Citation:

Mahir Anwar, 2026. AI Copilots for Warehouse Supervisors: Human-in-the-Loop Decisioning in SAP EWM,  Volume 6 Issue 3: 130-137.

Abstract:

AI copilots may enhance SAP Extended Warehouse Management beyond mere transactions, enabling warehouse supervisors to make better decisions on warehouse-related issues, such as wave release, workload balancing and reallocation, replenishment escalation, and exception resolution. Unlike fully automated control, the human-in-the-loop use case considered here keeps recommendations subject to human judgment, accountability, and intervention. This review examines the conceptual and empirical foundations of AI-supported warehouse supervision, drawing on warehouse automation, logistics digitalisation, human factors, algorithmic control, and hybrid-intelligence research. When task boundaries are clear, confidence information is communicated, and override rights are available, the literature suggests that AI support can enhance information processing and decision consistency. The reviewed literature also identifies risks including automation bias, algorithm aversion, opaque recommendations, skill erosion, and conflicts between local and system-level objectives. There is significant uncertainty about the effectiveness, safety, governance, and user acceptance of an embedded SAP EWM supervisor copilot as no peer-reviewed journal study identified in this review directly assesses such an implementation. To progress, then, it will be necessary to rely on field experiments, decision architectures that can be tracked, calibrated explanations, and an evaluation against combined operational and human-centred outcomes.

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Keywords:

Algorithmic Decision Support, Human-In-The-Loop, SAP EWM, Warehouse Copilots, Warehouse Supervision