According to Tyrone Machado, the HDMM industry is currently facing severe labour shortages because of aging populations as well as the requirement for highly skilled personnel in HDMM operations.
“Would you work in an environment where your body is subjected to adverse working conditions such as dust, snow, rain, loud noises, and vibrations?” Machado asks. These labour shortages negatively affect the profitability of daily operations. Thus, automated and autonomous HDMMs are here to primarily solve the problem of labour shortages, in addition to improved process efficiency and safety of HDMM operations.
Automation of HDMMs requires flexibility within business and society
Automation capabilities are often misunderstood or misinterpreted by society, due to a lack of common understanding. In his doctoral dissertation, Tyrone Machado addresses this lack of common understanding by proposing standardised definitions for different levels of automation for HDMMs.
For example, HDMMs perform two main tasks: they change the shape, size, form, and/or location of external materials such as soil and rocks; and they drive or propel themselves from one location to another. Thus, the significance of each task and its automation capabilities depends on the type of HDMM used, for example, wheel loaders or excavators.
“Automation of HDMMs should not be viewed as an all or nothing approach. When you replace the human operator, certain human sensory capabilities such as tactile feeling, sight, hearing, and thinking, need to be replaced by hardware such as vision (e.g., camera, lidar) and acoustic (e.g., radar and ultrasonic) sensors, vibration/motion sensors, and high-performance computing, which are not necessarily cheap.” Machado explains.
Accordingly, the standardised definitions proposed in his dissertation introduces 36 different possibilities of automated HDMMs. Among the 36 possibilities for automated HDMMs, 32 possibilities still need human input in some form. Thus, it may be more feasible to have a single human supervising multiple automated HDMMs, rather than deploying fully autonomous driverless HDMMs.
Successful deployment of Automated HDMMs requires novel approaches to business
Automation of HDMMs requires skills, resources, and competences extending beyond mechanical engineering principles, for example, from robotics and artificial intelligence. Thus, different business approaches, such as interdisciplinary collaborations, are required to account for the variety of automation possibilities for HDMMs. Accordingly, Machado’s dissertation maps out the different stakeholders in the HDMM industry and attempts to unify the research and development related to automated and autonomous HDMMs under a single umbrella.
“When I began this research in 2020, there were almost no research papers that dealt with the business and management aspects of traditional HDMMs, let alone automated HDMMs. There has been a slow emergence of such research, but it is still not enough. Novel business approaches require novel research insights from the fields of business and management,” Machado elaborates.
Thus, Machado’s research, which was predominantly performed in the HDMM industry, proposes several interdisciplinary and practical frameworks that are developed using theories from business and management. This enables interdisciplinary decision-making within the HDMM industry.
“Typically, strategic management personnel in the HDMM industry began their careers as engineers and transitioned into management positions. The pragmatic frameworks developed in my dissertation can enable such engineer-managers to assess collaborative ecosystem partnerships or novel business models. For example, selling an outcome such as ‘X Euros per ton of material moved autonomously’ rather than simply selling a physical HDMM product, without the need for a deep dive into business and management theories,” Machado explains.
In doing so, Tyrone Machado’s dissertation attempts to bridge the gap between research and practice in the HDMM industry. Machado hopes that, in addition to the HDMM industry and academic researchers, other stakeholders such as legislation, standards organisations, and the broader society, can utilise the research results to develop a better understanding of the practical implications and nuances of the next generation of autonomous driverless HDMMs.
Public defence on Friday 22 November
MSc (Tech) Tyrone Machado's doctoral dissertation in the field of Automation Science and Engineering titled A Path Towards Business Cases for Automated and Autonomous Heavy-Duty Mobile Machines: An Interdisciplinary Approach will be publicly examined at the Faculty of Engineering and Natural Sciences at Tampere University on 22 November 2024, at 12 o’clock. The venue is auditorium K1702, Konetalo building, Hervanta campus, Korkeakoulunkatu 6, Tampere. The Opponents will be Professor Aki Mikkola from LUT University, Finland, and Doctor Harri Kulmala from DIMECC Oy, Finland. The Custos will be Professor Reza Ghabcheloo from the faculty of Engineering and Natural Sciences.