Good company in a journey makes the way seem shorter. — Izaak Walton
In July 2018 we held the first meeting for all the investigators in the newly-funded DigiTOP project. DigiTOP has at its heart the aim to develop a series of tools and theories that help us to understand and predict the impact of digital manufacturing on the way that people work. For the first meeting we shared our own visions of the future of digital manufacturing, listened to our industrial partners’ descriptions of the challenges they face as they implement digital technologies in their manufacturing settings, and identified some of our priorities for the first 6-12 months of DigiTOP.
The notion of ‘productivity’ is pervasive within discussion around Digital Manufacturing and Industry 4.0. Within DigiTOP we have a number of challenges relating to predicting the impact of Digital Manufacturing on future productivity, both for individual people, and for organisations and systems as a whole. Firstly, it is clear that digitisation is changing the nature of jobs. This might be automation of a previously manual job (not a new phenomenon, but now happening at pace); We will increasingly have the ability to see the performance of a manufacturing system in real time, in higher level of detail, and with additional interpretation due to the inclusion of machine learning in the analysis and synthesis of data; And we will have new forms of sensing, which enable us to have new information, that we did not previously have, about the performance of people and system.
So, a major theme that emerged for me from our first retreat, was – in this new world of Digital Manufacturing, how can we measure productivity? Productivity is something that can be defined on a national scale (e.g.
https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/labourproductivity) but tends towards quite traditional notions, such as ‘output per hour’ or ‘output per worker’. In a collaborative and complex environment where jobs might rapidly evolve, decisions might be shared between machine learning technologies and people, and data might be drawn from multiple sources, ranging from real-time product use to analytics of manufacturing operations, what are the types of measures of productivity that are going to be meaningful to help us understand how the introduction of digital technologies is changing work, and what the impact of those changes is on individual and system performance.
The second theme that emerged for me was around wellbeing, and the interaction between wellbeing and acceptance of new technologies, the ways that new technologies can support operator wellbeing, and the issues of trust, privacy and ethics when embedding sensing in a complex sociotechnical system. Our multidisciplinary team has expertise in many of the methods that can help us understand these issues, but we need to work out how to bring them all together in a way that both contributes to theories and develops useful tools to help industry to implement digital manufacturing effectively.
I’m really excited that the DigiTOP project is now underway, and really looking forward to seeing how our team combines our expertise in different manufacturing contexts and theoretical approaches to meet our project goals.
Prof. Sarah Sharples
University of Nottingham