With the pace of change accelerating in today’s industrial organizations, the expectations of the maintenance and operations departments continue to grow as well. In fact, this change is advancing in lock-step with the underlying equipment that must be more productive, efficient, and reliable than ever before.
For maintenance organizations to keep up, new approaches and solutions to maximize equipment productivity and uptime are needed. In this ARC Advisory Group strategy report, we examine these approaches along with the evolving enterprise asset management (EAM) market and the key trends driving changes in this and adjacent markets.
The heightened expectations for asset management solutions brings focus to the primary trends driving changes in the overall EAM market. With technological and product changes across the enterprise fundamentally changing EAM, users in today’s industrial organizations now have new tools to better assess how they monitor, maintain, and manage their assets.
Key findings include:
- Much data is available to maintenance users, but a pressing need remains to access and share real-time, actionable information throughout organizations.
- The reach and influence of asset management systems, and EAM in particular, continue to expand as these solutions have become an important system of record for enterprise asset data. Increasingly, these data need to be shared with adjacent systems.
- The trend toward a democratization of IT, analytics in particular, can provide access to non-IT users, including maintenance and operations personnel.
- The role of EAM is expanding as it offers a foundation for accessing capabilities such as mobility, predictive maintenance (PdM), Industrial IoT, predictive analytics, artificial intelligence (AI), machine learning (ML), augmented reality (AR), and virtual reality (VR).
Actionable, Real-time Information
These are both challenging and exciting times for maintenance and operations users in industrial organizations looking to find actionable information in a sea of disparate data. With a wide variety of data sources available today, finding the right information, at the right time, and – ideally – in the right context, is more important than ever. This is why we’re seeing an increasing demand for updated EAM systems and why these systems are needed to support more effective asset lifecycle management (ALM) and asset performance management (APM). Today’s enlightened professionals need to look beyond the standard reports based on static, historical data in month-end printouts, since these are insufficient in today’s fast-paced maintenance environments.
Maintenance information today increasingly requires visibility into predictive information, forecasts, and projections for preventive and other planned and recommended work. The availability of tools such as data analytics, data visualization, and predictive analytics to augment EAM and PdM initiatives can provide organizations with a strong competitive differentiator. This is particularly true in maintenance and operations functions, as there is an ongoing challenge to make sense of disparate data. 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.
Furthermore, because many organizations have traditionally considered analytics to be under the umbrella of dedicated quant staffs, they are often reluctant to undertake analytics initiatives at the business unit level, including maintenance teams. This is driven by the perception that analytics capabilities are only available to those in large organizations that have access to costly analytics solutions and trained data scientists and statisticians assigned to organizations’ quant staffs. This perception is beginning to change, however, as there has been an increase recently in “self-service”-style analytics tools that can be used by more plant personnel. These solutions can allow an expanded array of users – and particularly maintenance users – to begin to leverage the power of analytics.
The underlying purpose of many EAM systems has grown from more basic work request, work order, and inventory management information to an expanded role in asset lifecycle management, including the financial implications of managing equipment, personnel, and other resources in asset-intensive industries. Looking ahead, EAM’s role as a foundation for capturing and managing Industrial IoT, ERP, and similar information that permeates today’s industrial organizations. Included are IIoT-enabled monitoring, assessments, and data sharing across maintenance and operations functions, and increased real-time information into equipment availability, performance, trends, and the condition of individual components throughout asset hierarchies.
EAM’s extended reach is largely driven by the movement from siloed, monolithic, and proprietary systems to cross-organizational, interconnected, and open enterprise systems that can be used throughout maintenance and operations organizations.
Rising Expectations for Asset Management
Expectations for asset management capabilities, often led by EAM systems, are increasing. These are driven largely by new requirements to support digital transformation initiatives. The result is a need for greater connectivity, visibility, and information-sharing both from with-in and outside of enterprises.
New and expanded features can range from broadening traditional work management planning, scheduling, and execution capabilities; to industry-specific functionality and spare parts inventory management. These capabilities are enhanced by expanded connectivity and visibility to help users make better, more informed, and more timely decisions about preventive and corrective maintenance. In addition, many organizations are already using or at least considering on-premise, SaaS, or hybrid business models.
The implications are significant, as many organizations are reviewing their current EAM strategies within the context of their existing or planned digital transformation projects and initiatives. These conversations often center on how and where these systems are deployed and accessed, the expected ROI, the cost and effort that would be required to upgrade or replace the current system, and if integration with adjacent systems would be needed.
EAM Evolves at an Accelerated Pace
Asset management system capabilities continue to increase, elevating the state of maintenance and operations management to new levels. Next-gen systems are moving beyond the relatively modest, reactive, and largely manual maintenance capabilities seen in previous CMMS and EAM systems. Expanded EAM capabilities are centered around broader and deeper features and extensions of features to support adjacent systems. In addition, both on-premise and SaaS deployment options are now available.
The adjacent systems include IoT-based sensor networks and condition monitoring systems, plant and enterprise historians, as well as systems intended to support both asset lifecycle management and asset performance management. For ALM, this means broader features in such areas as asset specifications, design, acquisition, and disposal.
To more fully support APM, advanced EAM solutions can include interoperability with a wide range of enterprise inputs, including IoT and edge computing, reliability-centered maintenance (RCM), predictive maintenance, and predictive analytics solutions.
Further examples of the evolution of IT, OT, and EAM systems can be seen below, which shows how far the evolution of these systems has occurred over the recent past. They have evolved in many ways, but most notably in such areas as the development process, app architecture and development, and infrastructure.
Included in this transition is a movement from siloed, functional solutions to seamless, real-time, enterprise-level asset management systems. These solutions can manage maintenance activities across the board. These can include horizontal solutions for plant, facilities, and field service use; vertical variants for trucking and transportation, public transportation, aviation and aerospace; and other industry-specific asset management systems.
Using EAM to Identify Appropriate Asset Management Strategies
As a central repository for much asset information, expanded EAM capabilities can be particularly useful when mapping out appropriate maintenance strategies for the wide variety of possible asset types. This can help identify areas currently receiving either too little or too much maintenance.
Mining EAM data can provide a foundation for thoughtful review of recommended maintenance management strategies. For example, assets that do not affect equipment downtime or worker safety can some-times be run-to-failure and then repaired or replaced as needed. Often, imminent failure of these non-critical assets can be identified during routine PM or other periodic inspections.
However, for assets that can impact uptime and/or safety, an increasing stringent and holistic maintenance maturity model may be warrant-ed, including the use of condition-based, predictive, and prescriptive models.
Striking the Right Balance
Finding the right balance of maintenance activity can be a challenge for maintenance teams. Often, this will depend on how much data and what kind of data is available. Too little data can mean missing key information needed to manage assets. Too much data, in turn, can be overwhelming and have limited practical use. The latest-generation of EAM systems that offer visibility and connectivity to adjacent systems can help users achieve a proper balance.
EAM systems can offer visibility into a wide variety of maintenance performance metrics. In addition to planning and scheduling metrics, other examples include mean time to failure (MTTF), mean time between failures (MTBF), and mean time to repair (MTTR). These can help users identify failure trends and develop appropriate maintenance, job, and safety plans and PM and corrective maintenance schedules. Increasingly, EAM customers are using common maintenance terms and standards through industry standards, such as ISA95 and ISO 55000, to communicate information about assets in a more consistent manner.
In addition, today’s maintenance personnel are often challenged to determine the risk of under-maintaining (and risking unwanted unplanned downtime) and over-maintaining (which can increase both costs and downtime). This can be particularly challenging in plants where production lines are at or near full capacity and must be shut down to perform PM tasks.
Table of Contents
- Executive Overview
- Actionable, Real-time Information
- Rising Expectations for Asset Management
- EAM Evolves at an Accelerated Pace
- Using EAM to Identify Appropriate Asset Management Strategies
- Striking the Right Balance
- The Rise of Mobility in EAM
- Industrial IoT Supports Predictive and Prescriptive Maintenance
- AR and VR for Maintenance
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