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Article
Publication date: 9 December 2024

Sufyan Sikander, Afshan Naseem, Asjad Shahzad, Muhammad Jawad Akhtar and Ali Salman

In recent years, especially after the COVID-19 pandemic, home textile production orders decreased significantly. This sudden drop in production has increased industry competition…

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Abstract

Purpose

In recent years, especially after the COVID-19 pandemic, home textile production orders decreased significantly. This sudden drop in production has increased industry competition, making customer satisfaction more challenging. As a result, it has become imperative for the industry to deftly navigate such ongoing challenges.

Design/methodology/approach

This study examines textile production efficiency methodically. Customer requirements like quality, on-time delivery, better working conditions, cost-effectiveness and facility safety audits are understood first. Quality function deployment (QFD) turns client requirements into technical requirements. Prioritise and analyse risks using Monte Carlo simulation and Pareto charts. Consequently, experts and literature propose corrective measures, which are tested in a pilot run to see how they affect production.

Findings

QFD, define, measure, analyse, improve and control (DMAIC) and Monte Carlo simulation were used to reduce high-priority risks and meet client requirements in this study. The house of quality helped relate customers’ requirements and technical requirements. Monte Carlo simulation has also improved risk prioritisation by providing a flexible mathematical structure for identifying and managing the most important risks.

Originality/value

This study is novel in the way it applies this integrated approach to the understudied home textile sector. Unlike traditional DMAIC, this study introduces a novel matrix encompassing all defects. This study offers a data-driven approach to improve product quality, meet customer expectations and reduce prioritised risks in home textile manufacturing.

Details

International Journal of Productivity and Performance Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0401

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