This month’s column focuses on an area that you either love or hate; data manage-ment. I think it is worthwhile speaking about as it is one of the vital areas that support modern asset management. It is an area that we often neglect, creating opportunities for people with less than adequate levels of asset management knowledge to make large-scale mistakes.
View more data management content on PlantServices.com
Technological advances relating to how asset managers capture, organize, and use asset related data is, without a doubt, the most important advance that we have made within the 20 years that I have been consulting in this area. The implications of this are immense and it has the ability to permanently change the way that your company manages its physical asset base.
New technologies can also bring with them new dangers. Not the least of which is the potential for allocating a lot of time and money on tools, services and activities that do not support the central goals of asset management. Recently, I have seen many corpo-rations spending millions of dollars on areas of asset data management that are dubi-ous at best, counterproductive at worst.
The humble CMMS is the centre of most asset maintenance efforts to capture, store and analyze asset data. In larger operations, this has been superseded by Enterprise Asset Management, EAM, and Enterprise Resource Planning, ERP, systems but the goal remains the same. These are supplemented by mobile working and bar coding solutions, GIS integration, RFID tagging systems, online condition monitoring, plant management systems, and a range of detailed analytical software tools.
These technologies are now abundantly available, their costs are becoming affordable in terms of generating a good return on investment from them, and they are increas-ingly easy to use. Yet there are still asset maintenance departments that operate with-out even a CMMS, or worse, use only a fraction of their existing system.
A common issue is where companies implement a system, starting with asset register information, and then never progress from there to truly effective and dynamic work flow management. They remain stagnant, going in ever decreasing analytical circles focusing on collecting reams of static data, often without a real cost-benefit analysis of how they are going to use it.
So why is this so important? Because it can highlight areas of inefficiency, aid effec-tive reliability management when applied through a framework of RCM logic, reduce inventory, provide defensible asset replacement plans, and increase profitability. These benefits, however, turn out just to be ancillary to the main benefit.
Even today, many maintenance companies make decisions, often very large decisions, based on expert judgement, opinion, and anecdotal information. If we look at this critically we can see that often these decisions are made based on strength of charac-ter, political manoeuvring, and coalition building. In summary, we can basically apply the old cliché “the squeaky wheel gets all the grease.”
When a company begins to make decisions based on data rather than on opinion, the entire dynamic of the company changes. Instead of influence and story-telling, deci-sions become based on fact; anecdotal benefits are replaced by provable and support-able business plans.
One of the key benefits of data-based decisions is that projects stop being initiated based on spurious claims and start being judged based on their ability to achieve iden-tifiable targets. This alone is a strong reason for any company to seriously get into the data-capture and analysis business.
So how does your company get to the point where they are able to make asset mainte-nance and asset management decisions based principally on data? The following are some tips. I have used them over the years to help numerous companies to advance their decision support frameworks.
1. Don’t focus on the quality, volume and integrity of the data until you know what data should be collected!
It has been my experience that a great deal of money is spent, often unwisely, on pro-grams aimed at perfecting data quality, integrity, and on monitoring volumes of data. If this needs to be done, and often that is a point for debate, then it should only be done after you have identified the critical data to be analyzed.