Industrial IoT

Industrial Analytics

Industrial Analytics

Industrial Analytics is the collection, analysis, and usage of data generated in industrial operations and throughout the entire product lifecycle. This includes data about a company’s operations including materials, processes, product performance, production performance, and customer feedback. Industrial analytics thus finds applications in any company that manufactures and/or sells physical products.

Industrial Analytics involves traditional methods of data capture and statistical modeling. However, most of its future value will be enabled by advancements in connectivity (IoT) and improved methods for analyzing and interpreting data (machine learning/big data) in order to optimize processes in real-time, improve efficiency, lower operating costs and enhance customer satisfaction.

Preventive Maintenance

Preventive maintenance is the maintenance performed on manufacturing equipment with the idea of reducing the likelihood of its failure. Preventive maintenance techniques are designed to help determine the condition of in-service equipment in order to predict when the maintenance of the equipment should be performed. By comparing current sensor readings to historical data, the system can use preventive maintenance to detect issues early in the manufacturing process, allowing the company to schedule maintenance activities at a time when the overall impact on the system will be minimal. This level of prediction can prevent costly and unplanned maintenance as well as lost earnings that might otherwise affect service level agreements.

Preventive Maintenance
Anomaly detection

Anomaly detection

Anomaly detection systems help manufacturers discover product defects early in the production pipeline. Early anomaly detection gives machine operators advance warning of issues in the manufacturing process downstream, allowing such issues to be resolved quickly, without having to shut down the entire production line.

Defect Density Improvement

In the process and discrete manufacturing industries, is it critical to keep defects below a certain threshold in order to maintain optimum levels of efficiency. A new set of possibilities is being enabled from granular data collected from digital factories. It is the ability to dig deeper into the next level of data to understand specifics of process states which increase defect density. A product may undergo a variety of operations and at each step of the operation, various data parameters are monitored. The system then uses advanced machine learning techniques to spot patterns and reduce the defect density of the product.

Taking this further, intelligent machine learning models help orchestrate actions requiring less manual intervention. For example, real-time fault detection on products during the manufacturing process helps in automatically reducing scrap-related costs.

Defect Density
Data-DrivenProduct-Optimization

R&D: Data-Driven Product Optimization

IOT analytics can reduce product costs. A manufacturer of specific lighting systems, for example, needs to guarantee a certain duration of product lifetime to his customers. Traditionally the manufacturer over engineers” certain components of the solution in order to ensure that the required lifetime can be guaranteed. With Industrial IOT Analytics, this manufacturer can now analyze the product usage in detail and can then reduce specifications for those components that do not have a large impact on the overall product lifetime.