Industry

 IIoT serves as a new vision of IoT in the industrial sector by automating smart objects for sensing, collecting, processing, and communicating real-time events in industrial operations. The major objective of IIoT is to achieve high operational efficiency, increased productivity, and better management of industrial assets and processes. This is achieved through intelligent monitoring applications for production operations and machine health and predictive and preventive maintenance of industrial equipment. Our technology platform with IoT, AI, and 5G supports our customers to maximize application and benefits in this evolving industrial landscape, from the warehouse to production facilities and distribution centres.

The production environment relies on equipment to work properly. A fault in a component or sub-system may cause a stop in the entire production line. The production stops are associated with huge costs, including increases in downtime, loss of product, poor quality and costs of repairs, and deterioration of equipment. Data-driven predictive maintenance systems utilize the data generated from IoT sensors attached to equipment in real-time, to detect signals for potential failures before they occur by using machine learning methods.

Optimizing manufacturing processes for efficiency can have a significant impact on your bottom line. The advent of smart manufacturing simplifies the exploitation of data provided by whole production plants, individual machines, or single sensors, enabling machine learning at different stages of complexity. Machine Learning exploits data collected to find complex, non-linear patterns in data, and transform them into models, which are then applied to fine-tuning process parameters, resulting in process optimization.

Optimizing manufacturing processes for efficiency can have a significant impact on your bottom line. The advent of smart manufacturing simplifies the exploitation of data provided by whole production plants, individual machines, or single sensors, enabling machine learning at different stages of complexity. Machine Learning exploits data collected to find complex, non-linear patterns in data, and transform them into models, which are then applied to fine-tuning process parameters, resulting in process optimization.

Operational excellence has many definitions, with the simplest being where an entire organization collaborates on achieving successively higher levels of quality, efficiency, and performance. Built on the idea that what gets measured gets managed, data-driven operational excellence KPIs not only drive a business to new innovations and efficiencies to fulfill customers’ needs, it also highlight which of their site metrics are levers to improve.

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With ThingWave’s knowledge in data acquisition, communication platforms, and data analytics, we are able to support our client’s data-driven operational excellence journey through:

• Collection of data from equipment using ThingWave wireless sensors (vibration, rpm, temperature, ultrasonic, humidity, and other)

• Overall Equipment Effectiveness (OEE) monitoring and reporting

• Consolidation of data from other sources (ERP, LIMS, SCADA & other databases)

• Equipment and process monitoring - Diagnostic analytics show why it happened

• Predictive monitoring/maintenance - Predictive analytics describe what’s likely to happen in the future

• Machine Learning - Prescriptive analytics tell you how you can change it