A Novel Hybrid Approach of QFD- Monte Carlo – DMAIC For Risk Prioritization in Textile Sector

Abstract

n recent years, particularly in the aftermath of COVID-19 pandemic, there has been a drastic decline in the number of production orders in the realm of home textile industry. This abrupt decline in production volume has significantly contributed to an increase in competition within the industry, exacerbating the already-difficult task of maintaining customer satisfaction. As a result, it has become imperative for the industry to deftly navigate such ongoing challenges; therefore, this scholarly work delves into systematic approach to efficiently improve production processes in textile sector. It begins by understanding critical customer needs, including higher quality, on time delivery, improved working conditions, cost-effectiveness, and safety audits within facilities. Thereafter, customer requirements are translated into technical specifications using the Quality Function Deployment (QFD). The study employs Monte Carlo simulation to prioritize risks and uses statistical tools like Pareto charts in Minitab software to analyse current risk conditions. As it is observed that home textile sector has remained an untapped domain, devoid of significant research, the application of this integrated approach in the home textile sector, and the commencement of research within textile industry at large, underscore the novelty of this study. Also, innovating beyond conventional DMAIC approach, this study pioneers a novel matrix that comprehensively encapsulates the entire spectrum of defects, transcending traditional matrices. Consequently, corrective measures suggested by experts and literature are implemented and evaluated in a pilot run, demonstrating the impact of improvements on the production processes. This research offers a structured, data-driven approach to enhance product quality, meet customer expectations, and mitigate prioritized risks in home textile manufacturing industry.