One of the implicit assumptions made in research related to inventory control is to keep products indefinitely in inventory to meet future demand. However, such an assumption is not true for a large wide of products characterized by a limited lifetime. The economic impact of managing such products led to substantial work in perishable inventory control literature. Investigations developed so far underline the complexity of modeling perishable inventory. Moreover, the dependency of the lifetime to temperature conditions in which products are handled adds more complexity since the lifetime of products stemming from the same order may vary from product to another. In this context, the ability of Time Temperature Integrators to capture the effects of temperature variations on products’ lifetime, offers an opportunity to reduce spoilage and therefore ensure product’s freshness and safety. The general aim of this thesis is to model perishable inventory systems. Particularly, three different problem areas are considered. The first one concerns perishable inventory with fixed lifetime, often referred as Fixed Life Perishability Problem, where an approximate (r;Q) inventory policy is developed. This model relaxes some assumptions made in previous related works. The second problem considered is a (T; S) perishable inventory system with random lifetime. Results of this model contribute to the development of a theoretical background for perishable inventory systems which are based on Markov renewal process approach. The third area incorporates the impact of temperature variations on products’ lifetime throughout inventory systems that use TTIs technology. More general settings regarding the demand and the lifetime distributions are considered throughout simulation analysis. The economic relevance stemming from the deployment of this technology is therefore quantified.