Manufacturers are implementing scalable, low-cost IoT-connected factories using wireless sensor networks for real-time monitoring of equipment and environments. They employ edge computing to process data locally, reducing latency and bandwidth requirements. Cloud platforms enable centralized data storage and analysis for remote monitoring and predictive maintenance. Integration with legacy systems is facilitated through industrial gateways and middleware, with standardized protocols like MQTT and OPC UA ensuring device interoperability. Modular IoT architectures are favored for scalability and ease of incorporating new technologies.
IoT connects everyday devices to the internet, allowing data collection, sharing, analysis and remote control. IoT is expected to transform industries, creating new products, services and business models that drive economic growth. Manufacturing is anticipated to benefit most from IoT, leading in IoT spending to develop smart, connected factories that enhance productivity, reduce costs and improve quality.
Intelligence Hub: IoT at the Edge (AWS, Azure, GCP)
The edge intelligence hub collects and processes sensor and device data for insights at the edge. It is an industrial DataOps framework or solution for data modeling, orchestration and governance at the edge then to any native cloud such as AWS, Azure or GCP. DataOps orchestrates people, processes and technology to securely deliver trusted, ready-to-use data. A microcontroller or microprocessor processes the data and sends it to a cloud-connected gateway for analysis. The hub provides real-time feedback to factory devices for immediate adjustments and sends alerts and notifications to workers for prompt responses.
Baselining Production KPIs and Metrics to be Set
The initial step in IoT implementation is understanding the problem to be addressed, which requires extensive discussions with factory personnel and management to identify the key performance indicators (KPIs) the IoT system will measure. Next, the current methods of measuring these KPIs must be analyzed to understand existing data collection processes and identify gaps the IoT system can fill. Following this, the development team and management collaborate to determine additional KPIs that the IoT system could measure, highlighting potential areas for production improvement. Finally, targets for each KPI are set in collaboration with management, defining what success looks like for the IoT system.
Model
The IoT system framework serves as the central nervous system of the connected factory, enabling communication among machines, systems and the cloud. It comprises sensors, connectivity modules, IoT gateways and the cloud. This framework is crucial for advancing automation and the digital supply chain in future factories. It collects and analyzes data from various sensors and devices to optimize factory performance by identifying and resolving bottlenecks, reducing waste and enhancing product quality. Additionally, it automates tasks typically performed by humans, such as monitoring and adjusting machine settings and sorting defective products. Moreover, the framework facilitates cloud connectivity, allowing data sharing and analysis across other factories and the supply chain.
Transform
Emerging business models are transforming manufacturing. The prevalent model is product servitization, where manufacturers offer products as a service through IoT connectivity, generating new revenue streams such as remote monitoring, predictive maintenance, equipment-as-a-service and output-as-a-service.
Output-as-a-service connects production equipment to the IoT, providing flexible, agile services and enabling entry into new markets like on-demand manufacturing. Equipment-as-a-service offers similar benefits by providing production equipment as a service.
Various solutions help manufacturers transfer plant floor data to the cloud. Some integrate with existing automation equipment, while others replace it. The optimal solution depends on the factory's current equipment, its age, operational nature and existing data infrastructure. Manufacturers may combine multiple solutions to prepare data for cloud analysis.
ISA-95 Foundation for a Digital Factory
The ISA-95 standard is fundamental for the digital factory, defining the interface between business and manufacturing systems. It establishes a hierarchy of manufacturing systems from the enterprise down to process control and field device levels, detailing information exchanges at each level. The standard aims to provide consistent information exchanges between business and manufacturing systems across various companies and industries.
Conclusion
Implementing an IoT framework for a connected factory varies significantly in costs, benefits and risks, depending on the manufacturer. Understanding available options and their implications is crucial. ISA-95 — widely adopted by companies — underpins much of the work in manufacturing execution systems (MES), linking enterprise and manufacturing systems for data collection, analysis and decision support. It addresses the inconsistency caused by proprietary definitions from different MES vendors. IoT in manufacturing offers unprecedented opportunities for efficiency, productivity and innovation through wireless sensor networks, edge computing and cloud platforms, enabling real-time monitoring, predictive maintenance and data-driven decision-making. Integration with existing systems, facilitated by industrial gateways and standardized protocols, ensures seamless connectivity and interoperability. As manufacturers adopt IoT technologies, they can transform operations, develop new business models and drive economic growth. The future of manufacturing lies in intelligent IoT application, where data-driven insights and automated processes lead to more agile, efficient and competitive factories. To maximize benefits, manufacturers must consider specific needs, set clear KPIs and choose appropriate IoT solutions.
