Supply Chain Risk Management and Compliance Glossary

Agent memory is the capability of an AI agent to retain and reference previous actions, decisions, or context across time. It supports consistent, personalized, and context-aware behavior.

Agent role specialization refers to the assignment of specific functions or domains to individual AI agents. It allows Agentic AI systems to scale across enterprise operations.

Agentic AI refers to artificial intelligence systems endowed with autonomy, enabling them to perceive, reason, and act on behalf of users to achieve specific goals. These systems go beyond passive data processing to actively manage tasks and make decisions in dynamic environments.

Agent orchestration refers to the coordination of multiple AI agents working together to complete complex tasks that require multi-step, cross-functional execution.

An AI agent is a modular, software-based system that leverages artificial intelligence to autonomously perform tasks within a specific domain or function. In enterprise settings, AI agents are tailored to monitor, analyze, and act upon domain-specific data and workflows.

AI Mapping uses artificial intelligence technologies to automatically map and visualize complex supplier networks, product flows, and risk exposures across the supply chain.

APIs (Application Programming Interfaces) allow different software systems to communicate and exchange data seamlessly.

An autonomous agent is a self-directed software entity that perceives its environment and takes actions to meet defined goals without continuous human intervention. It is a core component of Agentic AI.

Autonomous mapping is AI-powered mapping technology that provides automated, multi-tier visibility into complex supply chain networks.

BOM Intelligence enables visibility into how disruptions affect products at the bill-of-materials (BOM) level. It connects components, parts, and suppliers to end products to calculate risk exposure and simulate mitigation strategies.

Compliance automation refers to the use of AI-powered tools within Resilinc to continuously monitor and assess supplier and site adherence to regulatory frameworks such as UFLPA, CHIPS Act, and EU import bans. It automates document collection, risk scoring, and reporting.

Resilinc's data backbone is the foundational data infrastructure that supports Resilinc’s AI agents, analytics, and automation at scale.

Dataflow Integration (DFI) is Resilinc’s automated pipeline for loading customer supply chain data quickly, accurately, and at scale.

DDRT 2.0 (Forced Labor Due Diligence Reporting Template) is an industry-standard assessment framework released by AIAG to help automotive and manufacturing companies collect, standardize, and validate supplier data related to forced labor risk across multi-tier supply chains.

The Disruption Vulnerability Index (DVI) measures a supplier’s or site's susceptibility to operational disruptions based on various risk factors.

ESG compliance refers to an organization’s adherence to environmental, social, and governance regulations and standards across its operations and supply chain.

The ESG risk score quantifies a supplier’s exposure to environmental, social, and governance risks. It is used in procurement, compliance, and supplier segmentation strategies.

The EU Chips Act is a European Union initiative aimed at strengthening Europe’s semiconductor ecosystem by boosting domestic production, research, and innovation to reduce reliance on non-EU suppliers and improve supply chain resilience.

The EU Deforestation Regulation is a 2023 European law that requires companies to ensure that certain products sold in the EU are not linked to deforestation or forest degradation anywhere in the world.

Event severity score is a dynamic metric that quantifies the potential impact of a supply chain disruption on operations, revenue, or continuity. It helps prioritize mitigation and triage activities.

EventWatchAI is Resilinc’s AI-powered global monitoring platform that continuously scans over 104 million sources in 100+ languages to detect supply chain disruptions in real time. It classifies risks into more than 50 disruption types and delivers supplier- and site-specific alerts for proactive mitigation.

Explainability is the ability of an AI system to clearly articulate the rationale behind its decisions or actions. It supports transparency, trust, and regulatory alignment in Agentic AI.

This capability monitors global supply chains for exposure to regions or suppliers flagged for forced labor, including enforcement under the Uyghur Forced Labor Prevention Act (UFLPA).

Graph-based architecture is data modeling framework that uses relationships between entities to power fast, scalable supply chain mapping.

Guardrails are predefined policy, regulatory, or business constraints that limit or shape an AI agent’s actions. They are essential for responsible AI deployment in enterprise systems.

Human-in-the-loop refers to a governance model where human oversight is embedded into automated AI workflows. It ensures accountability, compliance, and intervention in sensitive or high-impact decisions.

Incident response automation refers to the use of AI agents and playbooks to orchestrate responses to supply chain disruptions. It enables faster and more standardized mitigation.

Industry 5.0 represents the next evolution in manufacturing and supply chain strategy—where human creativity and AI-driven intelligence work together to create more resilient, sustainable, and adaptive operations.

LangChain is a development framework that simplifies building applications powered by large language models (LLMs) by connecting them with external data sources, tools, and decision chains.

LangGraph is a framework for building agent-based AI workflows that use graphs to define complex, dynamic decision-making processes.

Material breakdown is the process of breaking down finished parts into their raw material components to improve supply chain visibility and risk analysis.

A multi-agent system is a coordinated network of AI agents that work together—each handling specific tasks—to achieve complex objectives.

Multi-Tier Mapping is Resilinc’s AI- and supplier-powered system that maps supply chains down to the part-site level across all tiers, exposing hidden dependencies, bottlenecks, and disruption risks. It blends machine learning, ERP integration, and supplier input to deliver verified, dynamic supply network visibility.

What are PFAS? PFAS (per- and polyfluoroalkyl substances) are a group of man-made chemicals widely used in industrial and consumer products for their resistance to heat, water, and oil. Often called “forever chemicals” due to their persistence in the environment and the human body, PFAS have been linked to serious health risks and are now […]

A proactive mitigation workflow anticipates supply chain disruptions and activates pre-defined response actions. These workflows are triggered by AI insights or risk thresholds.

Prompt engineering is the process of designing inputs for large language models (LLMs) to guide desired outputs. It plays a foundational role in shaping Agentic AI behavior.

Pydantic is a Python library used for data validation and settings management, ensuring that structured data conforms to defined models.

R-Score is a resilience rating that evaluates the overall operational resiliency of suppliers, based on their ability to prepare for, respond to, and recover from disruptions.

This feature automatically correlates supply chain disruptions to specific supplier sites and parts to assess real-time operational impact.

Playbooks are predefined response protocols for common disruption scenarios, including supplier failure, weather events, or regulatory noncompliance.

RiskShield is Resilinc’s solution for quantifying, scoring, and managing supply chain risk exposure at the part, site, and supplier level. It delivers configurable risk profiles, supplier collaboration workflows, and real-time impact analysis for strategic decision-making.

Sub-tier dependency refers to reliance on suppliers beyond the first tier of the supply chain. These hidden links can introduce significant risk due to limited visibility and resilience at deeper levels.

Supplier assessments are evaluations conducted to measure a supplier's capabilities, compliance, and risk profile.

Supplier collaboration is the coordinated engagement between buying organizations and their suppliers to improve risk response, data quality, and compliance.

Supplier onboarding is the process of engaging and validating supplier data to ensure readiness for supply chain mapping and risk assessment. Resilinc automates this with agent-led workflows that streamline outreach, data collection, and compliance validation.

Supplier Risk Score is a quantitative measure of a supplier’s likelihood to cause operational disruption. It accounts for financial, geopolitical, environmental, and compliance-related factors.

Compliance refers to an organization’s adherence to laws, regulations, industry standards, and internal policies that govern ethical and legal operations across its supply chain.

A digital twin is a virtual model of an organization's real-world supply chain. It integrates live supplier, site, and part data to simulate events and predict impact.

A supply chain disruption is any unexpected event that interrupts the normal flow of materials, information, or production across a supply network, delaying delivery, increasing cost, or putting business continuity at risk.

Governance refers to the policies, systems, and accountability structures that manage risk, data quality, and compliance in the supply chain.