All Foundries

Commodity Management & Trading

Transforming trading ecosystems through data-driven insights and secure, agile platforms that empower global commerce.

Customer & Marketing Strategy

Reimagining customer engagement with personalized, connected experiences that build trust and long-term value.

Digital Engineering

Turning ideas into scalable digital solutions—accelerating transformation through design, technology, and engineering excellence.

Station Studio

Redefining mobility hubs with digital innovation to create smarter, sustainable experiences for people and businesses on the move.

Supply Chain & Industry 5.0

Building resilient, intelligent supply networks that anticipate change and deliver efficiency with purpose.

Sustainability

Driving responsible innovation to help organizations achieve net-zero goals and create a positive impact for future generations.

Physical AI

Physical AI gives machines, robots and physical operations the ability to perceive their environment, decide what matters and act in real time. Factories, plants, stores and infrastructure become systems able to sense conditions, learn from them and improve on their own, closing the gap between the physical and the digital world.

Next-Gen Infrastructure

From networks as infrastructure to connected platforms that enable critical, autonomous and intelligent operations.

Retail

From satisfaction surveys to EBITDA: closing the loop on customer pain points

A department store retailer in Mexico turned a biannual NPS programme into a closed-loop customer listening mechanism, connecting feedback to pain-point initiatives and incremental EBITDA.

From satisfaction surveys to EBITDA: closing the loop on customer pain points

Retail

Unified returns management

A retailer processing more than two million returns a year moved from channel-specific workflows toward one digital returns system. The initiative connects request creation, status tracking and routing across sales and return channels.

Unified returns management

Contact

Predictive Failure Prevention for LNG Plants

An LNG plant moved beyond reactive failure response by using multi-year pressure, temperature and other sensor histories to detect failure precursors and give operators earlier, actionable warning.

Coastal LNG regasification terminal at sunrise, with cryogenic storage tanks and process pipework viewed from above.

Prediction horizon

More than 3 days in advance

Data visibility

Less sensor history to inspect manually

Maintenance planning

Earlier scheduling of interventions

Failure coverage

Multiple failure types modeled

Two maintenance technicians inspect a valve train inside an LNG plant during a planned intervention.

Predictive intelligence for LNG plant reliability

A connected decision layer helps plant teams focus on the signals that matter, validate predictions against sensor behavior and plan maintenance before operational impact.

From reactive failure response to earlier operational warning

Objectives

The initiative set out to anticipate multiple LNG plant failure types early enough for maintenance to be scheduled rather than triggered only after detection. It also aimed to move asset management from fragmented, reactive activity toward a condition-, risk- and data-informed model, directing limited field and maintenance capacity to the interventions with the greatest operational relevance.

Opportunity

The documented approach could be extended across additional LNG plants, creating a more consistent basis for proactive maintenance, operational risk management and cost-aware planning. Each plant presents a distinct operating context, so any broader rollout would depend on local assets, data quality and operating conditions.

When critical signals arrive too late

The challenge was to turn a large, distributed evidence base into decisions operators could trust and act on within the plant’s maintenance workflow.

Key Challenges

Maintenance technician approaches a compressor area in an LNG plant after an equipment alert.

1: Reactive response
Plant failures were addressed once detected, creating urgent maintenance requirements and limiting the time available to plan an intervention.

Instrumentation engineer inspects pressure and temperature sensors along a frost-covered LNG process line.

2: Sensor data at scale
Years of real-time pressure, temperature and other sensor histories were available, but operators could not analyze the full volume manually.

LNG maintenance planner compares a paper equipment diagram with a rugged field computer beside process equipment.

3: Fragmented operational evidence
Asset, condition, maintenance and operational information needed to be brought together across different systems, formats and organizational teams.

Two engineers review multiple LNG valve trains from an elevated walkway to prioritize maintenance work.

4: Risk-based prioritization
The initiative needed to rank work by operational relevance while keeping a traceable link between underlying signals and the resulting maintenance decision.

Solution

From plant signals to maintenance action

The delivery connected sensor analysis, predictive models, a common decision workflow and operator validation in an end-to-end path from evidence to intervention.

Multi-failure prediction

Prediction models were developed for failure types including high-pressure, low-pressure and low-temperature conditions, enabling earlier warning than reactive detection alone.

Time-series signal analysis

Real-time and historical sensor data from key plant components was analyzed to identify the variables most closely associated with impending failures.

Integrated decision workflow

Relevant asset and operational data was connected in a common workflow, where analytical and rules-based logic could identify priorities.

Operator validation and execution

A dashboard combined model predictions with the relevant sensor series so operators could validate the evidence and connect findings with maintenance planning and execution.

Impact

More time to act before operational impact

The capability predicted plant failures more than three days in advance. It reduced the volume of sensor history operators needed to inspect manually and improved their ability to schedule maintenance instead of responding only reactively. The resulting decision layer supports more proactive maintenance, clearer prioritization and better-informed operational risk and cost management.

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