Innovative Asset Management Tools Increase Plant Reliability, Throughput and Availability
Most organizations do not have a fully trained and qualified asset manager, however, and even if they did, how should asset managers formulate strategies to provide lasting results? Fundamental questions include:
- Is maintenance focusing on the right assets?
- Is the organization recouping its investment in predictive maintenance tools?
- Is the maintenance budget being spent effectively?
- Which assets fail most often and what are the associated direct costs?
- Which assets represent the greatest risk to safety and plant availability?
Many managers “know” the answers to these questions, but supporting this knowledge with statistics can be difficult because asset-related data is often spread across the business within various systems and data “silos”. Drawing reliable conclusions from a mass of potentially conflicting data can also be difficult. The challenge is therefore to deliver asset management information that is easy to understand and supports improved decision-making. In addition, managers choosing between different asset management strategies need to know the likely consequences of each.
The Need to Change
In today’s competitive global market, an organization can truly differentiate itself from others by installing a “boardroom-to-shopfloor” asset management strategy which can not only direct day-to-day activity but also provide a plan for the years ahead. The organization and its stakeholders need to develop a vision for tracking performance.
Most organizations monitor their businesses through “silo data centers”. Metrics are obtained from individual departments or systems and this information is correlated to help understand how the business is performing against its own objectives. The introduction of plant-wide Enterprise Asset Management (EAM) systems has brought slight improvements in performance, but these mainly have resulted from cost accounting identification of department spends or asset failures. The information is therefore predominantly retrospective.
The figure on page 52 shows the structure of a typical process industry company, with the base process layer at the bottom, rising through to the business layer where EAM software operates. Even in the most efficient organizations, the challenge of retrieving and acting on data throughout these levels is difficult and requires multiple interfaces.