Rolling horizon product quality estimation and online optimisation for supply chain management of perishable inventory

We introduce new methods inspired from dynamical systems and control theory for estimating the quality of perishable products in inventory in a supply chain based on measurable data. A state-space representation of the supply chain with perishable inventory is constructed from which controllability and observability properties are established to derive inventory management and quality estimation strategies with guaranteed performance. Rolling horizon state estimation is formulated to estimate the quality of inventory at locations where measurements are not available. Observability and controllability properties then allow us to formulate an online optimisation framework inspired by model predictive control, that defines an implicit supply chain management policy. Numerical experiments demonstrate the performance of the proposed state estimation and online optimisation approach, as well as its benefits for supply chain optimisation ( $ \sim 40 $ ∼40% improvement in the cost objective relative to the baseline model without state estimation).