Predictive and Adaptive Manufacturing

Renjithkumar Surendran Pillai; Patrick Denny; Eoin O'connell

doi:10.62127/aijmr.2026.v04i01.1193

Predictive and Adaptive Manufacturing

Author(s)	Renjithkumar Surendran Pillai, Patrick Denny, Eoin O'connell
Country	Ireland
Abstract	This comprehensive analysis explores the integration of digital technologies into the manufacturing industry, with a particular focus on developing and implementing predictive and adaptive frameworks aimed at achieving digital maturity. The study outlines the creation of a Minimum Viable Product (MVP) and its design specifications, primarily intended to enhance digital transformation in manufacturing. The report also emphasises the importance of assessing digital maturity as a prerequisite for implementing predictive systems. It identifies significant gaps in current systems through a thorough literature review. The research highlights the transformative potential of digital technologies in driving digital transformation and streamlining manufacturing processes. By delving into the complexities of implementing predictive and adaptive systems, the study identifies critical gaps in existing frameworks. It emphasises the necessity of structured models, such as the Digital Plant Maturity Model (DPMM), to enhance digital capabilities. Additionally, the role of digital twins is highlighted for their ability to optimise processes and enable real-time monitoring, which significantly improves operational efficiency and product quality in pharmaceutical manufacturing. Furthermore, the research outlines future directions and emerging trends in digital transformation, including the ongoing evolution of digital twins, advanced analytics, and scalable integration solutions. The study encourages the development of a culture centred on continuous improvement, empowering employees to embrace innovation and adapt to dynamic industry standards, thereby ensuring long-term sustainability. In summary, this research aims to provide a clearer understanding of the transformative impact of digital technologies within the manufacturing sector. By reshaping maintenance practices and optimising operational efficiency, the study seeks to drive the industry towards achieving digital maturity, overcoming challenges associated with legacy systems, and maximising the benefits of advanced digital frameworks. It offers evidence-based recommendations and practical solutions, highlighting the importance of a holistic approach to digital transformation in fostering a sustainable and competitive manufacturing ecosystem
Keywords	Gazer 3D, Digital Twin (DT), Predictive Maintenance, Immersive Visualization, Artificial Intelligence, 3D Visualization.
Discipline	Engineering
Published In	Volume 4, Issue 1, January-February 2026
Published On	2026-02-14
DOI	https://doi.org/10.62127/aijmr.2026.v04i01.1193

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Advanced International Journal of Multidisciplinary Research

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Predictive and Adaptive Manufacturing

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