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Course Overview and Objectives

Welcome, Outcomes, and Use Case Definition

Data Foundations and Pipelines for Dynamic Min/Max

Data Model, Sources, and Quality Controls

Pipelines, Orchestration, and Monitoring

Modeling and Decision Policy Design

Forecasting and Uncertainty Estimation for 'A' Items

From Forecasts to Min/Max: Policy Optimization

AI Agent Architecture and Control Loop

Agent Design: Perception, Reasoning, and Action

Enterprise Integration, APIs, and Security

Operations, Experimentation, and Compliance

Monitoring, Feedback, and Continuous Improvement

Risk, Compliance, and Ethical Considerations

Quizzes and Knowledge Checks

Quiz: Data Foundations

Quiz: Forecasting and Uncertainty

Quiz: Policy and Optimization

Quiz: Agent Architecture and Control Loop

Quiz: Integration and Operations

Final Survey

Learner Feedback Survey

Designing AI Agents for Dynamic Inventory Min/Max Optimization is a practitioner-focused course that teaches you how to build, deploy, and govern autonomous decisioning for high-value “A” items across retail, e-commerce, distribution, and manufacturing. You will learn how to connect robust data pipelines to forecasting services, quantify uncertainty, translate demand and lead-time risk into optimal min/max policies, and orchestrate an agent loop that senses, plans, validates, and acts—safely and at enterprise scale. The curriculum blends data engineering, applied forecasting, operations research, and MLOps so you can reduce stockouts, lower working capital, and respond faster to promotions, disruptions, and supplier variability. Through concrete architectures, policy stress testing, and integration patterns for ERP/WMS, you’ll gain the skills to productionize near real-time min/max updates with clear SLAs and guardrails. By the end, you will implement agentic control with explainability cards, human-in-the-loop thresholds, rollback plans, and comprehensive auditability—delivering measurable improvements in fill rate, backorder reduction, inventory turns, and value-at-risk. If you’re ready to move beyond static rules and unlock dynamic optimization for the SKUs that matter most, this course provides the end-to-end blueprint.

Designing AI Agents for Dynamic Inventory Min/Max Optimization

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            <p>Inventory teams managing high-value, high-velocity “A” items face a relentless balancing act: protect service levels while minimizing working capital and reacting quickly to demand, lead time, and supply shocks. Designing AI agents for dynamic min/max optimization turns this challenge into a repeatable, auditable control loop—continuously sensing change, reasoning over uncertainty, and acting with guardrails. This course equips you to build that system end-to-end: from data foundations and forecasting to policy design, agent architecture, and enterprise integration. By the end, you’ll be able to reduce stockouts, lift fill rates, shorten response times, and improve inventory turns—while maintaining SLAs and value-at-risk limits.</p>

<p>We begin with the business context and success metrics tailored to “A” inventory in an ABC framework, aligning outcomes to service-level goals, backorder reduction, and working capital impact. You’ll establish robust data pipelines across SKUs, locations, and channels; harmonize facts like daily demand, receipts, and on-hand; and incorporate external signals such as promotions, holidays, weather, and supplier reliability. With batch and streaming ingestion, feature stores, and strong data quality controls, you’ll support near real-time updates for the items that matter most.</p>

<p>The modeling segment moves from demand forecasting to calibrated uncertainty: hierarchical time series, covariate-rich ML models, and conformal approaches that translate into actionable predictive intervals. You’ll model stochastic lead times and vendor variability, employ transfer learning for cold starts, and validate through rolling-origin backtests using cost-aware metrics. Turning forecasts into decisions, you’ll construct safety stock and reorder frameworks, configure order-up-to policies with real-world constraints (MOQs, pack sizes, budgets), and run what-if simulations for promotions and disruptions—extending to multi-echelon considerations and value-at-risk limits.</p>

<p>The core of the course is an AI agent designed for perception, reasoning, and action. You’ll implement a state builder, planner, policy engine, validator, and actuator in a tight decision loop: sense changes, detect anomalies, propose min/max updates, validate against constraints and governance, execute via APIs, and learn from outcomes. Tooling includes forecasting services, optimization solvers, rules engines, knowledge bases, and an audit store, all supported by episodic and semantic memory for context retrieval. Safety mechanisms—hard constraints, human-in-the-loop thresholds, rollback plans, and explainability cards—ensure reliability and trust.</p>

<p>Finally, you’ll integrate with ERP/WMS systems using secure APIs and idempotent writes, design event-driven triggers via CDC and webhooks, and implement versioning and governance through model registries and policy snapshots. Operations focus on monitoring KPIs (service level, stockouts avoided, inventory turns), online evaluation with shadowing and A/B tests, drift detection for demand regimes and lead times, and human workflows for approvals and post-mortems. The course closes with risk, compliance, and ethics: auditability for every min/max change, lineage and retention practices, fairness across channels and regions, and change management for stakeholder alignment. Quizzes throughout reinforce mastery, and a final survey gathers feedback to improve future cohorts.</p>
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