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AI-Powered Retail & Merchandising Operations: From Sales Analysis to Order Optimization

This hands-on course teaches you how to use AI to analyze sales and inventory data and create evidence-based ordering plans. You’ll learn how to clearly communicate your objectives, constraints, and desired outputs to AI, then apply those skills to sales analysis, demand forecasting, and multi-item order optimization. Using the provided training data, prompts, and code, you’ll verify calculation results and test the basis for your decisions by changing the conditions. By the end, you’ll complete an ordering plan for 30 products and a one-page decision-making report, which you can use for job preparation and to expand your practical skills.

3 learners are taking this course

Level Intermediate

Course period Unlimited

AI
AI
prompt engineering
prompt engineering
Python
Python
ChatGPT
ChatGPT
Business Productivity
Business Productivity
AI
AI
prompt engineering
prompt engineering
Python
Python
ChatGPT
ChatGPT
Business Productivity
Business Productivity

What you will gain after the course

  • Designing analysis requests that clearly communicate goals, constraints, deliverables, and completion criteria to AI

  • Break down sales changes into price, volume, and product mix, and analyze the incremental profit from promotions

  • Decompose sales changes into price, quantity, and product mix, and analyze the incremental profit from promotions; forecast demand by store and product, and validate forecast errors and uncertainty.

  • Decompose changes in sales into price, volume, and product mix, and analyze the incremental profit from promotions; forecast demand by store and product, and validate forecast errors and uncertainty; develop an ordering plan for 30 products reflecting budgets, available space, and product-specific order units.

  • Decompose sales changes into price, volume, and product mix, and analyze the incremental profit from promotions Forecast demand by store and product, and validate forecast errors and uncertainty Develop an ordering plan for 30 products that reflects budget, space, and product-specific order-unit constraints Change demand and budget conditions to compare alternatives, and check AI analysis for errors, omissions, and assumptions

  • Complete the analysis project with a product-specific purchase order sheet, a constraint validation sheet, and a one-page decision report.

This is a hands-on course that uses AI to analyze sales, inventory, and ordering issues in retail and merchandising.
You will make specific requests about the desired outcomes and completion criteria, then run AI-generated code to verify the calculation results and constraints.
The course covers sales change analysis, promotional profitability, demand forecasting, and multi-item order optimization step by step. Using educational sample data for 30 products, you will complete an order quantity table and a one-page decision-making report, along with course notes, practice data, AI prompts, and templates.


Recommended for
these people

Who is this course right for?

  • Those preparing for careers in distribution or merchandising (MD) who want to create a project showcasing their analytical skills

  • Those who want to expand their learning into AI-powered sales analysis, demand forecasting, and order optimization after learning the basics of distribution and merchandising.

  • Practitioners who want to explain the rationale behind product management and ordering decisions based on sales and inventory data

  • Those who want to learn how to accurately communicate work objectives and conditions to AI, and even verify errors in the analysis results.

Need to know before starting?

  • Understanding basic distribution terms such as sales, purchase costs, inventory, and purchase orders will help with learning.

  • You should be able to open the CSV file in Excel or a similar program and check the rows, columns, and numbers in the table.

  • We recommend a basic user experience of asking questions to generative AI and checking the answers.

  • The coding exercises require a Python execution environment. Basic experience running the provided code and modifying inputs such as the budget is recommended; this is not a course that explains Python syntax from the beginning.

Hello
This is andrew0904

Career Verified

Hello. I’m Kang-ho Ryu, sharing 24 years of retail experience through learning that can be applied in practice.

Since 2002, I have worked in large-scale retail, broadening my experience from serving as a Category/Section Manager to becoming an assistant store manager, leading online and strategic marketing teams at headquarters, and overseeing regional operations. From engaging with products and customers on the sales floor to developing strategies at headquarters, I have approached retail from a variety of perspectives.

Key Professional Experience

• Sales and Operations Management: Managing key KPIs such as sales, customer count, average transaction value, inventory, and service, and standardizing store operations
• Strengthening Store Competitiveness: Executing complex projects including refit and renovation project management, merchandise assortment changes, trade area and competitor analysis, and reopening marketing
• Online and Digital Marketing: Managing online commerce, CRM, targeted marketing, UI/UX improvements, and partnership marketing

In the course, based on this experience, we clearly and concretely explain how product planning, sales and inventory management, customer analysis, store operations, and marketing are connected in actual work. We also cover what to check in practice and what evidence to use when making decisions.

I will share hands-on experience and practical methods with those who are new to retail, want to strengthen their practical skills in the field, or are preparing for an MD role and looking to build a product planning portfolio.

• Experience coaching frontline employees and leaders, improving customer experience (CX), and coordinating among headquarters, regional headquarters, stores, and partners

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Curriculum

All

14 lectures ∙ (52min)

Course Materials:

Lecture resources
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