Agenda

• Introducing PHILIPS Drachten and the industrial environment
• Our journey to predictive maintenance
• The solution and methodologies applied
• The results and lessons learned

Bas Snijders, Program Manager Public-Private Partnerships, Philips Drachten

• Role of air compressors in modern manufacturing
• Case studies: From Sustainability KPIs to actual results
• How high-speed technology and active magnetic bearings shifts manufacturing to more efficient and sustainable direction?
• Suppliers as part of reliability network in compressed air production
• Utilization of compressor system data for improved production

Timo Sihvo, Sales Manager, Nordics, Tamturbo

• Fundamental causes of the syndrome
• Potential remedies
• Setting the vision at the Plant manager level
• Restoring the confidence between maintenance and production with a multi-week schedule outlook.
• Making an agreement between maintenance and production on when the work must be done to prevent equipment failure that is clearly communicated to everybody involved.
• Identifying Potential failure intervals and risks of failure based on AI predictive analytics, or experience.
• Ensuring the work required to prevent equipment failure is always done on time.
• Measuring compliance of the maintenance and production time execution agreements, in order to maximize trust and teamwork between maintenance and production

Jean Charbonneau, President, iVisual

• Connecting Maintenance to the core business drivers
• Leveraging Production process improvements
• How to integrate asset data with production and maintenance
• Role of digital tools and devices in maintenance and production

Dr. Marcel Mourits, VP Expert Digital Manufacturing, CGI

• Use of predictive maintenance in the mix of different maintenance strategies
• From concepts to practical applications
• Sensor overkill – how condition monitoring creates P&L impacts
• Prioritizing reliability problems to target the most important ones for your business

Hannes Pasemann, Partner, ConMoto Strategie und Realisierung GmbH

• Using 5S approach in anchoring Asset and Maintenance Management Strategy
• Importance of early equipment management
• Master data for CMMS
• Maintenance of Legacy Equipment/Machinery

Darko Palalic, Strategic Plant Maintenance Senior Manager, Carlsberg Supply Company Danmark A/S

• Maintenance Strategy
• Maintenance Concepts
• Maintenance Maturity
• Maintenance Policy
• How to embark on the Maintenance Maturity Journey

Jens Ulrik Gullacksen, Head of Maintenance, MAN Energy Solution

• Power skills
• Neuromanagement
• Pragmatics of communication
• Active listening
• Persuasion
• Effective delegation

Mauro Pinna, Maintenance Manager, Alfagomma Group

• Nanoprecise’s AI+physics-driven solutions detect issues early, shifting Maintenance from reactive to proactive and enhancing financial, environmental, and operational benefits.
• Energy-Centered Maintenance goes beyond Reliability-Centered Maintenance by reducing energy waste while optimizing operations making it ideal for less critical equipment.
• Explore our case studies, which show how our 6-in-1 IIoT sensor has achieved efficient results in the cement, chemicals, mining, oil and gas, and automotive industries.

Qais Aldababneh, Regional Sales Manager, Nanoprecise Sci Corp

• Bonfiglioli Company
• Experiences in implementing lean with the help of digitalization
• TPM: what is TPM in Bonfiglioli Company
• Our goal and our target

Giovanni Polosa, Global Manufacturing Director, Bonfiglioli

• Condition monitoring techniques.
• AI pilot project made in Nordic Sugar Nakskov
• Challenges of transforming the organisation
• A take on how to evolve in practice
• Conclusion
• Q&A

Thomas Nielsen, Head of Maintenance, Nordic Sugar A/S

• Digitalization as great opportunity in Pharma
• Strategy on digital manufacturing
• Process Analytics as priority on manufacturing in Pharma
• MVA as a process analytics technology to improve reliability
• Early AI application in Pharma Supply Chain instead of Adoption of Machine Learning algorithms in the Pharma

Jose Antoranz, Digital Manufacturing Manager, Johnson & Johnson
Dr. Gerolamo Carboni, Scientist – Process Modelling Lead, Johnson & Johnson

• Simplifying AI adoption: Easy model building for increased operational reliability and sustainability compliance
• Empowering frontline Workers: Involving them in AI integration fosters successful deployment and continuous improvement
• Utilizing AI for predictive maintenance: Predictive analytics help organizations anticipate maintenance needs, enhance product quality, and meet sustainability goals

Charles-Emmanuel Frenoy, VP Southern Europe, Intelecy

• Online monitoring of electric machines
• Non Invasive and deterministic approach, exclusively based on electric variables measurements
• No need of historical data or learning periods
• Easily integrated into existing monitoring platforms

Gualter Sampaio, CSO, Enging Make Solutions, SA

• Discover how to revolutionize your maintenance operations using I-care’s “Analyze, Manage, Connect, Collect” methodology
• Collect high-quality data from various sources to ensure accurate insights
• Seamlessly connect systems and IoT devices for comprehensive data integration
• Apply advanced analytics to predict potential failures and gain actionable insights
• Manage with a clear strategy, setting KPIs and continuously improving processes
• Join our presentation to learn how these success factors can minimize downtime and optimize your maintenance strategy for maximum efficiency

Andrew Blair, Customer Care Manager, I-care Group

• Advantages of sensors over traditional condition monitoring techniques
• Performance of different sensors in the field and laboratory
• Safe and Sustainable by Design methodology and its relevance for maintenance and operations
• Role of advanced sensors in supporting fluid redesign to meet performance needs whilst reducing impact on users and the environment

Francesco Beltritti, Global Branded Services Manager – Technical Services, Petronas Lubricants International

• Data- driven prognostics of remaining-useful-life of aircraft systems
• Optimization of maintenance decisions using prognostics
• Remaining challenges and potential solutions for data-driven predictive maintenance
• Industry-University collaboration paths towards predictive maintenance

This presentation will give an outlook on predictive maintenance in commercial aviation based on the presenter’s recent works, focusing on the application of data-driven models and AI methods. The remaining challenges and potential solution approaches will be discussed based on university-industry collaborations to realize predictive maintenance in various industrial assets.

Dr. Juseong Lee, Assistant Professor, Eindhoven University of Technology

• What to do when there is no data – we are at the start of life of our Offshore assets
• Sometimes we have too much data and are still in the dark. How we create light at TenneT
• How can we transform our data into usable information using AI
• How we ensure our data collection is repeatable and at lowest risk – introducing “Sparky”, our version of BOLT

Graeme Phibbs, Operations and Maintenance Excellence Advisor, Tennet
Joost de Vreugd , Lead Operations and Maintenance Engineering, Tennet