IoT has got locked-up in a box. The box is really getting more data for decision making. And the job of most IoT projects is to automate the data collection in a structured way. To realise the true potential of IoT (cash!!), businesses need to think out of the box!
IoT current focus Collect data
- Report incidents
- Spot the problems
- Help to optimise on a parameter
Out-of-the-box IoT focus
- Faster decisions
- Predict incidents
- Tell the solution
- Understand where to optimise for best results
Crafsol has been working with leading manufacturers to deliver some really out of the box IoT solutions. We discuss the power of ‘prediction’ applied by Crafsol in maintenance of your equipment and the manufacturing processes.
IIoT can do much more than just doing faster reporting in maintenance
Sudden Machine snags or failures causing downtime is a challenge for most manufacturers. Most IIoT projects focus on condition monitoring, which involves using sensors to do real-time monitoring of key parameters, and alert the user on any deviation. But that’s just faster reporting.
The real question that needs to be answered is when the machine most likely to fail? and why?
To really save on maintenance costs and improve OEE of equipment calls for going beyond condition monitoring. Crafsol offers predictive models for maintenance that help in the proactive maintenance of key assets. While condition monitoring will report, predictive maintenance will tell you in advance.
With Crafsol, you can jumpstart your journey by beginning with automated condition monitoring and quickly graduate to real-time predictive maintenance with Automated Machine Learning. Given the lowering costs of data transfer and data acquisition devices, such solutions can easily fit your budgets too.
IIoT can give you insights from inside of the manufacturing process
Every production manager is concerned about one thing. Will we get the production volumes at the end of the shift?
IIoT can help at multiple levels. The first of course, is predict the probability of production throughput using the WIP data – typically reflected in shop-floor dashboards. But enhancing productivity can go way beyond that.
For instance, there is machine performance data that can be analysed to optimise the machining process itself. Very few companies are leveraging the readily available controller data from the machine to reduce their cycle time, without affecting quality.
