La recherche en management manque de praticité. Les chercheurs en management ont tendance à se consacrer à des travaux théoriques déconnectés de la vie en entreprise et de n’écrire que pour leur communauté. Une revue systématique des implications pratiques des articles publiés dans des revues de management montre qu’elles contiennent une majorité de propositions triviales, c’est-à-dire des propositions qui n’avancent pas des recommandations qui puissent être mise en pratique. Pour améliorer cette situation, les chercheurs en management sont encouragés à utiliser un langage synthétique lorsqu’ils écrivent les implications pratiques de leurs travaux.

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Robotic warehouses have transformed logistics, prioritizing speed and efficiency. However, traditional static priority systems often leave low-priority customers facing excessive delays, raising concerns about fairness. This research, based on Invia, a robotic warehouse company, proposes a dynamic priority allocation model to balance efficiency and fairness. By adjusting order priorities over time, this approach ensures that both high-priority and long-waiting low-priority orders receive timely fulfillment. Through stochastic modeling and simulations, we demonstrate that dynamic prioritization reduces delays compared to static and first-come, first-served (FCFS) models. Case studies in e-commerce and healthcare logistics illustrate the broader impact of fairness in automation. As industries increasingly rely on AI-driven decision-making, the balance between efficiency and equity becomes critical. This research challenges the assumption that robotic warehouses should optimize for speed alone and advocates for a future where fairness plays a central role in automated commerce.
YUAN Zhe - EMLV |
- Recherche
- Logistique et Supply Chain, Transformation Digitale