Boosting operational resilience in utilities

In the past five decades, maintenance as a practice has evolved to better serve the manufacturing in the areas of reliability and availability, says Luc Chantepy, Regional Sales Vice President, MENA, AspenTech.

Luc Chantepy, Regional Sales Vice President, MENA, AspenTech
Luc Chantepy, Regional Sales Vice President, MENA, AspenTech

In creating a world that doesn’t break down, a huge $20B opportunity is on the table for the process industry to address. Astute manufacturers should focus on reducing unplanned downtime and increasing asset utilization, as both processes represent the biggest opportunities for financial improvement in production operations.

The evolution of maintenance

In the past five decades, maintenance as a practice has evolved to better serve the manufacturing in the areas of reliability and availability. However, change is imminent. The current approaches, such as run-to-failure, calendar, usage-based, condition-based and reliability centered maintenance (RCM), are less than ideal. Two key challenges remain. First, despite the increasing complexity of these maintenance initiatives, the exact science of when to conduct inspections and service the machines is less than scientific.

Second, the current slew of maintenance methodologies focuses on wear and tear as the root cause of failure. This literally sidesteps the fact that 80% of degradation and failure in mechanical equipment is process driven. This view is further reinforced by Boeing, a company at the cradle of RCM and the aircraft industry. Boeing basically acknowledges that up to 85% of all equipment failures happen on a time-random basis, no matter how much you inspect and service.

However, the industry reality today is that in order to maximize profitability, processes tend to be operated as close to key limits as possible. This can be detrimental, as process excursions quickly place an asset in an undesirable operating point, where damage or excessive wear and tear to the asset occurs.

This means that maintenance decisions need to be further mitigated by better understanding the impact on asset and process. A new generation of analytical capabilities is required to provide deeper insights into the asset, process and interaction between them. While operators need predictive solutions to red flag impending trouble, the software needs to be able to guide them away from trouble with prescriptive guidance. This requires the preferred solution provider to have deep domain and process expertise with the ability to extract data from design, production and maintenance systems.

The next generation Asset Performance Management (APM)

Using data from various sources, anomalies in drilling can be detected in real-time and decisions can be made earlier to shut down if necessary to prevent any large environment risks. According to a recent survey conducted by IDC, GCC-based companies have started to move beyond the hype, and are increasingly considering, or piloting, Big Data initiatives. Big Data is now perceived as not only a 'nice-to-have' technological innovation, but also as a critical investment choice that is contributing significantly to the rise of an intelligent economy. Big Data adoption in the GCC is still in its early days when compared with other regions like Europe and North America, but it is clear that awareness around the concept and its potential business impact is rapidly rising across the region.

Extractive industries, particularly the oil and gas vertical, are other key areas of growth for Big Data adoption in the GCC. This is due to their relative importance in most local economies, and the critical need for companies in these sectors to turn the massive amounts of raw data they collect in their fields into actionable insights that can guide operations or business decision making.

In taking a step into the future of manufacturing, APM 2.0 incorporates the advanced analytics that predict issues and prescribe operator actions. With a holistic view of the process and asset, software such as the Aspen APM suite combines asset analytics, reliability modeling and machine learning to analyze, understand and guide. Principles of data analytics and data science enable the reliability strategy, which includes machine learning. A dominant predictive analytics technology in information technology today, machine learning on manufacturing assets requires domain specific knowledge of chemical processes, mechanical assets and maintenance practices, etc.

For industrial prowess, machine learning needs to interpret and manage complex, problematic sensor and maintenance event data. Eventually, it can determine the operating conditions and patterns that can have a deleterious impact on the asset by capturing the patterns of process operation and merging them with failure information.

A system of success is long overdue

While predictive analytics can reduce downtime, disruption seldom happens in isolation. Instead, dozens of reliability, process and asset issues happen simultaneously. This presents a systemic problem for RCM, a current maintenance approach that conducts static assessments, by delaying the decision-making process. As such, dynamic assessment is required, as new warnings need to be evaluated alongside other active conditions to prioritize and allocate resources. However, we cannot address everything at once. A system of success is mandatory to address problems and prioritize them, according to the level of risk they represent. With APM software, each new alarm triggers a recalculation of risk profiles to guarantee that the most current financial and risk probability assessment is used.

However, to be thoroughly successful, companies need to adopt a holistic approach in implementation. First, they need to communicate their goals clearly. This helps in effective problem solving. Second, it is necessary for companies to genuinely embrace a data-driven world. Third, they need to differentiate between lagging and leading indicators, as well as how to respond accordingly.

Fourth, the right mix of people, technology, strategy and solution is essential – along with the use of relevant case studies. Fifth, companies need to invest time and master the technology well. Sixth, the adopted analytics program needs to be well aligned with business goals. Seventh, companies need to deploy the appropriate software and hardware to solve problems. Eighth, companies need to execute well and with a keen sense of urgency. With operational excellence and profitability at stake, it is imperative that organizations are successful in developing the best asset performance strategy.

Indeed, failure is not an option – in creating a world that doesn’t break down!



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