Industry of Things World USA 2025

March 16 – 18, 2025 | Boston, MA

Industry of Things World USA | The IIoT and Smart Manufacturing Event

From theory and visions to practical implementation of IIoT-driven manufacturing

The diverse and new technological possibilities of Industry 4.0 have been in the focus of industrial companies for many years now. But what are the real benefits of Industrial IoT for manufacturing? What are the best practices and solution approaches with regard to Network & Connectivity, interoperability & protocol conversion? What are the most effective ways of dealing with technical obstacles in the context of Data Management & Real-Time Data Analytics? How do IIoT innovations productively drive Asset Tracking & Condition Monitoring, Device Management or Predictive Maintenance? What new technologies from IIoT Cloud to Industrial Edge enable stable and reliable systems that provide a successful IT/OT integration as well as the efficient use of AI/ML, Robotics or Digital Twins, while at the same time advancing the level of Cybersecurity?

At the Industry of Things World more than 150 decision-makers, practitioners and solution providers will discuss use cases and business strategies from the Industry 4.0 universe.

Latest technological trends, opportunities and risks as well as direct practical examples from the manufacturing industry – the Industry of Things World is designed to evaluate and discuss your technology strategy for a scalable, secure and efficient IIoT implementation around your production. Don’t miss the opportunity to meet all relevant IIoT stakeholders under one roof. We look forward to welcoming you at the event.

Keytopics of the conference

• 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 Quality, Performance & Production Management: How to leverage IIoT to predict and improve quality and performance in production management, and what challenges must they overcome to implement these systems effectively?
• 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?