6 Asset Management Myths

Most companies agree asset optimization is a good thing; many strive to optimize each and every asset—or at least achieve a broad level of optimization across all assets. The problem is you can't optimize something until you first are able to measure and manage (control) it. Through this process we get the concept of asset management.

By Dave Harrold September 1, 2005
  • Continuous improvement

  • Fieldbus role

  • Asset classes

  • Multi-dimensional assessment

Most companies agree asset optimization is a good thing; many strive to optimize each and every asset—or at least achieve a broad level of optimization across all assets. The problem is you can’t optimize something until you first are able to measure and manage (control) it. Through this process we get the concept of asset management.

Companies successful in establishing asset management systems and procedures report that, when fully implemented, these practices and products provide a corporate-wide business process and decision-making framework that is guided by performance goals, covers an extended-time horizon, draws from economics as well as engineering, and considers a broad range of assets—especially people.

TS Raghavendra Prasad, asset management product manager, Honeywell Process Solutions, explains it like this: “Asset management is the beginning of a journey encompassing continuous work process improvements and optimization. It’s a collection of tools and technologies that help ease improvement implementations, but in the absence of an appropriate culture, know how, and well-established work processes, it cannot guarantee results.”

Asset management is viable and achievable, but it’s not quick or easy; and unfortunately its adoption continues to languish, partly because of lingering myths.

Debunking myths

Within the process industry, myths that tend to permeate asset management include the following:

Myth #1 : Digital fieldbus technologies (such as FOUNDATION fieldbus, Profibus, and DeviceNet) are all that’s needed to manage assets.

Certainly digital fieldbus technologies supply an abundance of data, but more data doesn’t necessarily equate to managed assets. Asset management is far more than extracting health and diagnostic data from field instrumentation. It includes understanding and monitoring equipment performance and its criticality to product quality. Increasingly, this is achieved using dynamically calculated KPIs (key performance indicators) aligned with business strategies.

KPIs are developed using engineering, design, and operational knowledge of the process and are included on operator displays. Operators then manipulate individual KPI factors (such as flow, temperature, and level) to maintain a desired KPI value range.

Many successful asset management implementations extract field instrumentation health and diagnostic information from the HART content that often co-exists on traditional 4-20 mA wires, and judiciously use that information as part of the KPI calculations.

Myth #2 : Asset management solutions are control-system specific.

It’s true that control system suppliers that also sell asset management-related products should provide a more tightly integrated system. However, today’s communication standards, such as OPC, make it easy to extract information from different vendor systems and populate asset management databases. From there, data can become part of a KPI calculation, analyzed by knowledgeable staff and equipment vendor experts, and turned into operational and maintenance planning information.

Remember, asset management isn’t just tools and technologies. It’s also processes and methods leading to optimized assets. In the end, asset management will require connectivity and integration of multiple tools, processes, and technologies, not just products of a single supplier.

Myth #3 : If an asset management system fails, the 4-20 mA loop also fails.

Robert Schosker, product manager, Pepperl+Fuchs, says he’s heard a lot of reasons not to deploy asset management, but this particular myth is expressed most often and represents users’ greatest fear.

Although it’s probably possible to implement an asset management system that places control loop integrity at risk, that would be a poorly engineered implementation.

Asset management systems are passive implementations. For example, the HART communication protocol is field-proven, easy to use, backward compatible, and provides digital communication simultaneously with the 4-20 mA analog signal. Properly engineered, an asset management product is less likely to cause a 4-20 mA-loop failure than a blown fuse.

Myth #4 : Different asset classes (mechanical, discrete, process) require different products and tools.

This myth often leads to the belief that asset management requires large amounts of money and resources to begin.

A project and maintenance manager for a global chemical company suggests implementing asset management in manageable chunks. Most successful asset management deployments have been those that commit a handful of resources to learn everything possible about smart field devices, he says.

That knowledge became the proving ground for developing methodologies and processes most effective for different departments in different locales. For example, the asset management implementation that works in one area of the world may require some adjustments for use elsewhere.

Several asset management packages support multiple asset classes and many are modular. This facilitates creating an implementation plan that supports implementing the all-important cultural and work process aspects of an asset management system, one asset class at a time.

Asset management is about eventually managing all assets. Where staff expertise about a specific asset is minimal or non-existent, part of the process may include outsourcing the monitoring and analysis to an experienced third-party.

Implementing an asset management system can be accomplished on a “pay as you go” basis. In other words, begin by identifying the most critical assets, then institute a monitoring, analysis, and improvement process and use some of the savings to pay for the next segment of your asset optimization journey.

Myth #5 : Users will intuitively know how to adapt to an asset management environment.

This may well be the biggest reason many deployments fail to achieve anticipated results.

Imagine what would happen if patients were handed EKG printouts and left to interpret the data and determine appropriate lifestyle changes on their own. Asset data are nothing more than plain data until knowledgeable resources analyze and explain what the data indicate, determine if additional data are required, and then form an appropriate action plan.

It’s still fairly common to perform mechanical maintenance on a calendar date rather than from expertly interpreted device and process data. This approach leads to unnecessary equipment or process downtime for replacing parts that data analysis would have shown capable of performing for a much longer time.

Developing, adapting, and preparing an asset management-oriented workforce is critical to achieving the ultimate goal of optimized assets. Until a company is mentally prepared to embrace an asset-management culture, investments in related products and technologies will be largely wasted.

Myth #6 : We’ve just gone through this or that; now is not the time to introduce an asset management system.

Just having gone through downsizing, a merger or acquisition, or any other business event is frequently an excuse to delay an asset management implementation.

Any company that waits until it’s not coming out of or just going into a change before it decides to implement something like asset management will be waiting until they are boarding up the windows and shackling the doors.

And just because senior management recently spearheaded a rightsizing exercisedoesn’t necessarily mean they know how to accomplish more with less. They very well may be hoping that the people who remain will help identify ways and means to accomplish more with less.

Honeywell’s Prasad says, “It’s important to remember that implementing asset management is one of the first steps on a business’ journey toward asset optimization. This is going to change not only how a business works; it’s going to change the culture within the business, and it’s going to stretch across department boundaries. Therefore, it requires senior management support and sponsorship.”

Getting started

Glenn Schulz, director of global business development for Rockwell Automation, says that he sees many users limiting their definition of an asset and thus too narrowly setting their expectations. He suggests segregating the physical resources of a plant into asset types.

Schulz’s advice is reinforced by ARC Advisory Group’s multi-dimensional approach to asset management process development.

ARC views asset management as six dimensions that must be simultaneously considered (see “Asset management” graphic).

Accompanying each dimension is a maturity model designed to decompose the dimension’s functions into a set of attributes. These attributes are used to capture key issues that must be considered in designing an effective asset management process (see “Maturity model” graphic).

ARC’s multi-dimensional assessment approach helps identify where excellence as well as deficiencies exist. With knowledge gained from assessing the maturity of each dimension, managers can better identify where and what improvements are necessary—as long as everyone’s onboard and old habits are truly abandoned.

Make it last

Augie DiGiovanni, vice-president, PlantWeb services for Emerson Process Management, sums it up when he says, “Many times users think they have an asset management solution in place because their supplier offers machinery that includes some amount of built-in diagnostics or self-evaluating capabilities. Users often fail to recognize that an asset management solution is not just about individual pieces of machinery; it’s about the whole plant and everything in it.”

Mark Bitto, asset optimization systems marketing manager, ABB, adds, “For any asset management solution to be successful long term, there must be acceptance and use of the solution by plant personnel. Many companies have learned that asset management solutions are most effective when operations and maintenance groups are integrated. It’s this common working environment that enhances communications, solidifies common goals, fosters identification of operational improvements, and shrinks the amount of time between when a problem is identified and when it’s resolved.”

Before any company can dream of completing the journey toward asset optimization, it must understand that asset management is more about establishing the right culture than about buying the right hardware and software.

Asset management dimensions

Dimension Description
Source: Control Engineering with data from ARC Advisory Group
Asset class Kinds or classes of assets included within the asset management team’s responsibility.
People Management strategies for the individuals who must assume the roles in the asset management process.
Parts Management strategies for the classes of spare parts to support the asset base.
Processes Practices, policies, and procedures used to control the execution of asset management activities.
Measures Measures used to determine asset health and the need for action. These can reflect performance (quality, throughput, etc.) or be surrogate measures that indicate pending performance problems (temperature, vibration, etc.)
Information Management of the kinds or classes of information used by the asset management processes.

Maturity model for people management dimension (typical)

Maturity level Management Staffing strategy Training strategy Supporting technology
Source: Control Engineering with data from ARC Advisory Group
4 Enterprise Responsibility outsourcing Improvement-based training Comprehensive, state-of-the-art
3 Business unit Strategic outsourcing Competency-based training Normal, state-of-the-art
2 Site Specialty outsourcing Budget-based training Normal, legacy
1 Department Scheduled As-needed Limited, legacy
0 Ad hoc As-needed On-the-job only Ad hoc