Enterprise Asset Management Market

[257 Pages Report] The enterprise asset management market is expected to grow from USD 4.7 billion in 2023 to USD 7.6 billion by 2028 at a Compound Annual Growth Rate (CAGR) of 9.8% during the forecast period. Enterprise asset management is a business solution to manage organizations' assets across different departments, facilities, business units, and geographic locations. Companies can view their infrastructures throughout their entire lifecycle, from design, commission, or procurement to operation, maintenance, disposal, and replacement by implementing enterprise asset management solutions. Most market vendors offer robust and comprehensive solutions and encompass the agility and flexibility necessary to provide global organizations with the tools they need to manage their business operations, control costs, and increase overall productivity. Enterprise asset management has evolved. The most primitive form of asset management was using run–to–failure systems for railroads. In 1950, preventive maintenance systems were installed in railways and railroads. Since the 1980s, asset management practices, such as reliability-centered and condition-based maintenance, have been used in airline, military, and power verticals. After that, enterprises felt that tracking and managing assets and activities would significantly impact organizational efficiency regarding profit and resource optimization. Since the 20th century, advanced enterprise asset management solutions and services have been used in all asset-intensive verticals, including energy & utilities, transportation & logistics, government & defense, manufacturing, and healthcare & life sciences.

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Recession Impact on the Enterprise Asset Management Market

The global enterprise asset management (EAM) market is intricately connected to various economic and geopolitical factors, and the simultaneous occurrence of a recession, the Russia-Ukraine war, inflation, and rising interest rates have profound implications for this sector. Recessionary periods often lead businesses to prioritize cost-cutting measures, prompting a reevaluation of asset management strategies. The Russia-Ukraine conflict introduces geopolitical uncertainties, disrupting global supply chains and affecting enterprises' ability to manage and maintain assets efficiently. Inflation poses a dual challenge, increasing operational costs and potentially diminishing purchasing power for EAM solutions. Rising interest rates can add further pressure by increasing the cost of capital for businesses looking to invest in EAM technologies. In such a complex environment, companies often reduce output and manufacturing activity during recessions due to the rising cost of raw materials. A drop in exports and economic activity might follow such a shift. Therefore, the enterprise asset management market will experience a transformation as it moves away from broad-based upgrading toward a focus on producing specific business value.

Enterprise Asset Management Market Dynamics

Driver:  Emergence of technologies to offer competitive advantage

Organizations increasingly recognize the strategic importance of technology-driven solutions in optimizing asset performance. Continuous innovation in sensor technologies, data analytics, and artificial intelligence has enabled the development of advanced EAM systems, fostering a paradigm shift from reactive to proactive asset management. Companies like IBM, SAP, and Oracle are at the forefront of incorporating predictive analytics and AI-driven insights into EAM solutions, enabling organizations to make data-driven decisions for maintenance, asset performance optimization, and overall operational efficiency. These technological advancements streamline asset management processes and contribute to significant cost savings and increased asset reliability. The rise of the Internet of Things (IoT) has been particularly impactful, as it allows for real-time monitoring of assets, enabling predictive maintenance and minimizing downtime.

Additionally, Artificial Intelligence (AI) and Machine Learning (ML) play a crucial role in EAM by analyzing vast datasets to predict equipment failures, optimize maintenance schedules, and improve overall asset performance. For instance, IBM's Maximo Asset Performance Management leverages AI to provide insights into equipment health, helping organizations make informed decisions. These technological advancements collectively contribute to more efficient asset management, reduced operational costs, and increased productivity, defining a new era for EAM solutions.

Restraint: Concerns over data security & confidentiality

As organizations increasingly rely on digital platforms to manage their assets, the vulnerability of sensitive information becomes a paramount issue. Data breaches and cyberattacks have raised apprehensions among businesses, as EAM systems often store critical data related to asset performance, maintenance schedules, and operational details. Instances such as the SolarWinds cyberattack in 2020, which impacted various sectors globally, including critical infrastructure, have underscored the vulnerability of digital systems. According to a study by IBM Security and the Ponemon Institute, the average cost of a data breach reached USD 4.24 million in 2021. In the context of EAM, industries dealing with sensitive information, like healthcare and finance, are particularly wary of adopting comprehensive asset management solutions due to the potential exposure of critical operational data. The fear of unauthorized access, data leaks, or malicious activities has made many enterprises hesitate to adopt EAM solutions, particularly in industries such as healthcare, finance, and defense dealing with highly confidential information. Compliance with data protection regulations, like the General Data Protection Regulation (GDPR) in Europe, adds a layer of complexity and scrutiny.

Opportunity: Data-driven asset management

Data is the driving force for change in many organizations. Today, organizations are creating data-driven business intelligence to gain deeper insights into their clients' needs and better understand businesses. To achieve this, enterprises need strong data science capabilities to implement the cross-functional model across different functionalities. Intelligent asset management provides real-time data-driven insights for predictive maintenance, monitoring, and management across industries. For example, in January 2022, IBM enhanced its Enterprise Asset Management platform, IBM Maximo Application Suite (MAS), with IBM Cloud Pak for Data to provide a framework for combining various data from different areas of an organization. Data-driven asset management offers the ability to make informed business decisions to avoid unplanned downtime due to critical problems and identify ways for cost saving and process optimization. According to IFS EAM Trend Report 2023, Among asset managers surveyed, 54% expressed a positive response, while 38% indicated a negative response, citing a lack of access to such data. Despite this, participants were questioned about the significance of data analytics and predictive modeling in enhancing asset management decisions, with more than 50% expressing that it holds either very or somewhat important; this implies that even among those without current access to the data, there is a shared belief that possessing such information would prove advantageous in optimizing asset management decisions.

Challenge: Cybersecurity concerns

The adoption of Enterprise Asset Management (EAM) solutions brings forth significant cybersecurity concerns that organizations must address to safeguard sensitive asset data and maintain the integrity of their operations. EAM systems, often reliant on interconnected networks and cloud-based platforms, become potential targets for cyber threats. The digitization of asset management processes increases the attack surface, exposing vulnerabilities that malicious actors may exploit. Breaches in EAM security could lead to unauthorized access to critical asset information, disruption of operations, and compromise of sensitive data. As organizations integrate EAM solutions with other enterprise systems, the potential for cyber threats grows, necessitating robust measures such as encryption, access controls, and continuous monitoring. Furthermore, the evolving nature of cyber threats demands a proactive approach, including regular software updates, employee training on cybersecurity best practices, and collaboration with cybersecurity experts to stay ahead of emerging threats. As EAM adoption continues to rise, prioritizing cybersecurity is paramount to ensure the resilience and reliability of asset management systems in the face of an ever-evolving threat landscape.

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Based on application, the predictive maintenance segment will grow at a higher CAGR during the forecast period.

Predictive maintenance is a proactive approach that relies on data analytics, sensor technology, and machine learning algorithms to analyze real-time and historical data from assets. By monitoring equipment conditions, performance patterns, and potential failure indicators, predictive maintenance aims to optimize asset performance. This approach minimizes unplanned downtime, reduces maintenance costs, and extends the lifespan of critical assets. EAM systems with predictive maintenance capabilities enable organizations to transition from reactive and scheduled maintenance to a more efficient and strategic model. The integration of predictive maintenance into EAM enhances overall operational efficiency. It empowers organizations to make data-driven decisions, prioritize maintenance tasks, and allocate resources more effectively for sustained asset reliability.

Based on vertical, the transportation & logistics segment will grow at the highest CAGR during the forecast period.

In the dynamic and complex world of transportation, EAM solutions play a pivotal role in optimizing the maintenance, tracking, and utilization of diverse assets, ranging from vehicles and trailers to warehouse equipment. These solutions offer real-time visibility into the condition and location of assets, enabling companies to implement proactive maintenance strategies, minimize downtime, and extend the lifespan of their fleets. EAM systems also contribute to efficient inventory management, helping logistics providers track spare parts and manage the maintenance supply chain effectively. With route optimization and performance analytics features, EAM solutions enhance overall operational efficiency, reduce fuel consumption, and improve asset utilization in transportation and logistics operations. Integrating EAM with other systems, such as Transportation Management Systems (TMS) and Warehouse Management Systems (WMS), ensures a seamless flow of information, enabling organizations to make data-driven decisions and adapt quickly to changing logistics requirements.

Based on region, North America holds the largest market share during the forecast period.

North America stands at the forefront of global technological innovation, with robust technology and IT adoption across its diverse landscape. Organizations in North America are leveraging advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML) to enhance the capabilities of EAM systems. Integrating IoT sensors with assets allows for real-time monitoring, predictive maintenance, and data-driven decision-making. This approach minimizes downtime, reduces maintenance costs, and prolongs the lifespan of critical assets. For example, in the energy sector, companies utilize IoT-enabled sensors to monitor equipment health, predict potential failures, and optimize maintenance schedules. Moreover, the shift towards mobile EAM applications has gained traction, allowing maintenance teams to access and update asset information on the go, enhancing overall operational efficiency.

Key Market Players

The enterprise asset management market is dominated by a few globally established players such as IBM (US), SAP (Germany), Oracle (US), Hexagon (Sweden), and IFS (Sweden), among others, are the key vendors that secured enterprise asset management contracts in last few years. These vendors can bring global processes and execution expertise; the local players only have local expertise. Driven by increased disposable incomes, easy access to knowledge, and fast adoption of technological products, buyers are more willing to experiment/test new things in the enterprise asset management market.

Scope of Report

This research report categorizes the enterprise asset management market to forecast revenue and analyze trends in each of the following submarkets:

By Offering:

• Software
• Services
• Professional Services
• Managed Services

By Application:

• Asset Lifecycle Management
• Inventory Management
• Work Order Management
• Labor Management
• Predictive Maintenance
• Facility Management
• Other Applications

By Deployment Model:

• Cloud
• On-premises

By Organization Size:

• Large Enterprises
• SMEs

By Vertical:

• Manufacturing
• Energy & Utilities
• Healthcare & Life Sciences
• Transportation & Logistics
• IT & Telecom
• Government & Public Sector
• Education
• Other Verticals

By Region:

• North America
  • United States
  • Canada
• Europe
  • United Kingdom
  • Germany
  • France
  • Italy
  • Rest of Europe
• Asia Pacific
  • China
  • Japan
  • Australia and New Zealand
  • India
  • South Korea
  • Rest of Asia Pacific
• Middle East & Africa
  • GCC
• Saudi Arabia
• UAE
• Rest of the GCC Countries
  • South Africa
  • Rest of the Middle East & Africa
• Latin America
  • Brazil
  • Mexico
  • Rest of Latin America

Recent Developments:

• In June 2023, American racing team AJ Foyt Racing announced a multi-year partnership with IFS to utilize IFS Ultimo's asset management solution for parts management, lifting, and maintenance.
• In October 2022, Hexagon expanded its customer base in Asia Pacific through organic growth and partnerships to optimize digital asset management with the help of HxGN EAM.
• In October 2022, Oracle introduced a new asset-based service solution for Oracle Fusion Cloud Applications Suite. It would help businesses predict and prevent asset downtime by reducing costs, optimizing service efficiency, and enhancing the overall customer experience.
• In September 2022, Hitachi Energy announced that Energinet accelerated its digital transformation by utilizing the Lumada APM solution. The collaboration between the companies would support TSO in better assessing the grid reinvestments needed to achieve their 2030 carbon neutrality goals.
• In January 2021, IBM collaborated with Atos to accelerate digital transformation in enterprises with AI and RedHat Open Shift technologies. The collaboration offers asset life cycle management solutions, asset monitoring and MRO optimization solutions, SMART waste/energy monitoring and optimization solutions, and packaging, transportation, and logistics optimization solutions to end users.

Frequently Asked Questions (FAQ):

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