Analyzing the Impact of Big Data Analytics on Supply Chain Efficiency in the Fashion Industry
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Abstract
The fashion industry has seen tremendous growth in recent years, fueled by rising consumer spending and the expansion of fast fashion brands. However, the industry is also plagued by inefficient supply chains that lead to excessive waste, lost sales, and sustainability issues. Big data analytics has emerged as a solution to enhance supply chain efficiency in the fashion industry. This research paper analyzes the impact of big data techniques on key supply chain processes including demand forecasting, inventory optimization, supplier selection, and delivery management. Both quantitative and qualitative data were collected through surveys of fashion companies and supply chain experts as well as case studies of leading fashion brands implementing big data analytics. The findings indicate that big data enables more accurate demand forecasts, personalized product recommendations, optimized inventory levels, improved supplier reliability, and on-time delivery performance. Fashion companies utilizing big data supply chain solutions demonstrated 20-50% improvements on efficiency metrics compared to industry benchmarks. However, challenges remain regarding data quality, integrating disparate data systems, and reluctance adapting to data-driven culture. To maximize the effectiveness of big data in the fashion supply chain, companies need to ensure information accuracy, provide actionable insights for decision-makers, and invest in change management and skills training while being cognizant of ethical risks.
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Last updated: 05-02-2023