La industrial equipment design is based on the know-how and experience of your design office and R&D teams. Part of this experience is acquired thanks to the feedback from your customers on the use they make of your equipment, the problems they may have encountered, their unmet functional expectations...
These feedback elements allow you to define improvement actions to be implemented in the design of new products based on a reality: customer expectations. They also make it possible to update and refine the sizing assumptions used in the design of your products, to improve their performance and their level of reliability.
In this process of improvement, the collection of data associated with the use of equipment by your customers becomes critical. Indeed, data relating to the use of the equipment is the best way to know the real use that it is made of.
Whether it is the parameters used, the values of the measurements carried out by the sensors or the defects or anomalies detected, this mass of data is invaluable in feeding the product improvement loop. Unfortunately, this data is only rarely or difficult to access.
La digital transformation ongoing in factories with the arrival of new technologies related to the Internet of Things and the digital factory is an opportunity to change this situation.
Data collection and equipment connectivity are being democratized thanks to secure, reliable, accessible and increasingly economical solutions. For you, this is an opportunity to be able to monitor your equipment throughout the product life cycle. In particular with the implementation of tools to recover data from the fleet of equipment installed at your customers.
In parallel with this data collection, it is necessary to provide the data analysis solutions (Data Analytics) adapted to process them, especially since the volume of data will increase with the number of equipment concerned.
Collecting data on operating equipment will allow you to quickly capitalize on thousands or even millions of hours of cumulative operation.
As soon as the commercial launch of the product equipped with data feedback, it will be possible to have feedback on the use made of it, the anomalies... With a little more perspective, the analysis of this data will make it possible to quantify event frequencies, physical quantities or any other measured element, in a statistical and reliable way to update the sizing assumptions.
Data analysis can also make it possible to detect different behaviors according to the types of customers or uses and thus propose new areas of product segmentation to better meet the needs of users.
In the event of the introduction of a modification to an equipment, it becomes possible to measure very quickly whether the modification proposed to the customer was relevant to the issues it is supposed to address.
The end user of the equipment will benefit from the benefits of this digitalization process by having more reliable equipment that is better adapted to its needs and this with more frequent changes. Concretely, this will mean an improvement in the availability rate of his production line and in better performances for the process on which he uses the equipment.
When renewing its equipment or buying new equipment to increase its production capacities, historical data will allow it to be guided to the best choice of equipment. The latter has benefited from improvements designed using the same data.
Likewise, you can offer it a retrofitting on its existing equipment to provide them with improvements adapted to its uses.
If you transform your product design and improvement processes so that they start from collecting data from equipment in service with your customers, you will be able to: