Modern textile supply chain systems are both large and complicated, with global sources and suppliers feeding into production lines that can span continents. A substantial amount of defects can’t be directly traced back to defective batches that entered the supply chain along the way, causing waste and frustration downstream. Traceability is almost impossible due to the number of stages the product goes through and the size of data involved. No single system is globally utilized to record and trace the product throughout the supply chain. By the time the root cause of the issue is discovered, no recourse is possible except to discard the end product, resulting in losses that could reach 40% of the end product value. Communicating quality issues cross-stream is virtually nonexistent due to the challenges in identifying the source and recognizing that the other systems can deal with utilizing it. While traceability is an obvious problem in textile supply chain, transparency is a more impactful issue that is not well addressed. Cross supply chain and lack of transparency exacerbates the problems facing each participant and forces each entity to work locally using the localized information. This approach is fundamentally flawed as it deals with a global problem from a localized point of view. Not all industries are ripe for taking advantage of blockchain technology. Blockchain requires an industry with a complicated and widely distributed supply chain, containing an increased number of middle stages. This cannot apply more than in one of the world’s oldest industries, textile. In this paper, we propose a complete blockchain-based framework for textile quality improvement that enables in near real time, cross chain information sharing with guaranteed authenticity and accuracy allowing quality defective batches to be identified in all systems as soon as they are detected in any few.
CITATION STYLE
ElMessiry, M., & ElMessiry, A. (2018). Blockchain framework for textile supply chain management: Improving transparency, traceability, and quality. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10974 LNCS, pp. 213–227). Springer Verlag. https://doi.org/10.1007/978-3-319-94478-4_15
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