Anouncement: Partnership SSAMM
Recently we started a partnership with SSAMM.NL
SSAMM.NL fills the gap for specific commercial activities and organizational questions. In line with the body of thoughts of this page, SSAMM.NL act likewise to support organizations.
For more information please go to SSAMM.NL
or mail contact@ssamm.nl
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Onderzoek & Social Media
Het onderzoek heeft een aantal social media platforms. Wordt lid voor de updates.
- Sustainable Asset Management
- Sustainable Asset Management
- Sustainable Asset Management
- Sustainable Asset Management
- Mail maincontract
Research Resulta
4IR
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/ 4IR, Assetmanagement, NEN-ISO55000, NEN-ISO73, RCM, Risk
Human factor in Industry 5.0
Authors & Review: SSAMM, Review: J. Stoker Each of us has some intuitive notion of what constitutes a failure. We would all agree that an automobile engine, […]
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/ 4IR, Assetmanagement, NEN-ISO55000, NEN-ISO73, RCM, Risk
Failure
Authors & Review: SSAMM, Review: J. Stoker Each of us has some intuitive notion of what constitutes a failure. We would all agree that an automobile engine, […]
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/ 4IR, Assetmanagement, NEN-ISO55000, NEN-ISO73, Risk
Risk Tables
Authors & Review: SSAMM, Review: J. Stoker The nature and degree of uncertainty requires an understanding of the quality, quantity and integrity of information available concerning the […]
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/ 4IR, Assetmanagement, Circulair, IAM, Maintenance, NEN-ISO55000
LIVE Digital Twin for Smart Maintenance
Authos: N.G. Malek Kheli, M. Tayefeh, A. Barari and Dylan Bender LIVE Digital Twin refers to four phases of Learning, Identifying, Verifying, and Extending for a physical […]
Consulted Articles & Literature
- Consulted Article’s
- – Framework IAM
- – Qualifications Maintenance Personnel
- – Framework EFNMS
- – Industry 5.0
- – Industry 4.0 vs. Industry 5.0: Co-existence, Transition, or a Hybrid
- – Maturity assessment for Industry 5.0: A review of existing maturity models
- – Industry 5.0 and Society 5.0—Comparison, complementation and co-evolution
- Human-centric amidst the IR 5.0 (Article)
- – Outlook on human-centric manufacturing towards Industry 5.0
- – Maintenance 5.0: Towards a Worker-in-the-Loop Framework for Resilient Smart Manufacturing
- – Industry 5.0: Prospect and retrospect
- – Industry 5.0 defind
- – Rethinking engineering education at the age of industry 5.0
- – Industry 5.0: A survey on enabling technologies and potential applications
- – Identifying industry 5.0 contributions to sustainable development
- – Industry 4.0 and Industry 5.0—Inception, conception and perception
- – IR 4.0 Readiness Tool
- – Industry 4.0
- – Disruptive Innovation: An Intellectual History and Directions for Future Research (Wiley)
- – Strategic servitization design method for Industry 4.0-based smart intralogistics and production
- – Development of Digital Twin for Intelligent Maintenance of Civil Infrastructure
- – Machine Learning and Deep Learning
- – Digital Twin: Enabling Technologies, Challenges and Open Research
- – What is analytics?
- – IT/OT Convergence: From Device to Database
- – Digital Twin: Generalization, characterization and implementation
- – LIVE Digital Twin for Smart Maintenance in Structural Systems
- – Capturing the true value of industry 4.0
- – An overview of Industry 4.0 Applications for Advanced Maintenance Services
- – The Essential Components of Digital Transformation
- – Enabling technologies and tools for digital twin
- – A review of Industry 4.0 characteristics and challenges, with potential improvements using blockchain technology
- – Approach for industrial inspection in the context of Industry 4.0
- – Digital Twin: Enabling Technologies, Challenges and Open Research
- – A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications
- – Predictive Maintenance Market: The Evolution from Niche Topic to High ROI Application
- – Maintenance and digital health control in smart manufacturing based on condition monitoring
- – Introducing an application of an industry 4.0 solution for circular supply chain management
- – Technology Futures: Projecting the Possible, Navigating What’s Next
- – Big Data, Predictive Analytics and Maintenance
- – PF-Interval and Big Data
- – Impacts of Industrial Revolutions on the Enterprise Performance Management
- – A survey on decision-making based on system reliability in the context of Industry 4.0
- – IR40 enabeler of TQM
- – Education within the Industry4.0 Time Frame(Presentation)
- – Industry 4.0 – A Glimpse
- – Data driven management in Industry 4.0: a method to measure Data Productivity
- – Research on the Theory and Method of Grid Data Asset Management
- – How will change the future engineers’ skills in the Industry 4.0 framework?
- – Aspects of Risk Management Implementation for Industry 4.0
- – Contextualizing the outcome of a maturity assessment for Industry 4.0
- – Boston Consulting Group: Industry 4.0
- – Industry 4.0 implementation for multinationals
- – The expected contribution of Industry 4.0 technologies for industrial performance
- – Requirements for Education and Qualification of People in Industry 4.0
- – The challenge of integrating Industry 4.0 in the degree of Mechanical Engineering
- – Servitization and Industry 4.0 convergence in the digital transformation of product firms: A business model innovation perspective
- – Industry 4.0 – An Introduction in the phenomenon
- – Services enabler architecture for smart grid and smart living services providers under industry 4.0
- – Backshoring strategy and the adoption of Industry 4.0: Evidence from Europe
- – Requirements for Education and Qualification of People in Industry 4.0
- – Maintenance for Sustainability in the Industry 4.0 context: a Scoping Literature Review
- 10. On the importance of combining “the new” with “the old” – One important prerequisite for maintenance in Industry 4.0
- – Decentralized Data Analytics for Maintenance in Industrie 4.0
- – The challenge of integrating Industry 4.0 in the degree of Mechanical Engineering
- – Servitization and Industry 4.0 convergence in the digital transformation of product firms: A business model innovation perspective
- – Internet of Things, Big Data, Industry 4.0 – Innovative Solutions in Logistics and Supply Chains Management
- – Collaborative Maintenance in flow-line Manufacturing Environments: An Industry 4.0 Approach
- – Using an interdisciplinary demonstration platform for teaching Industry 4.0
- – Reliability Assessment in the Context of Industry 4.0: Data as a Game Changer
- – Structuring Data for Intelligent Predictive Maintenance in Asset Management
- – A review of building information modeling (BIM) and the internet of things (IoT) devices integration: Present status and future trends
- 20. Fuzzy AHP analysis of Internet of Things (IoT) in enterprises
- – A review of asset management literature on multi-asset systems
- – Reliability Assessment in the Context of Industry 4.0: Data as a Game Changer
- – The degree of readiness for the implementation of Industry 4.0
- – Capturing value at scale in discrete manufacturing with Industry 4.0
- – Requirements for Education and Qualification of People in Industry 4.0
- – The role of big data analytics in industrial Internet of Things
- – Smart Maintenance: an empirically grounded conceptualization
- – Implementing Intelligent Asset Management Systems (IAMS) within an Industry 4.0 Manufacturing Environment
- – Asset Management
- – Principles of Sustainable Production in the context of Circular Economy and Industry 4.0
- Disruptive Innovations: Christensen updates disruption theory
- – Disruptive Innovation: Intellectual History and Future Paths
- – A systemic perspective on transition barriers to a circular infrastructure sector
- – How Asset Management Can Enable the Circular Economy.
- – A conceptual construct on value for infrastructure asset management
- – The organic growth of an asset management system: Case ProRail
- – Asset Management Council AU- AMBoK
- – IAM: Asset Management Maturity Scale and Guidance
- – Improving Asset Management under a regulatory view
- – Living Asset Management
- – The Bosh problem solving funnel
- – Multi-criteria decision-making considering risk and uncertainty in physical asset management
- – Asset maintenance optimisation approaches in the chemical and process industries – A review
- – Reliability Assessment of Passive Safety Systems: State-of-the-Art
- – Smart Maintenance in Asset Management
- – Video: Transforming Infrastructure Performance
- – Transforming Infrastructure Performance: Roadmap to 2030
- – Asset Health Index
- – AM maturity second edition
- – Thuiswerken en de gevolgen voor mobiliteit
- Optimized Expansion Strategy for a Hydrogen Pipe Network with Compound Real Options Analysis
- – Improving Asset Management under a regulatory view
- – Improving Asset Management under a regulatory view
- – Alignment between Engineering Asset Management and Finance
- – The role of life cycle cost in engineering asset management
- – Value in Asset management
- – Asset Management decision support tools: A conceptual approach for managing their performance
- – ISO 55001 – A Strategic Tool for the Circular Economy – Diagnosis of the Organization’s State
- – Civiele Kunstwerken Kennis- en Innovatieprogramma
- – Circular Business Models: Overcoming Barriers, Unleashing Potentials
- – Integrale Circulaire Economie Rapportage 2021
- – Life Cycle Cost versus Life Cycle Investment
- – Physical Asset Management Core Practices and Their Influence on Operational Performance
- – MCD making considering risk and uncertainty in physical asset management
- – Integrated Asset Management – An Investment in Sustainability
- – Integrating lifecycle asset management in the public sector
- – The strategic role of Engineering Asset Management
- – A Big Data Analytical Architecture for the Asset Management
- – Asset management techniques
- – Asset Performance Management Solutions
- – Maintenance Managmnt
- – Metaverse, Multiverse & Maintenance
- – Rethinking Maintenance Terminology for an Industry 4.0 Future
- – Maintenance optimization in industry 4.0
- – Decision Framework for Predictive Maintenance Method Selection
- – RVB Beslisboom 2022
- – Maintenance 2022 Report
- – Development of a flexible predictive maintenance system in the context of Industry 4.0
- – A Survey of Predictive Maintenance: Systems, Purposes and Approaches
- – A digital twin-based decision analysis framework for operation and maintenance of tunnels
- Framework for data-driven maintenance planning and problem solving in maintenance communities
- 2008 RCM (Reliability Centerd Maintenance) Guide For Facilities and Collateral Equipment
- 2000 2008 RCM (Reliability Centerd Maintenance) Guide For Facilities and collateral equipment
- – Vibration Sensors Are Essential to Maintaining Machine Health
- A Decision-Based Framework for Predictive Maintenance Technique Selection in Industry 4.0
- – Plant health index as an anomaly detection tool
- – Risk Tables
- – KSPMI: A Knowledge-based System for Predictive Maintenance in Industry 4.0
- – Using industry 4.0 to face the challenges of predictive maintenance: A key performance indicators development in a cyber physical system
- – IoT for predictive assets monitoring and maintenance: An implementation strategy for the UK rail industry
- – Gone in 2s: Analysing the Maintenance Pitstop of Formula 1.
- – Developing prescriptive maintenance strategies in the aviation industry based on a discrete-event simulation framework for post-prognostics decision making
- – Lean Maintenance 4.0: implementation for aviation industry
- – A Digital Twin Design for Maintenance Optimization
- – Inspection schedule for prognostics with uncertainty management
- – A predictive Markov decision process for optimizing inspection and maintenance strategies of partially observable multi-state systems
- – A deep learning predictive model for selective maintenance optimization
- – A predictive maintenance model for k-out-of-n:F continuously deteriorating systems subject to stochastic and economic dependencies
- – A general model for life-cycle cost analysis of Condition-Based Maintenance enabled by PHM capabilities
- – Sustainable maintainability management practices for offshore assets: A data-driven decision strategy
- – Ageing assets at major hazard chemical sites – The Dutch experience
- – An RUL-informed approach for life extension of high-value assets
- – Managing infrastructure asset: Bayesian networks for inspection and maintenance decisions reasoning and planning
- – Dynamic Risk Assessment for CBM-based adaptation of maintenance planning
- – Predictive maintenance of pumps in civil infrastructure: State-of-the-art, challenges and future directions
- – Development of a risk assessment selection methodology for asset maintenance decision making: An analytic network process (ANP) approach
- – Toward cognitive predictive maintenance
- – Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges
- – Approach for a Holistic Predictive Maintenance Strategy by Incorporating a Digital Twin
- – Data science applications for predictive maintenance and materials science in context to Industry 4.0
- – Maintenance transformation through Industry 4.0 technologies: Asystematic literature review
- – Data-driven decision-making for equipment maintenance
- – Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective
- – Data-driven failure mode and effect analysis (FMEA) to enhance maintenance planning
- – Maintenance decision with AHP
- – Advances of Digital Twins for Predictive Maintenance
- – Risk assessment by failure mode and effects analysis (FMEA) using an interval number based logistic regression model
- – Predictive Maintenance in Industry 4.0: Current Themes
- – Maintenance cost-based importance analysis under different maintenance strategies
- – 9 Principles of a modern preventive maintenance program
- – Reliability-Centered Maintenance (RCM)
- – MAINTENANCE AND RELIABILITY MANAGEMENT MODEL PROPOSED FOR THE PROJECT: THIRD SET OF LOCKS IN THE PANAMA CANAL
- – On the importance of combining “the new” with “the old” – One important prerequisite for maintenance in Industry 4.0
- – Rethinking Maintenance Terminology For An INDUSTRY 4.0 FUTURE
- – NASA RCM Guide for Facilities and Collateral Equipment
- – Reliability, Maintainability, and Availability Analysis of a Computerized Numerical Control Machine Tool Using Markov Chains
- – Facilities management: The strategic selection of a maintenance system
- – Knowledge Management and World Class Manufacturing
- – Integrating Physics and Data Driven Cyber-Physical System for Condition Monitoring
- – Towards multi-model approaches to predictive maintenance
- – Detecting Asset Cascading Failures Using Complex Network Analysis
- – An Intangible Asset Management Proposal based on ISO 55001 and ISO 30401 for Knowledge Management
- – Evaluation of Lean Principles in Building Maintenance Management
- – Descision Making in Lean Smart Maintenance
- – Designing a hybrid methodology for the Life Cycle Valuation of capital goods
- – Adopting machine learning and condition monitoring P-F curves in determining and prioritizing high-value assets for life extension
- – A Proposed Maintenance Performance Measures for Manufacturing Companies
- – STANDARDIZED TIME CLASSIFICATION FRAMEWORK FOR MOBILE EQUIPMENT IN SURFACE MINING: Operational Definitions, Time Usage Model, and Key Performance Indicators
- – A state of the art PdM techniques
- – Integration of Recursive Operability Analysis, FMECA and FTA for the Quantitative Risk Assessment
- – Maintenance As A Value Center
- – A proactive decision making framework for condition-based maintenance
- – RCA Root Cause Investigation Best Practices Guide
- – Big Data Analytics for Predictive Maintenance Strategies
- – Human factor in FMECA-based risk evaluation
- – Dissertation: Condition Based Maintenance
- – Responsibility Charting (RACI)
- – Maintenance 4.0 Technologies for Sustainable Manufacturing – an Overview
- – Smart Society and Artificial Intelligence: Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance
- – A new model for RCM prioritisation of distribution feeders
- – Wind turbine reliability data review and impacts on levelised cost of energy
- – Integration of a MM model into an AM process
- – IoT-based predictive maintenance for fleet management
- – A new dynamic predictive maintenance framework using deep learning for failure prognostics
- – Preventive Maintenance for Priority Standby Redundant System
- – Improvement indicators for Total Productive Maintenance policy
- – Iterative Cost Assessment of Maintenance Services
- – Perspectives on trading cost and availability for corrective maintenance at the equipment type level
- – Failure Mechanism Identification expert System
- – Maintenance Database
- – Lean maintenance roadmap
- – Evaluation of strategic building maintenance and refurbishment budgeting method Schroeder
- – Optimal maintenance scheduling for local public purpose buildings
- – Developingprescriptive maintenance strategies in the aviation industry decision making
- – Data management in maintenance outsourcing
- – Case for alternative approach to building maintenance management of public universities
- – Risk-based maintenance: Techniques and applications
- – Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics
- – Data management in maintenance outsourcing
- – Decision support system for condition monitoring technologies
- – A framework for maintenance concept development
- – CIBOCOF: A framework for industrial maintenance concept development
- – The Computerized Maintenance Management System An essential Tool for World Class Maintenance
- – Requirements Specification of a Computerized Maintenance Management System – A Case Study
- – Preventive Maintenance of Critical Assets based on Degradation Mechanisms and Failure Forecast
- -Discussion on key successful factors of TPM in enterprises
- – Business Processes Improvement on Maintenance Management: A Case Study
- – Maintenance Contracting
- – Digital building twins and blockchain for performance-based (smart) contracts
- – An ontology for reasoning over engineering textual data stored in FMEA spreadsheet tables
- – Data management in maintenance outsourcing
- – Condition-based maintenance under performance-based contracting
- – Pricing of full-service repair contracts
- – Optimal maintenance service contract negotiation with aging equipment
- – Outcome-based Contracts – Towards Concurrently Designing Products and Contracts
- – What brings the value to outcome-based contract providers? Value drivers in outcome business models
- – Optimizing maintenance service contracts through mechanism design theory
- – Outcome attributability in performance-based contracting: Roles and activities of the buying organization
- – Performance-based and functional contracting in value-based solution selling
- – Measuring service outcomes for adaptive preventive maintenance
- – Condition-based maintenance under performance-based contracting
- – Relational base contracts – Needs and trends in Northern Europe
- – Exclusive contracts with complementary inputs
- – Contracting abroad: A comparative analysis of contract design in host and home country outsourcing relations
- – A behavior-based decision-making model for energy performance contracting in building retrofit
- – Assessing maintenance contracts when preventive maintenance is outsourced
- – Using performance-based contracts to foster innovation in outsourced service delivery
- – Outcome-based contracts as a driver for systems thinking and service-dominant logic in service science: Evidence from the defence industry
- – Bathtub Curve
- Student Thesis & Articles
- – Investigation to an assessment tool to determine the criticality of devices
- – Dura Vermeer Infrastructuur als ‘Maincontractor’
- – Changing our DNA: Embedding Sustainability in the Asset Management Strategy of water based infrastructure
- – Repositioning the client order decoupling point to improve the quality of suspended ceilings
- Bibliografie
- Papers
Het Theoretisch Kader
Assetmanagement wordt normatief door de NEN-ISO 55000 gedefinieerd en vindt plaats als een organisatie Waarde aan het creëren is met Assets. Het realiseren van waarde is veelal het bewerkstelligen en waarborgen van een evenwicht tussen kosten, risico’s, kansen en prestaties van een Asset.
Ongeacht in welke sector de Asset zich bevind heeft een organisatie te maken met vier pijlers waarop het Assetmanagement is gebaseerd. Deze vier pijlers zijn Waarde, Afstemming, Leiderschap en Waarborging (WALW). Deze vier pijlers, en dus het Assetmanagement, staan los van de sectoren waarin de Asset volgens het SBI (Standaard Bedrijf Indeling) zich bevindt.
Of de Asset zich in de Burgelijke & Utiliteitsbouw (B&U), industrie of Grond,- Weg,- en Waterbouw (GWW) bevindt is niet relevant.
Rol Technisch Beheer & Onderhoud
Het Technisch Beheer & Onderhoud is één van de vier pijlers van het Assetmanagement en wordt omvat door de pijler Afstemming. De pijler Afstemming, en dus o.a. het Technisch Beheer & Onderhoud, vertaald organisatiedoelstellingen in technische en financiële beslissingen, planning en activiteiten.
Om de integriteit van het Assetmanagement te waarborgen worden op basis van risico’s, betrouwbare informatie, vastgelegde besluitvormingsprocessen en activiteiten de organisatiedoelstellingen worden vertaalt naar gedocumenteerde informatie die onder andere mensen, middelen en tijdskaders omschrijven die vereist zijn zodat de doelstellingen van de Asset(s), en dus waarde creatie, wordt bereikt.
Hierdoor heeft Technische Beheer & Onderhoud een belangrijke rol tijdens de levensduur en verschillende levenscyclussen van Assets
ISO55000

Uitbesteding Technisch Beheer & Onderhoud
De mate waarin en de wijze waarop Technisch Beheer & Onderhoud wordt uitbesteed kent verschillende vormen. Welke vorm uiteindelijk wordt gekozen hangt af van de doelstellingen van het management (de directie). Zo kan het onderhoud geheel of gedeeltelijk uitbesteed worden in bijvoorbeeld een inspanningscontract, prestatiecontract, Integraal Beheer Contract of Maincontract.
Ongeacht welke keuze er wordt gemaakt; de eigenschappen van het Technisch Beheer en Onderhoudsactiviteiten veranderen niet.
Mate van succesvol uitbesteden
Voorliggende vraag is wanneer het uitbesteden van het Technisch Beheer en Onderhoud succesvol is, voldoet aan vooraf gedefinieerde kaders en doelstellingen en de integriteit van het Assetmanagement niet aantast.
Het onderzoek beoogt antwoord te vinden op deze vragen door een referentiekader te definiëren waaraan de kwaliteit en volwassenheid van deze onderhoudsconcepten getoetst kunnen worden.