SCOR+Enable: Extended Supply Chain Model

• SCOR+Enable is an extension of the traditional SCOR model that introduces a sixth 'Enable' process stage to support cross-functional supply chain operations.
• It offers a detailed taxonomy for CSCBench, mapping diagnostic items to specific stages and facilitating granular performance evaluation.
• The model enhances supply chain AI diagnostics by pinpointing process-stage misclassifications, thereby improving precision in benchmarking commodity supply chains.

SCOR+Enable is an extension of the foundational Supply-Chain Operations Reference (SCOR) model that integrates an explicit “Enable” process stage alongside the canonical five SCOR stages (Plan, Source, Make, Deliver, Return). This six-stage process taxonomy provides the backbone for advanced diagnostic benchmarks such as CSCBench, especially within the domain of commodity supply chain (CSC) reasoning governed by institutionalized rule systems and feasibility constraints (Cui et al., 5 Jan 2026).

1. Formal Definition and Principal Structure

The SCOR model, as defined by the Supply Chain Council (2010), is a 5-tuple of process categories: $$\mathrm{SCOR} = \{\mathrm{Plan}, \mathrm{Source}, \mathrm{Make}, \mathrm{Deliver}, \mathrm{Return}\}$$ SCOR+Enable formally incorporates a sixth “Enable” process, creating: $$\mathrm{SCOR}^{+} = \mathrm{SCOR} \cup \{\mathrm{Enable}\} = \{\mathrm{Plan}, \mathrm{Source}, \mathrm{Make}, \mathrm{Deliver}, \mathrm{Return}, \mathrm{Enable}\}$$ The “Enable” category comprises cross-functional and supporting activities—such as performance management, compliance, and infrastructure setup—not cleanly assignable to the original five SCOR processes but indispensable for end-to-end supply chain orchestration.

2. Process Stages and Sub-Process Taxonomy

At the top level, SCOR+Enable consists of six process stages, each comprising distinct sub-processes relevant to institutional CSC reasoning:

Stage	Example Sub-Processes	Scope
Plan	Demand/supply planning, S&OP, capacity planning	End-to-end orchestration
Source	Supplier selection, procurement/order management	Upstream procurement and inbound logistics
Make	Production scheduling, manufacturing execution	Transformation and assembly
Deliver	Distribution planning, warehousing, outbound transportation	Outbound flow and customer fulfillment
Return	Returns management, reverse logistics	Reverse flow, repair, refurbishment
Enable	Performance monitoring, IT enablement, compliance	Cross-functional support and infrastructure

In CSCBench, each benchmark item is associated with exactly one of these stages. This explicit alignment enables granular evaluation of domain models with respect to institutional supply chain process structure (Cui et al., 5 Jan 2026).

3. Mathematical and Logical Formalization

Let $Q$ denote the set of diagnostic items (questions) in CSCBench. The categorical assignment of process stages is encoded by a mapping: $f_{X}: Q \to \mathrm{SCOR}^{+}$ For each $q \in Q$, $f_X(q)$ yields a process label in $$\{\mathrm{Plan}, \mathrm{Source}, \mathrm{Make}, \mathrm{Deliver}, \mathrm{Return}, \mathrm{Enable}\}$$. Benchmark performance for the Process axis relies on macro-averaged accuracy across three sub-benchmarks: $$\mathcal{B}_X = \{\mathrm{CIPS}, \mathrm{CSCP}, \mathrm{SCMP}\}$$

$$\mathrm{Acc}_X = \frac{1}{|\mathcal{B}_X|} \sum_{b \in \mathcal{B}_X} \mathrm{Acc}(b) = \frac{1}{3} (\mathrm{Acc}_{\mathrm{CIPS}} + \mathrm{Acc}_{\mathrm{CSCP}} + \mathrm{Acc}_{\mathrm{SCMP}})$$

General process accuracy leverages the standard confusion matrix: $$\mathrm{Acc} = \frac{\sum_k C_{kk}}{\sum_{i,j} C_{ij}}$$ where $C_{ij}$ is the count of examples with true label $i$ and predicted label $j$.

4. Operationalization in CSCBench

Process-stage annotation in CSCBench follows a controlled data curation protocol. Each item receives a primary PVC triple: $(X, Y, Z)$, with $X$ representing the SCOR+Enable stage, $Y$ reflecting variety constraints, and $Z$ denoting cognitive demand. An expert domain verifier assigns $X$ using the SCOR+Enable taxonomy. Sub-benchmarks leverage authoritative certification syllabi:

• CIPS (Chartered Institute of Procurement & Supply): Focuses on Source and Enable.
• CSCP (APICS Certified Supply Chain Professional): Covers all Plan–Return stages.
• SCMP (Supply Chain Management Professional): Encompasses Plan, Source, Make, Deliver.

Model evaluation employs a direct low-temperature, single-choice prompting regime, ensuring focus on process-stage discrimination. The resulting $X$-axis accuracy is reported as $\mathrm{Acc}_X$ via macro-averaging, as formalized above.

5. Process-Stage Misalignment and Model Diagnostics

A key diagnostic application is the identification of process-stage confusion errors. For instance, a CSCP-derived item involving Advanced Planning System (APS) usage in end-to-end workflows was gold-labeled as Plan (consistent with APS's core planning role), whereas an LLM predicted Source. This misclassification exemplifies failures in recognizing adjacent but semantically distinct process boundaries (Plan vs. Source). Two principal insights arise:

• Process-Grounded Reasoning: Accurate mapping from domain-specific concepts (e.g., APS) to SCOR+Enable stages is fundamental to operational validity in CSC settings.
• Diagnostic Value: Isolating the $X$-axis enables precise error localization, revealing that even with high overall accuracy, significant process-mapping errors can persist in LLM reasoning (Cui et al., 5 Jan 2026).

6. Integration with Professional Certification Benchmarks

SCOR+Enable underpins the structure of CSCBench by integrating three major professional certification benchmarks, each with distinct coverage:

Sub-Benchmark	Covered Stages	Source Materials
CIPS	Source, Enable	Procurement syllabi
CSCP	Plan, Source, Make, Deliver, Return	APICS CSCP materials
SCMP	Plan, Source, Make, Deliver	SCMP certifications

This partitioning allows macro-level performance measurement and fine-grained process-stage diagnostic analytics, enhancing the benchmark’s interpretability and actionability for CSC-focused reasoning systems.

7. Significance and Benchmarking Implications

The explicit extension from SCOR to SCOR+Enable in CSCBench advances the fidelity of reasoning benchmarks for high-stakes commodity supply chains. While contemporary LLMs can achieve over 90% accuracy on the Process axis, persistent errors in process-stage assignment—especially near boundary cases—demonstrate the need for more nuanced, process-grounded model evaluation and training. Anchoring benchmark tasks to the SCOR+Enable taxonomy provides actionable feedback loops for the development and assessment of supply chain AI systems (Cui et al., 5 Jan 2026).