SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination

Source

Source name: arXiv
Source kind: paper
Authors / organization: Yunhao Liang, Xianqi Cao, Pujun Zhang, Yuan Qu, Yongzhi Qi, Ningxuan Kang, Max Z. J. Shen
Venue: arXiv: cs.AI
Published: 2026-07-30
Collected: 2026-07-31
URL: http://arxiv.org/abs/2607.28488v1
PDF: https://arxiv.org/pdf/2607.28488v1
DOI: UNKNOWN

Plain-Language Brief

The paper proposes a new model called SCOPE (Supply-Chain Operations through Coupled Policies for End-to-End Coordination) that aims to improve the efficiency of supply chain AI by coordinating various decision-making processes, such as product assortment, source assignment, and delivery routing. The model was tested in urban fresh-retail replenishment and showed better performance compared to methods that optimize each decision stage separately and practice-oriented baselines.

What the Document Says

The paper presents SCOPE: a composite policy model for end-to-end supply chain coordination.
SCOPE represents supply-chain entities as tokens, contextualizes them using a shared operational representation, and maps each token type to the corresponding decision interface.
Each decision in SCOPE builds on the partial plan formed by earlier decisions.
The completed plan is evaluated using a shared system-level utility.
The paper focuses on urban fresh-retail replenishment.
SCOPE was tested using real operational data from Dingdong and JD.com.
SCOPE outperformed methods that optimize each decision stage separately and practice-oriented baselines.
Learning and coordinating cross-department operational couplings lead to more effective end-to-end supply-chain decisions.

Key Concepts Mentioned

Supply Chain AI
Unified Operational Planning
Composite Policy Model
Coupled Policies
End-to-End Coordination
Urban Fresh-Retail Replenishment
Dingdong
JD.com

Claims Made by the Document

SCOPE improves end-to-end supply chain decisions.
Separate optimization of decision stages can lead to inefficiencies.

Evidence Used

Results showing SCOPE outperforming other methods in real operational data.

Methods / Approach

Composite Policy Model
Shared Operational Representation
System-level Utility

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Can supply-chain AI move beyond isolated decision modules toward unified operational planning? A complete replenishment plan specifies which products each location carries, which upstream facility supplies it, how often it is replenished, and how deliveries are routed. These decisions are operationally coupled: the selected assortment changes the demand and load passed to later stages; source assignment and replenishment frequency reshape the delivery requests; and route feasibility and cost, in turn, determine the system value of the earlier choices. Yet in modern supply chains, these decisions are often handled by separate departments and optimized through separate systems, which can lead to stockouts, inventory exposure, and avoidable transportation. We propose SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination, a composite policy model that represents supply-chain entities as tokens, contextualizes them through a shared operational representation, and maps each token type to the corresponding decision interface. Each decision builds on the partial plan formed by earlier decisions while the completed plan is evaluated using a shared system-level utility. We instantiate this framework in urban fresh-retail replenishment, where service frequency, assortment, capacity pressure, and road-network routing interact strongly, and evaluate it on real operational data from Dingdong and JD.com, two large-scale supply chains operating at different replenishment echelons. Across both settings, SCOPE consistently outperforms methods that optimize each decision stage separately, as well as practice-oriented baselines commonly used in supply-chain operations. These results show that learning and coordinating cross-department operational couplings lead to more effective end-to-end supply-chain decisions.