Uptime, engineered: how SLB cut downtime 55% and avoided $7.8M with predictive maintenance

SLB is the world's largest oilfield services company — a 100-year-old energy technology leader operating in more than 100 countries, generating over $35 billion in annual revenue, and employing more than 100,000 people worldwide. Its business runs on thousands of high-value assets deployed in some of the harshest environments on the planet, assets that only earn their keep when they stay reliable. Keeping that global fleet running is SLB's superpower. Turning it into a repeatable, scalable science is what this story is about.

By transforming decades of equipment data and engineering expertise into an operational intelligence ecosystem on Dataiku, SLB cut equipment downtime by 55% and lifted fleet availability from 84% to 93% — creating the capacity of nearly six additional assets without buying a single one, and unlocking an estimated $2.5M to $15M in economic impact over three years.

A four-year track record, not a one-off project

What began in 2022 as a pilot on fewer than 10 assets has grown, over four years, into a global framework spanning 110+ assets, 24+ models, and five countries — and it's still expanding. That kind of multi-year reliance is earned one deployment, one region, and one proven result at a time.

The data was there. The decisions weren't.

SLB was sitting on large volumes of equipment data, maintenance records, and engineering knowledge, with no way to turn it into decisions it could act on. The strain was sharpest for cementing equipment in harsh, variable field environments: assets never designed with advanced monitoring built in, maintenance that was mostly reactive or calendar-based, and threshold alarms that were blind to developing issues until it was too late. SLB needed a scalable way to catch failures early, plan smarter, and extend asset life.

One ecosystem, not a collection of models

SLB set out to make asset intelligence an operational capability, not a pile of isolated models. Building it on Dataiku brought together a rare cross-functional team of about 20 contributors, from the Digital Asset Performance Manager who set the roadmap to the data scientists, data engineers, and reliability engineers who built and validated the models. Teams across five countries now use it.

The core innovation is an operational context framework: equipment data is ingested automatically, then classified into meaningful operating stages like pumping, mixing, and pressure testing. Traditional systems evaluate sensor values without that context, flooding teams with false alarms. By judging behavior only under relevant conditions, the system sharpens precision, feeding a library of 24+ equipment-specific models covering pumps, engines, lubrication, cooling systems, transmissions, and more.

Trust engineers can see

In an environment where failures carry safety and financial consequences, trust came first. Development and production environments stay separated, and every workflow is traceable through Dataiku flows, recipes, and applications. Reliability engineers validate every finding before it reaches field teams — the platform identifies risk, but qualified people decide what happens next. For a CIO, that governance is the difference between a model and a system you can actually run a business on.