition
- Condition based maintenance on engine remanufacturing and component pairing
- Delivered $80M of productivity – op profit and cash – through analytics products which provided operational prescriptive directions for servicing and remanufacturing our assets with greater precision & efficiency.
- Reduced annual operating expenses by 30% while growing platform reach by 240% and maintaining platform availability of 99.6%. Delivered by being on the bleeding edge - the first to host technologies in the cloud and transitioning to open source platforms.
- Formation of a business organization to continue analytics investments and growth across the enterprise. Individuals working analytics in pilot forms throughout the enterprise were pulled together under 1 leader to drive more comprehensive strategic focus & progression while also reaching beyond organizational lines,
The GET data lake is an ecosystem of technologies that continuously evolves to meet the growing analytics demand. With the strength of the team & robust architecture, the environment scales & evolves with ease, measuring new technology introductions in weeks from idea to full environment automation & configuration. Some of the technologies used in this journey include Pivotal, Hortonworks, Sisense, H2O, Zeppelin, Machine learning algorithms, Kafka, HVR, Talend, and Qlikview.
Moving critical business decisions from leadership to an analytics-based prescription required trust & confidence-building. Through the early stages of analytics deployments, this became essential to full operationalization and, therefore, benefit recognition. Harnessing the skepticism through this process created a forum to educate leadership and operators to understand the technology, how the data is transformed in an asset and ultimately guide the organizations through how we create the asset valuation using analytics. For instance, when engines arrive at the remanufacturing facility, they are now dispositioned and destined for remanufacturing workscopes as prescribed by analytics, savings thousands on each rebuild. The process once decisioned with limited information is now precisely prescribed without intervention.
In GE’s effort to invest in their Digital Industrial journey, the IT function formed several horizontal teams designed to deliver products & solutions that would accelerate the digital industrial journeys of each industrial division. The GE Transportation team, experience and architecture was modeled as the architecture to support the entire GE organization in their recent deployment of a single GE data lake.