The Industrial IoT is a new tool available to companies to improve their productivity. To make the most of its considerable potential, it is important to understand the challenges involved in its implementation and the steps crucial to the successful deployment of the Industrial Internet of Things.
Involved in an IoT deployment process within his industrial group, Didier Henry, Managing Director at Setforge Engineering, introduced us to the different steps he followed and the main obstacles he had to overcome in order to prepare the deployment of IoT within the factories.
Could you introduce us to your company and your missions?
Setforge Engineering, which I manage, is the support company of the Setforge Group, a subsidiary of Farinia Group, specializing in forging, casting and machining. As such, we are in charge of supporting the industrialization of ranges in support of our sites and carefully studying all new technologies and innovations that could be of interest to Farinia Group.
The turnover of the group is about 180 million euros with about ten companies located in France. Approximately 50% of our activity is dedicated to the automotive industry, but we also work for all other sectors: off highway, aeronautics, petroleum, textile, etc.
Where do you stand concretely in the use of IoT in your group’s industrial activities?
We have been actively preparing the deployment of IoT in our industrial sites for about 2 years, and we really launched it on a large scale a few weeks ago. Several sites have started work to connect the machines and we are currently setting up the communication platform.
What were the main preparation steps before the deployment of IoT on sites?
First, we took care to secure our industrial networks, before connecting any machines. And as I said, we are now moving on to the deployment stage on the sites.
What inspired you to develop IoT within your industrial group?
We are constantly looking for ways to improve and optimize our ranges, and a good part of our work is to improve the processes to use the machines at their best.
Today, if we want to improve productivity, we have to find new spaces, because the known variables of our processes are under control, thanks to our long experience (some of our sites are almost 100 years old).
To find these new spaces, we need to know our machines and processes better and therefore capture more parameters and associate them with the actual activity throughout the process.
It is in this context that the IoT is a valuable tool that allows us to connect space-time, production and machine data, including those we do not monitor today, to discover the new spaces I was talking about.
For example, it may be data that is already captured today by a machine but that is only used for internal operation: vibrations, temperatures, environmental data such as humidity… We need to be able to associate fairly fine-grained data with a scrap rate, an OEE (Overall Equipment Effectiveness) or productivity, to be able to carry out analyses and find opportunities for improvement.
What do you think are the main challenges for IoT implementation?
The first obstacle is the cost: contrary to popular belief, this IoT approach has a long-term ROI, the initial investment can be significant and the gains are to be explored and based on improved future control. It is therefore important to structure the approach properly.
The second issue is the organization and security of networks: with the IoT, we connect our machines to the Internet, which represents a challenge in terms of IT security. This also highlights the flaws linked to old automatons still in service. We also have to take into account the human factor, because maintenance workers are generally very skeptical about the idea of connecting a machine.
As for the third lock, it is technical. Indeed, it is not always easy to connect machines, automatons and sensors, of different generations and different brands, in an industrial environment, especially with wireless connections: there are pits, metal walls, etc.
Could you tell us more about the issue of costs associated with IoT implementation?
There are 3 major cost drivers:
- data interpretation, dashboarding, etc. For this we use the Diapason tool developed by our IT company SAFIR, which suits us very well;
- the data transmission and storage protocol, also via SAFIR, which we master quite well today;
- the sensors and connection to the automatons part, on which we are still working.
If the ROI to be expected from the IoT is generally long, as I mentioned, there are nevertheless use cases with rapid gains, for example on the connected lines we now have a real-time vision, shared by both operators and managers, including on smartphones. This allows us to be very responsive for maintenance, tool changes, and anticipation of production by support services: we have quickly gained a few points in OEE.
Another example is the installation of sensors to measure energy and water consumption, enabling rapid savings actions.
What is the role of Mobility Work CMMS in your IoT strategy?
Mobility Work is our maintenance management tool. With this CMMS and IoT, we can marry digital data that comes from the machines with the interpretations of the maintenance technicians.
In addition, Mobility Work will allow us to set up automated tasks based on sensor information and trigger inspections.
What advice would you give to companies considering using IoT?
Generally speaking, I would say that you should take the time to choose reliable suppliers who offer solutions adapted to your needs.
On the other hand, what seems important to me in the link between maintenance, IoT and production is to bring the production data closer to the machine data. For example, the alert levels can be different on the same machine depending on the part being produced.
Finally, what do you think are the major upcoming trends in industrial IoT?
In my opinion there will be 2 major trends in industrial IoT in the coming years:
- The data will be increasingly sent to the Cloud, therefore in a universe quite far from the field, to make advanced analyses thanks to artificial intelligence in particular;
- At the same time, some sensors will be able to process data directly without external help, thanks to their own calculations and interpretations, to allow immediate reactivity on the machine.
In my opinion, these 2 trends will develop in parallel.
A big thank you to Didier Henry for his testimony and his availability!
