Build a Supply Chain Inventory App with R & Azure.

INVENTORY APP · R SHINY · RSTUDIO SERVER · MYSQL · AZURE · CLOUD DEPLOYMENT · SUPPLY CHAIN DEVOPS · CRON · INVENTORIZE · REPLENISHMENT ALGORITHM · SHINY SERVER

★ The Only Course on Udemy That Builds a Complete Inventory App in R — From Algorithm to Cloud Deployment

Most R courses stop at statistical analysis. Most inventory courses stop at spreadsheet models. Most Shiny courses build toy dashboards. This course combines all three into one end-to-end engineering project: a production-grade inventory replenishment algorithm in R, wrapped in a multi-tab Shiny web application with alarms, rankings, and category analytics, connected to a MySQL database, scheduled with Cron, and deployed to Azure via RStudio Server and Shiny Server. There is no other course on any online learning platform that does this in R. It is genuinely one of a kind.

★ Shiny Goes Far Beyond Dashboards Here — Multi-Tab Application with Alarms, Categories, and Live Database

Most Shiny courses teach you to build single-page reactive apps. This course builds a multi-tab production inventory application: Tab 1 covers inventory KPIs, bar plots, pivots, margin and turns analysis, top sales/revenue, rankings, and pie charts with file download. Tab 2 is a full alarm system with orange and red logic, alarm datatables, and a categories page. A dedicated size and colour tab adds a third analytical dimension. Every tab connects to a live MySQL database and updates in real time. This is Shiny at the level of a commercial SaaS product.

★ Built by the Co-Founder of Keip — a SaaS Platform for Retail and Supply Chain Management

Haytham is the co-founder of Keip — a software-as-a-service product built specifically for retail and supply chain management and analytics. The Shiny application architecture, MySQL database design, RStudio Server configuration, and Azure deployment approach in this course reflect real decisions made building and maintaining a live commercial software product. You are not learning from a developer who built a demo. You are learning from a SaaS founder who builds and ships supply chain software.

★ Inventorize in Its Native Language — R, Built by Its Creator, for ABC Classification at Scale

Inventorize was originally developed in R before it was ported to Python — making this course the natural home of the library. Section 5 uses Inventorize for ABC dynamic classification across the full product assortment, integrated directly into the inventory algorithm pipeline. The library is used by over 90,000 supply chain professionals worldwide. In this course you use it in R, taught by the person who built it, within a live production inventory system.

★ Full-Stack Supply Chain in R: Algorithm → MySQL → Shiny → RStudio Server → Cron → Azure

The R ecosystem for supply chain application development is powerful but rarely taught end to end. This course covers every layer: R and dplyr for data manipulation and algorithm development, Inventorize for ABC classification, MySQL for the operational database, Shiny for the front-end application, RStudio Server for the cloud R environment, Shiny Server for serving the app to the internet, Cron for automated scheduling, and Azure for virtual machine hosting and deployment. All from scratch. No previous knowledge required.

COURSE DESCRIPTION

Most R courses stop at statistical analysis. Most Shiny courses stop at single-page dashboards. This course goes all the way. Across 10 sections and 18 hours, you will build a complete cloud-deployed inventory management application in R — from a Python-free algorithm that runs on real supply chain data, through a multi-tab Shiny application with live database callbacks, to full Azure deployment using RStudio Server and Shiny Server so the app is accessible from any browser, anywhere, running automatically without human intervention.

The course is structured as a single consulting project. Mango Incorporation has hired you to optimise their stock replenishment and automate the process. You will investigate their data, build an inventory algorithm in R (seasonality, ABC classification with Inventorize, stock status, reorder points, service level, min-max policy), send results to MySQL, build a multi-tab Shiny application with KPI charts, alarm systems, category analytics, and ranking dashboards, schedule the pipeline with Cron, and deploy everything to Microsoft Azure via RStudio Server and Shiny Server.

This is the R twin of “Build a Supply Chain Inventory App with Python & Azure” — same project, same database structure, same Azure deployment target, R and Shiny all the way through. It is taught by the co-founder of Keip and the creator of the Inventorize R package. No previous knowledge is required. The course genuinely starts from zero.

WHAT MAKES THIS COURSE DIFFERENT

[ END2END ]

Algorithm to cloud app — nothing skipped

Inventory algorithm → MySQL → Shiny multi-tab app → RStudio Server → Shiny Server → Cron → Azure. Every layer built from scratch. The full R stack, in one project.

[ SHINY ]

Shiny at SaaS depth — not a tutorial dashboard

Multi-tab app: KPI bar plots, inventory turns, alarms with orange/red logic, categories page, size and colour tab, pie charts, file download, live database callbacks. Commercial application depth.

[ R ]

R preferred? This is the right choice

If your team, your client, or your organisation runs on R, this course delivers the same complete inventory application as the Python version — in R and Shiny, natively.

FULL TECHNOLOGY STACK COVERED

R / RStudio → dplyr → Inventorize → MySQL → Shiny → RStudio Server → Shiny Server → Cron → Azure

WHAT YOU WILL LEARN

✓ Learn R from scratch: installation, RStudio, data structures, dplyr manipulation, joins, pivot tables, and for loops — applied to supply chain data throughout

✓ Build reactive Shiny applications: UI design, input/output binding, reactive expressions, callbacks, and dynamic data tables

✓ Design a complete inventory replenishment algorithm in R: seasonality definition, beginning/ending/receiving inventory, stock status, total requested and current stock, ABC with Inventorize

✓ Implement demand lead-time calculation, service level targets, min-max reordering policy, and reordering quantity across all SKUs simultaneously

✓ Connect R to a MySQL database: initialise the schema, filter and send stock and sales data, fetch real-time data, create unique keys, and parse dates for time series alignment

✓ Build a full multi-tab Shiny inventory application: bar plots, pivot tables, inventory turns and margin KPIs, top sales/revenue, ranking tables, pie charts, and file download functionality

✓ Build a complete alarm system in Shiny: define orange and red alarm logic, build alarm datatables, a full alarms page, a categories page, and a size and colour analytics tab

✓ Schedule the inventory algorithm with Cron for fully automated, unattended operation

✓ Deploy the complete application stack to Microsoft Azure: set up a virtual machine, install RStudio Server, configure Shiny Server, set up remote MySQL, and verify the full system is running

✓ Configure multi-server network connections so the Shiny application, the R algorithm, and the MySQL database communicate correctly across Azure virtual machines

COURSE CONTENT — 10 SECTIONS · 147 LECTURES · 18 HOURS · 33 DOWNLOADABLE RESOURCES

PHASE 1 — FOUNDATIONS

SECTION 1: Introduction: why R, why Shiny, why cloud

Set the context for the entire project. Understand what makes this course different from other supply chain and Shiny courses. Learn why R is the right language for this application, what Shiny enables that static reports cannot, the difference between application dynamics, and a full review of the supply chain applications landscape. Complete curriculum overview.

Concepts

SECTION 2: R crash course for supply chain developers

No R experience? No problem. Install R and RStudio, set up your project, and install packages. Learn R from scratch with a supply chain data mindset: data structures and types, arithmetic and vectors, lists, importing and exploring data, selecting from dataframes, if-else logic, functions with conditions, and for loops applied to dataframes. Two-part graded assignment.

R RStudio

SECTION 3: Data manipulation with dplyr

Build the dplyr toolkit that powers the inventory algorithm. Investigate data with dplyr, calculate unique invoices, average invoice values per country, average items per invoice, perform joins, handle datetime, apply pivot wider/longer, and use separate and paste. Includes the New York Airlines graded assignment across seven questions — the data engineering foundation the entire application depends on.

R dplyr

PHASE 2 — SHINY APPLICATION FUNDAMENTALS

SECTION 4: Building reactive Shiny applications

Your first supply chain web application in R. Install Shiny packages, build your first app, design a UI, connect inputs and outputs, and create reactive expressions. Build a fully reactive Fibonacci sequence assignment to master Shiny’s declarative programming model. Then move to a real supply chain Shiny app: get unique identifiers, design the full user interface, build the app structure, add date/country/SKU reactivity, plot line charts, and build interactive datatables.

R Shiny

PHASE 3 — THE INVENTORY ALGORITHM

SECTION 5: Inventory replenishment algorithm for Mango Incorporation

The analytical and engineering core of the course. Design the full algorithm workflow. Set up MySQL Server and Workbench. Orient to Mango Inc.’s data. Prepare and filter stocks, send stocks and sales to the database, define algorithm assumptions, process purchase orders. Connect to the database, fetch real-time data, create a unique key, and parse dates. Define seasonality in the data and the algorithm. Calculate beginning, ending, and receiving inventory. Calculate stock status, total requested and current stock. Apply ABC classification using Inventorize. Calculate inventory metrics, demand lead-time, reordering quantity, and service level targets. Adjust for seasonality, finalise the algorithm, calculate last stock on hand date, and send the complete output back to the database.

R MySQL Inventorize

SECTION 6: Scheduling and automation with Cron

An algorithm that runs manually is a tool. An algorithm that runs automatically every day is a system. Configure Cron to schedule the inventory algorithm in R, debug the scheduling job, and verify that the algorithm triggers, executes, and writes to the database without any human intervention.

R Cron

PHASE 4 — THE SHINY INVENTORY APPLICATION

SECTION 7: Multi-tab real-time inventory Shiny application

Build the commercial-grade Shiny application that presents the algorithm output as a live, multi-tab inventory dashboard. Design the key input layer and the Mango Stock user interface. Manage user inputs reactively. Build Tab 1 completely: bar plots, pivot tables, inventory turns and margin KPIs, top sales and revenue rankings, pie charts with file download functionality, and the finalised tab layout. Build Tab 2: define alarm logic (orange and red conditions), build alarm datatables, construct a full alarms page, build the categories analysis page with overview, and add a size and colour analytics tab. Full application overview walkthrough.

R Shiny MySQL

PHASE 5 — AZURE CLOUD DEPLOYMENT

SECTION 8: Azure deployment Part 1: RStudio Server and Shiny Server

Deploy the R environment to the cloud. Set up a free Azure account, deploy a virtual machine, configure the server, install RStudio Server, install essential R libraries, set up Shiny Server to serve the application, configure the remote database connection, verify the full system is operational, install R packages with sudo permissions, and deploy the application to the public Shiny Server.

Azure R Shiny MySQL

SECTION 9: Azure deployment Part 2: second VM and database configuration

Set up the second Azure virtual machine for database isolation. Connect to the new VM remotely, install all required libraries, configure MySQL in the Azure environment, create the production database, verify the full end-to-end system is working across both virtual machines, and receive the final confirmation that the inventory application is live, accessible from the internet, and running automatically.

Azure MySQL R

SECTION 10: Final message and next steps

A brief closing section confirming what you have built — a complete, cloud-deployed, automated inventory management application in R and Shiny — and pointing toward the next courses and projects in the supply chain analytics series.

Discussion

THIS COURSE IS NOT FOR YOU IF...

✗ You prefer Python — the Python version of this exact project (Build a Supply Chain Inventory App with Python & Azure) is available separately; same algorithm, same database, same Azure deployment, Python and Dash throughout

✗ You are looking for a conceptual inventory management course — this course builds a working cloud application; if you want inventory theory and Excel or Python models, the Stock Control & Inventory Management: Excel to Python course covers that

✗ You want a general Shiny tutorial course — every Shiny component in this course is applied specifically to the supply chain inventory problem; standalone Shiny training is a separate category

✗ You need a statistical analysis or forecasting course — this course focuses on application engineering; the R forecasting catalogue (A Deep Dive into Forecasting — Excel & R and Business Statistics with R) covers statistical analysis

WHAT STUDENTS AND CLIENTS SAY

“Excellent course. The instructor is very engaging and the way he walks through building a real supply chain application in R and Shiny — from the algorithm all the way to Azure deployment — is unlike anything else available online.”

Bill — Verified Udemy student

“In Q4 2018, I attended a Supply Chain Forecasting & Demand Planning Masterclass conducted by Haytham and the possibilities seemed endless. We retained Haytham as a consultant to implement inventory guidelines for our business — a clear indication of his capabilities as a specialist in R, data analytics, demand planning, and inventory management.”

Shailesh Mendonca — Commercial Lead — Adventure AHQ, Sharaf Group

“Haytham mentored me in my role of Head of Supply Chain Efficiency. He is extremely knowledgeable about supply chain concepts, latest trends, and benchmarks. His analytics-driven approach was very helpful to recommend and implement significant changes to our supply chain at Aster Group.”

Saify Naqvi — Head of Supply Chain Efficiency, Aster Group

WHO THIS COURSE IS FOR

Supply chain and inventory professionals who use R

You manage stock, replenishment, and purchasing decisions in an R-based environment and want to automate your inventory processes with a real algorithm and a live Shiny web application.

R developers and data scientists entering supply chain

You know R and want to apply it to a complete, real supply chain engineering project — building an inventory management system, connecting to MySQL, and deploying a Shiny app to Azure.

Supply chain analysts and consultants

You build R-based analyses and models for clients and want to take the next step: delivering live, cloud-hosted Shiny applications rather than static RMarkdown reports that require manual updating.

Shiny developers who want a real-world project

You have built basic Shiny dashboards and want a complete, multi-tab, database-connected, cloud-deployed application project to stretch your skills and build your portfolio.

Python course completers who prefer R

You have seen the Python version of this course (Build a Supply Chain Inventory App with Python & Azure) and prefer to work in R. This course delivers the same complete system — same project, same deployment, R all the way through.

Students and career changers into supply chain tech

You want a portfolio project that demonstrates full-stack supply chain engineering in R: algorithm design, database management, Shiny application development, and Azure cloud deployment.

REQUIREMENTS

● No previous knowledge required — this is the only requirement listed on the course page, and intentionally so. No R, no Shiny, no database, no cloud experience assumed.

● Section 2 is a complete R crash course covering all the fundamentals before any application code is written — installation through for loops.

● Section 4 teaches Shiny from scratch — from the first reactive expression through to a complete multi-tab inventory application.

● A computer with R and RStudio (both free) — setup guided step by step in Section 2. MySQL Community Edition, Shiny, and all R packages are free and open-source.

● A Microsoft Azure free account — the Azure free tier covers everything needed for the deployment sections. Account setup is guided inside the course.

WHAT IS INCLUDED

● 10 sections, 147 lectures, and 18 hours of on-demand content covering the complete pipeline: R → inventory algorithm → MySQL → Shiny multi-tab app → Cron scheduling → Azure deployment

● 33 downloadable resources: R project files, Shiny application code, database setup scripts, and Cron job templates

● A complete, working multi-tab Shiny inventory application by the end of the course — not exercises, but a deployable system you own, with alarm logic, category analytics, and a size and colour tab

● Mango Incorporation project dataset: real-format supply chain data used throughout the algorithm development and Shiny application sections

● Full Azure cloud deployment across two virtual machines — RStudio Server, Shiny Server, and remote MySQL configuration covered in full

● Inventorize R package for ABC dynamic classification — taught in Section 5 by its creator, in its native R environment

● Lifetime access to all content and any future updates to the curriculum

● 30-day money-back guarantee — no questions asked

● Certificate of completion upon finishing the course

YOUR INSTRUCTOR

Haytham Omar, Ph.D.

Supply Chain & Business Intelligence Consultant · Developer · Trainer — UAE & France · Co-Founder, Keip (SaaS for supply chain & retail management)

Haytham holds a Ph.D. in Supply Chain and Forecasting from the University of Bordeaux and a Master of Science in Global Supply Chain Management from Bordeaux École de Management. He is the co-founder of Keip — a SaaS platform for retail and supply chain management — which means the Shiny application architecture, RStudio Server configuration, and cloud deployment approach in this course come from building and maintaining a live commercial software product.

He is the creator of the Inventorize package for R and Python — originally developed in R and now used by over 90,000 supply chain and retail professionals worldwide. He has deployed inventory replenishment algorithms for retail and supply chain companies across the UAE and France. He has trained over 70,000 professionals across 70+ workshops in the UAE. Additional consulting clients include Sephora France, Aster Group, DNO, PWC Training Academy Dubai, Qarar, and the Higher College of Technology.

This course is the R twin of “Build a Supply Chain Inventory App with Python & Azure”. Both courses build the same end-to-end inventory management system for Mango Incorporation using the same algorithm logic, the same database structure, and the same Azure deployment target — but this one does it entirely in R and Shiny.

Your R skills deserve a real project. This is it.

10 sections · 18 hours · R → Algorithm → MySQL → Shiny → RStudio Server → Cron → Azure · SaaS founder instructor · Real project