Adapting Statistical Thinking in Traditional Manufacturing Contexts - Nugroho, Fajar (Journal of Sustainability Industrial Engineering and Management System, 2024)
This study explores the adaptation of statistical thinking in traditional manufacturing contexts, emphasizing its significance as a cognitive and cultural framework for improving quality, reducing process variation, and fostering data-informed decision-making. While statistical thinking has become a cornerstone in modern manufacturing systems, its integration into traditional environments—dominated by legacy processes, experiential judgment, and minimal technological infrastructure—remains limited. The purpose of this research is to analyze the current landscape of statistical awareness in such settings and to identify both enablers and barriers to its broader adoption. Employing a qualitative methodology based on an integrative literature review, this study synthesizes evidence from peer-reviewed articles, industry case studies, and theoretical frameworks published between 2000 and 2024. Thematic analysis reveals a persistent gap in statistical literacy among employees, cultural resistance among leaders, and technological limitations that impede implementation. However, emerging practices—such as contextualized training, policy interventions, and leadership-driven cultural change—offer promising pathways for sustainable integration. The findings contribute to both academic and managerial discourse by reframing statistical thinking as a holistic organizational capability rather than a technical function. This research advocates for a strategic and inclusive approach that combines education, infrastructure, and leadership to foster statistical maturity in resource-constrained manufacturing environments. In doing so, it supports long-term resilience, continuous improvement, and competitiveness in the face of global industrial transformation.