Future of Sri Lankan Apparel Industry: Proposal for the B2B Sales Trend Analysis Using Machine Learning Approach

Abstract

ABSTRACT In the backdrop of global economic challenges, Sri Lanka's apparel export industry, a significant contributor to the nation's economy, faces threats amidst the country's severe economic crisis. Despite its reputation for ethical sourcing and high-quality garments, Sri Lanka's market share in the global garment industry is relatively small compared to the dominating country, China. Recognizing the challenges associated with this reduced market share, Expo Group of Industries, a leading engineering plant in Sri Lanka, acknowledges the necessity of adopting a data-driven approach to navigate complexities and maintain competitiveness. The company is committed to leveraging data-driven strategies to overcome industry challenges, ensuring it can continue to provide tailored solutions for its clients in the fashion industry. Sri Lanka is grappling with a severe economic crisis since March 2022, marked by a drastic drop in foreign reserves and a subsequent impact on industries, notably the apparel sector. The crisis, rooted in a dollar shortage and exacerbated by electricity tariff hikes and unfavorable tax policies, has led to increased production costs, shipping challenges, and delays in order fulfillment. The political and economic instability has eroded trust among foreign buyers, resulting in reduced orders and job losses in the apparel industry. Amid these challenges, a proposed research project aims to develop a tailored forecasting model using machine learning and time series analysis to improve B2B sales predictions in the Sri Lankan apparel industry, addressing a critical knowledge gap and offering practical insights for industry stakeholders. The study investigates sales forecasting techniques in the B2B apparel industry, revealing that SARIMAX, Random Forest Regression, and XGBoost are effective models. While LSTM lags due to data limitations, Random Forest Regression and XGBoost consistently outperform ARIMA-based models, with XGBoost emerging as the superior performer based on lower MSE, higher R2, and Explained Variance Score. These findings align with prior research highlighting the efficacy of machine learning models in sales prediction. The study fills gaps in B2B sales forecasting literature for the apparel industry, emphasizing the importance of data-driven decision-making and customer profiling for maximizing financial performance. Despite limitations, the research provides a robust foundation for evidence-based decision-making in navigating challenges and capitalizing on opportunities in the Sri Lankan apparel industry.