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 and the steps for a successful result.  
• Simulation and digital twins – where does the accountability lie?  
• How to grow the implementation of digital twins across industries. 

• Supply chains building resiliency through the IoT. 
• Responding to demand – the reboot of supply chains post-pandemic. 
• Combining IoT sensors and digital capabilities like blockchain and digital twins to enhance efficiency. 
• The next move to ‘sustainable’ supply chains.  

• A use-case of digital twins aiding the construction space.  
• Considering the next steps for optimisation – input as well as output data, progressing from digital twins to digital prototypes? 
• The next phase for digital construction.  

• 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.  

• Improving business resilience through data led decisions.
• A look at sustainable practices and having a future focus – the shift to a more sustainable and human-centric approach.
• Human centricity – empowering the team and utilising digital services like connected work applications.

The latest IoT innovations improving predictive maintenance capabilities including:  

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

• 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. 

• 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 …