Read Part 1
Asset management (AM) is a recognized value lever for asset intensive organizations. It encompasses a broad vision of possibility and action. Within this new paradigm, AM practitioners need a new toolbox of skills, competencies and tools. This article, written in two parts, opens the lid of this asset manager’s toolbox and takes a look inside.
Figure 1: Asset productivity risk
Modeling Risk and Opportunity: Effective Risk Model
Most organizations understand risk reasonably well from an enterprise perspective. Usually, there is an enterprise risk department or activity where broad organizational risks are well identified and managed. However, incorporating the asset productivity risk into the risk framework is less mature and most risk matrices do not adequately address this. Often, there is a mismatch of conceptual skills in compiling the enterprise risk register and incorporating the elements of asset productivity risk.
Alternatively, maintenance departments deal at a more static asset risk level. This is referred to as criticality, which directs efforts at allocating maintenance activities and resources to ensure appropriate plant or asset reliability.
There are proven high value opportunities in bridging this divide, creating a scenario where the asset productivity risk is incorporated into the corporate risk framework, thus facilitating the strategic alignment of asset risk. (To read additional information on this, see #4 in the References section at the end of this article.)
Understanding Complexity: Understanding the Relevance of Complexity Theory Will Increase the Chance of Success and Establish Effective Asset Management Solutions
In industry, there is always a push for standardization and predefined solutions or business processes. They are seen as the solutions to complex situations without truly understanding why situations are complex. These “best practices” are often promoted as the only way forward. However, experience shows that each situation can be very different within the same organization. Complex systems have many elements, adding to the uncertainty.
Figure 2: The Cynefin framework
When looking at an organization’s specific situation, certain characteristics are evident that, when understood correctly, allow for an appropriate approach or solution. To assist in this evaluation, the Cynefin framework is used. This decision-making methodology, developed in 1999 by Dave Snowden and Cynthia Kurtz, is enormously useful in characterizing problems in order to apply a systematic and aligned approach to evaluating and making appropriate decisions. The argument being that a problem characterized as a simple problem has a very different solution path to that of a complex or chaotic problem.
The framework offers five decision-making contexts or domains: Obvious (known until 2014 as Simple), Complicated, Complex, Chaotic and, at the center, Disorder. These domains help managers identify how they perceive situations and make sense of their own and other people’s behaviors. The framework draws upon research into systems theory, complexity theory, network theory and learning theories.
Figure 3: Aligning appropriate solutions to the correct domain
Experience shows that characterizing problems provide a clear approach as to how they should be solved. In real-world situations, most problems fit into the complicated and complex domains, and a sure route to failure is trying to apply simple solutions to complex problems.
The idea is that as knowledge and experience increase in organizations (i.e., expertise), there is a clockwise drift from chaotic, through complex and onto complicated, where it can be captured by procedures.
Most modern-day business problems exist in the complicated or complex domain and, ultimately, have many differing effects. The low-hanging fruit items are usually solved using systems and processes. These items are usually in the obvious domain. As organizations rise higher into the tree, they move through the different domains and the level of complexity increases.
The manner in which an organization addresses an obvious domain issue is to sense, categorize and respond, drawing on previous solutions or best practices. In the same respect, if an organization addresses a chaotic domain issue, the relationship between cause and effect is nonexistent. Categorization of the issue and the selection of a solution are not easy. The way to address chaotic issues is to act, sense and respond. The ultimate decision and solution are based on the team’s experience in recognizing the issue. Malcolm Gladwell’s book, Outliers, is a good reference, noting 10,000 hours of experience.
This is why industry standards, like ISO55001 for asset management systems, cannot be at a prescriptive level. One size certainly does not fit all. Likewise, when an organization establishes an asset management system, the organizational maturity, situation and ambition must be reflected in the solution implemented. As any of them change, the inherent processes and systems must support complex decision-making and adjustments.
Figure 4: The relative contribution from enablers according to Ledet
Figure 5: The role of integrated planning in asset management
Maintenance Reliability Tool Kit: Selecting the Right Improvement Tools
In the book, What Tool? When? A Management Guide for Selecting the Right Improvement Tools, author Ron Moore concisely describes the maintenance management/reliability engineering toolbox. Each of the 15 chapters in the book describes a tool that has an effective application in the appropriate circumstance. Some are technical, such as reliability-centered maintenance (RCM), Six Sigma and kaizen. There are also organizational elements, such as lean and total productive maintenance (TPM).
This tool kit would be incomplete without mentioning the condition monitoring/predictive maintenance tools, which are often key to any effective maintenance management strategy.
A core knowledge element in the application of the maintenance reliability tool kit comes from Winston P. Ledet’s work at DuPont, where he modeled the influence of a number of maintenance reliability enablers. He clearly demonstrated that the value was to apply a tightly integrated approach, where single point solutions applied in isolation have limited and, in some cases, a negative contribution.
Integrated Planning: Realizing the Value from Seeing the Big Picture
One of the transitions an asset manager has to make is to be able to see the big picture and ensure there is an integrated way of thinking that supports the realization of all the business functions working together. Strategic planning needs to cross traditional organizational boundaries, which is a formidable challenge. However, the value contribution from such a joined approach can be a strategic differentiator, with dimensions larger than the contribution of a number of independent, individual initiatives.
Looking back at Michael E. Porter’s (see Reference #2) value chain, one can see that a business is only as good as the elements that are aligned and working together. In a company’s haste to create organizational structures to support business delivery, it often creates functional silos. Although these silos are often well managed and, in some cases, optimized, the impact of them on the other functional silos is lost. Effective asset management can only occur when the functional silos are aligned and integrated.
Integrated planning is more than just planning. It ensures that all costs, benefits, risks and opportunities are examined and understood. Often, value only can be created when all functions and departments understand their role in the value chain and are fully aligned. In the effective asset management delivery model, there are no departmental boundaries since it is fully understood how to ensure alignment of the business.
It is very important when aligning business functions and departments to deliver value that the correct level of accountability and responsibility is established.
Figure 6: Resolving a large data set into the time domain
Data: Realizing the Value of Data Analytics
Big data, digitization and the promise of a new future with interconnectivity between machines are hot topics. However, when one digs around, there are few real use cases in the asset management world. It is not because the premise is not real, but rather the existence of several clear barriers to transform the concept and create tangible value.
These barriers include, but are not limited to:
1. The perception that incomplete data or data with potential errors/inaccuracies has no value;
2. The challenge of integrating and matching different data sets, probably in different data formats; For example, combining technical, performance and financial data, which reside in multiple systems;
3. The lack of skills to work with big data;
4. A poor conceptualization of what to do with the data and where value realization lies;
5. Poor management of the data to the information, action and knowledge management process.
Foundational to working with big data in asset management is finding the common basis. In most cases, data is time stamped, so consequently, looking for variations, transitions and anomalies is done in the time domain. A typical time domain set of data is illustrated in Figure 6, which is an amalgamation from a series of data sets consolidated to facilitate a time series analysis.
Using big data tools, like principle component analysis or multivariant analysis, one is able to create new elements of knowledge and insight. Ultimately, the development of artificial intelligence and machine learning will enable these advanced analysis techniques to be incorporated into the day-to-day decision-making tools and dashboards available to managers.
Many organizations are feeling their way into the world of big data and have a naive approach, hoping to stumble into new actionable insights.
Effective asset management is a whole business activity that creates strategic alignment and tangible value. One of the most important issues is to be able to make informed decisions on available information and data. This is dealt with in barrier #3, but also falls into the arena of data analytics and has all the attributes that make creating the data value paradigm so important for asset management.
Figure 7: Illustrating supervised machine learning example from asset management big data
Going Forward
Asset management professionals must always remember that business and life are linear. As you grow in the field of asset management, your understanding of the operating context changes and develops. The asset management system (ISO55001) that you establish in your organization must be an ever-developing one. One size does not fit all, even within the same organization.
The mission of asset management professionals is to ensure that whatever systems, tools, or processes they establish, they must realize tangible value from them while delivering on business objectives. There isn’t a single way of doing it, so doing something is better than doing nothing!
References
1. International Organization for Standardization (ISO). ISO55000:2014 Asset management – Overview, principles and terminology. https://www.iso.org/standard/55088.html
2. Porter, Michael E. Competitive Advantage: Creating and Sustaining Superior Performance. New York: Free Press, 1998.
3. Snowden, David J. and Boone, Mary E. “A Leader’s Framework for Decision Making.” Harvard Business Review, November 2007: pp 69–76.
4. Fogel, G. and Swart, P. “Does Relying on Criticality Put Your Organization at Risk?” Uptime Magazine, April/May 2018.
5. Fogel, G., Stander, J. and Griffin, D. “Creating an Effective Asset Management Delivery Model.” Uptime Magazine, June/July 2017.
6. Fogel, G. and Kemp, T. “The Role of Asset Management in a Constrained Economy.” IAM Annual Conference, Edinburgh, Scotland: 2016.
7. Fogel, G. and Swanepoel, S. “Chapter 8: Declaring Value from an Asset Management System.” The New Asset Management Handbook. Fort Myers: Reliabilityweb.com, 2014.