Blog 6: How asset management can be a massive victory for IT

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1 Blog 6: How asset management can be a massive victory for IT (Getting Ready for ISO Part 6 of 10) Insights from the "Asset Management for the 21st Century - Getting Ready for ISO 55000" Seminar, May 2013, Calgary: This blog is based on a series of interviews with John Woodhouse from The Woodhouse Partnership (TWPL), who delivered this wellreceived seminar. John Woodhouse is CEO and Managing Director of asset management consulting firm TWPL, is a founder member of the Institute of Asset Management. He chaired the development of the PAS 55 standard and is UK Principal Expert in the development team for the ISO standard. But asset management is actually a management science, with a business dimension, and this allows IT to play a more significant role to improve the component processes as well as the larger system of managing the assets for maximum value over their whole life cycle. Here are ways in which IT supports asset management and how that support can be extended: Data processing. Asset management involves good decision-making, and decisions should be based on facts wherever possible. This requires collection, interpretation, and retention of data. IT s involvement in this can be positive if the purpose of the data collection and retention is clear, or negative if it creates or compounds confusion, or merely automates inappropriate processes. Information management. The volume, diversity, and complexity of assets and asset systems means that information is correspondingly large scale, varied, and complex. So information systems are needed to hold it, navigate it, and sustain its integrity. In addition, IT should have the capability to properly present this complex data to the target audiences that need to understand and use such information. For example, geographic information systems (GIS) can help to correlate and navigate asset technical information, work information, and systems, providing geographical or spatial information in a visual context. Control of workflow. IT support is vital for control, coordination, and recording of the multidimensional activities of managing assets. This is because planning work, achieving consistency in task specifications, and capturing feedback are also high volume, complex processes. An example of a process is the planning and work management of a major plant

2 shutdown, which could entail a million man-hours of diverse, often safety-critical tasks and involve multiple contractors. Objectivity in identifying and solving problems. Continual improvement requires focus on the real problems and opportunities, and IT systems can assist with identifying, investigating, quantifying, and prioritizing performance, reliability, or cost improvement opportunities. If done right, IT can be the linkage the single source of truth to different parts of the business, providing disparate functional interests (finance, operations, and safety assurance, for example) with the same critical source material, so everyone contributes to, and works with, the same set of facts. PAS 55 and ISO standards help to define what needs to be done for coordinated asset management. And IT is one of the vital enablers that can facilitate individual activities and also help to integrate and align the different processes to deliver better overall value the business.

3 Blog 7: Don t Confuse an EAM (or CMMS) for an Asset Management System (Getting Ready for ISO Part 7 of 10) Insights from the "Asset Management for the 21st Century - Getting Ready for ISO 55000" Seminar, May 2013, Calgary: This blog is based on a series of interviews with John Woodhouse from The Woodhouse Partnership (TWPL), who delivered this wellreceived seminar. John Woodhouse is CEO and Managing Director of asset management consulting firm TWPL, is a founder member of the Institute of Asset Management. He chaired the development of the PAS 55 standard and is UK Principal Expert in the development team for the ISO standard. One of biggest mistakes that is made in trying to improve asset-intensive businesses is the thought that an asset management system is simply a software solution. Yet is it surprisingly common to find people who think that because they have EAM or CMMS software that holds a register of assets and helps to plan and schedule work orders for maintenance, that this somehow equates to an asset management system (a coordinated and integrated system of management like a quality management system). While it s good to have an EAM or CMMS tool, because you want to be able to track assets and control work, an Asset Management System is a much higher level construct a governance and coordination layer that involves alignment of strategic business objectives with the contributions you re getting from each of the asset systems and the life cycle activities that are worth doing. Asset management is about getting best value-for-money from assets, not just maintaining them. So by looking at things from a maintenance perspective, you re leaving a lot of the opportunities and benefits of asset management on the table. Here are a few things that asset management does that go beyond what asset maintenance can do. Asset management looks at the whole asset life cycle, such as considering at the design stage how to eliminate the need for maintenance, how to improve operability, or how to extend the achievable life cycle. And in the environment of aging assets, when a maintainer s concerns are usually How do I keep these assets going? an asset manager s viewpoint would be How should I manage the risks?, and, Is enhanced maintenance worthwhile or should I replace the assets, and if so, when, and with what? Asset management makes value realization the primary, shared goal for everyone, in contrast to the siloed objectives of separate groups fighting each other (e.g., operations sweating the assets, finance trying to cut costs, and project management seeking on time and under budget whatever the consequences for others). And value manifests itself not just with asset performance and financial success, but also through

4 satisfying other stakeholder expectations such as safety assurance, brand reputation, and environmental responsibility. So value realization involves handling competing priorities, and asset managers have to understand and deal with various trade-offs to find and demonstrate the optimal compromise. Such tradeoffs include costs versus risks versus performance, short term versus long term, capital costs versus operating costs, and so on. An effective asset management system aligns business objectives with what gets done day-to-day. Values are clearly defined and quantified, including risks, criticalities, and decision-making criteria. The entire organization has the same agenda: everybody is part of asset management. Asset care (maintenance) is an important contributor to this, but is only one dimension of asset management. Similarly, while an EAM or CMMS system can be a powerful tool for information management and work control, such technology is just one of the enablers for better activity and resource coordination, process integration, and data-driven decision-making. A management system for asset management includes all these and a whole lot more as identified in the PAS55: 2008 and ISO55000 standards (see

5 Blog 8: The Four Most Common Failures When Implementing Enterprise Asset Management Software (Getting Ready for ISO Part 8 of 10) Insights from the "Asset Management for the 21st Century - Getting Ready for ISO 55000" Seminar, May 2013, Calgary: This blog is based on a series of interviews with John Woodhouse from The Woodhouse Partnership (TWPL), who delivered this wellreceived seminar. John Woodhouse is CEO and Managing Director of asset management consulting firm TWPL, is a founder member of the Institute of Asset Management. He chaired the development of the PAS 55 standard and is UK Principal Expert in the development team for the ISO standard. When an organization implements EAM software, it s a challenge because you are introducing a system to track assets and manage maintenance work at the same time as enabling (or constraining, for greater consistency) some complex, cross-disciplinary business processes such as planning, resource coordination, performance reporting, and decision-support. So two levels of thinking are needed the daily mechanics of data models, work orders, and information flows, as well as the more strategic level: What do we want or need to do in the first place, and how can we use the information system to get better at it? To successfully implement EAM software in line with the full range of asset management activities (and not just maintenance management), you have to address both levels at once. Any large enterprise software application can be a challenge to implement. But an EAM system, because it affects most of the organization, is one of the most difficult to effectively deploy. Here are a few of the most common mistakes that people make when implementing enterprise asset management software. Big is bad, small is good. The chance of failure rises geometrically with the scale of IT projects, especially if they are cross-disciplinary. And if the project gets too big, the truth about its poor cost/benefit ratio is often hidden through embarrassment, vested interests, or a sense of powerlessness to tame the beast. It s better to go through a prioritized series of smaller, more manageable stages than to try to do it in one big integrated systems project. Mismatch between IT capability and the organization s level of understanding of asset management processes. Underexploited technology is an expensive waste, and insufficient sophistication leads to frustration and disillusionment. IT innovation occurs at a very different pace to that of organizational maturity or workforce understanding of the technology. Tied into this common misalignment of capability versus readiness is the lure of the flashing lights the overselling of (and gullible belief in) a fancy technology that will somehow make all the problems go away.

6 Instead, mistimed or overly sophisticated technologies can even make problems worse, such as helping you to do the wrong things quicker, or introducing more cost and confusion. Insufficient investment in training, communications, and engagement. System developers and integrators rarely appreciate the importance and scale of efforts needed to address human factors, and when IT budgets overrun (not unusual!), the training budget often gets raided. And training methods are often naive and shallow out of touch with the human factors needed to establish competency and confidence. Data quality is a moving target. Setting a fixed target such as I want all data to meet a plus or minus 5% accuracy is a completely inappropriate and false hope. A fixed target for data quality is a distraction from reality. Spurious accuracy is an endemic weakness of most EAM systems (e.g., the system forcing you to enter a cost to 2 decimal places even if the value is only known to +/- 30%). So too is the common perception that available hard data is either pretty good or total rubbish. Uncertainty and confidence limits, range estimates, and fuzzy knowledge are all areas where EAM systems struggle, yet they are a reality of asset management. Forcing uncertain information into EAM hard-edged boxes, or believing information just because it is presented in a multiple digit format, leads to loss of long-term credibility and support for the system.

7 Blog 9: How Thinking of Assets as Systems Improves Asset Management Processes (Getting Ready for ISO Part 9 of 10) Insights from the "Asset Management for the 21st Century - Getting Ready for ISO 55000" Seminar, May 2013, Calgary: This blog is based on a series of interviews with John Woodhouse from The Woodhouse Partnership (TWPL), who delivered this wellreceived seminar. John Woodhouse is CEO and Managing Director of asset management consulting firm TWPL, is a founder member of the Institute of Asset Management. He chaired the development of the PAS 55 standard and is UK Principal Expert in the development team for the ISO standard. When trying to support asset management, CIOs typically subdivide the problem to get their heads around it and create models that center around individual physical asset records on the asset register. This makes the data library simpler (asset attributes are more consistent if they are structured by asset type, and at least some transactional data is asset type-specific) but, from the business value perspective, it is usually better to look at the way in which diverse assets work together in functional systems to deliver value and business results (which is why the assets exist in the first place). This system is harder from the IT modeling perspective, since systems usually comprise a mix of asset types, functional locations, and overlapping system and sub-system hierarchies and have complex performance interdependencies. However, it does reflect how we actually use assets to generate value so it aligns better with performance outcomes, risks, criticalities, and value focus. We also tend to do things to individual assets (e.g., inspection, maintenance, or renewal) but measure and get benefits from the systems in which they operate. So determining the right thing to do, and when to do it, requires us to evaluate impact at the systems level for costs typically incurred at the component asset level. A systems perspective is therefore essential for optimized decision-making, work prioritization, and delivering best value-for-money. It also means that you must understand what business outcomes are desired, how the various asset systems contribute to these, and only then how the individual components (assets) contribute to the performance of the systems. Dealing with assets as if there were independent data elements, needing maintenance and accumulating performance history, is like grouping parts of a jigsaw puzzle by their similarity of shape instead of referring to the overall picture and how neighboring pieces fit together. Let s look at electric motors as an example. Some asset management requirements and data attributes are evidently linked to the fact that it is an electric motor (rather than, for example, a pipe or an instrument).

8 But the same design of motor should receive very different asset management strategies, data and performance tracking depending on its functional location (or system context): such as if it has an installed standby alongside it, or if it is used only two hours a day, or if it is in a business critical role with big failure consequences. These factors are usually considered in strategy development (e.g. RCM and RBI studies) but not often mirrored in EAM systems and data structures. Strategic planning of business priorities (e.g., investments and improvement programs) is primarily a topdown process, informed by bottom-up asset realities (constraints and opportunities in what is possible, given the assets condition, performance, flexibilities, etc.). Asset systems are where these two influences collide the performance desires of the business versus the component asset capabilities and constraints. So, in the pyramid of assets in the total portfolio, it is the operational systems level at which strategy needs to be most carefully optimized: it is the negotiation layer, where stretch targets get set and challenged, and it is the layer where success or failure needs to be measured and reported.

9 Blog 10:Avoiding the Data Swamp in Asset Management (Getting Ready for ISO Part 10 of 10) Insights from the "Asset Management for the 21st Century - Getting Ready for ISO 55000" Seminar, May 2013, Calgary: This blog is based on a series of interviews with John Woodhouse from The Woodhouse Partnership (TWPL), who delivered this wellreceived seminar. John Woodhouse is CEO and Managing Director of asset management consulting firm TWPL, is a founder member of the Institute of Asset Management. He chaired the development of the PAS 55 standard and is UK Principal Expert in the development team for the ISO standard. In the not so distant past, most enterprise applications were based on a range of data that was heavily constrained by what was collectable. It was hard to acquire data and ensure it was of high quality (and it still is for some types of data!). Now in the modern world, with the Internet of Things and the ability to collect data from a variety of different sources, automatically in many cases, you can easily end up with overwhelming amounts of data. But Big Data can often result in Big Confusion. Some of the data has a natural home, such as asset registers and technical records. Some can be distilled, analyzed and converted into useful management information. But a large amount of it falls under the category of it might be useful one day a large, often unstructured mix of activity records, asset performance and condition attributes, sometimes having localized or temporary usage but often collected just because it is now easily collectable. The real challenges therefore are to understand what data is worth collecting in the first place, and why (how it would and should be used). Then we have to put it into organized repositories that are more like a library and less like a swamp. Here are some ideas on how you make sure that you can store more relevant data, with clearer understanding of why it is needed, without it becoming a messy liability that is neither used nor trusted. First, think of data as part of a demand-driven supply chain in which justification for collection, retention and usage has to be made from the business risk or cost of not having it (to the appropriate standard at the right time). The apparently low cost of acquiring data and the motive that it might prove useful are not enough to justify collection and retention. This bucks the trend of data provision being seen as an availability-driven process that triggers a search of usages. Demand-driven thinking requires greater understanding of how the data will be used, selective extractions from it, and what business value is achieved from using it.

10 The SALVO Project, a multi-industry R&D program to develop innovative approaches to asset management decision-making, has yielded good examples of this approach. Three of the necessary six steps in the SALVO decision-making process illuminate the demand-driven data specification. Step 1, Identify problems and improvement opportunities spells out the business impact criteria for which assets need what attention in the first place, and the desirable evidence to support this identification. This includes definition of asset health indices (relevant mix of performance and condition features) and criticality measures. Step 2 is the drill-down into the identified problems or improvement opportunities to ask why they are problems (root cause analysis). This often reveals a mismatch between expectations and realities in the use of data to demonstrate patterns and correlations. The noise in the system, the inherent limitations of data samples, and the volatile business environments in which data is collected (including consistency of collection method) mean that pattern-finding or non-randomness is rarely provable, irrespective of the clever data analytics that are applied. Except in very rare cases, the available data will normally be constrained and censored in various directions, so the collectable evidence needs to be used with great care and with a healthy dose of realism and tacit knowledge from asset design, operations, and maintenance experts. Step 3 of SALVO covers the selection of potential actions or interventions, and these can be a far wider range of options than the technical tasks normally considered (such as inspection, maintenance or renewal). SALVO has identified 42 practical options that might be applicable to solve asset management problems. Step 4 then covers the business value-for-money evaluation of the potential solutions, requiring assumptions and, if obtainable, evidence of costs and short-term and long-term consequences. This step combines observable facts (mostly helpful in quantifying the do nothing implications) with external data needs and the tacit knowledge of the experts in forecasting and estimating the degrees of improvement that might be achievable. This is a stage where reliance on collectable hard data is fairly limited, but at least we can be clear about the questions that need to be asked (that is, what data is desirable to support the decisions). SALVO has mapped the information needs for all 42 common decision and intervention types the information required to determine if the interventions are worthwhile and, if so, when. For example, there are 13 specific questions or data elements that must be considered in deciding whether to buy a critical spare part and how many to hold. These decision-specific checklists help to focus on the relevant and useful information within the background swamp of confusing evidence. They, and a what if? approach within the evaluation process, reveal the role of the data to support decisions. They demonstrate the business value of collecting the right stuff, by quantifying the cost of uncertainty when forced to rely on range estimates or assumptions.