INDUSTRY
Mining
- BryteFlow ingests operational data and data from devices which can be used via IoT to predict when a particular equipment or machinery could fail or need downtime.
- Automated ways of loading railway cars can yield data that can be used to pinpoint efficiencies within logistics which can be rectified with informed business decisions or by following an improved process.
- Automated ground control systems analyse vibrations in the ground to test its strength and send warnings to evacuate in the face of dangers like anticipated landslides or tunnel collapses. The data captured over time by the ground monitoring system is collected by BryteFlow and can be used in framing better and safer processes for blasting and drilling.
- Procurement and inventorying becomes more efficient- present and future requirements of services and spares can be anticipated by collecting and analyzing time series data from various sources using BryteFlow. This can drive better negotiations with price and spend analytics and lower procurement costs.
The role of data in the mining industry and how BryteFlow helps
In commoditized, asset-centric industries like mining, business differentiation is all about how you can exploit and leverage your assets to realize lowest cost and maximum availability. It is imperative to optimize maintenance and reliability systems of physical plants and equipment. Mining companies are realizing by aligning information technology and operational technology via IoT they can use their data to achieve greater productivity, efficiency and cost savings. BryteFlow ingests and transforms time series as well as device / sensor data to help in equipment maintenance and safety protocols.
Predictive Maintenance Models
The mining industry is extremely capital and asset-intensive. With a wide array of equipment and machinery in play much of which has to be monitored closely and a broad spectrum of data available, AI enabled predictive maintenance models can predict critical asset downtime and reduce operational risks successfully. You can select attributes to help AI determine the health of an asset. These could include temperature, pressure, noise and/or vibration. Data models using historical data that monitors and quantifies these attributes are key in predictive maintenance. Bryteflow ingests all types of data from AI systems, devices and industry-specific applications to give you data to use in predictive maintenance models.
Monitoring processes with algorithms
Business processes are increasingly getting automated and algorithms can save millions of dollars for asset-intensive companies like mining. Algorithms are used in manufacturing control systems and marketing automation among other things. Machinery that is digitally operated throws out a lot of time series data through IoT that produces data patterns that analysts can use to decide the optimal way to use equipment and machinery, when replacements and maintenance would be due and the frequency of checkups to be done. With BryteFlow ingesting and preparing this crucial data, companies find it possible to predict failure and what it looks like. Maintaining equipment saves companies thousands of dollars in downtime and maximises effectiveness and availability of the asset.
Data helps to manage logistics better
Transportation is key in the mining industry. Rail cars cost money and it is critical they are kept moving rather than idle. Software like Minestar can be used to track vehicles on an ongoing basis. BryteFlow ingests this data and prepares it for analytics in real-time so companies can use this data to discover transit time delays, unloading times and other factors that may create issues. Based on data ingested by BryteFlow from the vehicles, companies can institute maintenance processes for their trucks and specialized vehicles. They can also collaborate with shippers, railroaders and unloaders to fix delays and other revenue leaks. BryteFlow replicates automated data about ETA of vehicles coming in and going out. This enables planning of loading and unloading in a cost-efficient way to keep vehicles moving and to lower labour costs.