Strategic Asset Management Planning (SAMP) in Major Industrial Electrical Networks
25 February, 2019 | Blog
Every installation faces challenges associated with maintaining asset value over their expected lifespan.
Defining budgets to alleviate asset degradation is difficult considering the justifications that must be given to allocate money as well as the scarcity of available lifecycle data associated with major electrical equipment in the industrial sector.
Most of the statistical data available worldwide in the electrical/energy field comes from utilities that do not have a choice but to gather this type of information. They use this information for their long-term asset management planning, as it is mandatory for them to quantify these costs and incorporate them into their revenue/cost analysis.
Usually, industrial installations do not have the same type of discipline to acquire and maintain such statistical information over many years. Another aspect is that equipment is less standardized in the industry, which is an important driver in the utility sector as a way to ensure reliability and drive down engineering and installation costs.
Equipment standardization helps correlate data across a much larger equipment base and refine quantity precision like equipment life expectancy, failure rate, time to repair, time to replace, etc.
This is rarely the case in the industrial sector, as the money needed to invest in standardization is often not available considering the short installation lifespan and equipment diversity, which is much larger than in a utility environment.
With insufficient data available, decisions to invest in replacements or major equipment/system overalls are not performed quickly, with major consequences for the economic survival of the assets/plant.
A data storm is coming
But things are changing. With Industry 4.0, individualized information about asset condition, at more reasonable prices than in the past, will allow industries to better quantify the life expectancy of each asset, define efficient predictive maintenance programs and implement a strategic asset management planning initiative that makes sense at every level of the organization, whether it is the department manager, the plant operator or even the investors who, ultimately, make the money available to carry out these initiatives.
But this will require the capacity to analyze and interpret loads of data on a real-time basis. There will be little tolerance for incidents (and losses) that happen when predictive data was available to take preventive actions. That is where tools like big data analytics, artificial intelligence and machine learning will be required. This is not a question of “if”, but of “when”, where every industrial plant will have to use these tools with proficiency or be left on the side of the road watching competitors pass by.
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