Supply chain decisions involve activities such as sourcing of material, production of goods, and logistics. Demand forecasting plays a critical role by providing accurate order quantities to meet consumer demands, reduce costs, improve replenishment processes and maintain optimal inventory levels. However, forecasting perishable goods can be a challenge. Furthermore, without proper information sharing between retailers and suppliers, mismanagement of order quantities and production can occur. This research aims to improve inventory management for a case study of a retailer located in the United Arab Emirates that is facing issues with its replenishment and forecasting processes. To achieve this purpose, forecasting models are tested for the case study. Also, to facilitate adopting the best forecast model, a collaborative framework is proposed. Finally, the collaboration benefits are illustrated using simulation, considering several scenarios and parameters. The results show that forecast models ARIMA and avg. ARIMA + triple exponential smoothing models perform the best and have the least errors. In addition, adopting the CPFR collaborative framework could reduce the transportation cost for some retail items by 42%.