Agile methodologies for Asset Management

Currently, there is a global trend to view asset management as a strategic activity. Maintenance is no longer seen as an unfortunate, expensive task, which is poorly understood by C-level executives, it now became an opportunity to increase organizational efficiency by putting assets in operation according to the business strategy.

The main motivation behind this paradigm shift is the pressure faced by companies to become more efficient either by the dynamics of the market where they compete or by regulatory compliance. In many contexts, other factors stress the need for this transition, such as increasing operation and maintenance costs due to ageing fleets and environmental & safety concerns.

Fortunately, years of operation have generated a substantial amount of information ready to be analyzed and used to optimize the operation and maintenance costs of installed and future assets. The emergence of smart sensors making available large volumes of data can also play an essential role in developing advanced predictive maintenance algorithms. Nevertheless, although the potential exists, it is still mostly unexplored.

How can analytics help?

Managing a portfolio of assets poses several challenges demanding engineering, technology, and management to work coordinately to provide the required level of service in the most cost-effective manner. Yet, to decide when and how to service assets, companies turn to their engineers and maintenance workers or view maintenance as an unwanted cost and try to minimize interventions. Such policies lead to low-performance plans and misalignment with companies' strategic objectives either by excessive operations & maintenance (O&M) costs or by inadequate asset availability.

Analytics in asset management can bring an integrated view of all the impacts allowing an informed decision covering all angles. Data-driven approaches can leverage these decisions in several dimensions.

  • Predict asset condition by combining engineering models and artificial intelligence, ensuring that insights coming from field data enrich expert knowledge.
  • Assess assets criticality through a complete measurement of unavailability impacts. Impacts cover economic and financial losses, risks to employee health and safety, environmental harm, security lapses, and regulatory sanctions.
  • Decision models integrating condition and criticality, establishing the best way to allocate resources while optimizing the trade-off between risk and cost.

By combining these skills, companies can understand how to prioritize OPEX and CAPEX investments, build what-if scenarios testing alternative policies, analyze the risk in their operations for different O&M budgets, and study the impacts of extending assets life cycle.

Towards an analytical aware asset management

The fact that historically asset management was an area where executives assumed a hands-off posture places analytics as an essential tool to foster change. Delivering this change requires a new and fresh perspective over personnel technical skills working on the area, data infrastructure & information systems, not forgetting the cultural adoption of the new methods.

By successfully addressing these challenges companies can unlock hidden value from their assets leading to:

  • Improved operating margins through increased productive resource availability coupled with the reduction in operating costs.
  • Increased capital efficiency by allowing to boost production resources' capacity while increasing their lifetime.
  • Guaranteed safety, compliance and quality by reducing the number of occurrences and ensuring the equipment performance.

 

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