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CSCBench: A PVC Diagnostic Benchmark for Commodity Supply Chain Reasoning

Yaxin Cui, Yuanqiang Zeng, Jiapeng Yan, Keling Lin, Kai Ji, Jianhui Zeng, Sheng Zhang, Xin Luo, Binzhu Su, Chaolai Shen, Jiahao Yu

TL;DR

CSCBench introduces a diagnostic benchmark for commodity supply chain reasoning using the PVC framework, which decomposes evaluation into Process, Variety, and Cognition axes. The native-source dataset (2,300+ items) anchors tasks to SCOR+Enable workflows, commodity-specific rule texts, and Bloom-based cognitive depth, with adversarial distractors to sharpen variety discrimination. Evaluation across frontier LLMs reveals strong Process and Cognition performance but a notable gap on Variety, especially for Freight Agreements, pointing to the primacy of rule- and constraint-consistency over general fluency for practical deployment. The framework enables interpretable, axis-level diagnostics for pre-deployment acceptance and remediation, with future work including data-quality improvements and interactive, multi-turn, or time-series extensions to better reflect real-world CSC decision-making.

Abstract

Large Language Models (LLMs) have achieved remarkable success in general benchmarks, yet their competence in commodity supply chains (CSCs) -- a domain governed by institutional rule systems and feasibility constraints -- remains under-explored. CSC decisions are shaped jointly by process stages (e.g., planning, procurement, delivery), variety-specific rules (e.g., contract specifications and delivery grades), and reasoning depth (from retrieval to multi-step analysis and decision selection). We introduce CSCBench, a 2.3K+ single-choice benchmark for CSC reasoning, instantiated through our PVC 3D Evaluation Framework (Process, Variety, and Cognition). The Process axis aligns tasks with SCOR+Enable; the Variety axis operationalizes commodity-specific rule systems under coupled material-information-financial constraints, grounded in authoritative exchange guidebooks/rulebooks and industry reports; and the Cognition axis follows Bloom's revised taxonomy. Evaluating representative LLMs under a direct prompting setting, we observe strong performance on the Process and Cognition axes but substantial degradation on the Variety axis, especially on Freight Agreements. CSCBench provides a diagnostic yardstick for measuring and improving LLM capabilities in this high-stakes domain.

CSCBench: A PVC Diagnostic Benchmark for Commodity Supply Chain Reasoning

TL;DR

CSCBench introduces a diagnostic benchmark for commodity supply chain reasoning using the PVC framework, which decomposes evaluation into Process, Variety, and Cognition axes. The native-source dataset (2,300+ items) anchors tasks to SCOR+Enable workflows, commodity-specific rule texts, and Bloom-based cognitive depth, with adversarial distractors to sharpen variety discrimination. Evaluation across frontier LLMs reveals strong Process and Cognition performance but a notable gap on Variety, especially for Freight Agreements, pointing to the primacy of rule- and constraint-consistency over general fluency for practical deployment. The framework enables interpretable, axis-level diagnostics for pre-deployment acceptance and remediation, with future work including data-quality improvements and interactive, multi-turn, or time-series extensions to better reflect real-world CSC decision-making.

Abstract

Large Language Models (LLMs) have achieved remarkable success in general benchmarks, yet their competence in commodity supply chains (CSCs) -- a domain governed by institutional rule systems and feasibility constraints -- remains under-explored. CSC decisions are shaped jointly by process stages (e.g., planning, procurement, delivery), variety-specific rules (e.g., contract specifications and delivery grades), and reasoning depth (from retrieval to multi-step analysis and decision selection). We introduce CSCBench, a 2.3K+ single-choice benchmark for CSC reasoning, instantiated through our PVC 3D Evaluation Framework (Process, Variety, and Cognition). The Process axis aligns tasks with SCOR+Enable; the Variety axis operationalizes commodity-specific rule systems under coupled material-information-financial constraints, grounded in authoritative exchange guidebooks/rulebooks and industry reports; and the Cognition axis follows Bloom's revised taxonomy. Evaluating representative LLMs under a direct prompting setting, we observe strong performance on the Process and Cognition axes but substantial degradation on the Variety axis, especially on Freight Agreements. CSCBench provides a diagnostic yardstick for measuring and improving LLM capabilities in this high-stakes domain.
Paper Structure (34 sections, 1 equation, 2 figures, 3 tables)

This paper contains 34 sections, 1 equation, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Main finding.
  3. Related Work
  4. General and Domain-Specific LLM Evaluation
  5. Supply Chain Evaluation and Constraint-Grounded Reasoning
  6. Supply Chain Theory Foundations and Commodity Constraints
  7. The PVC 3D Evaluation Framework
  8. Why three dimensions?
  9. X-Axis: SCOR+Enable-Aligned Process
  10. Y-Axis: Variety as Rule- and Constraint-Consistency
  11. Z-Axis: Bloom-Grounded Cognitive Operations
  12. Data Construction Pipeline
  13. Design rationale.
  14. Design rationale.
  15. Adversarial option perturbation.
  16. ...and 19 more sections

Figures (2)

  • Figure 1: Overview of the proposed PVC 3D Evaluation Framework for commodity supply chains. The framework decomposes evaluation along three orthogonal axes: Process (SCOR-aligned stages), Variety (commodity-specific rule systems and anchors), and Cognition (Bloom-style reasoning depth). Axis scores are macro-averages of their sub-benchmarks.
  • Figure 2: Data construction pipeline for CSCBench (high-level). Source discovery and collection are informed by domain experts; we curate the collected materials into axis-aligned source pools, draft items with an LLM under prompts, and apply human verification for decidability and internal consistency under a unified single-choice schema.