This paper explores the complexities of temperature-sensitive food supply chains and the role of third-party logistics (3PL) providers in managing them. Specifically, we partner with the world's second-largest cold chain 3PL provider to establish a dynamic Sales and Operations Planning (S&OP) process for their warehousing services. In this project, we propose a novel inventory forecasting framework that could complement the S&OP process proposed aimed at aligning supply and demand, optimizing inventory levels, and preserving the quality of temperature-sensitive food. We start by reviewing the related literature on food cold chain management and demand forecasting methods, complemented by qualitative interviews with key roles within the company. We select a specific warehousing site for our minimum viable product and extracted the data of inventory positions for every customer during a 3.5-year period. We propose segmentation criteria based on customer inventory size and variability and develop forecasting models for each segment and used Seasonal Autoregressive Integrated Moving Average (SARIMA) and Facebook Prophet. The accuracy of the models is measured using the Mean Absolute Percentage Error (MAPE) performance metric. The valuable insights offered by the forecasting models allows us to propose create additional freezer capacity at the site. We also identify underutilized space in the cooler segments that could potentially be repurposed to increase freezer capacity. Finally, we discuss next steps regarding the opportunities to improve the forecasting models and scale the dynamic S&OP process across the entire company network. Overall, our findings highlight the importance of a dynamic S&OP process for 3PL providers in managing temperature-sensitive food supply chains effectively. The insights from our inventory forecasting framework can help 3PL providers to optimize their operations, better align supply and demand, and preserve the quality of temperature-sensitive food throughout the supply chain.