INTEGRATING HETEROGENEOUS DIGITAL TECHNOLOGIES IN HEAVY EQUIPMENT SUPPLY CHAINS: A SYSTEMATIC LITERATURE REVIEW
Published:
2026-09-14Downloads
Abstract
Heavy equipment supply chains face increasing pressure to adopt digital technologies such as Digital Twins, Machine Learning, the Internet of Things (IoT), Blockchain, and Artificial Intelligence (AI). These technologies offer tangible benefits, including predictive maintenance, real-time visibility, and end-to-end traceability. However, integrating heterogeneous technologies creates technical and organizational challenges that remain insufficiently understood, particularly in the context of complex, long-lived, and capital-intensive equipment. This study conducts a systematic literature review of studies published from 2016 to 2026 to address three research questions: (RQ1) Which digital technologies are currently used in heavy equipment supply chains? (RQ2) What barriers hinder seamless integration among these systems? (RQ3) What middleware solutions or architectural approaches have been proposed to overcome these challenges? Following the PRISMA 2020 guidelines, literature searches were conducted across SciSpace, Google Scholar, ScienceDirect, and ResearchGate using terms related to digital technologies in the sector. From 679 initial records, 66 studies met the inclusion criteria and were analyzed. The findings indicate that Digital Twin is the most extensively studied technology, appearing in 34 studies (59.6%), followed by IoT in 25 studies (43.9%), Blockchain in 18 studies (31.6%), and AI/ML in 15 studies (26.3%). The central finding is that improving heavy equipment supply chain performance requires not merely technology adoption, but the integration of heterogeneous digital technologies through interoperable middleware and enabling architectures capable of overcoming data, connectivity, interoperability, governance, and organizational barriers.
Keywords:
Artificial Intelligence Blockchain Digital Twins Internet of Things Middleware ArchitecturesReferences
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