THE APPLICATION OF TIME SERIES ANALYSIS TO PREDICT MARKET TRENDS AND OPTIMIZE SUPPLY CHAIN PLANNING

This study explores the application of time series analysis in predicting market trends and optimizing supply chain planning from 2020 to 2024. The research employs a mixed-methods approach, integrating quantitative time series models such as ARIMA, SARIMA, and machine learning-based forecasting, alongside qualitative insights from industry experts. The major findings indicate a strong correlation (r = 0.91) between improved forecasting accuracy and supply chain efficiency. Market growth increased from 2.5% in 2020 to 5% in 2024, while supply chain disruptions declined from 15% to 5%. Demand forecasting error reduced from 4% in 2020 to 1.7% in 2024, demonstrating enhanced predictive precision. Regression analysis results show a decreasing variance in supply chain delay predictions, with errors dropping from 20% in 2020 to 0% in 2024. The study concludes that leveraging time series techniques significantly enhances supply chain agility and operational efficiency. It recommends the adoption of hybrid forecasting models, real-time data integration, and enhanced workforce training in predictive analytics to sustain these improvements.