Industry assets are at the heart of organizations’ production activities. The durability of both infrastructure and industrial assets (buildings, bridges, various networks, tanks, conveyors, crushers, lifting facilities, tailings ponds, etc.) is of paramount importance.
For reasons of economic profitability, maintenance of production operations and environmental social aspects, managers must maintain the overall health of their asset inventory. Extending the life of assets allows to postpone direct investments in the most important ones. The financial pressure naturally decreases over time, thanks to the extension of their lifespan.
Asset replacement should be the last option in asset lifecycle management, unless it is executed as part of a carefully considered, planned, and phased investment. Unplanned shutdowns are to be avoided, as they generate very important losses in terms of repair costs as well as in revenues. due to the production interruption. This is what motivates companies to find a way to achieve a viable predictive maintenance protocol. However, there are many challenges to achieving this, as the adoption of this desired best practice is complex.
In some cases, replacement of a critical asset is virtually unthinkable due to its unique characteristics, for example: the mining and processing line at a mine site. Maintaining its good condition is essential to proper functioning of production, even though it has already been in service since several years. An asset’s many years of service complicates the work of the maintenance teams because the maintenance methods have inevitably evolved over the years. For instance, engineering standards are not the same today as they were fifty years ago. Proper information management on the history of observations, inspections and maintenance activities is therefore worth its weight in gold.
It is therefore critical for asset management teams to have means to collect and manage the necessary data on the condition of an asset, and that, since its commissioning. This is true for most organizations, regardless of their sizes. However, improvements in certain business practices are needed, specifically within the area of data management.
First, the multiplication of formats and the decentralization of data seem to be very frequent, even generalized. In addition to this problem, an operational inefficiency in the processing of this data can be observed. For this reason, a complete and qualified asset health report can take months to produce. The engineers’ burden is to commission external analysis, review documents in all forms (paper, etc.) and cross-reference this scattered information as much as possible. Meanwhile, the worst can happen because a report revealing an impending problem arrived too late.
Managers, on the other hand, want the information they need to make informed, timely and effective decisions. No one wants to be in reactive mode. It is imperative for them to optimize their inspection and maintenance efforts in order to obtain a diagnosis reflective of the reality and to develop an effective action plan.
To achieve this, it is often difficult to increase the number of people in charge. Qualified candidates are scarce and, too often, experienced people retire without having transferred their valuable knowledge.
The solution could come from the implementation of an intelligent digital platform. These digital tools compensate for this gap and provide a clearer picture of the assets’ health, whenever needed. With the help of some powerful tools, it is possible to centralize and process, analyze and cross-reference information from past inspections, surveys, maintenance work and repairs, efficiently. Thanks to these tools, managers and engineering firms benefit from synthesized information that is always easily accessible, and that represents a significant advantage for making informed asset management decisions.
By digitizing and importing assets’ data, it also becomes possible to establish a complete assets’ life traceability, from the most recent data, through to the original plans. This wealth of information is unifying.
It is even possible to use algorithms such as artificial intelligence to better predict the actions required to improve the life of an asset. In this way, we can better respond to both economic and socio-environmental requirements.
Moreover, the complete digitization of asset management operations achieves major time savings. Indeed, greater efficiency is achieved as data collection, calculation, analysis and visualization can be performed by using the same platform. This eliminates the need for time-consuming documents searches under various traditional media, while risking to lose information.
Conclusion :
The problems experienced by asset managers could become a thing of the past. A digital solution such as described, can only become the norm in the future, in this new era of social responsibility awareness, in addition to economic and environmental motivations.