Conceptual framework for a stochastic (r,Q) inventory policy with storage-related environmental metrics

Many steps have been taken to estimate greenhouse gas emissions related to transport and operations of inventories, with a particular effort to adapt them to reordering policy models. When talking about inventory management, the construction of a warehouse is also a main driver of greenhouse gas emissions. This driver can be measured through the embodied carbon of the storage building, which quantifies the equivalent carbon dioxide emissions related to its construction process. Following a literature landscape characterization regarding different reordering policies and inventory-related environmental aspects, an original framework for sustainable inventory control is developed with the aim of finding the reorder level and quantity (r,Q) considering warehouse embodied carbon. One of the main parameters for a reordering policy is product demand and, especially for retailers which are in the downstream end of the supply chain, the inventory needs to be modeled considering some variability to cope with unpredictable customer orders. To deal with this variability, which has been a big focus in the inventory control literature, demand can be considered as stochastic. Depending on the type of a product and its consumption rate, the demand's stochastic representation needs to change for more realistic modeling of such a scenario. The proposed framework considers the relevant stochastic aspects related to product demand during supply lead-time and it focuses on the main embodied carbon drivers to allow the modeling of a bi-objective reordering policy for the continuous-review inventory of a single product. The bi-objective nature of this problem is given by the economic and environmental perspectives, and it can be tackled using multicriteria decision-making methods.