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This responsive R&D Future Fashion Factory funded project supported a collaboration between Burberry and the University of Leeds to explore digital-color and data-driven design workflows, evaluating their effectiveness within contemporary Fashion and Textiles supply-chains. In principle, data-driven approaches prioritize decisions based on technical processes and data analysis over individual experience or personal intuition with the aim being to produce the right product. The study incorporates perspectives from the design and technology interface to assess the reliability of technical color-judgments. It considers how trust in measured color-decisions impacts on the Digital Transformation objectives of streamlining processes, improving business efficiency, and delivering on sustainability goals through digitally driven manufacturing processes. Reliable color accuracy and consistency can be fundamental in the assessment and successful integration of digital design workflows. The implications of this innovative research are positioned within the broader context of industry transformation and the urgent need to make sustainable progress in the reform of traditional Fashion & Textile manufacturing. The opportunities it presents are closely aligned with the growing demand for compliance in meeting international legislation, the mandate for transparent reductions in CO2 emissions, material waste, and the adoption of low-impact manufacturing practices. Color, although recognized as integral to the creative design process, is seldom mentioned as a factor influencing successful Digital Transformation. This is perhaps reasonable considering the maturity of color-measurement technologies and their integration with established CAD-software, both analogue and digital manufacturing. A systematic evolution of the scientific instruments and routines used to measure color, spectrophotometry and calibration routines, has been conducted assessing reliability for absolute, quantitative, and reproducible color measurement. Experimental results raise questions regarding the complexities of technical-protocols revealing both scope for human-error and potential drifts in machine accuracy. At this initial scoping stage, the primary objective is to deepen our understanding of the complexities and challenges inherent in digital color workflows. This includes identifying existing skill gaps and exploring opportunities for upskilling that can be effectively addressed through targeted education and training initiatives. It is widely recognized that embedding industry-relevant skills into fashion and textiles education promotes positive change through broader sustainable development. Analysis of Future Fashion Factory success stories helps to evidence how utilizing Industry 4.0 digital textile innovations are enhancing the design and creation of fashion and textile products. In the longer term, this investigation holds significant potential to contribute to the reduction of CO2 emissions and material waste. By enabling accurate, first-time-right color decisions, it can reduce the need for costly sampling and fabric batch approval processes, thereby supporting more efficient, data-informed digital manufacturing practices.
Henry et al. (Thu,) studied this question.