Executive Summary
A leading global petrochemical major - operating across 50+ countries with a 30,000+ workforce, and recognized for its ESG leadership - needed to move from reactive, legacy asset monitoring to predictive, AI-led asset health across its plants. LTTS deployed its proprietary Asset Health Monitoring solution across 22 plants in KSA, US, and Europe - processing ~500,000 data points every 15 minutes across 1400+ assets - cutting unplanned incidents by >80% and saving the client ~..10 Mn in the first year.
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Download Case StudyThe Challenge
Petrochemical manufacturing runs on complex, interdependent process and equipment units - where a single unplanned failure can stall an entire plant. The client’s existing plants relied on conventional, legacy methods for asset performance and reliability that could not proactively flag asset breakdowns or plant shutdowns with enough lead time to prepare or mitigate. The gaps were structural: no effective monitoring of critical Key Performance Indicators (KPIs) - driving operational inefficiencies, material losses, and process leakages - and no reliable way to identify the right KPIs to track per asset or schedule periodic maintenance against them. What the client needed was a predictive, AI-led layer that could see failure coming across a diverse, geographically spread asset base.
Engineering Complexity
Predicting failure at this scale meant solving four very different problems in parallel — on plants that could never be paused, spread across KSA, the US, and Europe.
Data at industrial scale and velocity
~1400+ assets, 100 parameters each at 15-minute frequency, against 5 years of historical data - ~500,000 data points to ingest, clean, and score every 15 minutes without falling behind real time.
Diverse, hard-to-model asset physics
Extruders, steam turbines, centrifugal and reciprocating compressors, centrifugal pumps, and heat exchangers - each with distinct failure signatures that no single generic model could capture.
No clean baseline for what “normal” looks like
Legacy KPI monitoring meant the right variables per asset had to be discovered from the data itself - not inherited from an existing, trusted setup.
Predict early enough to act, across geographies
Detection had to arrive with enough lead time to prevent a shutdown - and reach the right owner across KSA, US, and Europe operations, not just surface on a dashboard.
AI advisory decision support system
A Machine Learning (ML) engine that turns live plant data into ranked, actionable maintenance guidance for operators.
Anomaly detection & failure prediction
Sensor-driven classifiers flag anomalies and predict failures before they cascade into unplanned shutdowns.
Custom models per critical asset
1500 individualized data models - tuned per asset type: extruders, turbines, compressors, pumps, heat exchangers.
Customizable data pipeline
~500,000 data points ingested every 15 minutes across 1400+ assets, on 5 years of historical baseline.
Workflow & alert notification mechanism
Automated workflow and email alerts route each anomaly to the right owner - closing the detection-to-action loop.
Business Outcomes
>80%
Drop in unplanned incidents within a year.
A step-change in plant safety and reliability that earned significant client recognition.
~..10 Mn
Saved
A step-change in plant safety and reliability that earned significant client recognition.
Up to 20%
Less equipment downtime
Optimal, prediction-led maintenance scheduling replaced reactive firefighting.
Up to 20%
Higher productivity, up to 30% lower maintenance cost
Deeper real-time analytics improved throughput while cutting maintenance spend and lifting Return on Investment (ROI).
Success Stories
A Global Petrochemical Manufacturer Saves Millions with LTTS’ AI, IoT & Data Analytics-Led Solution
Discover how LTTS enabled predictive asset reliability, real-time KPI monitoring, and data-driven operations for a global petrochemical leader.
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