Breaking the Silos: Reliability and Data Management

Many organizations spend several years collecting massive amount of data and information that are often of little value. The recent buzz word of analytics has often caused organizations to go into a tailspin to get ahead of the game. And in the effort to do so, spend millions without ever seeing the promised value. On the flip side, where data is limited or too complex to analyze, maintenance companies make decisions, based on expert judgment, opinion, and anecdotal information. If we look at this critically we can see that often these decisions are made based on strength of character, political maneuvering, etc.

The focus of my publication is on asset reliability data, especially in asset intensive companies. My intent is to provide an opinion and a guidelines on how to approach data management and reliability. I will put forward some fundamental questions and answers to those questions (based on at least 2 successful program implementations).

1.      What data and information should I collect for a successful implementation of an asset management program?

My suggestion in this area is to identify how each potential data set will be used and what value it brings to the table. Some data is extremely difficult (and costly) to collect and manage and may not provide the value required to justify collecting it.  By holding a table top exercise across a cross functional team, a model of how data and reliability correlate can be developed. As a starting point, the basic fundamentals are:

-         Solid asset hierarchy, defining the relationship between location, processes, equipment, sub equipment and components (based on ISO 14224)

-         Accurate equipment master data (BOMs, manufacturer name, model number and key physical attributes). This is essential for everything from spare parts analysis to warranty claims

-         Standardized naming conventions of equipment

-         Categorization of maintenance, operations, engineering and reliability work on asset. Work order categorization is foundational piece of any bad actor analysis and continuous improvement initiatives

-         Failure codes on the work order. Considerable though should be given to define codes that would categorize all corrective work on a component within an equipment. The relationship between a component and what damages it could see if often overlooked. Generic damage code lists are often built into the system resulting in inaccuracies and inefficiencies

-         Capturing costs on work order to ensure bad actor analysis is accurate

-         Equipment criticality and failure modes to ensure that all relevant risks are identified and mitigation actions are put in place

-         Equipment performance limits (on critical equipment) for purposes of managing the condition and performance of the asset

Although, this is by no means an exhaustive list, it provides a starting point for the organizations to gauge where they stand. In short, develop a model where critical data is identified and all stakeholders have a firm understanding on why a particular data set is important.

2.      Who should manage the data?

No IT please. More often than not, we get IT folks defining how the data should be managed for the business. In asset intensive industries, this often requires clear understanding of how the data will need to be used. When there is a clear data model built by the business for the business, the implementation and sustainment of data and information is much easier. Having said that, IT has a critical role in enabling such models. But the key message is, data should have business owners who are accountable for how it is managed.  Technology should only enable the implementation of such model.

3.      When should such data model be implemented

Building a data model should be the foundation of any transformation (green or brown field). This is typically a short but focused effort. The implementation of such a model require a road map that is custom to each organization.

My recommendation for green field is to build this data model and road map in early engineering phases in coordination with all your contractors. Having contractual agreements can go a long way. For brown field facilities, build the model based on the ground realities. The implementation can be small and in a focused area to show the benefits to the stakeholders. After a couple of iterations, improvements and lesson learnt can be incorporated for a broader implementation.