13 Oct 2022

CROCEMS interviews Esther Álvarez, professor at the University of Deusto.

On the occasion of the Erasmus Days, we interview Esther Álvarez Professor at the University of Deusto, where she teaches Production Management and Logistics, where in addition, she is head of the Industrial Organisation and Logistics research team.

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What are the challenges of Collaborative Robotics nowadays?

Human-Robot interaction and communication are key issues for the development of collaborative robotics. The level of confidence in a robot behaviour largely depends on how it reacts to human movements and gestures. On the other hand, it is also very important for human operators to know that they remain in control of the industrial process and that they will always retain the ability to make the final decision.
Other challenges include the need to find a good balance between the speed of robot movements and the safety of people, which must be guaranteed. Cobots are getting bigger and faster and this implies greater risks for humans sharing a workspace with them.

How Collaborative Robots could affect waste within the manufacturing process?

The use of collaborative robots could contribute to decrease the amount of waste generated in a manufacturing process. Sometimes, waste is not correctly identified during the process, or is not suitably manipulated. The correct identification and treatment of waste based on an optimized division of tasks between robots and operators could contribute to increase the degree of separation of components and materials, as well as the safe disposal of waste. Furthermore, the components and materials obtained could be reintroduced in a new economic cycle through reuse, recycle o remanufacturing, using a circular economy approach. In this way, it will be possible to save resources and reduce both the environmental and health impacts of waste.

What are the current challenges and what can we learn from other industries?

Not all waste streams are of equal importance. In some cases, such as e-waste, its continuous growth is of particular concern, as well as the fact that it contains valuable materials that should be recovered but also hazardous materials, which may seriously damage the environment. These priority waste streams have attracted much attention and have been the first to implement waste management processes, eliminate hazardous substances and increase recycling capacity but other waste streams will soon be targeted as well.
A very important issue to take into account is that the separation of components is intensive in manual tasks. In the case of e-waste recycling, most processes are performed by hand, due to the lack of uniformity of the discharged devices. Other industries with similar requirements are the agri-food industry, packaging, clothing, and, in general, industries with a wide range of materials to handle.

Due to the constant crises that are occurring, have companies become more flexible and open to reformulating strategies or making change decisions?

Unfortunately, it often happens that companies are reluctant to make changes during crises. The decision about integrating a collaborative robot in a production line implies a deep analysis of the process and a close collaboration with human workers, all of which is time consuming. And it does not always result in a lower cycle time, but it can introduce more flexibility in the process and relieve human operators from physically demanding tasks or from postures that may lead to musculoskeletal diseases. It is our responsibility to clearly set out the possibilities and benefits from the triple bottom line perspective to dispel these logical doubts and to get companies to invest in process innovation. 

How do you think AI could impact waste management in manufacturing sectors?

Artificial intelligence is the cornerstone of innovative robotic processes. Through integrating big data, sensor acquisition, or machine learning it can mean a great support for the improvement of waste management. It could help with one of the main challenges, which is the correct identification and separation of materials. If AI is able to collect enough data, learn from the experience of human operators, and successfully assign tasks to either humans or robots depending on the different situations and conditions of waste stream, this will allow for a higher degree of separation and a more effective treatment of waste.