Asset Management
Excellence: A Roadmap for Integrating Lean Six Sigma and ISO 55001 to Achieve Operational
Excellence

Gomaa, Attia  Hussien

doi:10.70322/ism.2025.10020

lssue 2, Volume 2

Article Open Access

Asset Management Excellence: A Roadmap for Integrating Lean Six Sigma and ISO 55001 to Achieve Operational Excellence

Attia Hussien Gomaa ^*

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Mechanical Engineering Department, Faculty of Engineering, Shubra, Benha University, Cairo 13511, Egypt

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Received: 23 April 2025 Accepted: 19 June 2025 Published: 23 June 2025

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Intell. Sustain. Manuf. 2025, 2(2), 10020; DOI: 10.70322/ism.2025.10020

ABSTRACT: Asset Management Excellence (AME) has become essential for sustaining operational efficiency and long-term competitiveness in today’s digitally driven and increasingly complex industrial landscape. This study introduces an integrated roadmap that aligns Lean Six Sigma (LSS)—specifically the DMAIC methodology—with ISO 55001 standards to enhance asset reliability, optimize lifecycle performance, and support continuous improvement. The proposed model embeds principles such as lifecycle value optimization, risk-based decision-making, and sustainability. It leverages proven tools, including Failure Mode and Effects Analysis (FMEA), Root Cause Analysis (RCA), Statistical Process Control (SPC), predictive maintenance, and real-time monitoring to enable proactive, data-driven asset management. This integration supports efficiency, reduces variability, and extends asset life. Performance is measured through key indicators such as Mean Time Between Failures (MTBF), Overall Equipment Effectiveness (OEE), and lifecycle cost-efficiency. These metrics enable organizations to monitor progress, validate improvements, and ensure alignment with strategic objectives. The study also addresses common implementation challenges across financial, organizational, workforce, technological, and structural domains. It proposes targeted mitigation strategies, including phased implementation, cost-benefit analyses, stakeholder engagement, digital readiness assessments, and capacity-building programs to enhance adoption and long-term sustainability. While conceptual, the roadmap offers a practical, scalable approach to embedding LSS within asset management systems. It fosters a transition from reactive to proactive practices, enhancing resilience, sustainability, and strategic value. Future research will validate the framework through sector-specific case studies and pilot implementations.

Keywords: Asset management; Operational excellence; ISO 55001:2024; Lean Six Sigma; Strategic roadmap; Continuous improvement

1. Introduction

In today’s rapidly evolving industrial landscape, driven by digital transformation, increasing complexity, and rising performance demands, effective Asset Management is a critical enabler of Operational Excellence. Organizations are increasingly tasked with managing a growing portfolio of assets while optimizing performance, minimizing costs, and ensuring sustainability. Asset management has evolved from traditional equipment maintenance to a strategic function that directly influences an organization’s competitiveness and long-term success. The integration of Lean Six Sigma methodologies into asset management enhances the ability to drive continuous improvement, reduce variability, and optimize both efficiency and effectiveness, ultimately contributing to sustained operational excellence [1,2]. According to ISO 55000:2024, an asset is any item, entity, or component that provides actual or potential value to an organization. Figure 1 depicts the Organizational Breakdown Structure (OBS), detailing the hierarchy from the organizational level to individual parts. An organization consists of multiple geographically distributed sites, each containing facilities that host systems—integrated components designed for specific functions. Within these systems are assets, such as machinery or equipment, composed of parts—the smallest maintainable units that ensure performance, reliability, and functionality.

Figure 1. Organization Breakdown Structure (OBS).

Achieving asset management excellence requires addressing several key factors: ensuring assets operate according to design specifications, maintaining reliability, controlling maintenance costs within budget while optimizing capital investment, and achieving high service levels. Additionally, efficient inventory management, real-time monitoring, and fostering a skilled, motivated workforce are essential. However, the path to asset management excellence is complex, as external factors—such as economic shifts, technological disruptions, and regulatory changes—often influence the industrial environment. Despite these challenges, the overarching goal remains operational excellence, although the unpredictable nature of dynamic variables complicates its achievement [3]. Asset Management Excellence has evolved from a reactive function into a central enabler of Operational Mastery. Achieving excellence in asset management requires a comprehensive and strategic approach that aligns asset management practices with broader organizational objectives, optimizes asset performance throughout its lifecycle, and mitigates risks. True asset management excellence goes beyond reducing costs and meeting compliance—it involves adopting digital technologies, fostering a culture of continuous improvement, and empowering a skilled workforce to create long-term value. When fully realized, Asset Management Excellence enhances resilience, improves operational efficiency, and positions organizations for sustained success [1,2]. International standards, such as ISO 55001:2024, offer a robust framework for transforming asset management practices, providing structured guidelines to align asset management with organizational goals. An Asset Management System (AMS), defined by the standard, is a coordinated approach to managing assets throughout their lifecycle—from acquisition to disposal—maximizing value. Achieving operational excellence goes beyond compliance; it requires a proactive, integrated approach that combines best practices, advanced technologies, and innovative methodologies. As illustrated in Figure 2, the Quality Management System (QMS) and Asset Management System (AMS) are closely interconnected, both aiming to enhance performance, ensure compliance, and drive continuous improvement. While QMS focuses on quality through planning, assurance, control, and improvement, AMS applies a similar methodology to asset planning, assurance, control, and optimization. Integrating these systems strengthens risk management, boosts efficiency, and aligns with standards like ISO 9001 and ISO 55001, ensuring high-quality outcomes supported by reliable assets.

Figure 2. QMS and AMS Interrelationship.

Central to this integration is the Plan-Do-Check-Act (PDCA) cycle, a model for continuous improvement that helps organizations plan, execute, monitor, and refine processes. The PDCA cycle ensures alignment of asset management with strategic goals, driving ongoing value and performance optimization. Table 1 and Table 2 show how the PDCA cycle applies to both quality and asset management, while Table 3 and Table 4 summarize the key clauses and objectives of ISO 9001:2015 and ISO 55001:2024, reinforcing the synergy between quality and asset management. Table 5 provides an in-depth overview of key tools and techniques used in asset management, categorized into areas such as Asset Lifecycle Management, Maintenance Management, Condition Monitoring and Performance Analytics, Risk and Decision Support, Spatial and Data Analysis, and Workforce and Field Service Management. The table outlines the purpose of each tool, provides examples, and highlights key features, offering insights into how these tools optimize asset management processes. Whether focused on managing the full asset lifecycle, automating maintenance, leveraging predictive analytics, or mitigating risks, these tools are essential for improving asset performance, reducing downtime, and aligning operations with organizational goals. This table serves as a valuable resource for organizations seeking to refine their asset management strategies by selecting the appropriate tools for their unique needs. This study outlines a Lean Six Sigma-based roadmap for Asset Management Excellence aimed at optimizing asset performance and fostering continuous improvement. The framework integrates the DMAIC methodology, aligns with ISO 55001 standards, and emphasizes principles like risk-based decision-making, sustainability, and lifecycle optimization. It leverages tools such as FMEA, RCA, and SPC, alongside predictive maintenance and real-time monitoring, to enhance asset reliability, reduce downtime, and extend asset life. By focusing on key KPIs like MTBF, OEE, and lifecycle cost-effectiveness, the approach helps organizations transition from reactive to proactive asset management, strengthening competitiveness in the digital industrial landscape. The paper is organized as follows: Section 2 presents a comprehensive literature review, Section 3 discusses research gaps, Section 4 outlines the research methodology, and Section 5 provides strategic insights, practical applications, and recommendations for future research and industry practices.

Table 1. Overview of the PDCA Cycle for Quality Management.

Stage	Objective	Description
Plan	Define strategy and objectives	Establish a quality management strategy, set clear and measurable quality objectives, and develop detailed action plans that align with customer requirements and organizational goals.
Do	Implement systems and plans	Put the quality management plans into action by deploying appropriate systems, processes, and controls to ensure consistent quality and compliance with standards.
Check	Monitor and assess performance	Continuously assess performance against quality objectives, customer expectations, and regulatory standards. Analyze the results to identify improvement opportunities.
Act	Take corrective actions	Address any quality issues with corrective actions and implement preventive measures to avoid recurrence, fostering ongoing improvement in quality management practices.

Table 2. Overview of the PDCA Cycle for Asset Management.

Stage	Objective	Description
Plan	Define strategy and objectives	Develop an asset management strategy, establish clear and measurable objectives, and create actionable plans that align with the organization’s goals, policies, and regulatory requirements.
Do	Implement systems and plans	Execute the asset management plans by implementing required systems, ensuring compliance with legal and regulatory standards, and carrying out necessary activities.
Check	Monitor and assess performance	Monitor and evaluate performance against established objectives, policies, and standards. Document findings and assess the results to inform decision-making.
Act	Take corrective actions	Make necessary adjustments based on performance results to meet objectives and promote continuous improvement in asset management practices.

Table 3. ISO 9001:2015 Clause Overview: Objectives and Descriptions.

Clause	Title	Objective	Description
0	Introduction	Introduce core concepts and principles	Explains the purpose of the standard, the process approach, and risk-based thinking.
1	Scope	Define the boundaries of the application	States where and how the standard applies to ensure consistent product/service quality.
2	Normative References	Identify referenced documents	Points to ISO 9000 as the source of key quality management terms and definitions.
3	Terms and Definitions	Standardize terminology	Ensures consistent understanding of terms across all users of the standard.
4	Context of the Organization	Align QMS with the organizational environment	Understand internal/external issues and stakeholders; define the QMS scope.
5	Leadership	Demonstrate top management’s commitment	Ensures leadership establishes quality policy, roles, and strategic direction.
6	Planning	Manage risks, opportunities, and quality goals	Plan actions to address risks and opportunities and set clear, measurable objectives.
7	Support	Provide necessary resources and communication	Ensure the availability of people, infrastructure, competence, awareness, and documentation.
8	Operation	Control the production/service process	Manage customer requirements, design, procurement, production, and nonconformities.
9	Performance Evaluation	Assess QMS performance and effectiveness	Monitor, measure, analyze data, conduct internal audits, and review results.
10	Improvement	Enhance the QMS continuously	Identify improvement opportunities, address nonconformities, and apply corrective action.

Table 4. ISO 55001:2015 Clause Overview: Objectives and Descriptions.

Clause	Title	Objective	Description
0	Introduction	Provide context for asset management	Introduces the principles and benefits of aligning asset management with organizational strategy.
1	Scope	Define the standard’s scope and application	Specifies the AMS requirements for managing assets across their full life cycle.
2	Normative References	Reference foundational documents	Refers to ISO 55000 for definitions and core concepts of asset management.
3	Terms and Definitions	Clarify terminology for consistency	Standardizes the terms and concepts used throughout the standard for uniform interpretation.
4	Context of the Organization	Align AMS with internal and external factors	Understand the organization’s strategic context and stakeholder needs and define the AMS scope.
5	Leadership	Ensure leadership commitment and accountability	Establishes top management responsibility for AMS, including setting policy, objectives, and ensuring alignment with organizational strategy.
6	Planning	Define objectives, assess risks, and plan actions	Establish asset management objectives, address risks, and ensure alignment with overall strategy.
7	Support	Ensure necessary resources and documentation	Provides the necessary resources, infrastructure, and documented information to support AMS implementation.
8	Operation	Implement asset management processes	Manages day-to-day asset management activities to meet defined objectives and requirements.
9	Performance Evaluation	Monitor and evaluate AMS performance	Measures and reviews AMS performance through audits, KPIs, and management reviews to ensure continuous improvement.
10	Improvement	Drive continual improvement in asset management	Identifies nonconformities, implements corrective actions, and drives continuous improvements within the AMS.

Table 5. Comprehensive Overview of Tools and Techniques in Asset Management.

#	Category	Tools	Purpose	Examples	Features
1	Asset Lifecycle Management	Asset Management Software (AMS)	Centralizes asset data and manages the asset lifecycle	IBM Maximo, SAP EAM, Infor EAM	Asset tracking, Maintenance scheduling, Performance Analytics
		Enterprise Asset Management (EAM)	Manages the entire lifecycle of assets	IBM Maximo, SAP EAM, Oracle EAM	Asset lifecycle management, Financial tracking, Resource allocation
		Building Information Modeling (BIM)	Uses 3D models and data for managing infrastructure assets	Autodesk Revit, Bentley Systems	Real-time asset tracking, Energy management, Lifecycle planning
		Life Cycle Costing (LCC)	Evaluates the total cost of asset ownership over its lifecycle	LCC analysis tools, Excel models	Cost optimization, Investment planning, Budgeting
2	Maintenance Management	Computerized Maintenance Management Systems (CMMS)	Automates maintenance tasks and tracks asset performance	Fiix, eMaint, UpKeep	Preventive maintenance, Work order management, Asset history tracking
		Total Productive Maintenance (TPM)	Increases asset reliability by involving all employees in asset management	OEE (Overall Equipment Efficiency), 5S methodology	Root cause analysis, Equipment reliability, Loss reduction
		Predictive Maintenance Software	Forecasts equipment failures and recommends maintenance actions	Predix, Fiix, IBM Watson IoT	Machine learning-based predictions, Preventive scheduling, Failure forecasting
		Asset Performance Management (APM)	Maximizes asset performance and minimizes downtime	GE Predix, Bentley APM	Performance monitoring, Risk-based analysis, Decision support
3	Condition Monitoring & Performance Analytics	Condition Monitoring & Predictive Analytics	Monitors asset health and predicts potential failures	Vibration sensors, Thermography, Ultrasound	Early fault detection, Predictive failure analysis, Real-time monitoring
		Asset Health Index (AHI)	Provides a measure of asset condition and performance	AHI models, Custom metrics	Condition-based assessment, Early warning indicators, Predictive analytics
		IoT-based Asset Tracking	Provides real-time tracking and monitoring using IoT sensors	Zebra Technologies, Honeywell	Real-time asset location, Usage monitoring, Automated data collection
		Performance Management Dashboards	Visualizes asset performance data and KPIs	Power BI, Tableau, Custom dashboards	Real-time data visualization, KPI tracking, Reporting
4	Risk & Decision Support	Risk-Based Asset Management (RBAM) Tools	Manages asset-related risks and supports strategic decision-making	Risk matrices, FMEA	Risk assessment, Asset prioritization, Risk mitigation
		Integrated Risk Management (IRM) Systems	Assesses and manages risks related to assets	Resolver, MetricStream	Risk identification, Risk prioritization, Compliance tracking
		Integrated Asset Management Systems (IAMS)	Combines asset data with financial and operational information	Oracle EAM, SAP S/4HANA	Performance analysis, Cost management, Business strategy alignment
		Supply Chain Management Systems (SCMS)	Manages flow of assets and spare parts through the supply chain	SAP SCM, Oracle SCM	Inventory management, Supplier collaboration, Demand forecasting
5	Spatial & Data Analysis	Energy Management Systems (EMS)	Optimizes energy usage of assets and monitors energy consumption	Schneider Electric, Siemens	Energy efficiency monitoring, Consumption tracking, Automated reports
5	Spatial & Data Analysis	Geographic Information Systems (GIS)	Integrates asset data with spatial information	ArcGIS, QGIS	Asset mapping, Spatial analysis, Field data integration
6	Workforce & Field Service Management	Mobile Asset Management Solutions	Allows field technicians to access and update asset information remotely	UpKeep, Hippo CMMS, Asset Panda	Mobile asset tracking, Field reporting, Work order management
6	Workforce & Field Service Management	Workforce Management Systems (WMS)	Manages field service teams and interactions with assets	ServiceTitan, FieldAware, Corrigo	Workforce scheduling, Work order assignment, Technician performance tracking

2. Literature Review

This section summarizes key contributions and insights into asset management drawn from a variety of studies and frameworks. These contributions span topics such as the integration of advanced technologies, including Artificial Intelligence (AI), Machine Learning (ML), and Digital Twins (DT), into Asset Integrity Management (AIM), as well as the adoption of innovative methodologies like Building Information Modeling (BIM) and Digital Asset Management (DAM). The studies reviewed demonstrate how these technologies and approaches enhance asset performance, optimize maintenance strategies, and improve operational efficiency across industries like construction, energy, oil & gas, and railways. The insights emphasize the shift from traditional asset management practices to data-driven, predictive, and integrated approaches that support long-term sustainability and operational excellence. Table 6 and Table 7 provide an overview of current trends and best practices, offering valuable insights for both academics and practitioners aiming to optimize asset management across diverse sectors.

1.: Value-Driven Asset Management: This category emphasizes the shift from traditional maintenance to optimizing the long-term value of assets. Store-Valen & Lohne (2016) [4] highlight the importance of a value-driven approach, where assets are managed not just for maintenance but for their strategic contribution throughout their lifecycle.
2.: AIMM Framework and Lifecycle Integration: Research in this area focuses on the Asset Integrity Management Model (AIMM), which integrates asset management across the entire lifecycle. Smith, Johnson & Brown (2017) [5] introduced AIMM as a transformative framework that optimizes asset management from design to decommissioning, aligning operational needs with strategic goals at each lifecycle phase.
3.: BIM and Digital Twin Technologies: This category explores the use of BIM and Digital Twins in asset management. These technologies enable real-time monitoring, simulation, and collaboration throughout the asset lifecycle. Lu, Lai & Tse (2018) [6] demonstrate how BIM, serving as a digital twin, enhances data sharing and decision-making across asset management processes.
4.: Predictive Maintenance and Optimization: Studies in this area examine the role of AI, ML, and Big Data in predictive maintenance. These technologies move organizations from reactive to proactive maintenance, improving asset reliability and reducing downtime. Wanigarathna et al. (2019) [7] explore how predictive analytics can optimize maintenance schedules and asset performance.
5.: Digital Asset Management (DAM) Strategies: This category focuses on managing digital assets (data, software, documentation) throughout their lifecycle. Re Cecconi et al. (2020) [8] advocate for metadata-driven strategies to improve the indexing, preservation, and retrieval of digital assets, ensuring long-term accessibility and security.
6.: Digital Asset Preservation and Governance: Research here outlines the need for preserving the integrity and usability of digital assets over time. Zhou (2023) [9] discusses standards for digital asset preservation and governance, emphasizing the need for frameworks that ensure authenticity and long-term access.
7.: Sector-Specific Applications and Case Studies: This category includes case studies from industries like oil & gas, energy, and railways. For example, Agala et al. (2024) [10] showcase a case study in offshore oil asset management, demonstrating how structured approaches can enhance reliability, reduce emissions, and foster sustainability in sector-specific contexts.
8.: Integrating Advanced Technologies with AIM: Research in this category explores the integration of technologies such as ML, DT, and traditional methodologies like Risk-Based Inspection (RBI), Reliability-Centered Maintenance (RCM), Total Productive Maintenance (TPM), and Lean Six Sigma (LSS). Gomaa (2025) [1,2] proposes a unified AIM framework that combines these advanced technologies and methodologies for enhanced asset performance, safety, and sustainability.

These categories reflect the ongoing evolution of asset management from traditional practices to predictive, data-driven, and value-oriented approaches. The integration of Industry 4.0 technologies, such as IoT, AI, Big Data, and Digital Twins, is transforming asset management, aligning it with modern organizational goals, increasing efficiency, and fostering sustainability. In conclusion, the integration of advanced technologies like AI, ML, Digital Twins, and BIM with established asset management frameworks such as AIMM, RBI, RCM, and TPM represents a transformative shift in the field. This integration enables more efficient, predictive, and data-driven maintenance strategies, optimizing asset performance, minimizing downtime, and improving sustainability. The studies reviewed highlight the importance of adopting a holistic approach that combines both traditional methodologies and emerging technologies. Insights from various industries—construction, energy, oil & gas, and railways—illustrate the potential for these innovations to enhance reliability, reduce operational costs, and drive long-term sustainability. Bridging the gap between conventional practices and cutting-edge technologies, asset management can achieve new levels of operational excellence and strategic value, positioning organizations to succeed in a competitive, technology-driven world.

Table 6. Summary of the Review on Asset Management.

#	Author(s)/Source	Focus Area	Key Concepts/Technologies	Main Contribution/Insight
1	Store-Valen & Lohne (2016), [4]	Asset Management & BAM	Asset lifecycle, Value-based management	Advocates value-driven asset management beyond physical maintenance.
2	Smith, Johnson & Brown (2017), [5]	AIMM Framework	AIMM, Lifecycle integration	Introduces AIMM as a transformative lifecycle framework in construction.
3	Lu, Lai & Tse (2018) [6]	BIM in AIMM	BIM, Collaboration	Highlights BIM as a digital twin enabling data-rich collaboration.
4	Wanigarathna et al. (2019) [7]	Emerging AIMM Technologies	AI, Machine Learning	Examines predictive maintenance and optimization via AI and analytics.
5	Re Cecconi et al. (2020) [8]	DAM Lifecycle Practices	Metadata, Retrieval, Preservation	Advocates metadata-driven lifecycle strategies for digital assets.
6	Zhou (2023) [9]	Digital Preservation	Long-term access, Preservation standards	Outlines standards for maintaining digital asset authenticity.
7	Agala et al. (2024) [10]	Oil & Gas Asset Management	FPSO, Reliability, Emissions reduction	Case study on improving asset reliability and emissions in offshore oil operations.
8	Granström & Söderholm (2024) [11]	Railway Asset Management	CCS, RAMS, ISO 55000	Develops a systemic approach to improving asset dependability in railways, aligned with ISO 55000.
9	Erhueh et al. (2024) [12]	Digital Transformation in Energy	IoT, AI, Big Data, Blockchain, Digital Twin	Explores digital technologies enhancing asset performance and sustainability in the energy sector.
10	Alkhard (2024) [13]	AIMM + DAM in Education Facilities	Metadata analytics, Facility data governance	Proposes a framework integrating AIMM and DAM for improved facility management.
11	Gomaa (2025) [1]	Maintenance 4.0 & Asset Integrity Management	IoT, AI, Big Data, Predictive maintenance, Operational efficiency	Explores Maintenance 4.0’s impact on AIM, focusing on predictive maintenance, efficiency, and sustainability.
12	Gomaa (2025) [2]	AIM Framework Integration	Machine Learning, Digital Twins, RBI, RCM, TPM, LSS	Proposes an integrated AIM framework combining ML, DT, and methodologies (RBI, RCM, TPM, LSS) for optimized asset performance and sustainability.

Table 7. Key Contributions and Insights in Asset Management.

#	Category	Key Concepts/Technologies	Main Contribution/Insight	Reference(s)
1	Value-Driven Asset Management	Asset lifecycle, Value-based management	Shifts from traditional maintenance to maximizing asset value throughout its lifecycle.	Store-Valen & Lohne (2016) [4]
2	AIMM Framework & Lifecycle Integration	AIMM, Lifecycle integration	Introduces AIMM as a framework to optimize asset management, aligning operational and strategic goals across the lifecycle.	Smith, Johnson & Brown (2017) [5]
3	BIM & Digital Twin Technologies	BIM, Digital Twin, Real-time monitoring	Explores BIM and Digital Twins for real-time monitoring and improved collaboration and decision-making.	Lu, Lai & Tse (2018) [6]
4	Predictive Maintenance & Optimization	AI, Machine Learning, Big Data	Examines the use of AI and ML for predictive maintenance, optimizing asset performance, and reducing downtime.	Wanigarathna et al. (2019) [7]
5	Digital Asset Management (DAM)	Metadata, Digital assets, Lifecycle management	Advocates metadata-driven strategies for efficient digital asset management, including storage, retrieval, and preservation.	Re Cecconi et al. (2020) [8]
6	Digital Asset Preservation & Governance	Digital asset preservation, Governance	Provides standards for preserving the authenticity of digital assets, emphasizing governance frameworks.	Zhou (2023) [9]
7	Sector-Specific Applications	Oil & Gas, Energy, Railways, Reliability	Showcases case studies from various sectors on how asset management improves reliability and sustainability.	Agala et al. (2024) [10]; Granström & Söderholm (2024) [11]
8	Integrated AIM Framework	Machine Learning, Digital Twins, RBI, RCM, TPM, LSS	Proposes an integrated AIM framework combining advanced technologies and traditional methods to optimize performance.	Gomaa (2025) [1,2]

3. Research Gap Analysis

Despite significant advancements in asset management, key research gaps remain that hinder organizations from fully realizing Asset Management Excellence. The rapid pace of digital transformation, the shift toward Industry 4.0, and the increasing complexity of global operations present opportunities for deeper exploration in the field. Table 8 outlines critical research gaps, challenges, and future directions to enhance asset management strategies.

1.: Technological and Strategic Integration Gaps: Despite the availability of advanced technologies like AI, IoT, and Digital Twins, their integration with traditional asset management (AM) frameworks remains limited. Many existing systems operate independently, which restricts the full potential of predictive analytics and real-time monitoring. Future research should focus on creating adaptable AM frameworks that harmonize technological innovations with long-term strategic objectives, enabling better decision-making and operational efficiency.
2.: Human, Organizational, and Risk Considerations: Human factors, such as leadership, workforce skills, and organizational culture, are often overlooked in asset management research, which tends to focus on technical solutions. Additionally, risk management is frequently not embedded into performance optimization processes. Research should explore the impact of human and cultural dimensions on asset management and investigate how proactive risk management can be integrated into asset performance strategies to reduce failures and enhance reliability.
3.: Data, Sustainability, and Security Challenges: As asset management becomes increasingly data-driven, challenges related to data integration, cybersecurity, and governance arise. Furthermore, sustainability goals are not always embedded within AM practices. Future research should focus on developing strategies to integrate sustainability objectives into asset management, improve data security, and ensure seamless data integration across platforms to enhance operational resilience.
4.: Interoperability and Standardization Deficiencies: The lack of standardized protocols and interoperable systems across platforms such as BIM and DAM hinders data exchange and integration across the asset lifecycle. This leads to inefficiencies and fragmented communication. Research should explore the development of standardized digital protocols and ontologies to facilitate seamless interoperability, ensuring the smooth exchange of information across all stakeholders and phases of the asset lifecycle.
5.: Limited Sector-Specific Customization: Many asset management frameworks are generic and not tailored to the specific needs of industries such as healthcare, transportation, and oil & gas. This limits their effectiveness and adoption. Future research should focus on developing sector-specific AM models that take into account industry-specific challenges, KPIs, risk profiles, and operational constraints, making AM systems more relevant and effective across different sectors.
6.: Decision-Making and Lifecycle Integration Gaps: Traditional asset management decision-making is often reactive or scheduled, lacking integration with lifecycle costing, risk analytics, and long-term value optimization. There is a gap in implementing data-driven, proactive decision-making across the asset lifecycle. Future research should develop frameworks that integrate lifecycle thinking with strategic decision-making, utilizing AI, digital twins, and risk analytics to optimize asset performance over time.
7.: Inadequate Integration of Risk Management in Asset Optimization: Risk management is often siloed from daily operations, limiting its effectiveness in asset performance optimization. Traditional risk assessment methods lack the flexibility to integrate real-time data, reducing their ability to mitigate risks proactively. Future research should focus on integrating dynamic risk models into asset management systems, enabling continuous risk assessment and response, thereby improving asset resilience.
8.: Fragmented Implementation of Maintenance 4.0: The adoption of Maintenance 4.0 technologies is inconsistent across industries, with barriers such as high initial costs, resistance to change, and a lack of skilled workforce. Many organizations implement isolated technologies without aligning them into a cohesive maintenance strategy. Research should focus on developing scalable and modular Maintenance 4.0 solutions that can be tailored to different sectors’ needs, budgets, and maturity levels, facilitating smoother and more widespread adoption.
9.: Underutilization of Digital Twin Potential: While Digital Twin (DT) technology holds significant promise for asset monitoring and predictive maintenance, its full potential is often underutilized, with many implementations focusing only on visualization rather than incorporating real-time data feedback and analytics. Future research should explore the integration of DTs with AIM systems, combining them with machine learning algorithms and real-time data to enable proactive insights, autonomous decision-making, and improved asset lifecycle management.
10.: Limited Focus on Human Factors and Change Management: Successful asset management transformation depends on human factors, including organizational culture and the readiness of the workforce to adopt new technologies. However, resistance due to inadequate training and change management strategies is common. Future research should focus on human-centered design, leadership-driven transformation strategies, and change readiness assessments to ensure the smooth adoption of digital asset management systems.
11.: Insufficient Standardization Across Tools and Platforms: Lack of standardization across asset management tools and platforms (e.g., BIM, DAM, CMMS) hinders seamless data integration and communication, affecting the overall effectiveness of asset management systems. Future research should focus on developing universal frameworks and open data standards that enable consistent and reliable data exchange, fostering automation and strategic decision-making across asset management platforms.
12.: Weak Link Between Asset Management and Sustainability Metrics: Asset management practices often fail to incorporate sustainability goals such as energy efficiency, emissions reduction, and lifecycle impact assessment. There is a disconnect between asset performance and sustainability outcomes. Future research should investigate ways to embed sustainability metrics into asset optimization models, using AI and digital technologies to promote green maintenance strategies and support sustainability-driven decision-making.
13.: Risk Management in Asset Performance Optimization: Dynamic risk management models, which integrate real-time data, are often underdeveloped in asset management systems. Traditional risk assessment methods are often static, making them less effective in proactively mitigating operational risks. Future research should focus on integrating AI, IoT, and Big Data into risk management frameworks, enabling continuous real-time risk evaluation and improving asset reliability and resilience.

In conclusion, addressing these gaps will require a more integrated and holistic approach to asset management, combining technological innovations with human-centered strategies and sector-specific solutions. This will enhance the efficiency, sustainability, and resilience of asset management systems, positioning organizations to thrive in a rapidly evolving technological landscape.

Table 8. Research Gap Analysis in Asset Management.

#	Research Gap Area	Challenges	Gaps	Future Research
1	Tech-Strategy Integration	Disconnect between digital tools (AI, IoT, DT) and traditional AM systems	Lack of integrated frameworks aligning technology with strategy	Develop unified AM models integrating digital tools with strategic goals
2	Human & Organizational Factors	Overlooked human aspects in AM (leadership, culture, skills)	Insufficient focus on change management and workforce readiness	Investigate human-centered strategies and leadership-driven transformation
3	Risk Management in Optimization	Siloed, reactive risk management practices	Absence of dynamic, real-time risk models in AM	Integrate AI-driven, real-time risk models into AM frameworks
4	Data & Sustainability	Data fragmentation, cybersecurity, and misalignment with sustainability goals	Limited integration of sustainability in AM	Develop frameworks for data governance, cybersecurity, and sustainable asset management
5	Interoperability & Standards	Lack of data standards and cross-tool interoperability	Absence of universal data exchange standards	Establish universal standards (e.g., ISO 55000) for seamless interoperability
6	Sector-Specific Customization	Generic solutions not tailored to industry-specific needs	Insufficient sector-specific AM models	Create customized AM frameworks addressing sector-specific KPIs and challenges
7	Lifecycle-Centric Decision-Making	Reactive decision-making, lacking lifecycle integration	Absence of lifecycle costing and proactive decision-making	Develop AI-driven systems for lifecycle-based decision-making
8	Fragmented Maintenance 4.0	Inconsistent adoption of Maintenance 4.0 technologies	Lack of integrated, scalable frameworks for Maintenance 4.0	Design scalable, modular Maintenance 4.0 models adaptable to organizational needs
9	Underutilization of Digital Twin	Limited use of Digital Twin for predictive analytics	Focus on visualization instead of predictive insights	Enhance Digital Twin integration with real-time data and AI-driven analytics
10	Digital Asset Management (DAM) Gaps	Lack of effective metadata management and lifecycle control	Fragmented digital asset governance	Develop advanced DAM systems with integrated metadata management and access control
11	Transformation Frameworks	Organizational resistance to digital transformation	Lack of frameworks for guiding AM transformation readiness	Develop tools for assessing and guiding digital transformation in AM
12	BIM–AIM Integration	Limited use of BIM in asset management beyond design	Disconnect between design and operational asset management	Integrate BIM with AM systems for continuous updates across the asset lifecycle
13	Knowledge Transfer & Feedback	Lack of integration between performance data and decision-making	Insufficient feedback loops in asset management processes	Leverage AI for continuous knowledge transfer and asset performance improvement

4. Research Methodology for Achieving Asset Management Excellence

This section presents the core methodologies, strategic frameworks, and guiding principles essential for achieving Asset Management Excellence (AME). It emphasizes the integration of international standards—particularly ISO 55001—with proven improvement approaches such as Lean Six Sigma (LSS) to enhance asset value, operational performance, and risk mitigation throughout the asset lifecycle.

1.: Core Principles of Asset Management Excellence: This subsection outlines the foundational principles underpinning AME, grounded in ISO 55001 and complementary asset management standards. Key concepts include asset reliability, lifecycle value optimization, sustainability, and continuous improvement. The importance of risk-informed decision-making and strategic alignment is highlighted to ensure that asset management contributes effectively to organizational objectives.
2.: Strategic Framework for AME Integration: A structured strategic framework is proposed to embed asset management within the broader organizational context. This framework emphasizes the alignment of asset strategies with corporate goals, the development of robust governance structures, and the integration of asset-related processes across functions. Such alignment enhances operational efficiency, resilience, and long-term value creation.
3.: Application of the DMAIC Methodology: The DMAIC (Define, Measure, Analyze, Improve, Control) framework is introduced as a data-driven methodology for continuous improvement in asset performance. By systematically identifying performance gaps, analyzing root causes, and implementing evidence-based improvements, DMAIC supports the optimization of asset management processes, promoting efficiency, reliability, and sustainability.
4.: Key Objectives and Performance Metrics: This subsection defines the strategic objectives of AME and identifies critical key performance indicators (KPIs) for monitoring progress. Metrics such as Overall Equipment Effectiveness (OEE), Mean Time Between Failures (MTBF), lifecycle cost-effectiveness, and asset uptime are discussed as tools for performance evaluation. The role of advanced analytics, real-time monitoring, and predictive maintenance is also addressed to support data-driven decision-making and continuous improvement.
5.: Mitigation Strategies for Implementation Barriers: Implementing AME through LSS and ISO 55001 is often challenged by barriers across financial, organizational, workforce, technological, and structural dimensions. This section identifies common barriers and presents tailored mitigation strategies, including phased implementation, structured change management, targeted capacity building, digital readiness assessments, and cross-functional collaboration. These strategies are designed to facilitate sustainable adoption and long-term success.

4.1. Core Principles of Asset Management Excellence Asset Management Excellence is founded on a set of core principles that guide organizations in optimizing the value, performance, and reliability of their physical assets throughout their lifecycle. These principles ensure that asset-related decisions are strategically aligned with organizational objectives, stakeholder expectations, and the demands of a rapidly evolving industrial environment. As businesses face increasing complexity, digital disruption, and sustainability pressures, embracing these principles becomes essential for achieving operational excellence, long-term resilience, and sustainable value creation. As shown in Table 9, the following principles form the cornerstone of a high-performing asset management framework:

1.: Value-Driven Decision Making|Whole-Life Asset Perspective|Strategic Alignment with Business Objectives: The foundation of Asset Management Excellence begins with Value-Driven Decision Making, where organizations aim to balance cost, performance, and risk to optimize asset value. This principle ensures that asset-related decisions contribute positively to overall business outcomes. Closely connected to this is the Whole-Life Asset Perspective, which emphasizes managing assets from acquisition to decommissioning. By considering the entire lifecycle, organizations can maximize the return on investment and minimize the total cost of ownership. Strategic Alignment with Business Objectives further reinforces this by ensuring asset management initiatives are not isolated efforts but are tightly integrated with the organization’s broader strategic vision, goals, and key performance indicators (KPIs). Together, these principles promote a focused and forward-thinking asset management culture.
2.: Risk-Based and Resilient Management|Strong Governance and Accountability|Technology Integration and Digital Enablement: As industries face increasing uncertainty and complexity, Risk-Based and Resilient Management becomes a critical principle. It involves identifying potential risks, assessing their impact, and developing mitigation strategies to safeguard asset reliability and business continuity. This requires a strong foundation in Governance and Accountability, where clearly defined roles, leadership structures, and collaborative decision-making processes drive effective implementation and oversight of asset strategies. Complementing governance is the Integration of Technology and Digital Enablement, which empowers organizations with advanced tools like IoT, artificial intelligence, and predictive analytics. These technologies enable real-time monitoring, smarter decision-making, and continuous optimization of asset performance—cornerstones of modern, data-driven asset management systems.
3.: Sustainability and Environmental Responsibility|Culture of Continuous Improvement: A future-focused asset management strategy must prioritize Sustainability and Environmental Responsibility. This principle calls for embedding environmentally conscious practices within asset planning and operation, reducing carbon footprints, improving energy efficiency, and supporting circular economy models. Meeting regulatory compliance and corporate social responsibility goals also hinges on this focus. Lastly, achieving excellence requires nurturing a Culture of Continuous Improvement, where innovation, learning, and employee engagement are central. By encouraging feedback loops, lean practices, and ongoing capability development, organizations ensure that their asset management practices evolve to meet emerging challenges, technologies, and opportunities for performance enhancement.
4.: Integration and Synergy Across Principles: While each principle serves a distinct function, its true strength lies in how they interconnect and reinforce one another. For example, value-driven decision-making is greatly enhanced when supported by real-time data from digital technologies. Similarly, long-term sustainability goals become more attainable when risk-based management is practiced in a way that accounts for environmental threats and regulatory changes. Governance structures not only provide oversight but also ensure that initiatives for continuous improvement, innovation, and environmental stewardship receive the necessary support and resources. These interdependencies underscore the importance of treating asset management not as a siloed function but as an integrated discipline that spans strategic, operational, and technological domains.
5.: Practical Application in Industrial Contexts: Applying these principles in real-world industrial settings demands customization and scalability. Different sectors—such as manufacturing, utilities, oil and gas, and transportation—will place varying emphasis on certain principles depending on their unique operational risks, asset criticality, and compliance requirements. For example, a manufacturing firm may prioritize predictive maintenance enabled by AI, while a utility company may focus more heavily on resilience and lifecycle optimization. However, by anchoring all efforts in these core principles, organizations can ensure that their asset management strategies remain robust, responsive, and results-oriented regardless of industry context.
6.: Driving Long-Term Organizational Transformation: Ultimately, these principles provide the foundation for long-term transformation. When embedded into the culture and decision-making fabric of the organization, they shift asset management from a reactive cost center to a proactive value enabler. This transformation supports not only operational efficiency and risk mitigation but also drives innovation, sustainability, and business growth. By committing to these principles, organizations can unlock new levels of agility and performance—turning their asset base into a strategic advantage in a rapidly changing industrial environment.
7.: Enabling a Culture of Accountability and Excellence: The implementation of asset management excellence hinges not only on systems and technologies but also on people and culture. These core principles promote a culture where accountability, ownership, and continuous learning are embedded across all levels of the organization. Strong governance frameworks ensure that roles and responsibilities are clearly defined, while performance measurement and feedback systems foster transparency and engagement. When employees understand how their roles contribute to asset performance and value creation, they become more empowered to innovate, optimize, and align their efforts with strategic goals. This cultural shift is fundamental to achieving sustainable asset management maturity.
8.: Continuous Evolution with Technological and Market Shifts: In today’s volatile business environment, where technological disruption and market dynamics evolve rapidly, asset management practices must remain agile and forward-looking. The core principles serve as guiding anchors, enabling organizations to adapt their frameworks without losing strategic alignment. For example, as new technologies such as digital twins, augmented reality, and machine learning become more accessible, organizations grounded in innovation and digital integration can rapidly adopt and scale these tools. Likewise, shifts in regulatory environments or stakeholder expectations around sustainability can be addressed more fluidly when risk-based decision-making and lifecycle thinking are already embedded.
9.: Benchmarking and Continuous Maturity Assessment: To maintain momentum and progress, organizations must commit to periodic benchmarking and maturity assessments. These activities help measure how effectively the core principles are being applied and where gaps or opportunities for growth exist. Frameworks such as ISO 55000 and maturity models provide structured criteria for evaluating progress across governance, technology, data management, and operational performance. By regularly assessing their alignment with the core principles, organizations can make informed decisions about capability development, investment priorities, and strategic direction—ensuring their asset management approach evolves with purpose and precision.

Together, these principles establish the foundation for designing and implementing a robust strategic asset management roadmap—one that maximizes asset value, supports digital transformation, and enables sustainable, future-ready operations.

Table 9. Core Principle for Asset Management Excellence.

#	Core Principle	Description	Key Focus Areas
1	Value-Driven Decision Making	Balances cost, risk, and performance to maximize asset value.	Asset optimization, cost-risk trade-offs, risk-based planning
2	Whole-Life Asset Perspective	Manages assets across their lifecycle to ensure sustainability.	Lifecycle planning, total cost of ownership, long-term performance
3	Strategic Alignment with Business Objectives	Aligns asset management with organizational goals and strategy.	Business integration, KPI alignment, strategic planning
4	Risk-Based and Resilient Management	Applies risk management to enhance reliability, safety, and continuity.	Risk identification, mitigation, operational resilience
5	Strong Governance and Accountability	Ensures clear leadership and cross-functional collaboration.	Governance, stakeholder roles, decision-making accountability
6	Technology Integration and Digital Enablement	Leverages advanced technologies for data-driven decision-making.	IoT, AI, predictive analytics, digital twins, Maintenance 4.0
7	Sustainability and Environmental Responsibility	Embeds sustainability to reduce environmental impact and ensure compliance.	Energy efficiency, circular economy, carbon footprint reduction
8	Culture of Continuous Improvement	Fosters innovation and learning for sustained excellence.	Lean thinking, employee empowerment, feedback loops
9	Benchmarking and Continuous Maturity Assessment	Measures and improves asset management maturity and performance.	Performance benchmarking, maturity assessments, best practices

4.2. Strategic Roadmap for Asset Management Excellence This section outlines a strategic roadmap that redefines asset management as a core driver of business performance, value creation, and organizational resilience, key to thriving in today’s fast-evolving industrial environment. As industries face ongoing technological disruption, organizations must transition from traditional reactive approaches to proactive, integrated asset management strategies that support long-term sustainability and sustained competitive advantage. The proposed roadmap follows a lifecycle-based model, ensuring that asset-related decisions—from acquisition and deployment to maintenance, optimization, and decommissioning—are fully aligned with the organization’s overarching strategic objectives. Achieving Asset Management Excellence requires a structured, step-by-step methodology. The following framework provides a comprehensive guide for developing and implementing an effective roadmap, one that integrates industry best practices, digital technologies, and strategic business alignment. Table 10 presents the key stages of this development process, with each step clearly defining the required actions and expected outcomes. This structured approach ensures consistency, strategic focus, and actionable guidance for building a resilient, high-performing asset management system.

1.: Define Clear Objectives and Scope: Establishing well-defined objectives is the foundation of the framework. Goals may include enhancing asset performance, minimizing costs, improving operational efficiency, or ensuring business continuity. Clearly setting the scope ensures alignment with organizational priorities and provides stakeholders with a unified understanding of the project’s intent and boundaries.
2.: Analyze Current State (Baseline Assessment): A thorough assessment of existing asset management practices, technologies, and performance benchmarks helps identify strengths, gaps, and areas for improvement. This baseline provides critical insights into current capabilities and forms the foundation for strategic planning and enhancement.
3.: Identify Key Drivers and Success Factors: Success depends on identifying the critical enablers of the framework—such as leadership support, digital technologies (IoT, AI, analytics), regulatory compliance, and cross-functional collaboration. Recognizing these early ensures the framework is designed for effective, sustainable implementation.
4.: Research and Integrate Best Practices: Incorporating global standards like ISO 55001 and analyzing industry case studies ensures the framework reflects proven methodologies. Benchmarking against best-in-class models enables organizations to adopt practices that are effective, scalable, and aligned with emerging trends.
5.: Develop Strategic Pillars: The framework should be anchored by key pillars such as governance, risk management, digital enablement, workforce development, and sustainability. These pillars provide structure and coherence, guiding the integration of technical, operational, and strategic elements.
6.: Align with Organizational Strategy: Asset management must support broader business goals, including growth, resilience, and competitive positioning. Strategic alignment ensures that asset-related initiatives are not siloed but directly contribute to enterprise-level success and adaptability.
7.: Define Actionable Initiatives and KPIs: Strategic goals should be translated into specific projects and measurable actions, supported by clear KPIs and timelines. This facilitates execution, accountability, and performance monitoring, ensuring progress is visible and aligned with expectations.
8.: Identify Technology Requirements: Determine the digital tools and platforms required—such as IoT sensors, predictive analytics, and AI systems—to support data-driven asset management. This step ensures appropriate technology selection and effective integration with operational systems.
9.: Develop Governance and Accountability Structures: Establish transparent governance, clear roles, and decision-making processes to oversee framework implementation. This structure ensures consistency, accountability, and continuous oversight across asset functions and departments.
10.: Pilot Testing and Validation: Conduct pilot tests within selected units to validate the framework, gather feedback, and refine approaches. Piloting reduces risk, improves stakeholder confidence, and enhances implementation effectiveness prior to full-scale deployment.
11.: Continuous Improvement and Feedback: Establish mechanisms for ongoing monitoring, feedback, and adaptation. As technologies and business conditions evolve, the framework must remain agile and responsive, fostering a culture of continuous improvement.
12.: Full Implementation and Scaling: Following successful validation, deploy the framework organization-wide. This requires a structured implementation plan, strong leadership, stakeholder engagement, and appropriate resources to drive widespread adoption and long-term impact.
13.: Review and Update: Periodically review and update the framework to reflect new challenges, technologies, and strategic priorities. Regular assessment ensures the asset management strategy remains relevant, future-ready, and fully aligned with business evolution.

This structured methodology enables the development of a dynamic, data-driven asset management framework that evolves with organizational needs. By aligning technical processes with strategic goals, organizations can achieve asset management excellence, driving operational performance and long-term value.

Table 10. Main Steps for Framework Development.

#	Step	Action	Outcome
1	Define Clear Objectives and Scope	Establish the goals of the framework (e.g., improving efficiency, reducing costs, enhancing resilience).	Clear understanding of objectives and boundaries.
2	Analyze Current State (Baseline Assessment)	Conduct an assessment of current asset management practices, technologies, and performance.	Baseline insights into strengths, weaknesses, and gaps.
3	Identify Key Drivers and Success Factors	Identify critical success factors such as leadership, technology, and regulatory needs.	List of key drivers for successful asset management.
4	Research and Integrate Best Practices	Review global standards (e.g., ISO 55001), industry case studies, and best practices.	Foundation of best practices and models to inform the framework.
5	Develop Strategic Pillars for the Framework	Define pillars like governance, technology integration, risk management, and workforce development.	Well-defined strategic pillars to guide asset management.
6	Align with Organizational Strategy and Objectives	Ensure alignment with business strategy, financial goals, and long-term sustainability.	Framework that supports business goals (cost efficiency, growth, etc.).
7	Define Actionable Initiatives and Tactics	Break down strategic pillars into specific initiatives, plans, and KPIs.	Clear, actionable initiatives with timelines and milestones.
8	Identify Technology and Tool Requirements	Assess and identify technologies (IoT, AI, analytics tools, etc.) needed for implementation.	List of recommended technologies and integration plans.
9	Develop Governance and Accountability Structures	Set up governance structures and accountability mechanisms for implementation.	Clear governance and accountability systems.
10	Pilot Testing and Validation	Test the framework with a selected asset group or business unit.	Feedback for refining the framework before full-scale implementation.
11	Continuous Improvement and Feedback Loop	Set up systems for monitoring, feedback, and continuous improvement.	A dynamic framework that evolves over time based on feedback.
12	Full Implementation and Scaling	Roll out the framework across the organization, ensuring full adoption.	Fully implemented framework that drives efficiency and resilience.
13	Review and Update	Periodically review and update the framework as per evolving needs.	Flexible framework that stays relevant to technological and organizational changes.

4.3. DMAIC Framework for Asset Management Excellence This study introduces a Lean Six Sigma-based roadmap that incorporates the DMAIC methodology (Define, Measure, Analyze, Improve, Control) to optimize asset performance, promote continuous improvement, and enhance operational efficiency throughout the asset lifecycle. The framework aligns with ISO 55001 standards and integrates key principles such as risk-based decision-making, sustainability, and lifecycle optimization, ensuring that asset management strategies align with both organizational goals and regulatory requirements. The DMAIC framework is a structured, data-driven approach designed to improve asset management practices. It consists of five key phases—Define, Measure, Analyze, Improve, and Control—each targeting specific aspects of asset management to enhance efficiency, reliability, and long-term value. Table 11 provides a comprehensive breakdown of the DMAIC process, detailing key objectives, activities, tools, and examples for each phase, ensuring organizations can achieve asset management excellence.

1.: Define Phase: This phase focuses on identifying asset management challenges, such as unplanned downtime or performance gaps. Clear goals, objectives, and performance metrics (KPIs) are defined to align with broader organizational priorities. Early stakeholder engagement ensures that the efforts focus on the most impactful areas. The phase concludes by setting concrete targets, such as reducing downtime by a specific percentage within a defined period.
2.: Measure Phase: In this phase, data is collected to assess the current performance of assets. Historical data on asset failures, downtime, and other relevant metrics establish a baseline for performance. Key indicators such as Mean Time Between Failures (MTBF) and downtime are tracked to measure reliability. Tools like Computerized Maintenance Management Systems (CMMS) and Enterprise Asset Management (EAM) software help to provide insights and identify gaps in asset performance.
3.: Analyze Phase: The Analyze phase focuses on identifying the root causes of performance gaps identified in the Measure phase. Analytical tools such as Root Cause Analysis (RCA) and Failure Modes and Effects Analysis (FMEA) are used to investigate the underlying issues, such as improper maintenance practices or aging equipment. Understanding these root causes ensures that the solutions implemented in the next phase directly address the core issues rather than just treating symptoms.
4.: Improve Phase: Following the identification of root causes, the Improve phase targets the implementation of solutions to enhance asset performance. This may include the adoption of predictive maintenance (PdM), condition-based monitoring (CBM), or process optimizations to prevent unplanned breakdowns. Solutions are tested, refined, and adjusted to meet performance targets, such as leveraging IoT-enabled predictive maintenance for real-time asset monitoring, reducing downtime, and improving reliability.
5.: Control Phase: The Control phase ensures that improvements are sustained over time. Regular monitoring and performance tracking are conducted to ensure that changes continue to meet targets. Best practices are standardized, and staff training is provided to maintain improvements. Additionally, automated reporting, audits, and feedback loops are implemented to monitor performance and adapt to evolving challenges, promoting a culture of continuous improvement.

In conclusion, the DMAIC framework offers a structured approach to optimizing asset management processes. By clearly defining goals, measuring performance, analyzing root causes, implementing effective solutions, and ensuring ongoing control, organizations can optimize asset performance, reduce operational costs, and ensure long-term sustainability. The adoption of the DMAIC methodology empowers organizations to elevate asset management practices, achieve operational excellence, and remain competitive in an increasingly complex and digitally integrated industrial environment.

Table 11. DMAIC framework for Asset Management Excellence.

Phase	Objective	Key Activities	Tools/Methods	Example
Define	Set clear goals, scope, and KPIs.	- Identify challenges and define scope. - Align objectives with organizational goals. - Define KPIs.	- Stakeholder engagement - KPI definition	Target to reduce unplanned downtime by 15% in 6 months.
Measure	Collect data on current performance.	- Analyze historical asset data. - Establish baseline metrics.	- CMMS, EAM software - Dashboards	Measure MTBF, downtime, and failure rates.
Analyze	Identify root causes of inefficiencies.	- Analyze trends and data patterns. - Perform root cause and gap analysis.	- RCA - Pareto analysis - FMEA	Identify causes like inadequate preventive maintenance.
Improve	Implement solutions to improve assets.	- Design, test, and refine solutions. - Implement best practices and new technologies.	- PdM - CBM - IoT, Automation	Deploy IoT-based predictive maintenance for asset health.
Control	Sustain improvements over time.	- Monitor performance. - Standardize practices. - Train staff.	- Control charts - Automated reporting - Audits	Standardize maintenance schedules and track them with dashboards.

4.4. Key Objectives and Performance Metrics for Asset Management Excellence This section outlines the key objectives of asset management excellence and introduces the performance metrics (KPIs) used to measure effectiveness. It highlights essential metrics such as asset uptime, maintenance optimization, cost efficiency, and reliability, showing how these indicators enable organizations to track success. The section emphasizes the importance of data-driven decision-making, leveraging predictive maintenance, real-time monitoring, and advanced analytics to enhance asset performance and drive continuous improvement. Table 12 presents a comprehensive framework for Asset Management Excellence, detailing metrics, objectives, and performance indicators (KPIs) across various categories. This framework assists organizations in optimizing asset management strategies, improving performance, and aligning operations with strategic business goals for long-term success.

1.: Asset RAMS (Reliability, Availability, Maintainability, Safety): This category focuses on enhancing the core performance aspects of assets. Reliability minimizes asset failure frequency and severity. Availability maximizes operational uptime. Maintainability measures the efficiency of restoring assets after failure. Safety ensures compliance with safety standards to reduce operational risks.
2.: Asset OEE (Overall Equipment Effectiveness): The goal here is to maximize OEE, which combines availability, performance efficiency, and quality to measure overall asset effectiveness. Improved OEE leads to higher productivity, reduced waste, and better asset utilization in production processes.
3.: Lifecycle Cost Optimization: This category focuses on optimizing the Total Cost of Ownership (TCO), which encompasses all costs throughout an asset’s lifecycle, from acquisition to disposal. Monitoring TCO helps organizations make cost-effective decisions, improve asset management practices, and ensure financial sustainability.
4.: Operational Efficiency: Key metrics like Asset Utilization and Mean Time Between Failures (MTBF) measure operational efficiency. Asset Utilization tracks the extent of an asset’s potential usage, while MTBF measures asset reliability, helping organizations minimize downtime and disruptions.
5.: Sustainability and Compliance: This category addresses the growing emphasis on sustainability. Energy Efficiency measures how effectively assets use energy, while Environmental Impact tracks emissions and waste, ensuring compliance with environmental standards and regulations.
6.: Strategic Alignment: Aligning asset management with organizational goals is critical for creating value. Return on Assets (ROA) assesses the financial return relative to asset investments, ensuring that asset management directly supports profitability and long-term strategic objectives.
7.: Risk Management and Resilience: This category focuses on managing asset-related risks and enhancing resilience. Risk Mitigation Effectiveness evaluates the success of strategies designed to minimize risks, improve asset reliability, and reduce the likelihood of unforeseen disruptions.
8.: Continuous Improvement: A culture of Continuous Improvement is essential for long-term success. Maintenance Compliance measures adherence to maintenance schedules, while Condition-Based Maintenance (CBM) Effectiveness evaluates how CBM strategies reduce downtime and enhance asset performance.
9.: Asset Health and Maintenance: This category aims to ensure asset longevity and minimize downtime. Mean Time to Repair (MTTR) measures the time required to restore assets after failure, while the Asset Health Index (AHI) tracks real-time asset conditions, enabling proactive maintenance and increasing asset life.
10.: Inventory and Spare Parts: Effective management of Inventory and Spare Parts is vital to minimize downtime. Inventory Turnover tracks the frequency of inventory replenishment, ensuring that essential spare parts are readily available to reduce repair delays and maintain operations.
11.: Asset Reliability: The focus here is to reduce asset failures and breakdowns. Asset Failure Rate measures the frequency of asset failures, enabling proactive actions to minimize unplanned downtime and improve reliability.
12.: Work Management: This category measures Work Order Completion efficiency by tracking the time taken to complete maintenance tasks. Work Order Completion Time ensures that maintenance is performed promptly, improving asset reliability and reducing delays.

In conclusion, this framework offers a structured approach to asset management by tracking key performance metrics across various categories. By aligning these metrics with organizational goals, businesses can improve operational efficiency, reduce costs, enhance asset reliability, and achieve sustainable growth in an increasingly complex industrial landscape.

Table 12. Key Objectives and Performance Metrics for Asset Management Excellence.

#	Metric Category	Objective	KPIs
1	Asset RAMS	Maximize asset RAMS (Reliability, Availability, Maintainability, Safety)	 Reliability: Frequency and severity of asset failures.  Availability: Percentage of time assets are operational.  Maintainability: Time and cost to restore assets after failure.  Safety: Number of safety incidents related to assets.
2	Asset OEE	Maximize asset Overall Equipment Effectiveness (OEE)	 OEE: Combined measure of asset availability, performance, and quality.
3	Lifecycle Cost Optimization	Optimize Total Lifecycle Costs (TCO)	 Total Cost of Ownership (TCO): Comprehensive cost across the asset lifecycle.
4	Operational Efficiency	Improve operational efficiency	 Asset Utilization: Percentage of asset capacity in use.  Mean Time Between Failures (MTBF): Average time between asset failures.
5	Sustainability and Compliance	Ensure sustainability and regulatory compliance	 Energy Efficiency: Energy consumed per unit of asset output.  Environmental Impact: Carbon emissions, waste generation, and resource use.
6	Strategic Alignment	Align asset management with organizational strategy	 Return on Assets (ROA): Profit generated relative to asset cost.
7	Risk Management and Resilience	Improve risk management and build asset resilience	 Risk Mitigation Effectiveness: Impact of risk management strategies.
8	Continuous Improvement	Foster a culture of continuous improvement and innovation	 Maintenance Compliance: Percentage of on-schedule maintenance tasks.  CBM Effectiveness: Impact of condition-based maintenance on downtime.
9	Asset Health and Maintenance	Improve asset health, reduce downtime, and enhance reliability	 Mean Time to Repair (MTTR): Average time for asset repairs.  Asset Health Index (AHI): Real-time condition of assets.
10	Inventory and Spare Parts	Optimize inventory and spare part management	 Inventory Turnover: Rate of inventory usage and replenishment.
11	Asset Reliability	Reduce asset failures and breakdowns	 Asset Failure Rate: Frequency of asset breakdowns.
12	Work Management	Improve work order completion efficiency	 Work Order Completion Time: Average time to complete maintenance tasks.

4.5. Mitigation Strategies for Implementation Barriers Achieving Asset Management Excellence (AME) through the integration of Lean Six Sigma (LSS) and ISO 55001 is a strategic endeavor that often faces multiple implementation barriers. These challenges span financial, organizational, workforce, technological, and structural domains. To ensure successful and sustainable adoption, organizations must proactively identify these barriers and apply context-specific mitigation strategies. Table 13 presents the major categories of barriers along with corresponding solutions to support practical implementation.

1.: Financial and Investment Barriers: High initial costs related to technologies, training, and system integration frequently hinder AME initiatives. In many cases, unclear return on investment (ROI) further complicates decision-making. Mitigation strategies include phased or pilot-based implementation to reduce risk and build confidence, supported by detailed cost-benefit analyses. Organizations should also seek external funding, leverage existing infrastructure, and define SMART (Specific, Measurable, Achievable, Relevant, Time-bound) KPIs to demonstrate tangible value.
2.: Organizational and Cultural Barriers: Employee resistance, limited executive sponsorship, and cultural misalignment can delay or derail implementation. Applying structured change management frameworks (e.g., Kotter’s 8-Step Model or ADKAR) can foster engagement and alignment. Early stakeholder involvement, clear communication of expected benefits, and demonstration of early wins help build trust. Aligning AME initiatives with strategic goals and ESG priorities while assigning executive sponsors reinforces leadership support.
3.: Skills and Workforce Barriers: Insufficient technical knowledge in LSS and ISO 55001, combined with overloaded personnel, often limits progress. Organizations should invest in targeted training and certification, integrate improvement responsibilities into daily operations, and use Lean tools to eliminate inefficiencies. These actions not only enhance technical capability but also embed a culture of continuous improvement.
4.: Technological and Digital Barriers: Low digital maturity, fragmented systems, and poor data quality undermine AME efforts. Conducting digital readiness assessments helps identify capability gaps. Implementing scalable, interoperable technologies enables smoother integration, while robust data governance frameworks and the appointment of data stewards ensure reliable and actionable data for decision-making.
5.: Structural and Compliance Barriers: Siloed operations and complex regulatory demands can limit collaboration and standardization. Forming cross-functional teams with shared KPIs promotes alignment. Standardizing workflows, aligning practices with ISO 55001, and applying methodologies such as Reliability-Centered Maintenance (RCM), Risk-Based Inspection (RBI), and Failure Mode and Effects Analysis (FMEA) enhance operational consistency. Regular internal audits help monitor compliance and reinforce a culture of accountability.

In summary, successful AME implementation requires a comprehensive and proactive approach to overcoming key barriers. By aligning strategic, cultural, technical, and operational elements, organizations can enhance asset reliability, improve operational performance, and achieve long-term competitive advantage in a digitally evolving landscape.

Table 13. Key Barriers and Mitigation Strategies for Implementing Asset Management Excellence (AME).

#	Barrier Category	Challenges	Mitigation Strategies
1	Financial & Investment	 High initial costs  Uncertain ROI	 Adopt phased or pilot implementations  Conduct cost–benefit analysis  Leverage existing resources and funding  Define SMART KPIs to demonstrate value
2	Organizational & Cultural	 Resistance to change  Limited leadership engagement  Cultural misalignment	 Apply structured change models  Communicate benefits  Deliver early wins  Align with strategic goals and assign executive sponsors
3	Skills & Workforce	 Skill gaps in ISO 55001 and LSS  Overloaded personnel	 Provide targeted training and certification  Integrate improvement roles into daily operations  Use Lean tools to reduce non-value-added work
4	Technology & Data	 Low digital readiness  Poor data quality and system integration	 Conduct digital maturity assessments  Implement scalable, interoperable solutions  Establish data governance and stewardship
5	Structure & Compliance	 Departmental silos  Regulatory complexity	 Form cross-functional teams with shared KPIs  Standardize workflows  Align practices with ISO 55001 and apply tools like RCM, RBI, and FMEA

5. Conclusion and Future Work

This study presents a structured roadmap for achieving Asset Management Excellence (AME) by integrating Lean Six Sigma (LSS)—specifically the DMAIC (Define, Measure, Analyze, Improve, Control) methodology—with ISO 55001 standards. In response to the increasing complexity of digital industrial environments, the framework aims to optimize asset reliability, lifecycle performance, and continuous improvement. Core principles such as risk-based decision-making, sustainability, and value optimization are embedded to align asset management strategies with organizational goals. The model incorporates proven tools, including Failure Mode and Effects Analysis (FMEA), Root Cause Analysis (RCA), Statistical Process Control (SPC), predictive maintenance, and real-time monitoring. These support proactive, data-driven decision-making that enhances operational efficiency, reduces variability, extends asset life, and improves resource utilization. Key performance indicators—such as Mean Time Between Failures (MTBF), Overall Equipment Effectiveness (OEE), and lifecycle cost-efficiency—are used to track performance and validate improvements. Recognizing common implementation barriers across financial, organizational, workforce, technological, and structural domains, the study proposes tailored mitigation strategies. These include phased deployment, cost–benefit analysis, stakeholder engagement, digital readiness assessments, and competency development programs to ensure effective and sustainable adoption. Although conceptual, the proposed roadmap offers a practical, scalable approach to embedding Lean Six Sigma within asset management systems. It promotes a shift from reactive to proactive practices, enhancing organizational resilience, sustainability, and competitive advantage. Future research will focus on validating the framework through sector-specific case studies and pilot implementations. Future research will focus on empirically validating the proposed Asset Management Excellence (AME) roadmap through sector-specific case studies and pilot implementations across asset-intensive industries such as manufacturing, energy, and infrastructure. Emphasis will be placed on assessing the framework’s scalability, effectiveness, and adaptability in diverse operational contexts. Further studies will explore the integration of emerging Industry 4.0 technologies—such as AI, IoT, digital twins, and advanced analytics—into the roadmap to enhance predictive capabilities, real-time decision-making, and lifecycle optimization. Additionally, the development of AME maturity models and performance assessment tools will be pursued to support benchmarking and continuous improvement. Attention will also be given to the human and organizational factors influencing adoption, including leadership engagement, change readiness, and workforce development, to ensure sustainable and holistic implementation.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Funding

This research received no external funding.

Declaration of Competing Interest

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