Oswaldo Rodriguez also recently discussed this topic on a podcast - listen here, or read the article below.
Since the oil price crash in 2014, oil and gas companies have focused more and more on how they can improve operational efficiency and with the added pressure of COVID-19, investing in digital technology has become even more urgent for 58% of those surveyed in a recent EY Survey. Equipment design data coupled with operational data from sensors to monitor specific asset conditions are often the first options that operators consider.
Individually, each data source makes businesses more effective and help track issues and provide insights. However, it often means that engineers have multiple point solutions creating terabytes of data in different formats with the end result that none of that siloed data can be connected together. This fragmentation increasingly leads to frustration when using data for decision making with team members from across O&M, reliability and safety unable to collaborate effectively. Further, without a consistent way of prioritising your most critical equipment, you might be making a lot of effort without materially impacting the bottom line: in fact, in the US alone, companies incur losses of USD $85 billion for unneeded maintenance.
The current reality is that we overspend on maintenance and still lose so much money due to downtime, as contradictory as that sounds. Around 80% of equipment failures are not wear-based, and cannot be addressed via scheduled maintenance, yet many companies continue to schedule unneeded maintenance because they lack the insight to prioritise their asset maintenance properly. At the same time, the industry also incurs around USD $1 trillion in annual loses from unplanned downtime for the same reason.
It is every operator's dream to have a central place where they can have a single view of their equipment data in order to make accurate decisions. Intelligent Operations - which connects together these various systems, then integrates analytics and cross-system business processes - is making that dream come true, solving the fragmentation issue and simultaneously optimising the decision-making abilities of field engineers everywhere.
A great example of Intelligent Operations in action is that of an asset performance management (APM) solution such as Lloyd's Register's AllAssets. It can interconnect different systems such as EAM, CMMS and data historians to create an intelligent digital twin of the operator's equipment with the pertinent information to provide a single view of equipment conditions for improved decision-making: work order history, inspection history, risk profile, inspection and planned maintenance activities.
For example, to optimise the operator's maintenance strategy and provide actionable insights, AllAssets helps identify assets that show patterns that could lead to failures. It considers the cost and risk associated with repairing the asset and loss of production versus the material and labour costs at certain intervals. The algorithm then identifies the optimum time to do maintenance based on risk of failure with the smallest value of total maintenance cost to determine the right activities at the right intervals based on this profile. The results can be quite spectacular; one recent implementation of LR's AllAssets reduced fire and gas device testing on seven offshore facilities by 26% with OPEX savings of $500K and increased safety.
A key challenge historically to implementing these methodologies was that it was perceived as time-consuming, however, driven by standardisation and technology, that is now changing, and in some cases, migration can take as little as a week. What's more, most companies already have data that can create actionable insights to improve decision-making, they just need to link it with industry experts and best practices to gain the most impactful results.
Almost all companies typically experience a decrease in the number of inspections and maintenance required when they first implement a risk-based approach. As the focus shifts to critical equipment it has the effect of reducing the risk of failure by 80% to 95% and the number of equipment items being opened by 30% to 60%. Altogether, new ways of managing data through Intelligent Operations can help drive significant efficiencies and cut operating costs, increase reliability and availability and reduce business risk in the process.