Partner Spotlight Series: Lexmark
From the start of the DiCiM project, the Lexmark project coordination team has greatly emphasized developing the use cases defined in the development plans and providing the necessary data scientist and AI technology engineer resources. The main elements of the technological developments are the collection and interpretation of HealthCheck data required for the condition assessment of Lexmark printers and the creation of usage models. To carry out the mentioned tasks, it is essential to involve teams of data scientists, who play a key role in designing, constructing, testing, and introducing the previously unused data management and AI/ML technology models. Lexmark, the University of Ljubljana, the Faculty of Mechanical Engineering (ULFS), and Signifikant cooperate closely in this work. To set up the experimental model, it is necessary to include nearly 1000 MS823dn printers.
Status data extracted from Lexmark's integrated systems are forwarded to the ULFS team, who use data markers to program AI/ML computation algorithms to support End-of-Life (EOL) decision-making. To make a connection between the producer and the users, Lexmark encourages customers to register to the “Collected by Lexmark” web platform so that their printers can be collected by Lexmark for free and get a second life. After processing the product data, the AI engine provides decision-support recommendations. Lexmark has Buyback and Takeback programs in place. In the Buyback program, for the products that represent value for Lexmark, an AI/ML algorithm calculates a repurchase offer for the user depending on the product's condition. Under the Takeback printers that have value get remanufactured for a second life or some spare parts that are still fit for purpose are collected. The rest of the printers and spare parts without any value get recycled (everything that cannot be reused).
The above-mentioned collaboration between the partners of the DiCiM consortium aims to implement a technology solution that will greatly contribute to the success of blockchain technology and AI/ML- supported circular farming.