Industrial Automation


Transform Traditional Manufacturing into Digital Manufacturing with Industrial IoT. Get ready for Global Competitions.

Machinery Predictive Maintenance

As the manufacturing industry going through massive global competitions, many veteran manufacturers are focusing more on operational efficiency, process improvement, less energy utilization to increase the profitability by producing higher volume goods, lowering maintenance and reducing the cost of repair (COR). With power of the Internet of things (IoT) and Artificial Intelligence (AI) manufacturing machine operations, efficiency and field conditions can be captured through sensors that generate massive data. These data can be analyzed through statistical models and a broader average algorithm to predict and improve overall equipment effectiveness and alert us on maintenance action even before it occurs.

Traditionally, manufacturers spent huge operation expenses by scheduling recurring maintenance to avoid unexpected production interruption. However, such scheduled maintenance also leads to unwanted expenses even before it is required. With the help of Internet of the things and Predictive Maintenance strategy, manufacturers can improve operational efficiency. We help manufacturers to achieve such level of efficiency through our Fogwing Industrial Internet of things (IIoT) Platform.

Manufacturing Process Transparency

Manufacturing process visibility and efficiency are still the biggest challenge for top management due to the nature of traditional processes followed by production managers. However, manufacturing is very competitive today due to global reach-ability, operation efficiency and process improvement which is always the key challenge to keep top on the race. With growing demands of transparency, it is time to collect the data from the manufacturing facilities at every stage of the production process with predictive quality analytics and plan the production operations effectively. Manual way o measuring the Overall Equipment Efficiency (OEE) does not effective due to delay in preparing reports. Thankfully, technology advancement in realtime sensor and communication helps to measure data at every stage of the production starting from supplier tracking, raw materials inventory, production floor, production output, product in stock to customer delivery. Data collection at every stage is critical to consolidate the events, analyze, compare with history to predict the process efficiency. Learn More.

Fogwing IIoT solution is designed by considering all state of the data and dynamic processes. Our model of delivery is customized according to your production processes to ensure accurate predictions in real-time.

Plant Safety Risk Analysis

The traditional approach of Manufacturing and Workplace inspections helps to prevents incidents, injuries, and Illnesses. However manual inspections are not as effective as it always suspect-able and can only identify the evidence of physically measurable. To prevent the incidents accurately required continuous monitoring and measurement of key indicators in the manufacturing plant/machinery to predict the vulnerability even before it is measurable by physical inspections. Industry safety and health regulations required to conduct safety measurement process, Hazard Analysis, Checkpoint Analysis, Boiler Pressure Analysis, Hazard Materials Handling to certify the workplace safety compliance’s.

Deploying Sensors and IoT Solutions at various checkpoints of the plant processes, to collect the performance data at real-time and analyze through Machine Learning algorithms to predict the vulnerability and threshold of the plant instantly enabling root cause analysis (RCA). We deploy various sensors and actuators for Air Pressure, Air Temperature, Moisture Level, Heat and Humidity measurements to collect the condition of the plant both inside and outside to build the predictive data metrics. We also provide Fogwing eco App to monitor and analyze with historic data environment condition monitoring and daily operations.


Apr 29, 2020 by Sancia Catherine

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