A factory floor where humans and robots work side by side is being redesigned from the ground up, with the human worker’s needs built into the software that controls the machines. Manufacturing is racing toward “Industrie 4.0”—fully digital, interconnected production lines. But the workforce is ageing, more mobile, culturally diverse, and less specialised. Simply dropping new robots or data systems onto a shop floor and expecting workers to adapt has failed before. The gap is that no reliable method exists to predict how a human–technology partnership will actually perform before it is installed. DigiTOP will build a Digital Toolkit that captures real-time performance data from sensors, analyses it with machine learning, and visualises the results to guide decisions. The team will test it on four industry-defined use cases with partners including Jaguar Land Rover and BAE Systems. If it works, manufacturers will be able to de-risk investments in digital technologies—raising productivity while ensuring that older, less specialised workers can operate the new systems effectively. This is not about replacing people; it is about designing workplaces where people and machines complement each other from the start.
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The manufacturing industry, with the drive towards 'Industrie 4.0', is experiencing a significant shift towards Digital Manufacturing. This increased digitisation and interconnectivity of manufacturing processes is inevitably going to bring substantial change to worker roles and manual tasks by introducing new digital manufacturing technologies (DMT) to shop floor processes. At the same time, the manufacturing workforce is itself also changing - globally and nationally - comprising of an older, more mobile, more culturally diverse and less specialist / skilled labour pool. It may not be enough to simply assume that workers will adopt new roles bestowed upon them; to ensure successful worker acceptance and operational performance of a new system it is important to incorporate user requirements into Digital Manufacturing Technologies design. In the past, Human Factors has shaped the tools used in manufacturing, to make people safe, to make work easy, and to make the workforce more efficient. New approaches to capture and predict the impact of the changes that these new types of technologies, such as robotics, rapidly evolvable workspaces, and data-driven systems are required. These approaches consist of embedded sensor technologies for capture of workplace performance, machine learning and data analytics to synthesise and analyse these data, and new methods of visualisation to support decisions made, potentially in real-time, as to how digital manufacturing workplaces should function. The DigiTOP project will develop the new fundamental knowledge required to reliably and validly capture and predict the performance of a digital manufacturing workplace, integrating the actions and decision of people and technology. It will deliver this knowledge via a Digital Toolkit, which will have three elements: i) Specification of sensor integration and data analytics for performance capture in Digital Manufacturing ii) Quantitative analysis of the impact of four industrial Digital Manufacturing use cases iii) Online interactive tool(s) to support manufacturing decision making for implementation of Digital Manufacturing Technologies The DigiTOP project brings together a team with expertise in manufacturing, human factors, robotics and human computer interaction, to develop new methods to capture and predict the impact of Digital Manufacturing on future work. This project will work closely with a range of industry partners, including Jaguar Landrover, BAE Systems, Babcock International and the High Value Manufacturing Catapult to co-create industry-specified use cases to examine. The overall goal of DigiTOP is to produce a toolkit, derived from new fundamental engineering and science knowledge, that will enable industry to increase productivity, support Digital Manufacturing Technology adoption and de-risk the implementation of future Digital Manufacturing Technologies through the consideration of human requirements and capabilities.
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