14th - 15th November 2024
Agenda
DAY 1
08:00 – 08:50
REGISTRATION
08:55 – 09:00
CHAIRPERSON’S OPENING REMARKS
09:00 - 09:30
DRIVE YOUR MAINTENANCE JOURNEY BY OPTIMIZING ASSET HEALTH AND PREDICT FUTURE FAILIURES TO STAY AHEAD OF THE CURVE
- Conditional-based predictive maintenance based on health insights from operational data and analytics helps you put your asset data to work
- Understand the status of critical business assets with insights from data and analytics to make smarter decisions about management and maintenance
- Using operational data into analytics-driven predictive maintenance models that help you optimize maintenance planning to improve asset reliability
- Manage the health of your assets by using IoT data from asset sensors
09:30 - 10:00
RISK BASED INSPECTION FOR AGEING EQUIPMENT: HOW FAR HAVE WE COME?
- Flaws or anomalies? What is the expected remaining lifetime of the asset?
- Oil and Gas portfolios: More intelligent assets
- End-to-end life cycle of our enterprise assets
- Best Approach for linear assets, IT assets and fleets -using a single, integrated platform
10:00 - 10:30
EMPOWERING THE ‘ENGINEERING DATA SCIENTIST’ AND OVERCOMING THE INDUSTRIAL SKILLS GAP
- What are the pressing needs in the area of human resources? How to attract more full-time proactive maintenance experts?
- Use existing digital asset maintenance platforms more effectively
- Why empowering engineers now with data is so important to drive the next level of efficiencies and business value
- Examples how data powered engineers are enhancing and pioneering new high value data use cases, leveraging synthetic data (simulation/CAE) and HybridAI approaches
- Best practice from the typical journey to empower an engineer with data, including ideas and strategies
10:35 - 11:25
COFFEE BREAK
11:30 - 12:00
INFLUENCING GLOBAL CHANGE IN SUSTAINABILITY WITH OIL-FREE COMPRESSED AIR FOR MAINTENANCE PROFESSIONALS
- Compressed air in the big picture of sustainability – a step towards net zero emissions
- Carbon handprint – achieving your companies goals of carbon neutrality
- Compressed air solutions – a how-to for setting and achieving your climate goals
- Total cost of ownership
12:00 - 12:30
REAL-TIME MAINTENANCE FOR INDUSTRIAL MACHINES: PUSHING CONDITION BASED MONITORING TO NEW LIMITS WITH ADVANCES IN NEXT GENERATION MANUFACTURING
- The application of condition monitoring tools are being pushed to new frontiers with Advances in Ai and wireless technologies
- The Cloud is enabling a shift towards Smart Condition Monitoring practices
- Real-time management of safety and business-critical assets by integrating new technologies and methodologies such as, vibration data, oil analysis, thermography, visual inspection and wireless condition monitoring devices
- Remote analysis of the health of asset
12:30 - 13:00
USING SENSORS AND PLATFORMS FOR OPERATIONAL DECISION-MAKING PROCESSES TO ACHIEVE MAINTENANCE 4.0
- Asset monitorization through Digital strategies and the impact on productivity and maintainability.
- Key elements to consider for building a sustainable and effective ecosystem for Maintenance 4.0
- What is the right path for choosing the sensors mix and how to get the maximum efficiency in the data acquisition?
- Type of data for monitoring and how to manage them for the decision-making process
- Implementing Online sensors, i.e Online lube oil analytics for maintenance in critical assets
PLANNED INVESTMENT AREAS
- Predictive Maintenance PdM 4.0
- Remote Management
- Non-intrusive Inspection
- Lubrication Systems
- HMI/SCADA
- Worker Safety
- Sound & Vibration Analysis
- Augmented & Virtual Reality (AR & VR)
- Artificial Intelligence
- Mechanical Protection
- Cybersecurity
- MES / Shop-floor Digitization
- Workforce Training and Development
- Process Digitalization & Automation
- Unified Communications / Wireless Connectivity
- Cloud, Edge & IoT Data Management
- Asset Lifecycle Management
- Energy Efficiency
- Machine & Deep Learning
- Wearable Digital Technologies
- Predictive Analytics
- Asset Performance Management (APM 4.0)
- Computerized Maintenance Management System (CMMS)
- Reliability Engineering
- Asset Optimization
- Sensors
- Data Analytics
- Condition Monitoring
- Fault detection, isolation, and recovery (FDIR)
13:05 - 13:55
LUNCH
14:00 - 14:30
WHAT DOES THE FUTURE MAINTENANCE WORKFORCE LOOK LIKE?
- Insights from 20 years of recruitment within the manufacturing engineering landscape
- The evolution of technology and Ai within manufacturing has spiked a need for Gen-Z
- The importance of executive and functional engagement in times of mass disruption
- How to attract, retain and develop the next generation of maintenance talent
14:30 – 15:00
THE LINK BETWEEN WORLD CLASS ASSET MANAGEMENT AND SUSTAINABILITY, INCREASING ASSET LIFE CYCLES TO IMPROVE SUSTAINABILITY USING CURRENT ASSET MANAGEMENT FRAMEWORKS
- Tracking component life cycles to determine the emissions related to every component, aligning asset management and sustainability within business processes
- Converting technical documentation into a digital asset register to improve data analysis,
- Understanding true capacity and demand within infrastructure to optimise business analysis and decision making
- Optimising the interface between operators and infrastructure owners to increase data sharing and improve asset management leading to increase asset lifecycles and reduce cost in parts and labour
- Incorporating tools to measure asset lifecycle emissions and social factors into your asset management systems, building the architecture to support decision making
- Improving demand analysis using RCM methodologies for structured decision making and criticality analysis to identify what the business want to reduce spare parts stocks
15:00 – 15:30
DESIGN THE PERFECT WORKFORCE OF THE FUTURE: ARTIFICIAL INTELLIGENCE (AI) TECHNOLOGY IS RAPIDLY CHANGING THE MODERN WORKFORCE BY CREATING NEW SKILLS AND ERADICATING OTHERS
- CNH Industrial business context with focus on manufacturing process before the application of Explainable Artificial Intelligence (XAI);
- Use case description, challenges, and motivation behind the needs of XAI application in manufacturing plant;
- Users’ stories on AI explainability: « what does the user want to know?»;
- Dashboard for XAI application within the XMANAI European project
- Conclusion considering the added value to business improving maintenance with XAI support
15:35 - 16:25
COFFEE BREAK
16:30 - 17:00
Machine Monitoring – Easy in every way
- Unplanned downtimes cause high costs and waste energy
- Many predictive maintenance solutions are too expensive and complicated
- Optimal lubrication and transparency of machine health avoid unplanned downtimes
- The simplicity of a solution is key for widespread distribution and use
17:00 - 17:30
CONTRIBUTION OF DIGITALIZATION AND ARTIFICIAL INTELLIGENCE TO THE VALUE PROPOSITION OF MAINTENANCE
- Maintenance as a value driver
- Role of digital tools in maintenance
- Example for process improvement using mobile apps
- Examples for applications of text mining and AI
PLANNED INVESTMENT AREAS
- Predictive Maintenance PdM 4.0
- Remote Management
- Non-intrusive Inspection
- Lubrication Systems
- HMI/SCADA
- Worker Safety
- Sound & Vibration Analysis
- Augmented & Virtual Reality (AR & VR)
- Artificial Intelligence
- Mechanical Protection
- Cybersecurity
- MES / Shop-floor Digitization
- Workforce Training and Development
- Process Digitalization & Automation
- Unified Communications / Wireless Connectivity
- Cloud, Edge & IoT Data Management
- Asset Lifecycle Management
- Energy Efficiency
- Machine & Deep Learning
- Wearable Digital Technologies
- Predictive Analytics
- Asset Performance Management (APM 4.0)
- Computerized Maintenance Management System (CMMS)
- Reliability Engineering
- Asset Optimization
- Sensors
- Data Analytics
- Condition Monitoring
- Fault detection, isolation, and recovery (FDIR)
17:30 – 18:00
PANEL DEBATE: DESIGN THE PERFECT WORKFORCE OF THE FUTURE: ARTIFICIAL INTELLIGENCE (AI) TECHNOLOGY IS RAPIDLY CHANGING THE MODERN WORKFORCE BY CREATING NEW SKILLS AND ERADICATING OTHERS. HOW DO WE READY OUR ENGINEERS AND TECHNICIANS FOR THE CHANGES TO COME
- What is AI’s impact on the skills of the future?
- Having a central platform to understand your workforce and how it compares to the market
- Empowering the technicians and engineers and creating organizational change
- Make informed decisions about where to build and grow talent to drive business growth in real-time
- Skills are the lifeblood of our organizations, how do you know what you’re working with today and what you’ll need to plan for tomorrow?
- How to deliver learning for effective skills development
- What are the must-haves for people-first organizations
18:05 – 19:00
DRINKS RECEPTION
DAY 2
08:55 – 09:00
CHAIRPERSON’S OPENING REMARKS
09:00 – 09:30
REAL WORLD EXPERIENCES OF OPTIMIZING ASSET MANAGEMENT AND STATISTICAL MODELING TO IMPROVE LIFE CYCLE MANAGEMENT IN FLEET MAINTENANCE
- What level of investment is required to construct digital infrastructure required to forecast future maintenance cost of on-board systems across the lifetime of the train
- New digital systems to build precise asset libraries, generating accurate data models of fleets maintenance plans, configurations, and logistics to increase reliability
- Challenges integrating asset management systems of different vehicles in terms of technology, available data, and age of fleets to improve forecasting and maintenance
- Examine how optimizing asset management can complement sustainability targets in rail, reducing waste, transportation, and material storage
- What systems and tools are operators deploying to optimise asset management of documents in rail, going paperless to reduce risk and improve accuracy in reporting
09:30 – 10:00
ASSET CONDITION MONITORING: SIGNIFICANCE AND BENEFITS – A IOT BUSINESS SOLUTION
- Technological advancements such as automation in manufacturing is triggering the use of smart condition monitoring systems
- Smart asset condition monitoring covers all possible parameters that are required to keep a machine in better condition
- Real-time status of the temperature, fuel requirement, environment, and other conditions enable successful machine production
- Machine learning adds intelligence to the assets and allows an interlink between machines to deliver seamless performance within the plant
10:00 - 10:30
How data analytics become a lever for production, maintenance, energy and CSR efficiency.
- Where and how to collect data
- Extracting more value from data
- Digital transformation of Efficiency, Maintenance and Energy management
- Measuring the impact on CSR performance
10:35 - 11:25
COFFEE BREAK
11:30 – 12:00
Seamless Operations: Empowering Efficiency with Mixed Reality
- Exploring Mixed Reality’s Evolution: Delve into the evolution of mixed reality, tracing its historical journey
- Unlocking Business Value with Mixed Reality (MR): Discover the tangible benefits of integrating mixed reality into your operations and how it can elevate your company’s performance
- Success Stories in Action: Explore real-world client case studies showcasing the seamless integration and return on investment (ROI) achieved through our mixed reality software
- Experience RemoteSpark™ Live: Engage in a dynamic, hands-on demonstration of our cutting-edge mixed reality software, RemoteSpark™, through an interactive live session
12:00 – 12:30
IMPLEMENTING MACHINE LEARNING AND AI TO FIND NEW INSIGHTS IN DATA, DELIVERING A BREAKTHROUGH BY ENHANCING GREEN MOBILITY
- Big data management, what are the challenges in extracting clean machine generated RCM data to pick up patterns and trends that might indicate an impending failure
- Increasing the repeatability and accuracy of algorithms to accurately forecast defects in a particular code with so many categories of codes to analyse from fleet components
- Building trust in the forecast to get the people using the tool, trusting the model
- Implementing machine learning to focus on issues that can impact maintainability, analysing congestion and weather data to understand impact on failures
12:30 – 13:00
HOW TO INTEGRATE THE MEGA TRENDS IN YOUR MAINTENANCE & OBSOLESCENCE MANAGEMENT
- The overall contribution of Maintenance within the digital maturity transition
- Critical assets obsolescence management integrating cybersecurity and energy efficiency
- The concept of energy centred Maintenance: a green maintenance system
- Potential of predictive technologies and the scale of smart maintenance practices
PLANNED INVESTMENT AREAS
- Predictive Maintenance PdM 4.0
- Remote Management
- Non-intrusive Inspection
- Lubrication Systems
- HMI/SCADA
- Worker Safety
- Sound & Vibration Analysis
- Augmented & Virtual Reality (AR & VR)
- Artificial Intelligence
- Mechanical Protection
- Cybersecurity
- MES / Shop-floor Digitization
- Workforce Training and Development
- Process Digitalization & Automation
- Unified Communications / Wireless Connectivity
- Cloud, Edge & IoT Data Management
- Asset Lifecycle Management
- Energy Efficiency
- Machine & Deep Learning
- Wearable Digital Technologies
- Predictive Analytics
- Asset Performance Management (APM 4.0)
- Computerized Maintenance Management System (CMMS)
- Reliability Engineering
- Asset Optimization
- Sensors
- Data Analytics
- Condition Monitoring
- Fault detection, isolation, and recovery (FDIR)
13:05 - 13:55
LUNCH
14:00 – 14:30
Terumo’s journey towards operational excellence
- Introduction Terumo Europe
- Highlights of Terumo’s journey – strategic goals
- Technology should support all aspects of the strategy
- Terumo’s cultural focus to support strategy
- Recognition supports the drive to move forward as a team
14:30 - 15:00
Physics-based, data-centric and hybrid predictive maintenance strategies
- Strategies for developing prognostics and health management solutions when historical data is limited.
- Need for domain-specific data processing strategies and overcoming the black-box nature of existing solutions.
- Creation of data-centric explainable machine learning models to enhance prognostics and health management.
- Development of hybrid models combining physics-based and data-centric approaches to improve prediction accuracy and reduce uncertainty.