Enhancing Demand Forecast Accuracy for FMCG Products Using SupplySeers Time Series Models and Permutation Complexity

This paper focuses on enhancing demand forecasting accuracy for Fast-Moving Consumer Goods (FMCG) using innovative methodologies, specifically SupplySeers Time Series Models and the concept of permutation complexity. With the challenges of traditional forecasting methods, which often fail to account for the complexities of consumer behaviour and market volatility, this research seeks to provide a robust framework to improve predictive performance. The study adopts a quantitative and experimental research design, which includes phases of data preparation, exploratory data analysis, model development, and evaluation. Key findings indicate that SupplySeers models significantly outperform traditional methods such as ARIMA and Holt-Winters, particularly in capturing non-linear and seasonal trends typical in FMCG sales data. Additionally, permutation complexity serves as an effective metric for evaluating time series predictability, facilitating tailored model selection based on the complexity level of the data. A proposed hybrid forecasting model integrates SupplySeers Time Series Engine with permutation complexity filtering, allowing for dynamic adaptation to varying demand patterns. This approach not only enhances forecast accuracy by up to 25% compared to standalone models but also offers a scalable solution applicable to diverse FMCG datasets. The implications of this research are far-reaching, providing FMCG companies with the tools to optimize inventory management and enhance decision-making processes. The study concludes with actionable recommendations for stakeholders to adopt complexity-aware forecasting systems, ensuring better anticipation of demand fluctuations and improved market responsiveness.