The mastery of manual skills and craftsmanship that once guided the textile and apparel industry is undergoing rapid metamorphosis, catalysed by technological advancements, most importantly, big data. This article analyses the various applications of big data in the textile value chain, ranging from trendsetting fashion forecasting to sustainable production processes and transparency in the supply chain.

The explosive technological advances being witnessed in the past few decades have led to the emergence of big data as one of the prominent industry disruptors, and the textile industry is no exception… What big data encompasses can be framed under the ‘3 Vs’ – volume, velocity, and variety; all of which need to be present simultaneously (Gandomi & Haider, 2015). The swift pace at which consumer needs and wants change, coupled with the dire necessity for sustainability, has propelled textile businesses to utilise data analytics to maintain operational efficiency and relevance in the market.

The Textile Ecosystem and Its Usage of Big Data Technologies
The textile sector is one of the industries that captures big data at different points of interaction:

  • Customer insights: Online shopping habits, customer reviews, social engagement
  • Process insights: Machine operation logs, sensor readings, production errors
  • Logistic insights: Delivery of goods, sales, stock levels, and purchase order timing
  • Environmental impact: Resource usage, waste generated, emissions output

Predictive analytics and prescriptive analytics offer guidance to companies after analysing these datasets, assisting businesses in waste reduction, faster go-to-market strategies, and personalised service offerings (Choi, Wallace, & Wang, 2018).

Big Data Integration into Textile Industries

1. Consumer Understanding and Fashion Prediction
The incorporation of textile big data has allowed real-time analytics to be utilised for fashion prediction. Edited and Trendalytics maintain AI systems that utilise big data to forecast colour, fabric, and silhouette trends for the upcoming seasons (Leach, 2022). The amalgamation of real-time data from Instagram posts, Google searches, and e-commerce facilitates smoother fashion trend adaptations. These data-centric predictions enable the reduction of overproduction by producing what consumers truly demand.

2. Optimising Production and Maintenance Predictions
The effectiveness of maintenance procedures relative to actual work done is heightened through big data, which facilitates workflow maintenance and predictive modelling. Machines equipped with IoT technologies monitor vibrations, temperature, and output, enabling system-driven failure predictions (Moeuf et al., 2020). In textile mills, this further enhances the production process by enabling longer-lasting machines, reduced downtime, and more efficient production mechanisms.

3. Dynamics of Supply Chain Transparency
Managing supply chains is yet another aspect that is currently benefiting from the implementation of big data. Demand analytics ensures accurate real-time goods monitoring, alongside other innovative algorithms designed to improve inventory. Documents authenticated through blockchain add another level of analytical power (Queiroz et al., 2019). An example would be the data-driven surge in sourcing and logistic strategies of H&M Group which aimed at adjusting to shifts in the international markets resulting in lower lead times and reducing surplus stocks.

4. Intelligent Quality Control
Defect detection AI and machine vision tools carry out quality inspections, which can identify more subtle defects compared to humans. As an example, Datacolor and Uster Technologies use high resolution cameras and processing fabrics in real time to monitor fabric consistency which result in reductions in rejections and returns (Datacolor, 2023).

5. Eco Friendly Manufacturing
Meeting sustainability goals has been made easy with data analytics. Regarding the operating impact of corporate activities, The Higg Index provides a research-based metric to evaluate environmental and social concerns (Sustainable Apparel Coalition, 2023). Brands can track energy and water usage as well as emissions throughout the entire production cycle, which allow for remediation and open reporting.

Case Studies

Zara (Inditex)
Zara is considered the earliest practitioner of data-driven fashion. The brand is able to bring new styles to the market in less than three weeks (Tokatli, 2008) using daily sales data, adjusting inventories, and designing new options based on store-level sales data.

Arvind Limited (India)
Arvind has implemented data analytics in the processes of manufacturing denim. With the aid of IoT sensors tracking water and energy usage, the company has achieved considerable reductions in resource usage (Arvind Ltd., 2022).

Levi Strauss & Co.
Levi’s employs predictive analytics to tailor collections for different regions, understand local preferences, and improve the efficiency of the global supply chains, thereby increasing customer satisfaction (Levi Strauss & Co., 2023).

Complications with Putting Strategies into Action
Even with all the promising opportunities big data presents, there are numerous challenges to consider:

  • Data Silo: IT infrastructure fragmentation usually inhibits the cross-functional integration of data (Ghosh, 2021).
  • Infrastructure and Skills Gap: SMEs in developing countries often do not have the necessary analytical capabilities or infrastructure to implement data solutions (UNIDO, 2020).
  • Ethical and Privacy Issues: The collection and processing of consumer data comes with concerns of consent, disclosure, and moral grounds (Tene & Polonetsky, 2013).

The Upcoming of Data-Powered Textiles
The integration of big data with AI, blockchain, and IoT is unlocking opportunities for smart textiles, hyper-personalisation, and fully traceable supply chains. Blockchain has already been implemented to verify organic cotton sourcing and AI models forecast demand at micro-market levels (Kamble, Gunasekaran, & Dhone, 2020). There is a need for educational institutions and industry bodies to actively participate in skill development programmes that promote the responsible use of data.