Stanzen Engineering increased the operational efficiency of their entire plan by 30% using our Industrial IoT Gateways and Lucrys Analytics.
Stanzen Engineering is one of the leading players in the automotive stamping and related solutions. In their three decades of operating in the industry, they have satisfactorily fulfilled hundreds of projects involving body, chassis, assembly and safety standards. Their range of solutions includes pressing, welding, assembling and tooling.
They needed one single platform that could communicate with machines of different makes, controllers and communication protocols, and compile all the necessary production details.
The first thing we did was install our industrial IoT gateway, which would transmit data from all of their machines. Once we finished our 10-minute setup, the platform started collecting data immediately. The platform soon started displaying data about the machine running time and idle time, production efficiency, stroke count, pressure, temperature, production forecast and many more.
Stanzen Engineering wanted to focus specifically on certain critical KPIs. Needless to say, we were more than happy to create a report with these KPIs for them.
UOverall Equipment Effectiveness (OEE) - To ensure their product quality is always on point, and their machinery performs perfectly every time, we created this dashboard. Overall Equipment Effectiveness (OEE) is the gold standard for measuring manufacturing productivity. It shows as a percentage of the time. In short, it indicates the percentage of time a machine has been productive, out of its total running time. It is divided into three parts:
- Quality - How often does the machine churn out optimum quality products? Is it producing any defective units? Do any of the units require rework? A score of 100% indicates that 100% of the time the machine produces optimum quality products.
- Performance - How quickly is your machine producing products? Is your machine slowing down? Is your machine running optimally? A score of 100% indicates that 100% of the time the machine is processing products as fast as possible.
- Availability - How often do you suffer unplanned shutdowns? Are your machines running as planned? A score of 100% indicates that 100% of the time, your machines and processes are performing during the scheduled production time.
Furthermore, their maintenance team provided us with 6 months of machine runtime data. This data showed us the following failures:
- Misaligned Shaft
- Bearing Defects
We fed the 6 months data mentioned above, potential diagnosis and the parameters in our AI algorithm. Additionally, our vibration sensors’ data indicated high levels of unusual vibrations and high temperatures. Then, our AI algorithm ran a correlation analysis between all these data sets, identified patterns and predicted further bearing defects.
We communicated this piece of information to the maintenance team, and upon verification, they confirmed that the parts had worn out. We were able to predict bearing wear accurately.
Actual vs Planned - This data shows the comparison between planned production and actual production. We have presented the expected data and the actual data, shift wise as well as week wise. This segregation has been done to provide deeper insights to the users.
Production Forecast - Additionally, our predictive AI showed a predictive analysis of their machine health. This information included the number of days until each of the components of a machine would fail, their maintenance priority and vibration levels (whether it’s normal or abnormal).
By being able to monitor comprehensive production details in real-time, they were able to increase the operational efficiency of their entire plant by 30%, achieve production targets and successfully predict machine failures and save unnecessary costs.
And that’s not all. When the management compared the amount invested in our products and their savings in downtime costs, they found the ROI to be 10 times their investment in Machstatz!
This way, we not only accurately predicted what component caused the recurring shutdowns, but we also identified why the part failed frequently. We removed costly guesswork and replaced it with an intelligent system that consistently warns them about imminent issues.
A Chemical Manufacturing Plant successfully predicted and prevented 12 hours of machine downtime using pattern recognition and AI (a 10x ROI!)