Operational IoT

Chairperson’s welcome and opening remarks. 

• Current pitfalls hindering digital twins across industries.   
• Providing unparalleled insights, are digital twins the best market product for reduced costs and risks? 
• Understanding the digital transformation process for implementing digital twins.   
• Incorporating other technologies like AI, ML and Blockchain for agile processes.  

• Knowing where to utilise digital twin technologies – when to implement the steps for a successful result and the value of Geospatial data
• Simulation and digital twins in Engineering & Construction – where does the accountability lie?  
• How to grow the implementation of digital twins across industries. 

• Storyline and strategy to be more productive and fully sustainable
• Roadmaps for enabling the connected worker
• Devices and solutions – hands-on experience
• Experience and learning shared

This presentation focuses on the utilization of the Internet of Things (IoT) as a powerful enabler for transforming traditional buildings into intelligent, connected, and sustainable ecosystems.

Around 70% of industrial electricity consumption is attributed to electric motors, with more than 300 million motor-driven systems in active use. Enhancing the efficiency of these systems plays a big role in advancing the journey towards achieving “net-zero” emissions and minimizing waste.

During this session, attendees will be taken through the full journey of becoming more energy efficient by:

• Making better decisions with data by identifying potential energy savings and CO2 emissions reduction
• Implementing energy efficiency solutions and services by modernizing motors and drives at the right time based on data and advanced analytics
• Keeping operations energy efficient through flexible business models that help mitigate risks

The latest IoT innovations improving predictive maintenance capabilities include:   

• Increased asset efficiency   
• Prolonger asset life span   
• Reduced costs and downtime 

• Practical considerations for providing predictive operational data from increasingly complex industrial systems.  
• Insights on how to provide enterprise-level transparency for multiple key decision-makers. 
• Analytical considerations for both the current state of play and preparation for the next wave of industrial technology eg AR/VR, 5G, 3D printing, and Robots. 

• Digitalising the factory floor – getting the digital transformation steps right to implement fully autonomous processes.   
• Optimizing lean processes through digital technologies.  
• Leveraging predictive analytics to stay ahead of potential errors or failures.   
• A sustainable focus on products and services – the advantages of a circular economy approach.   

• Looking at the limitations around data including sensitive data and capture limitations.   
• Transporting data from sensors securely, keeping in mind SSP (security, safety, privacy).  
• Optimizing and actioning insights for productivity benefits.  

• Working to collect ‘intelligent’ data and contextualising insights. 
• How to find the balance between standardisation and flexibility for quick response and optimisation. 

• Using ML to derive ‘relevant’ data.  
• A closer look at latency, robustness and reliability of the data being used for ML models.  
• Examples of ML in the manufacturing space.  

• AI and ML advancements to tackle errors in IoT. 
• Combining the capabilities of AI and IoT for risk predictions and rapid response.  
• Driving infrastructure resilience through AI and ML. 

To follow soon …