Enterprise asset management is not only caring about the physical assets in your plant. These days it’s even more important to have correct and reliable data representation in your operational and analytical systems.
Asset intensive organizations include companies in oil and gas, chemical, pharma, mining, utilities, rail, defense, and other industries. These companies operate thousands of highly complex plants, sites, and production lines, with millions of items of individual equipment.
A key priority of these organizations is to operate their infrastructure efficiently, effectively, and safely. For these reasons, the focus is on their physical assets.
These assets are often in place for decades and form a significant initial financial investment for these companies. The assets often require sophisticated maintenance processes, further adding to ongoing capital expenditure. However, successfully operating complex production sites in a global supply chain means the focus needs to shift towards having a holistic view of all asset data. More importantly, the information about the assets must be accurate, reliable, up to date, and trustworthy.
How can you keep track of data being scattered around for decades in plants all over the globe, maintained over the years by different employees and sometimes external contractors? The asset data you need resides in a multitude of decentralized systems ranging from ERP systems like SAP to CMMS systems from Oracle or IBM. The matter is further complicated by continuous acquisitions, carve outs and of course the fact that some information on equipment - so called brownfield assets - is only available in paper form, such as P&ID diagrams, and thus not recorded at all in a digital system. The answer is an intelligent data strategy.
At Utopia we have developed and executed such an intelligent data strategy, proven in hundreds of implementations to get data clean and then keep it clean. The overall approach is summarized in these three steps:
We will look at each of these steps in order, starting with the Build phase.
Enact Solid Business Rules to Assist with the Build Phase
In the Build phase, you must think of data standards within your organization and the business rules that should be executed against each data asset to ensure correctness, structural fit, accuracy, completeness, and uniqueness.
You need to carefully evaluate how you structure your assets, especially in the area of enterprise asset management. Given the fact that you may have global sites, often in different countries and sometimes operating independently, it is essential to agree on how to model a physical asset and how to get it into a standardized digital format – the so-called digital twin.
A proven industry standard in this respect is ISO14224. It helps to collect and structure asset data by providing guidance on the taxonomy, attributes, and a wide range of failure information, such as failure causes and what to do about them.
Following a standard like ISO14224 ensures that asset structures are always modeled the same way, follow the same nomenclature, and depicts essential information so that it is homogenous across your global sites. This allows operational CMMS and ERP systems as well as analytical applications to analyze, process, and share the data in an automated way (e.g., global spare part management).
Finally, the Build phase is to ensure and agree upon a data quality culture. Data is like an ideal gas – it takes the room you give it – so it is essential that rules are established, agreed, documented, and shared. But most importantly, they also need to be continuously checked! It is comparable to traffic rules – the rules are in place, and everyone knows them – but they also need to be enforced to achieve the desired effect.
Business rules make sure that, for example, an equipment record is completely entered. This starts with a standardized long and short description of the equipment, followed by the appropriate classes and characteristics, as well as unstructured information like operating manuals, technical drawings, or security bulletins. Business rules also check upon mandatory fields. These mandatory fields are essential for the processes that use the master data.
The equipment category can serve as an example of a mandatory field, as many operational processes need this information to execute the right downstream processes. For example, if the equipment category depicts the asset as being linear or rotating, then other fields will be needed. Business rules can further be used to ease the data collection process and help the user to complete the often-complex master data collection sequence.
A business rule can evaluate if the asset is critical or non-critical. If it is critical, then the ABC indicator can automatically be set to ‘A’ to reflect this criticality. This takes effort away from the end user and automatically ensures compliance in the collected master data.
Finally, the duplicate check falls into the category of business rules. Duplicates are probably one of the biggest evils in data processing. So, it is essential to keep your asset database clean with only unique entries. As you can imagine, entering duplicate equipment like a hydraulic pump is very easy. A slightly different description, or different non-standardized abbreviations, and most operational system will accept the new entry as being unique. Thus, a sophisticated duplicate checking mechanism is critical to avoid entering information twice or more.
The importance of making sure your asset database holds only unique entries cannot be reiterated often enough. Equipment are expensive assets – if they are duplicated across your systems all maintenance processes, analytical, and financial processes will derive wrong results. And wrong results lead to wrong decisions either by human beings or by automated processes. This can result in a huge financial loss, or in terms of safety critical assets, it may harm workers.
Clean Your Data in the Fix Phase
In the Fix phase, the focus is on getting your existing data clean. Ideally applying the business rules from the Build phase will help you in this step. The reality is that often the data is incomplete, out of date, not following the defined standards, as well as filled with duplicate entries.
From a Utopia perspective, we offer automated services that take this headache away from you to get you clean, standardized, de-duplicated, and enriched asset data. The Fix phase starts with analyzing and profiling your data. The usage of software tools allows a detailed profile of your data. The profile gives you a deep look into your data, including but not limited to these items:
- Percentage of missing information
- Common patterns to enter information
- Min and max ranges for numeric values
You can also easily see outliers. This is important, as outliers in the data might indicate that the data is not correct. This gives you an area to investigate.
The business rules are applied after the data has been analyzed. Data errors are corrected automatically if feasible. If automated correction is not possible, we apply internal and external expertise to correct the data issues.
Enrichment of the data occurs in the next step. We use internal data repositories with existing industry equipment and material information. If we can find the desired equipment or material in our repository a cleansed, standardized, and enriched record can be returned right away. Otherwise, we apply sophisticated machine learning algorithms combined with manufacturer web site crawl to automatically derive missing information. The Fix phase returns a clean asset database with accurate, reliable, standardized, and enriched information ready for your analytical and operational processes.
Sustain Your Data with Structured Governance
Finally, the Sustain phase is executed. As we all know, data is permanently changing as the reality is changing. Equipment is repaired, exchanged, moved, and retired. This means the data representation in your operational systems needs to be constantly updated to accurately reflect reality. Of course, you could run periodically the “Fix” phase again and again – but this is not a sustainable approach!
After the Fix phase, putting a structured governance process in place ensures that existing data is kept up to date and newly created data follows all your business rules. In an SAP environment, we are using SAP Master Data Governance for Enterprise Asset Management (MDG-EAM) to holistically sustain clean master data.
MDG-EAM allows you to execute workflow and rules driven data processes across your organization to request new master data or to change existing master data. The system allows you to orchestrate the complex data acquisition and sharing process amongst experts in your organization.
This process starts with requesting new master data, and continues by allowing experts to supply, for example, technical information like classes and their associated characteristics. The process continues with approving the changed or newly created equipment with a “four eyes principle,” meaning it must be reviewed and approved by at least two stakeholders. Finally, the clean master data record is published to the connected operational systems.
In summary – asset intensive organizations invest significant money and resources into their physical assets. To maximize the return on asset, it is crucial to have an accurate data representation of your assets in your operational and analytical systems. Operational processes depend on clean, accurate and standardized master data. Analytical applications can only return valuable reports and forecasts if the underlying data reflects reality. This can only be achieved with an intelligent data strategy.
Utopia has applied our “Build – Fix – Sustain” methodology numerous times to help asset intensive organizations drive their operations safe and profitable. For more information on how Utopia can help you, please get in touch with us here.