The adoption of advanced technologies is
changing business models, increasing output, and automating processes.
Internet of Things (IoT) is escorting in a new wave of sensor devices and their associated data streams. Sensor information and machine logs data files are granular and generated continuously. Such data needs to be captured, stored, validated, cleaned, visualized, analyzed and shared to enhance the processes and business.
Traditional BI and Analytics methods will not be sufficient in such era of high speed, high volume data streams.
Industrial Analytics and Efficiency
“Data is the new oil. It's valuable, but if unrefined it cannot really be
used. It has to be changed into gas, plastic, or chemicals tocreate a valuable entity that
drives profitable activity; so must data be broken down, analyzed for it to have
Clive Humby, Mathematician and architect of Tesco’s Clubcard, 2006
Our advanced predictive analytics are based on 'Machine Learning' algorithms to take smart and even autonomous decisions. Through Machine Learning computers can recognize patterns, learn from experience and continuously improve the efficiency and accuracy of the output.
We have capabilities of implementing IoT and Artificial Intelligence to automate the process of deriving 'Operational Efficiency'. The results are available in the form of reports or dashboards on multiple devices.
Predictive & Preventive Maintenance
In today's world, significant amount of money and efforts are being spent on maintenance activities, especially for addressing equipment breakdown or failure. Equipment ROI and operational efficiency demands industry need for higher equipment availability and utilization to its maximum potential. Maintenance function is evolving from being reactive to preventive, and then to predictive and finally self healing.
Sensors could collect the actual machine runtime and also sense the vibrations of the machine. This data could be used to derive the preventive machine maintenance schedule and also to predict machine breakdown.
Sales/Inventory forecasting using Machine Learning
In a conventional method, operational models are built on traditional data warehouses/databases where most of them are attributed to embedded rules in the code. Rules were developed in the past by domain experts and consultants who translated their experience and best practices to code to make automated decisions.
When the enterprise gets operational, one has to write 100 rules in case of 100 scenarios. Over a period of time as business grows, organization encounters more and more exceptions and so one has to start making more rules to keep exceptions under control. Data changes faster than one can keep up with the rules. At some point, nobody really knows how well the rules work or how many exceptions are resulting in havoc in enterprise systems with no useful actionable results.
Industrial image processing in the quality management
Computer vision technology provides quality control and real-time feedback for industrial processes, overcoming physical limitations and subjective judgment of humans.
The benefit of this is in its non-destructive testing process, meaning that all objects of the test are scanned and evaluated without contact.