Why IoT manufacturing makes sense:
Keeping assets up and running has the potential to significantly decrease operational expenditures, saving companies millions of dollars. With the use of sensors, cameras and data analytics, managers in a range of industries are able to determine when a piece of equipment will fail before it does. These IoT-enabled systems can sense warning signs, use data to create maintenance timelines and preemptively service equipment before problems occur.
By leveraging streaming data from sensors and devices to quickly assess current conditions, recognize warning signs, deliver alerts and automatically trigger appropriate maintenance processes, IoT turns maintenance into a dynamic, rapid and automated task.
This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when they are needed. The possibility to get the right information at the right time will allow managers to know which equipment needs maintenance; maintenance work can be better planned; and systems remain online while workers stay on task. Other potential advantages of predictive maintenance include increased equipment lifetime, increased plant safety and fewer accidents with negative environmental impact.
According to a study by Infosys and the Institute for Industrial Management at Aachen University, 85% of manufacturing companies globally are aware of asset efficiency practices, but only 15% of those surveyed have implemented such measures at a systematic level.
The goal of asset tracking is to allow an enterprise to easily locate and monitor key assets, including along the supply chain (e.g. raw materials, final products and containers) to optimize logistics, maintain inventory levels, prevent quality issues and detect theft.
Connected operations intelligence
This use case enables companies to connect disparate silos of operational data (e.g. manufacturing, supplier, and logistics) into unified, real-time visibility across heterogeneous systems, people and assets to make faster and better decisions and improve operational performance.
Through this use case, firms can aggregate and contextualize data from isolated manufacturing systems and assets into actionable web and mobile applications that provide role-based views into key indicators, while also allowing drill-down into correlated data to diagnose problems more quickly and improve performance.
Operations management improvements
This other use case allow companies in the manufacturing sector to quickly improve how complex processes are monitored, managed, optimized, and accelerate smart factory and ‘Industry 4.0’ initiatives. This can be achieved by extending existing equipment and ERP/MES systems with connectivity, interoperability, mobility and crowd sourced intelligence.