Real-time visibility on robotic lines and machines without connectivity
Company
Nissan Powertrain
Location
Barcelona, Spain
Sector
Automotive
Activity
OEM. Transmission and gearbox manufacturing
Initial challenge
Decentralized data
Solution
Minerva platform
Integration with existing MES system
Key benefits
- Comprehensive data visualization
- Real-time OEE
- Downtime reduction
- Increased operational efficiency
We would like to tell you about a monitoring project that we are particularly fond of and that was carried out for Nissan’s Powertrain plant in Barcelona. This plant manufactured transmissions, engines and suspensions for some of the Japanese brand’s models, as well as others such as Renault and Mercedes-Benz.
The plan was to carry out what is now known as a ‘Proof of Value’, which seeks to go beyond a proof of concept, although some experiments were carried out along these lines. As our technology was already well proven in the industry, Nissan Powertrain wanted to see the real return they could get from industrial monitoring with real-time visibility.
The challenge
The project began monitoring the plant, incorporating two very different lines:
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An automated line (100% robotized and connected). The challenge here was not to connect our Minerva platform to the industrial data collection systems already implemented by Nissan, which is something native to our platform, but rather how to manage that communication so that our platform would adapt to changes in the data and not the other way around. In this way, alarms and their severities were defined in the PLC and reached our platform dynamically, to then launch notifications and visual alerts.
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A manual line (with old, unconnected, manually operated machinery). These lines are always a big challenge, as they involved very old CNCs and machines that had no connectivity. In these cases, it was not usually worth trying to reverse engineer the machines, so we opted, working with the Powertrain team, to install a series of sensors..
The challenge here was to combine data from both lines with data from the self-developed MES system, which contained production targets, shifts, etc. This made it possible to calculate OEE, see if the pace was being maintained, etc. in real time, without waiting for the end-of-shift or end-of-day report, detecting problems much earlier and thus increasing production capacity.
Implementation of the solution
Automated Line
Due to the automated nature of this line, before starting the project, it already had all the equipment, connectivity and software needed to obtain data. This, together with the management tools already implemented at Nissan, allowed us to easily obtain the data.
The usual temptation here is to collect as much data as possible, with the cost that this represents, but what we did was work with the Nissan team to decide what data they might need to answer the questions they wanted to resolve with the project and only collect that data. The criterion here was to include any data where there was doubt as to whether or not it would be useful, but always to discard any data that was clearly not relevant to the purpose of the project; it could always be added later.
Once the data had been collected, a process that is always present in our projects began: iterations with the client’s teams. Through various meetings, we iterated on which visualization elements worked best or worst, which filters were needed, colors, alarms, etc. This phase was very important, because what you can think of on paper does not always work 100% on the shop floor.
Thanks to Minerva being a low-code platform, changes were made without taking requirements or putting them into production. In some cases, they could even be made during the meeting itself… in real time too 🙂
Manual Line
On the other hand, for the manual line, as there was no previous data collection, we had to obtain the data ourselves. To do this, we installed different types of sensors, which were external and easily removable and transferable to another facility, which we used to study the best possibilities for each situation:
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Consumption sensor: small devices that, connected to a machine’s current input, allowed us to monitor its consumption, thus obtaining different parameters. These types of sensors not only indicated when the machine was in operation or at rest, but they also allowed us to infer states and even count parts using an algorithm incorporated into Minerva. It was not always possible to obtain the parts count, but it was always possible to rise an alarm a machine was consuming without producing.
- Beacon sensor: a strip-shaped device that, when attached to a machine beacon, is able to detect its status and send it to our platform. It allowed us to detect finished parts, alarms and machine shutdowns on machines without connectivity.
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Inductive sensor: installed on the last machine in the line, it allowed direct counting of parts produced, key data for measuring productivity and OEE.
Interaction with management data
We didn’t just focus on obtaining real-time data. Another objective of this project was to link this data with Nissan’s management data.
To achieve this, we connected to a MES system that had been implemented in the company some time ago and where managers and operators could enter work data manually.
By cross-referencing their data with the line data, we were able to bring together two worlds that are normally separate or only linked through Excel, being able to compare plans with reality and add context to the line data… all on the same screen!
Benefits obtained
With this solution we have control of a key line for the factory which, being automatic, is unattended, being able to know and correct its efficiency failures.
Jorge Ferrís
Production Engineering and Facilities, Nissan Powertrain
The solution has also allowed us to save on structural personnel, since all management is done without having to travel thanks to mobile notifications.
Miguel Ángel González
Senior Supervisor Machining, Nissan Powertrain
Real-time visibility across lines, at a glance
One of the first and fastest benefits was the visualization of information in real time (parts, production rates, cadences, etc.). But thanks to Minerva’s calculation capacity, we used this input data to generate calculations of efficiencies, rates, etc., which added value to the information, provided data-driven answers to new questions and, above all, gave a true picture of how their lines were performing in relation to planning and ‘theory’.
As with almost all of our projects, being able to share this useful data for day-to-day planning, the Nissan Powertrain team decided to add TV screens in their offices, from which to see the platform in real time, visualizing the different lines at a glance, with their indicators and alarms. This allowed them to make informed decisions in real time, minimizing the impact of problems and increasing efficiency and production capacity.
It could also be viewed from any mobile device connected to Nissan’s corporate network.
In addition, the visualization allowed for both generic and specific views of each variable, from a plant view to the data for each machine, showing only what was necessary according to the user’s profile, which allowed the solution to scale seamlessly.
Access to historical data for problem analysis, bottlenecks, etc.
Besides being able to view data in real time, the tool also stored historical data that could be viewed at any time, allowing users to set the timeline to any point in the past to see what happened at a specific moment or when a certain reference was being manufactured. This historical data is contextual, as it recognizes shifts and production tickets, allowing it to be used for continuous improvement and analysis to address the causes that lower OEE.
This also freed intermediate industrial data collection systems from the need to store historical data and allowed other future systems to use it through the platform’s API.
Reduction in downtime response times and improved communication
All this data is not only useful for visualization, but also for generating alarms and sending notifications to the relevant people. For example, one of the most useful features has been the integration with Nissan’s alert platform via SMS, which was used, for example, to notify forklift operators when machines ran out of parts, or to notify maintenance personnel when there was a problem. The system can be reconfigured according to different shifts, etc., ensuring that the message is delivered to the most appropriate person to deal with it in the shortest possible time.
It is of course possible to consult the history of these alarms, as well as leave notes about them, mark them as dealt with, view the alarm periods, etc.
Vista de alarma específica con todos sus estados a lo largo del tiempo
Conclusion
Muutech’s Minerva platform provided a real-time visibility solution for on-site decision-making and was a key ally in continuous improvement for manufacturing at Nissan Powertrain. Its flexibility to integrate into automated and manual lines, as well as its ability to unify plant and management data, enabled Nissan to anticipate problems, optimize its resources and maximize productivity at its plant.
A tool that transformed the way workers monitored, acted and improved on a daily basis, demonstrating how intelligent, real-time monitoring can make a difference in industrial performance.
Would you like to see how it could work in your plant? Request a personalized demo and discover Minerva’s potential to transform your operations.