Published & working papers
The AVC Theorem: On the Impossibility of Simultaneous Autonomy, Veracity, and Coordination in AI-Augmented Supply Chains
Formal proof that no AI agent operating in a multi-tier supply chain can simultaneously satisfy Autonomy (independent action), Veracity (truthful signaling), and Coordination (consistent multi-agent alignment) — an explicit analog to the CAP theorem in distributed computing.
AVC TheoremAI CoordinationAutonomous AgentsSignaling TheorySC Governance
Emergent Opacity in AI-Augmented Supply Chains: The Epistemic Degradation Function and the Resilience-Opacity Tradeoff Frontier
Introduces Φ, the Epistemic Degradation Function, which models how human decision-makers lose interpretive access to AI-generated signals as AI agent density grows in the supply chain network. Formalizes the Opacity Accumulation Theorem and derives the Resilience-Opacity Tradeoff Frontier.
Opacity AccumulationEpistemic DegradationAI GovernanceResilienceHuman-AI Teaming
SCaL™ — Supply Chain as Leverage: An Algorithmic Sovereignty Framework for AI-Driven SC Strategy
Proposes SCaL™ (Supply Chain as Leverage) as a strategic framework positioning supply chain as a primary source of competitive leverage. Introduces Algorithmic Sovereignty as a design constraint for autonomous SC architectures. Includes a maturity model across SCRA domains.
SCaL™Algorithmic SovereigntySC StrategyAutonomous SCCompetitive Leverage
SCM-AI Theorem Series: 10 Papers on the Mathematical Foundations of AI-Driven Supply Chain Signaling
A ten-paper series developing the mathematical foundations of AI behavior in supply chain signaling environments. Papers cover the AVC Theorem, Trust Mechanics, Epistemic Degradation Accelerators, Dynamic Hedging, DSS Accountability, Multi-Org Dynamics, and Procurement Autonomy. Targeting Management Science and MSOM.
Signaling TheoryMechanism DesignAI Decision SupportManagement Science
Frontier research areas — open questions where this work contributes
Agentic AI · Hot
Agentic AI Governance in Multi-Tier Supply Chains
By 2026, 45% of G2000 companies are deploying agentic AI in supply chain operations. The critical unsolved problem is governance: when agents make irreversible procurement, routing, and production decisions autonomously, who is accountable and how are errors detected before they cascade?
This research's contribution
The AVC Theorem provides the formal impossibility proof that defines the governance design space. The Opacity Accumulation Theorem quantifies when multi-agent SC systems become ungovernable.
Epistemic Theory · Emerging
Epistemic Opacity & Human-AI Teaming in Operations
MIT, Stanford, and IAPS are actively studying how AI systems erode human epistemic access to operational state. In supply chain, this creates a paradox: the more AI improves SC efficiency, the less humans understand what the SC is doing — reducing resilience when disruptions occur.
This research's contribution
The Epistemic Degradation Function (Φ) formalizes this mechanism in SC-specific terms. The Resilience-Opacity Tradeoff Frontier operationalizes it as a measurable design constraint.
AI Policy · 2026 Priority
AI Coordination Failures & Systemic SC Risk
The Partnership on AI, EU AI Act, and NIST AI RMF are all grappling with multi-agent coordination failures as a systemic risk category. In supply chain, the bullwhip effect is already a well-documented coordination failure — AI agents may generate an analogous but faster and less predictable version.
This research's contribution
The AVC Theorem's Coordination component provides the formal basis for modeling AI-induced coordination failure as a systemic SC risk category, connecting to the CAP theorem literature in distributed systems.
Autonomous SC · Active
Autonomous Supply Chain Architecture & Algorithmic Sovereignty
As supply chains become increasingly autonomous — AI agents placing orders, routing shipments, adjusting prices — the question of who owns the decision logic becomes strategically critical. Companies risk ceding their SC competitive advantage to the algorithm providers.
This research's contribution
The SCaL™ framework and its Algorithmic Sovereignty construct directly address this gap — providing a strategic and architectural language for retaining competitive SC control in an autonomous environment.
Signaling Theory · Active
AI-Distorted Demand Signals & the New Bullwhip
Classical bullwhip effect theory assumes human decision-makers distorting demand signals. When AI systems make replenishment and ordering decisions, the distortion mechanisms are different — faster, correlated across companies using the same platforms, and invisible to standard monitoring.
This research's contribution
The SCM-AI Theorem Series (T2 Trust Mechanics, T3 Epistemic Degradation Accelerators) provides the mechanism design foundation for modeling AI-mediated demand signal distortion.
Geopolitics · 2026
Tariff Uncertainty, Nearshoring & AI-Driven SC Reconfiguration
The 2025–2026 tariff environment (US-China, T-MEC renegotiation) is forcing SC network reconfiguration at unprecedented speed. AI-driven scenario modeling is being used to evaluate nearshoring economics in real time — but the models themselves embed geopolitical assumptions that are rarely made explicit.
This research's contribution
The SCRA D17 (Risk & Resilience) and D11 (Trade & Global Ops) frameworks provide the architectural vocabulary for analyzing AI-driven SC reconfiguration decisions under geopolitical uncertainty.
About the researcher
Ismael López
PhD Candidate, Universidad Anáhuac · Manager, Deloitte SCNO · Mr. Supply Chain®
Supply chain practitioner and researcher with 15+ years across Fortune 500 operations (Cisco Systems, Samsung, Intel/Solidigm) and management consulting (Deloitte SCNO). PhD research focuses on AI-driven signaling failures and epistemic degradation in autonomous supply chain systems. Creator of the AVC Theorem, SCaL™ framework, and SCRA v2.2. Holder of the Mr. Supply Chain® trademark (IMPI, Classes 35/41/42).