Digital Transformation and Analytics Take Root in EAM

Building a Digital Framework

These are both challenging and exciting times for EAM users looking to find actionable information in a sea of disparate data. With a wide variety of data sources in use in industrial organizations today, finding the right information, at the right time, is more important than ever.  This is why there is an increasing demand for updated EAM systems that are part of larger digital transformation efforts, and increasingly include digital transformation, data and predictive analytics, and IIoT connectivity.    

Maintenance information today increasingly requires forward-looking insight, including forecasts and projections for preventive and other planned and recommended work.  The availability of such tools as data analytics, data visualization, and predictive analytics to augment EAM and predictive maintenance initiatives can be a strong competitive differentiator for organizations.

For many enlightened maintenance and operations professionals, adopting a digital-centric philosophy is key to competing more effectively in today’s hyper-competitive markets.  Many maintenance professionals are seeing the need to look beyond backward-looking reports to manage their operations.  Interest in basic reports based on static data in month-end printouts has largely waned, and is often insufficient today all but historical comparisons and basic trend data. Hence the need for predictive analytics and other tools to augment EAM and predictive maintenance initiatives. 

This is particularly true in today's fast-paced and complex maintenance and operations functions, as there is an ongoing challenge of making sense of disparate data across systems that inherently include much latency.  Maintenance personnel in these departments are often desperately seeking ways to extract nuggets of relevant and useful information in a timely (often real-time or near-real-time) manner. 

A major challenge is understanding what digital transformation and analytics resources are available and appropriate for use in maintenance and operations environments.  For example, many organizations have traditionally considered analytics to be solely under the umbrella of IT quant staffs, 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.  These resources, such as trained data scientists and statisticians assigned to organizations’ quant staffs, and investments in traditional, and costly, analytics solutions.

This perception is beginning to change, however, as 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. 

Figure 1: Maintenance Organizations Function Better with Access to Data at the Point of Origin

Digital Transformation in EAM and ALM

With the underlying purpose of many EAM systems has grown from more basic work request, work order, and inventory management information, digital transformation is becoming more important in EAM and ALM.  This includes the financial implications of managing equipment, personnel, and other resources across asset-intensive industries.

Looking ahead, the role of EAM systems is expanding beyond a simple foundation for capturing maintenance data.  It is expanding to include the management of actionable data, IIoT inputs, and similar information that permeates today’s industrial organizations, all of which are critical components of effective maintenance operations. 

Examples of needed capabilities include IIoT-enabled monitoring, assessments, and data sharing across maintenance and operations functions,.  Increased access to real-time information about equipment availability, performance, trends, and condition of individual components and entire systems throughout asset hierarchies are also important.

EAM’s extended reach is largely part of an enterprise-level movement from siloed, monolithic, and proprietary systems to cross-organizational, interconnected, and open enterprise systems that can be used throughout maintenance and operations organizations.