This study investigates the quantitative relationship between common weather variables and weekly sales in the Non-Alcoholic Ready-To-Drink (NARTD) industry in the Dutch market. Furthermore, this study explores how these weather variables can improve demand forecasting. The analysis is conducted on segment-level data, provided by Coca-Cola Europacific Partners (CCEP). The study is structured in two parts: (1) an explanatory analysis using Simple Linear Regression, Multiple Linear Regression and Random Forest. In this section, the models are evaluated based on statistical significance, explained variance and feature importance scores. (2) a predictive analysis using Seasonal Naïve, SARIMA, SARIMAX and XGBoost. In this section, the models’ accuracies are evaluated based on Mean Absolute Percentage Error (MAPE).  Across segments, the results indicate that temperature- and sunshine-related variables have a positive impact in most segments, whereas precipitation and wind speed often have a negative impact on sales. COLA, ICE TEA and WATER were identified as the most weather sensitive segments, as they show the highest explained variance and the greatest improvement in MAPE when adding weather variables. In contrast, FRUIT BASED and FUNCTIONAL were identified as the least weather sensitive segments. Furthermore, the non-linear models outperform the linear models, suggesting a non-linear relationship between weather variables and sales. Although data at the national level offers useful insights, future research could improve by including additional external variables such as promotions and events, adding international-level data, exploring more models tailored to time-series such as Prophet and Long Short-Term Memory (LSTM) Networks, and accounting for the inherent error of weather forecasts.