AI Adoption in Textile Supply Chains Faces Integration Challenges: Textiles Intelligence Report

A report titled “Artificial intelligence (AI) in the textile and apparel supply chain” by Textiles Intelligence examines the use of AI across design, manufacturing, and distribution. The study highlights efficiency gains alongside structural and operational challenges in implementation.

A 13-page report, “Artificial intelligence (AI) in the textile and apparel supply chain”, published by Textiles Intelligence, outlines how companies are applying artificial intelligence to improve efficiency across different stages of the textile and apparel value chain.

The report states that AI adoption is contributing to a more data-driven and responsive value chain, with increased focus on managing waste. In the design phase, AI is being linked to historic sell-through data to support decision-making based on past performance. In manufacturing, it is used to enhance process stability, reduce defects, and identify potential issues before they result in downtime. In distribution, AI supports logistics planning through route optimisation, shipment consolidation, exception management, and risk detection with alternative routing or production adjustments.

According to the report, the full potential of AI across these stages depends on its use as an integrated system rather than as separate tools. This level of integration is not yet widely achieved.

One factor identified is the fragmented nature of the textile and apparel value chain, where different stakeholders operate with distinct economic priorities. For example, a mill focused on machine utilisation may not directly benefit from a brand aiming to reduce markdown risk, despite commercial links between them.

The report also notes organisational challenges related to transparency and coordination. Integrating systems across stages requires data sharing, which may face resistance. Design teams may be reluctant to adopt constraints based on manufacturing data, manufacturers may be cautious about revealing inefficiencies, and commercial teams may question forecasts influenced by production factors.

It states that achieving integration requires alignment of objectives across stakeholders and acceptance of trade-offs at a system level rather than optimising individual stages independently.

Despite these challenges, the report indicates that reducing inefficiencies such as overproduction, oversupply, and markdowns is becoming necessary. It also notes that a fully connected system linking design, manufacturing, and distribution is both technically feasible and strategically relevant.

The current situation is described as a network of partially connected systems rather than a unified structure. The report suggests that as incentives and data systems develop, these systems may become more integrated over time.

It concludes that, in such a scenario, AI would function as an embedded infrastructure supporting decision-making across the value chain, aligning outcomes between different stages in both apparel and home textiles markets.