Dynamic Stochastic Inventory Management in E-Grocery Retailing
David Winkelmann, Matthias Ulrich, Michael R\"omer, Roland Langrock,, and Hermann Jahnke

TL;DR
This paper develops a stochastic, multi-period inventory management model for e-grocery retailing, using a sequential decision process and Monte Carlo methods to improve replenishment decisions under demand uncertainty.
Contribution
It introduces a novel stochastic lookahead policy that explicitly incorporates probabilistic forecasts and uncertainty propagation for inventory replenishment in e-grocery.
Findings
The proposed policy outperforms myopic and deterministic approaches in simulations.
Explicit uncertainty modeling improves service levels and reduces stock-outs.
Case study demonstrates practical applicability with real-world data.
Abstract
E-grocery retailing enables ordering products online to be delivered at a future time slot chosen by the customer. This emerging field of business provides retailers with large and comprehensive new data sets, yet creates several challenges for the inventory management process. For example, the risk of a single item's stock-out leading to a complete cancellation of the shopping process is higher in e-grocery than in traditional store retailing. As a consequence, retailers aim at very high service level targets to provide satisfactory customer service and to ensure long-term business growth. When determining replenishment order quantities, it is of crucial importance to precisely account for the full uncertainty in the inventory process. This requires predictive and prescriptive analytics to (1) estimate suitable underlying probability distributions to represent the uncertainty caused by…
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Taxonomy
TopicsSupply Chain and Inventory Management · Forecasting Techniques and Applications · Aviation Industry Analysis and Trends
Methodstravel james
