GenAI for Retail Managers: AI-Powered Inventory Optimization

Introduction to the section, key topics to be covered, and call to action.

Introduces LLMs, predictive models, and automation using a store manager asks, AI answers frame. Explains how GenAI differs from traditional analytics tools and why retail managers do not need a technical background to use it.

Live demonstration using ChatGPT and the Kaggle Retail Store Inventory Forecasting Dataset: the instructor generates a demand narrative, a stockout prediction explanation, and a markdown optimization insight. Learners see the exact prompt inputs and AI outputs.

Reviews real industry benchmarks and maturity models. The instructor uses Claude to summarize a retail AI adoption report and highlights the most common adoption barriers, including data quality, IT resistance, and skill gaps.

Explains all lifecycle stages: assortment planning, purchasing, allocation, replenishment, markdowns, and liquidation. Uses a visual stage map to show where AI fits and where it does not.

Loads the Kaggle dataset into ChatGPT and uses a structured prompt to identify the top three AI opportunities by estimated revenue impact. Learners see the exact prompt, the AI output, and how to interpret the results.

The resource uses Claude to map the AI opportunities identified in the previous video back to specific lifecycle stages and generates a one-page AI opportunity summary. Learners see how to structure this output for a team review.

Introduces the framework: overstock cost reduction, stockout revenue recovery, and markdown efficiency gain. Explains each metric with retail-specific definitions and shows what inputs are needed to calculate them.

Uses the Kaggle dataset and a structured ChatGPT prompt to calculate overstock carrying cost, estimate stockout revenue loss, and generate a one-page ROI summary ready for leadership review.

Shows how to use Claude to convert a data-heavy ROI analysis into a plain-language executive summary. Covers how to handle the top three objections: AI accuracy, data privacy, and implementation cost.

Introduction to the setion, key topics to be covered, and call to action.

Explains how GenAI supplements, not replaces, statistical forecasting models. Covers the role of LLMs in generating demand driver narratives and why plain-language explanations matter for store manager decision-making.

This loads the Kaggle dataset into ChatGPT and generates a 4-week demand forecast for three SKUs with driver explanations, then calculates reorder points and safety stock for the same SKUs using a second structured prompt.

Shows how to use ChatGPT to model promotional lift using the Kaggle promotional flags, generate a pre-positioning recommendation, and draft a vendor purchase order email. Learners see a complete workflow from data to email draft.

Introduces the markdown optimization framework: sell-through rate targets, weeks of supply thresholds, and price elasticity basics. Explains what data inputs are needed and why timing is the most critical variable in clearance decisions.

Loads a slow-mover SKU list from the Kaggle dataset into ChatGPT, generates markdown timing recommendations with elasticity insights and projected sell-through rates, then feeds weekly sales variance data into Claude to generate an anomaly alert report with root cause explanations.

Shows how to build a reusable AI copilot prompt library using the module dataset. The instructor creates 5 ready-to-use store manager queries covering inventory status, restocking advice, fulfillment priority, and vendor communication drafts.

Loads the Kaggle Superstore Sales Dataset into ChatGPT, calculates GMROI and sell-through rate by category, benchmarks results against industry averages, then uses Claude to generate a complete executive-level weekly inventory performance summary with anomaly flags and recommended actions.

Uses ChatGPT to generate a complete 90-day rollout plan from a structured input prompt, then uses Claude to create a one-page executive summary with a risk register and success metrics table. Shows how to sustain leadership support beyond the pilot.

Defines GMROI, sell-through rate, and stockout frequency with retail-specific examples. Explains what a good result looks like versus a warning signal, and covers what data inputs are needed to calculate and benchmark each KPI.

Recap of the course and final thoughts. The instructor synthesizes the AI inventory transformation journey and encourages learners to pilot their first GenAI tool in the coming week.