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Inspection Data Management Systems and the Evolution of Reliability Data

Introduction to Inspection Data Management Systems

Heavy processing industries generate thousands of data points each day. This overwhelming amount of data is important for maintaining mechanical integrity of operating assets. But how is the data able to be utilized to make decisions surrounding asset integrity? Each organization will have its own way of doing things, but generally, the data is gathered, stored, and organized in a number of different systems—an Inspection Data Management System (IDMS) being one of them.


What Is an Inspection Data Management System?

Inspection Data Management Systems (IDMS) are used by the oil and gas and chemical processing industries to organize inspection data for fixed equipment such as pressure equipment relief devices, tankage, and piping. These applications can establish and track inspection tasks, collect and maintain inspection results and, if managed correctly, provide reliable proactive inspection plans. Many IDMS programs today now also include Risk-Based Inspection (RBI) functionality.


Inspection Data Management System Limitations

Inspection Data Management Systems are good for managing inspection programs; however, they are limited in that this is all they do. This limited focus creates gaps that either go unresolved or require implementation of other software programs, contributing to siloed data.

IDMS programs are limited in the following ways:

  • They only include inspection data, meaning they do not look at reliability or how each asset impacts reliability to the entire system. Therefore, you can’t truly optimize resources across your facility.
  • IDMS programs use static data. Assets are not connected to the system with live data, and as such, updating each asset require tedious manual data input and analysis.
  • The inspection models used in IDMS programs are conservative. Time-based programs, for example, are extremely conservative since they don’t factor risk into decision making. Risk-Based Inspection programs use conservative risk calculations that leave room for further data refinement.

Because the capabilities of an IDMS are limited to managing inspection programs, relevant industries must use a multitude of programs and tools to manage various aspects of their facilities. But what does the future hold for reliability data?

What Does the Future of Reliability Look Like?

Complex manufacturing systems are constantly trying to make better and faster decisions to improve reliability and maintenance. To make those decisions, they must work through millions of pieces of data that are stored separately—for example in IDMS, Computerized Maintenance Management Systems (CMMS), and Asset Performance Management (APM) programs. However, the industry is continuing to see major advancements in data acquisition, warehousing, modeling, and analytics. With these capabilities in mind, we have the opportunity to take the next leap in reliability analysis, allowing us to improve reliability further while decreasing total maintenance and inspection spend.

This leap is being made possible through Quantitative Reliability Optimization (QRO). The QRO method connects every data point, analyzes the data to generate the most advanced and accurate risk models to date, and enables system-based strategies—meaning you can prioritize actions across your entire facility based on current market conditions, availability goals, and/or budgetary constraints.

QRO is a powerful way to connect all asset data and see key performance indicators, such as availability, risk, and cost. It can also be used to simulate multiple asset management plans to develop the best path forward across units, facilities, and enterprises.

QRO allows users to do things like:

  • Near real time optimization of all maintenance spend based on short/mid/long term reliability targets.
  • Understand the economic value of every inspection or maintenance activity performed.
  • Understand the economic value of every piece of data that is currently being gathered or could possibly be gathered in the future.
  • Near real time scenario modeling, including the implications of deferred turnarounds, feedstock pricing changes, or various capital projects.
  • Drive effective economic decisions in the event of reliability based operating excursions.

The evolution of Reliability Data requires an evolution of the systems we use to manage it. That evolution lies in QRO and the enablement of near real time data analysis and the modeling of effects across a multitude of systems at once. QRO allows for the bridging of gaps between your IDMS and other asset management systems into a single system that enables you to make better, faster, and more educated decisions to improve your reliability and maintenance performance.

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