e-space
Manchester Metropolitan University's Research Repository

    Human-centric research of skills and decision-making capacity in fashion garment manufacturing to support robotic design tool development

    Thiel, Katharina and Postlethwaite, Susan (2023) Human-centric research of skills and decision-making capacity in fashion garment manufacturing to support robotic design tool development. In: 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023), 20 July 2023 - 24 July 2023, San Francisco, California, USA.

    [img]
    Preview
    Published Version
    Available under License Creative Commons Attribution.

    Download (821kB) | Preview

    Abstract

    This paper examines the findings of research combining Human Factors methods with Fashion Design Practice Research to identify existing skills levels of UK sewing machinists, assessing the interest in integrating robotic tooling into low-volume high-value fashion design workflows to help an upskilling and onshoring agenda for UK SME fashion manufacturing. Despite its international reputation for creative design and contributing £32.3 billion to the UK economy (Oxford Economics, 2018), the UK’s fashion industry's levels of automation are much lower than other sectors. Amongst young people who might enter the industry a lack of interest in manufacturing, anxieties about modern-day slavery, poor working conditions, precarity in the jobs market, low levels of pay and training are exacerbating the situation. The challenges of integrating automation, robotics and engineering into a highly creative UK fashion sector with a need for very high levels of agility in micro-production processes can be addressed through joint research from Human Factors and design-led research. This project explored skills levels in garment manufacturing, to inform the steps in research of new tooling concerned with identifying tasks that can be performed by robots, or those needing to remain performed by skilled human makers - importantly identifying requirements for promoting worker satisfaction via new technology and automation. The research evidences sewing machinists’ need for better work fulfilment and personal reward. Currently, the UK fashion manufacturing sector lacks systems that support the application of transferable skills to rejuvenate the jobs market with opportunities that can inspire and entice a young workforce to enter what could be a dynamic field. In a mixed methods study, researchers used questionnaires, desk research, eye-tracking and heart-rate monitoring to evidence cognitive decision-making and tacit/tactile knowledge of sewing machinists. Participants of the questionnaire and eye-tracking trials stressed a sense of reward as one of the main drivers for fulfilment during a sewing project. Investigating the development of new tooling in the context of creatively rewarding activity is therefore a critical next step in design research with Human Factors. This study has delivered perspectives on ways to increase collaboration capability between social science and fashion design research to innovate within manufacturing processes amidst a growing skills shortage in the UK. This tightly limited scope study has been an ideal way of demonstrating the value in this area of research as a platform for a larger collaborative piece of work in the future with a focus on co-investigating, with micro and SME fashion design and robotics businesses, what kind of small-scale tools might need to be designed to enable new forms of on-shored production, leading naturally to a new design aesthetic. These cobot systems could support decision-making for fabrication sequencing. There is already potential for interactive robots to be mobile on desktops as well as self-assembling swarms - concepts that can help to address further development aims for garment manufacturing.

    Impact and Reach

    Statistics

    Activity Overview
    6 month trend
    313Downloads
    6 month trend
    133Hits

    Additional statistics for this dataset are available via IRStats2.

    Altmetric

    Repository staff only

    Edit record Edit record