Smart, Predictive and Personalized Products
Innovation and new technology are what will drive us into a more sustainable world and society. At Ekkono, sustainability is in our company DNA. It is the very reason why we exist. Smart IoT represents an enormous sustainability opportunity.
We use tech to do good. We know that new technology is a key-driver for changing the way we use the planet’s resources. Our mission is to transform production lines, going from hardware manufacturing to software updates and new features on existing products.
Sustainable IoT Within the Planetary Boundaries
In 2015, all united nations member states adopted the 2030 agenda for sustainable development. The outcome of the agenda was a shared blueprint for peace and prosperity for people in the planet in the form of 17 Sustainable development goals. At Ekkono, we always aim to do our part of fulfilling the goals.
As an example, Ekkono directly contribute to UN Sustainable Development Goal #9 (Industry, Innovation & Infrastructure) and #12 (Ensure sustainable consumption and production patterns). However, the way we truly contribute is how we asses each and every customer project to the impact on the goals. In our work with customer use cases, we have found several ways where our edge machine learning has a positive impact:
- Optimal use of every individual device. Edge machine learning enables constant auto-tuning of devices to run optimally all the time. This means saving a lot of energy as this is multiplied by millions.
- Predictive maintenance extends product-life by avoiding fatal wear on machines and vehicles.
- Performance optimization also extends product-life as every unit runs at its best for its unique environment, conditions and application.
- Software updates instead of hardware swapping.
- Energy-efficient individual operation reduces emissions.
- Individual smart battery management is a key driver for electrification.
- Less traveling when experts and maintenance are no longer needed onsite. The product self-tunes, self-heals, and identifies which support cases really require manual attention.