Comparative Analysis of Demand Forecasting Methods to Optimize Supply Chain Efficiency in PharmaHealth Group

Abstract : PharmaHealth Group encounters significant challenges in its supply chain distribution due to the pharmaceutical industry’s demand for rapid responsiveness and the high risk of demand fluctuations, particularly during events like the COVID-19 pandemic. Additional complexities include the short shelf-life of pharmaceutical products and extensive quality control processes mandated by strict regulations. This study compares advanced demand forecasting methods to address these issues and optimize supply chain efficiency. The research examines three forecasting techniques: Holt-Winters, ARIMA (Autoregressive Integrated Moving Average), and the hybrid ARHOW (ARIMA & Holt-Winters additive) model. The Holt-Winters method, effective for time series data with trends and seasonal patterns, improves supply chain management but has limitations in inventory forecasting. ARIMA, known for its simplicity and effectiveness in capturing trends and seasonality, faces challenges with non-linear data and the need for stationarity. The hybrid ARHOW model combines the strengths of both Holt-Winters and ARIMA, offering enhanced forecasting accuracy and efficiency. By analyzing these methods, the study highlights the potential of hybrid approaches like ARHOW to address PharmaHealth Group’s unique supply chain challenges, leading to improved inventory management and overall supply chain performance.