Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{204591,
        author = {Shamdutt Kamble},
        title = {From Data to Decisions – Rethinking Smart Manufacturing for High-Mix Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2796-2800},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204591},
        abstract = {High-mix, make-to-order (MTO) manufacturing environments present significant challenges in achieving truly data-driven operations due to dynamic routing, variable cycle times, and non-uniform process execution. While Manufacturing Execution Systems (MES) and Track & Trace solutions have improved operational visibility, they have not enabled the transition from data availability to decision intelligence. This limitation arises from incomplete data architectures and the absence of integrated closed-loop mechanisms. 
This paper proposes a five-stage data integrity framework—comprising Digital Plan, Digital Execution, Control, Decision, and Actions—integrated with a nine-dimensional traceability model to ensure complete, contextual, and reliable manufacturing data. The study demonstrates that analytics maturity is structurally dependent on both lifecycle completeness and data dimensional completeness. It further shows that isolated Track & Trace implementations capture discrete events but fail to represent operational reality, resulting in limited diagnostic insight and unreliable predictive capability. 
The proposed framework redefines the pathway from data to decisions by establishing a closed-loop cyber-physical system, enabling descriptive, diagnostic, predictive, and prescriptive analytics. By ensuring data completeness and embedding decision and control loops, the framework provides a systematic foundation for self-correcting, intelligent manufacturing systems operating in high-mix, variable environments.},
        keywords = {Industry 4.0, Make-to-Order Manufacturing, Data Integrity, MES, Traceability, Closed-Loop Manufacturing, Smart Factory},
        month = {June},
        }