Strategic Integration of Machine Learning in Polymer Composite Development: A Framework for R&D Portfolio Management and Technological Adoption

Year : 2026 | Volume : 14 | Special Issue 01 | Page : 1272 2286
    By

    Anik Ghosh,

  • Ritam Rajak,

  1. Assistant Professor, Department of Management, Brainware University, Kolkata, West Bengal, India
  2. Assistant Professor, Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Moodlakatte Institute of Technology, Karnataka, India

Abstract

The progress of advanced polymer composites is slow, costly and unpredictable due to traditional methods of trial-and-error research. As materials informatics and data-driven modeling speed up the process of discovering technology, there exists a huge disconnect between computational predictions on one hand and strategic decision-making on the other in research and development (R&D). To solve this issue, this paper presents the Agile Materials-Intelligence (AMI) Framework, a systematic combined methodology that integrates materials informatics, techno-economic analysis, and portfolio management into a unified framework of governance. The AMI Framework offers formalized mechanisms for turning technical and probabilistic data into actionable business intelligence, which allows R&D leaders to make Go, No-Go, or Pivot decisions more objectively and with fewer uncertainties. Its useful implementation is exhibited using an automotive lightweighting case study where the framework helped in realizing significant cuts on the development period and total cost. In terms of establishing a fit between computational findings and strategic management activity, the AMI Framework redefines R&D as a responsive, data-driven, and value-generating activity. This integration gives organizations the capability to speed up innovation and efficiently allocate resources as well as enhance competitiveness in material-intensive industries. Finally, the AMI Framework will provide a revolutionized avenue for modernizing materials R&D to bridge the gap between digital prediction and possible real-world decision-making and open up novel innovative results within a shorter time and with higher reliability.

Keywords: Decision support systems, innovation management, materials informatics, polymer composites, R&D portfolio management, strategic management, techno-economic analysis.

[This article belongs to Special Issue under section in Journal of Polymer & Composites (jopc)]

How to cite this article:
Anik Ghosh, Ritam Rajak. Strategic Integration of Machine Learning in Polymer Composite Development: A Framework for R&D Portfolio Management and Technological Adoption. Journal of Polymer & Composites. 2026; 14(01):1272-2286.
How to cite this URL:
Anik Ghosh, Ritam Rajak. Strategic Integration of Machine Learning in Polymer Composite Development: A Framework for R&D Portfolio Management and Technological Adoption. Journal of Polymer & Composites. 2026; 14(01):1272-2286. Available from: https://journals.stmjournals.com/jopc/article=2026/view=237357


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Special Issue Subscription Review Article
Volume 14
Special Issue 01
Received 19/11/2025
Accepted 01/12/2025
Published 21/02/2026
Publication Time 94 Days
149

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