Integrating Data-Driven Continuous Improvement Frameworks within ERP-Enabled Manufacturing Environments

Abstract

Aims: This study aims to explore the integration of data-driven Continuous Improvement (CI) frameworks with Enterprise Resource Planning (ERP) systems in manufacturing environments to enhance operational efficiency and competitiveness. Specifically, it seeks to determine how ERP-generated data can support real-time monitoring, predictive maintenance, process optimization, and strategic decision-making. Study Design: A systematic literature review was conducted, synthesizing empirical and conceptual studies published between 2010 and 2025 across databases including Scopus, ScienceDirect, and IEEE Xplore, as well as reputable industry reports. The approach emphasizes data-driven methodologies and evidence-based insights from manufacturing contexts across various regions. Methodology: The review analyzed studies addressing ERP-enabled CI architectures, analytics engines, visualization dashboards, and data governance mechanisms within both on-premise and cloud-based infrastructures. Key themes analyzed include data integration architectures, analytics engines, visualization dashboards, governance mechanisms, and scalability within cloud-based environments. The review further assesses critical success factors and implementation challenges reported across the studies. Results: Evidence shows that ERP-integrated CI systems yield 15–45% reductions in cycle time, 10–30% improvements in cost efficiency, and up to 25% defect reduction across manufacturing sectors. High-performing implementations consistently demonstrated robust data governance, real-time analytics, and employee engagement in feedback loops. Conversely, recurring barriers included poor data quality (reported in 38% of studies), organizational resistance (29%), and integration costs (24%). Conclusion: The findings confirm that ERP-driven, data-centric CI frameworks substantially enhance manufacturing performance by transforming ERP systems from transactional platforms into analytical and learning-oriented ecosystems. The study proposes a structured reference framework linking ERP data architecture, CI methodologies, and organizational capabilities, offering actionable guidance for firms pursuing digital transformation and operational excellence.