Manufacturing process flow
In manufacturing shop floors of Singapore, the buzzword IIOT (Industrial Internet of Things) is gaining a lot of momentum. Production wireless machine sensors, which gather and collect data on gateway devices and use data visualisation tools, are leading a new wave of factory productivity improvement. Indoor positioning systems for factories can help streamline process flow
One obvious benefit of IIOT is better visibility of the plant floor to the facilities and operations management, and possibly improved communication between machines. However, when you combine wireless sensor data with anomaly detection algorithms, even more, possibilities open.
IOT analytics on production machines are:
- Improving Overall Equipment Effectiveness (OEE) — The measurement of how much time your factory equipment is actually making products.
- Reducing Preventive Maintenance costs, which can improve OEE, but also can lower overall equipment repair and replacement efforts & costs.
- Improving the quality of the product coming off the manufacturing line.
Industrial Internet of Things (IIOT) is helping the manufacturing companies to improve their OEE evaluation with in-depth understanding of legacy equipment performance through deployment of retrofit wireless sensors, gateways, data visualization tools, and anomaly detection software. IIOT solutions help to improve the OEE values in many ways:
- Analyzing machine process and performance data to optimize maintenance planning, schedules, and resources.
- Get warnings in advance about the degradation of their machines, with anomaly detection to avoid downtime.
- Leading to lower preventive maintenance costs, reduced material & supplies, and greater equipment availability.
- Production line quality will be carefully monitored. It will help you to monitor process parameters, find out the calibration, temperature, speed, vibration, ambient light, VOC and production time & utilization of the machines.
- It will help in the management of supply chain logistics. Manufacturing units will be able to compare. It will help them to decide how they can work on their future schedule
IIOT & improving manufacturing process flow
The first item on this list is often overlooked when discussing wireless sensor and anomaly detection because most tend to focus their efforts on using sensor data to predict various production machine failures. Equipment usage and idling patterns, ground leakage, etc can help trigger information on utilization, besides saving on power consumed.
However, we’ve found that factories can make major improvements in OEE by focusing mainly on small adjustments happening on the shop floor. For example the hand-overs from one production shift to the next and the change-overs from one product line to the next.
When we worked on change-over reduction programs, we knew that Single Minute Exchange of Dies techniques measured time from the last part of the previous run to the first good part of the next run.
But we also found a lot of wasted time, both in waiting for the last part to clear out as well as wasted time as the machine gets back up to full speed on the next part. We discovered that shop floors could achieve additional OEE improvements by focusing on this part of the change-over as well.
There are similar problems surrounding lunch/tea breaks and shift changes, as well as small downtime adjustments during a production run. We’ve found there are plenty of OEE improvements available in these areas on the shop floor. To find these problems, we use wireless sensors to time-stamp when each job was finished as well as the various events and faults coming from the machines.
We can then analyze this wireless sensor data with simple, anomaly detection techniques, which enable us to find the patterns that lead to a loss of time.
Think of all of this as using IIOT for Manufacturing process flow monitoring & improvement.