Knowledge of asset location, its characteristics, usage, environment where its commissioned and other asset attributes determine how assets perform. Organizations strive to capture information about asset attributes and their changes over time, information that will help them understand the health of assets. Internet of things and sensors is making it easier for organizations to monitor assets remotely and capture operational, health and maintenance information real time and store that information in the cloud. While exception based dashboards are able to flag and predict asset failures, several organizations have started using deep learning to build asset failure patterns based on asset operational characteristics. These efforts enable organizations to build asset profiles and analyze failure risks that may impact operations. Artificial intelligence based intelligent reliability centered maintenance (RCM) reduces the skill requirement of RCM analysts, shortens the development period of RCM program, and enhances the cost-effectiveness of RCM process driving better outcomes from EAM systems.

Safety, environmental and health regulations play a crucial part in asset tracking and maintenance especially for assets that are in hazardous areas. Virtual reality offers great promise as maintenance technicians have the ability to simulate inspections, train with visual models and assess competency in a virtual classroom and be better prepared for their job. More training content will be generated with the time that will enable organizations to know their assets better and devise personalized maintenance strategies based on asset age, condition, and environment.

Human Intervention can Accentuate Value from Asset Data

The success of EAM system is driven by changes in processes and procedures that accompany the associated technology. Stakeholder buy in and process governance is key to enforce behavioral changes that are needed to enforce data quality. How humans implement and use the technology will play a key part in determining the amount and quality of asset information that will be captured and subsequent ROI that organizations can extract from the data.

What’s Ahead

Within a decade, more advanced technologies such as conversational user interfaces may become mainstream and will probably take mobile EAM solutions to a different level. Maintenance and field service technicians may use voice to find solutions to key asset problems and maintain work orders and parts. Machine learning models for assets will enable machines to converse with human beings and have machines express problems before they happen. Analysis of asset data and maintenance history will help organizations understand the characteristics and risk profile of assets and help organizations understand why certain assets perform better than others. Assets will no longer be just another asset ID but will assume a character that organizations can manage to optimize performance and drive improved ROI from investment.