Network & Connectivity: How can manufacturers ensure robust, scalable, and secure network connectivity across diverse environments that often include both legacy machinery and modern IIoT devices?
Interoperability & Protocol Conversion: What strategies can companies implement to achieve seamless interoperability among devices using different communication protocols, and how can they effectively manage protocol conversion without sacrificing performance?
Data Management & Real-Time Data Analytics: How to effectively manage massive volumes of data generated by IIoT devices, and how can companies perform real-time data analytics to enable immediate operational decision-making?
Asset Tracking, Asset Condition Monitoring & Device Management: What are the best practices for integrating asset tracking and condition monitoring systems to enhance device management processes and ensure operational continuity?
Predictive Maintenance: What technologies and approaches are essential for developing accurate predictive maintenance systems, and how can manufacturers integrate these into their existing operations without major disruptions?
Cybersecurity & IoT/OT Security: What are the key challenges in securing IoT and OT networks, and what new technologies or practices can provide effective defense mechanisms?
Industrial Edge Computing: What role does edge computing play in processing IIoT data, and what are the major hurdles in deploying and maintaining robust edge computing solutions in an industrial setting?
IIoT Platforms & IIoT Cloud: What are the main considerations for selecting an IIoT platform, particularly regarding cloud versus on-premise solutions, and how do these choices affect scalability and integration with existing IT infrastructure?
IT/OT Integration/Fusion: How to achieve IT / OT systems convergence, what are the significant barriers to integration, and how can organizations effectively address them to streamline processes and enhance data utilization?
AI/ML/Robotics: How can artificial intelligence and machine learning be integrated effectively into IIoT systems to enhance automation and decision-making processes, and what are the key challenges in training AI models with the data generated from industrial environments?
Digital Twin: What are the critical factors for successfully implementing digital twin technology in manufacturing, and how to overcome the challenges associated with creating and maintaining accurate and real-time digital replicas of physical assets?