Integration of Quality Function Deployment and Monte Carlo Simulation for risk prioritization in a textile sector
Sufyan Sikander, Afshan Naseem, Asjad Shahzad, Ali SalmanPurpose
Following the COVID-19 pandemic, the home-textile industry has experienced a decline in production orders, heightening competition and making it more difficult to maintain consistent customer satisfaction and operational performance. To address these challenges, this study employs a structured, simulation-based Quality Function Deployment (QFD) framework integrated with Monte Carlo Simulation (MCS) to identify, quantify and prioritize key process risks within textile manufacturing. Through this integrated approach, strategic performance alignment is achieved by mapping customer-driven requirements to operational risk data, enabling the systematic evaluation of process efficiency and reliability under uncertainty. The framework thus contributes to Strategic Alignment Theory by providing an empirical foundation for evaluating and maintaining a strategic fit between external market demands and plant-level operational capabilities.
Design/methodology/approach
The study starts with the identification of core customer requirements, specifically targeting improved quality, prompt delivery schedules, better working conditions, cost-effectiveness and overall safety in the workplace. These requirements are translated into technical requirements through QFD. MCS is then used to prioritize the identified risks by modeling uncertainty in the risk matrix. The study is conducted in the home-textile industry, where the following integrated approach remains underexplored.
Findings
The risks were categorized according to severity, and four high-priority risks, i.e. R1, R9, R11 and R3 were identified at the 95th percentile level. These risks represent major operational vulnerabilities, ranging from production-process disruptions to efficiency bottlenecks.
Originality/value
This study contributes to Strategic Alignment Theory (specifically the concept of Strategic Fit) within the performance management field by introducing a structured, probabilistic framework that operationalizes customer-driven requirements. By integrating QFD with MCS, it extends the literature on Strategic Performance Measurement Systems (SPMS) beyond traditional deterministic scoring. Specifically, existing theories of Strategic Fit are amended to incorporate statistical variability, effectively bridging the theoretical gap between static quality planning and stochastic operational risk management. The study proposes a novel extension to the conventional House of Quality by embedding simulated probability distributions directly into risk prioritization. This advancement shifts the theoretical focus from static, descriptive risk assessments to dynamic, probability-driven evaluations, redefining how operational vulnerabilities are quantified and managed in process-driven manufacturing sectors.