Today, siloed data and reliability models result in an estimated 10% to 30% wasted maintenance spend, unplanned downtime, and unmitigated process safety risk.

Large amounts of design and asset condition data are not digitized

Data is siloed between systems or not organized around proper asset hierarchies

Disconnected reliability models offer poor support for business decisions

Gaps between engineering models and data science frameworks impair technical insights

Our data-driven reliability framework ensures the right data is fueling the right intelligence, helping you make confident, strategic decisions.

Digitizing and organizing your data is the first step. Our framework then leverages models that combine reliability data science and engineering, surfacing better insights and breaking down silos between your systems.

Explore Pinnacle’s Data-Driven Solutions

Collect and store the right data with quality and efficiency.

Today, data collection processes are time consuming and human dependent. Data is often replicated across multiple systems and is vulnerable to human error, making it difficult for facilities to scale data-driven initiatives. The key to improvement here is knowing where to automate and leverage technology, while having appropriate human oversight. Trusted data is the foundation of a strong reliability program.
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Make data useful by building relationships across all your data and data systems

Without context and structure, data is useless. The data we use today is often static and siloed, requiring an abundance of human intervention and assumptions to finalize any analysis. Converting raw data into digitized and connected data across your entire facility is critical to scale data-driven initiatives and value at scale.
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Use the right data science and engineering models to drive reliability informed decisions.

Today, our reliability models are either heavily reliant on subject matter expertise or black box data science algorithms. While these approaches can add significant value individually, they are often too resource intensive or require significant quality review and human oversight. The key to improvement here is knowing when to leverage data science, human expertise, or a hybrid of the two to model reliability scenarios effectively.
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Prioritize reliability decisions across your facility and understand their impact real-time

Improving industrial reliability today is complicated. We are often overwhelmed by multiple competing initiatives and a constant requirement to make decisions in the moment without a clear understanding of the impact that decision is going to have on our facility. Decision clarity and confidence is critical to sustaining reliability performance improvement in the long-term. Understanding the complete data-supply chain across your people, process, and technology is the first step in driving strategic decision making.
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How Data-Driven Reliability Reduced a Refiner's Maintenance Budget by 14% Through QRO

Learn how a major refiner that maintained 99% availability on
its assets was able to identify an opportunity to reduce its maintenance budget on a partial crude feed.

Reliability Focus Areas

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Greenfield

A greenfield project provides the opportunity to get reliability right, from the start.

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Asset Reliability

Maintaining asset reliability is critical for keeping your facility functional and profitable.

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Mechanical Integrity

Maintaining mechanical integrity is critical for your facility’s safety.

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Operations & Maintenance

A well-executed O&M program helps facilities save money by maximizing asset reliability and life span.

Pinnacle Process to Getting Started

30 Minutes
10 Days
100 Days
Ongoing

Ready to see how data-driven reliability will impact your facility?