Leveraging Analytics to Boost EAM Performance

By Ed O'Brien

Category:
Industry Trends

These are both challenging and exciting times for business users -- and specifically EAM users --    seeking actionable information in a sea of disparate data.  With a wide variety of data sources in use in organization today, finding the right information, at the right time, is more important than ever. 

For many enlightened professionals, the need to look beyond standard reports based on historical data in month-end printouts has waned, and is insufficient in today’s fast-paced maintenance environments for all but historical comparisons and trend data. 

This is particularly true in maintenance and operations functions, as there is an ongoing challenge of making sense of all this data.  Personnel in these departments are often desperately seeking ways to extract nuggets of relevant and useful information in a timely (often real-time) manner. 

  

Figure 1

Predictive Analytics Can Be A Competitive Differentiator in EAM
Predictive Analytics Can Be A Competitive Differentiator in EAM

Source: Halo Analytics

Now Available: Analytics for EAM Business Users    

Maintenance information today increasingly requires visibility into predictive information, forecasts, and projections for preventive and other planned work.  This is in addition to analyzing available data for corrective maintenance work. 

The availability of such tools as data analytics, data visualization, and predictive analytics to augment EAM and predictive maintenance tools can increase the efficiency and productivity of maintenance teams.  Harnessing these capabilities can be a strong competitive differentiator for organizations.  Because of this, these capabilities are piquing the interest of many EAM users to learn more about analytics solutions.      

With analytics offering broad visibility into a wide-range of cost, performance, and operating data, users can make better use of maintenance data than ever before.  Included are extrapolations of likely trends and predictions based on data originating from various systems. This data can be instrumental in identifying the appropriate levels of maintenance to balance equipment uptimes and cost requirements to best meet organizational goals.  Organizations continue to seek useful information on such topics as mean time to failure, optimum equipment replacement cycles, and the inflections point between too little – and too much – maintenance. Organizations need to keep this equilibrium in mind to be competitive in the market.  

Availability of Analytics Beyond the IT Department     

Because many organizations have traditionally considered analytics solutions to be under the umbrella of IT technical teams, they are often reluctant to undertake analytics initiatives at the business unit level, including maintenance teams.  This is underscored by the perception that analytics programs require specialized expertise, such as statisticians and data scientists assigned to organizations’ quant staffs, and investments in traditional, and costly, analytics solutions.

There is a revolution of sorts occurring today – analytics for the masses (well, sort of).  While some of today’s analytics solutions still require an understanding of basic statistics and analytics concepts, they can be used by many tech-savvy business users.  These newer solutions do not always require the deep comprehension of statistics and coding that has traditionally been needed – and used almost solely – by quant experts. 

There has been an increase recently in analytics tools that can be used by business users.  These solutions can allow an expanded array of users – and particularly maintenance users – to leverage the power of analytics. 

Still, with today’s maintenance workers often requiring knowledge management and assessment skills, some, if not many, should have at least a working knowledge of such concepts as randomized controlled experiments, A/B testing, regression analysis, and statistical significance to make the most of available analytics tools, and to make informed and insightful decisions.  

Many of the newer solutions can now be used by business users with a working knowledge of analytics. Examples of business intelligence and analytics providers that offer such capabilities include IBM, Microsoft, MicroStrategy, Oracle, SAP, SASTableau, Qlik, and others. 

In addition to being relatively easy to use, such solutions can offer connectivity to a wide range of data source. In many cases, these newer solutions also come with a lower total cost of ownership, making life easier and more productive for maintenance teams.    

 

 

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