RA: Data Science and Supply chain analytics.(A-Z with R)

SUPPLY CHAIN DATA SCIENCE · R PROGRAMMING · INVENTORIZE · INVENTORY OPTIMIZATION · DEMAND FORECASTING · MACHINE LEARNING · REVENUE MANAGEMENT · SIMULATION · HIGHEST RATED

★ Highest-Rated Supply Chain Course on Udemy — 4 Consecutive Years

This course has earned and held the Highest Rated badge on Udemy for four consecutive years. Not the most enrolled — the highest rated by students who completed the program and returned to leave a review. Four years of sustained excellence is the most credible quality signal any online course can carry. The Python twin of this course carries the Bestseller badge. This R edition carries something more durable: the endorsement of its students, year after year.

★ Twin of the Udemy Bestseller — the original R edition that started it all

The Python version of this course carries the Udemy Bestseller badge. This R edition is the original — the one Haytham built first, that earned the Highest Rated Supply Chain badge for four years, and from which the Python bestseller was born. Same curriculum depth, same instructor, same real consulting cases — in R, the language of statistical computing preferred by analysts, researchers, and supply chain data scientists worldwide.

★ Inventorize — 60,000+ downloads. Taught only here, by its creator.

Haytham built the Inventorize package for R. Over 60,000 professionals use it across R and Python. This is the only course in the world where you learn Inventorize in full depth — directly from its developer, applied to real supply chain cases across inventory optimisation, safety stock, pricing, and product recommendations.

COURSE DESCRIPTION

There is no course like this anywhere on the internet. Most supply chain courses teach theory without code. Most data science courses teach generic skills without supply chain context. This program does something no other course on any platform does: it combines supply chain fundamentals, R programming from scratch, and eleven applied supply chain data science disciplines — forecasting, inventory, revenue management, segmentation, simulation, machine learning, and product recommendations — in one structured 40-hour program built by a practising supply chain data scientist and consultant.

The program is structured in three phases. Phase 1 — Supply Chain Fundamentals: cost and service dynamics, supply chain flows, financial flow, and the full spectrum of data produced by suppliers, production, stocks, and customers. Phase 2 — R Programming Fundamentals: R from absolute scratch through data structures, loops, functions, cleaning and manipulation with dplyr, statistical analysis including distributions and regression, and data visualisation. Phase 3 — Eleven Supply Chain Applications: every technique applied step by step to real supply chain data in R, from product segmentation to machine learning forecasting with Tidymodels.

At the heart of the program is Inventorize — the specialist supply chain analytics library Haytham developed, with over 60,000 downloads across R and Python. You will use it across inventory optimisation, safety stock, pricing, EOQ, and product recommendations. This is the only course in the world where Inventorize is taught in full depth by its creator. The program has held the Highest Rated Supply Chain badge on Udemy for four consecutive years — the most durable quality signal a course can earn.

WHAT MAKES THIS COURSE DIFFERENT:

[ ONLY ]

The only complete program of its kind on the internet

No course anywhere combines supply chain fundamentals + R from scratch + all 11 application disciplines in one 40-hour program. This is not a course. It is a professional data science program built specifically for supply chain professionals.

[ INVZ ]

Inventorize — taught by its creator

Haytham built Inventorize. 90,000+ professionals use it. You learn it here at full depth, directly from its developer, across inventory, pricing, forecasting, and recommendations.

[ SCALE ]

From one SKU to 1,000,000 simultaneously

Forecasting, inventory policy, and pricing models run across your entire product assortment in R. What takes hours in Excel takes minutes here — automated, reproducible, and scalable.

TOOLS AND TECHNOLOGIES COVERED

R / RStudio | Inventorize | Tidyverse / dplyr / tidyr | ggplot2 | Tidymodels | randomForest / rpart / glm

WHAT YOU WILL LEARN

✓ Master R from A to Z: syntax, data structures, loops, functions, data manipulation, statistical analysis, and visualisation with real supply chain data

✓ Understand supply chain fundamentals: cost and service dynamics, financial flows, and the complete data landscape across suppliers, production, stocks, and customers

✓ Perform supply chain statistical analysis: measures of centrality and spread, correlations, outlier detection, linear regression, and probability distributions

✓ Clean, manipulate, and explore supply chain data with dplyr: invoice analysis, date parsing, pivoting, joining, and aggregation across large transactional datasets

✓ Segment products, suppliers, and customers: single-criteria ABC analysis, multi-criteria ABC, Kraljic matrix for suppliers, and RFM analysis for customers with 3D visualisation

✓ Forecast demand at scale: multiple regression, time series analysis (ARIMA, exponential smoothing, SARIMA, dynamic harmonic regression), hierarchical aggregation, and SKU classification by demand pattern

✓ Optimise inventory: EOQ with and without quantity discounts, safety stock (multiple methods), re-order point with lead-time variability, and all four inventory policies (min-Q, periodic review, min-max, base stock) using Inventorize

✓ Apply revenue management: price response functions, elasticity modelling, optimum pricing across all SKUs, competing product models, markdowns for multiple periods, and critical ratio for seasonal products

✓ Build supply chain simulations: queue theory, waiting lines, 400 Monte Carlo simulations simultaneously, capacity and sequential service optimisation

✓ Apply supervised and unsupervised machine learning: k-means clustering, decision trees, random forests, logistic regression, and machine learning forecasting with Tidymodels

✓ Build product recommendation systems using market basket analysis and association rules (Apriori) on real transactional supply chain data

✓ Move beyond Excel: automate and scale supply chain decisions across 1,000,000 products simultaneously using R and Inventorize

COURSE CONTENT — 27 SECTIONS · 399 LECTURES · 40 HOURS · 165 DOWNLOADABLE RESOURCES

PHASE 1 — SUPPLY CHAIN FUNDAMENTALS

SECTION 1: Introduction to supply chain analytics

Why data science for supply chain? Visualise how supply chains work, understand cost and service dynamics, service level trade-offs, customer and supplier characteristics, supply chain flows, the financial flow, and why supply chain complexity demands data science beyond spreadsheets. Supply Chain Framework

SECTION 2: Supply chain data

Supply chains produce data at every node. Understand the types of data from suppliers, production, stocks, and sales/customers. Learn the four types of analytics applied in supply chain and why data science is the only scalable path to extracting value from this data. R Discussion

PHASE 2 — R PROGRAMMING FUNDAMENTALS

SECTION 3: Installation and overview of R

Set up your complete R data science environment. Install R, RStudio, configure your project workspace, and install the packages used throughout the program. A full walkthrough tutorial ensures you are coding-ready before Section 4. R RStudio

SECTION 4:R programming fundamentals

Learn R from absolute scratch: data structures and types, arithmetic, vectors, lists, dataframes, import and exploration, selection, if-else logic, conditions, for loops, custom functions, and applying functions across dataframes. Includes two-part graded assignment on real supply chain data. R

SECTION 5: Supply chain statistical analysis

Apply R to supply chain measurement. Calculate measures of centrality, spread, and correlations. Detect outliers. Introduce linear regression. Work with probability distributions — the Normal, Chi-square testing — both in Excel and R. Model demand distributions. Includes two graded assignments. R Excel

SECTION 6: Supply chain data manipulation with dplyr

Clean and reshape real supply chain data at scale. Master dplyr: investigate invoice data, compute average invoice value per country, calculate average items per invoice, join datasets, parse and transform date-time fields, pivot wider and longer, and apply the full pipeline to a real New York airlines dataset. Four-part graded assignment.R dplyr

SECTION 7: Working with dates and time series in R

Parse, manipulate, and make inferences from dates in R using Lubridate. Model customer inter-arrival times. Build time-aware supply chain analyses. Includes graded assignment across six questions on real supply chain date data.

R Lubridate

SECTION 8

Data visualisation with ggplot2

Master supply chain data visualisation: line plots, scatter plots, bar charts, distribution plots, boxplots, and histograms. Build publication-quality charts on real supply chain data with ggplot2. Includes two-part graded assignment.

R ggplot2

PHASE 3 — SUPPLY CHAIN DATA SCIENCE APPLICATIONS

SECTION 9

Product and supplier segmentation

Segment your entire product and supplier portfolio analytically. Apply multi-criteria ABC analysis at product and store level. Build the Kraljic matrix for supplier positioning and strategic sourcing. Visualise segmentation outputs. Two graded assignments on real supply chain data. R Excel Inventorize

SECTION 10: Demand forecasting: regression-based methods

Build regression-based demand forecasting models from scratch. Prepare data for regression, apply multiple linear regression in Excel and R, generate forecasts, and evaluate accuracy. Includes two-part graded assignment covering the full regression forecasting workflow. R Excel

SECTION 11: Demand forecasting: time series methods

Master the full time series forecasting toolkit in R. Convert data to time series, analyse components, measure strength of trend and seasonality, apply exponential smoothing, ARIMA, dynamic harmonic regression, SARIMA with grid search, and battle-test models for accuracy. Full train/test evaluation workflow. Includes two-part graded assignment. R

SECTION 12: Hierarchical and aggregate forecasting

Scale forecasting across your entire product and channel hierarchy. Apply top-down, bottom-up, and middle-out aggregation approaches. Structure hierarchical data, generate aggregate forecasts, test accuracy at each level, and compare approaches systematically. Graded assignment with two-part solution. R

SECTION 13: Big data SKU classification for forecasting

Classify thousands of SKUs to assign the right forecasting method to each. Identify slow-moving and intermittent demand using average demand intervals and CV². Check for holiday effects. Visualise demand classifications across the full assortment. Three-part graded assignment.

R Inventorize

SECTION 14: Supply chain simulations: queues and capacity

Model operational uncertainty with simulation. Understand waiting line and queue theory, build simulations in Excel and R, run 400 simulations simultaneously, optimise call centre staffing and capacity with the right K, add sequential services, model multiple service channels, and optimise capacity constraints. Two graded assignments. R Excel

SECTION 15: nventory management: EOQ and total cost

Build the analytical inventory foundation. Understand why we hold inventory, inventory strategies and types, EOQ derivation, quantity discounts, sensitivity analysis, EOQ with lead time — both in Excel and R with Inventorize. Includes graded assignment and two-part summary. R Excel Inventorize

SECTION 16: Safety stock and reorder point optimisation

Calculate safety stock using multiple methods: demand-lead time sigma, Method 1 and Method 2. Prepare SKUs for calculation, set cycle service level in R, calculate reorder points with and without lead time variability using Inventorize. Wrap the full indeterministic inventory workflow. Graded assignment with full solution.

R Excel Inventorize

SECTION 17: Inventory policies: simulation and visualisation

Apply and compare all four inventory policies across your assortment. Min-Q, periodic review, min-max, and base stock — demonstrated, explained in Excel, and implemented in R. Simulate the s,Q policy, visualise policy variations and all policies simultaneously, compare metrics. Two graded assignments. R Excel Inventorize

SECTION 18: Revenue management: seasonal products and critical ratio

Optimise purchasing decisions for seasonal and perishable products. Model the point of maximum profit, apply critical ratio analysis in Excel and R with Inventorize, calculate expected profit, prepare data for optimum quantity calculation. Graded assignment included. R Excel Inventorize

SECTION 19: Revenue management: price response and elasticity

Model how customers respond to price. Build price response functions in R, apply elasticity modelling with Inventorize, prepare real retail SKU data, model optimum price for all SKUs simultaneously, validate results. Graded assignment with full solution.

R Inventorize

SECTION 20: Revenue management: logistic regression pricing

Model the probability of purchase as a function of price using logistic regression (logit) with Inventorize. Build, interpret, and apply logit models to pricing decisions. Graded assignment included. R Inventorize

SECTION 21: Revenue management: competing products and multivariate pricing

Model competing products simultaneously. Calculate correlations among products, fit multivariate regression, apply ANOVA, predict with the model, apply multinomial choice models, and optimise competing prices for 40,000 observations using Inventorize. Two graded assignments. R Inventorize

SECTION 22: Markdown optimisation and customer segmentation by RFM

Optimise markdowns across multiple periods and one-period scenarios with Excel Solver. Apply salvage value, integrate forecasting. Segment customers by Recency, Frequency, and Monetary value (RFM): prepare data, calculate KPIs, join, rank, group into tiles, and visualise in 3D scatter plots. Graded assignment included. R Excel Inventorize

SECTION 23: Machine learning: unsupervised and supervised

Apply machine learning to supply chain classification and prediction. Unsupervised: k-means clustering with elbow method, silhouette analysis, and interactive 3D scatter plots. Supervised: linear regression, decision trees and random forests, model comparison, logistic regression classification with confusion matrix and ROC curve. Graded assignment.

R ML Packages

SECTION 24

Product recommendations: market basket analysis

Build product recommendation systems for your customers. Introduce market basket analysis, identify top-10 products, read transactional data, apply the Apriori algorithm, extract and subset top association rules. Graded assignment included.R

SECTION 25

Machine learning forecasting with Tidymodels

The most advanced section of the program. Apply Tidymodels — the standard ML framework in R — to multi-level time series forecasting. Convert data to tsibble, generate time series features, handle missing data per level, split and log-transform data, build recipes, define models and workflows, resample with cross-validation, collect and compare metrics, stack models, predict and visualise the future across multiple hierarchical levels. R Tidymodels Tidyverse

THIS COURSE IS NOT FOR YOU IF...

✗ You want a Python course — this course uses R throughout. The Python twin (RA: Data Science and Supply Chain Analytics A-Z with Python) is available on Udemy

✗ You want a quick 2-hour overview — this is a 40-hour complete program; it rewards 4–5 hours per week of commitment over 12–16 weeks

✗ You need ERP, WMS, or TMS software training — this course builds analytical models and R programming skills, not software configuration

✗ You are looking for one specialisation only — this program covers eleven supply chain data science disciplines; single-topic courses are available separately in Haytham’s catalogue

WHAT STUDENTS AND CLIENTS SAY

“It’s incredible to see what is possible with Python in terms of supply chain planning and optimization. Haytham is doing a great job as a trainer — starting with explanation of basics and ending with presentation of advanced techniques supply chain managers can apply in real life.”

Larsen Block — Director, Supply Chain Management — Freudenberg Home & Cleaning Solutions

“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

“I participated in the Supply Chain Forecasting & Management training conducted by Haytham. It helped me enormously in my daily work in the purchasing department. Haytham has the pedagogy to explain very difficult calculations and formulas in a simple way. I highly recommend this training.”

Djamel Bouremiz — Purchasing Manager, Mineral Circles Bearings W.L.L.

“A fantastic course, you truly learn a lot and Haytham is very quick with his responses. You will be able to use the skills and techniques he goes through immediately. This course was worth every cent.”

Wesley — Verified Udemy student

WHO THIS COURSE IS FOR

Supply chain managers moving into data science

You have deep domain expertise and want to move from Excel to R — automating the analysis you do manually every day and scaling decisions across your entire product range.

Demand planners and inventory managers

You manage forecasting and stock levels and want to apply statistical and ML methods to improve accuracy, optimise safety stocks, and automate replenishment across thousands of SKUs with Inventorize.

Data scientists entering supply chain

You know R and data science but want a structured program that applies your skills to real supply chain problems — forecasting, inventory, pricing, segmentation, simulation, and recommendations.

Finance and budget forecasting professionals

You model budgets and revenue and want to apply time series, hierarchical aggregation, and machine learning with Tidymodels to forecasting problems that go far beyond spreadsheet capabilities.

Excel users ready to move to R

You are frustrated with spreadsheet limitations and want to automate, scale, and professionalise your supply chain analytics. This course is your complete, structured path from Excel to R.

Absolute beginners at coding

No coding experience? This program starts from the absolute basics of R and builds to machine learning and supply chain automation — step by step, with real supply chain data throughout.

REQUIREMENTS

● Microsoft Excel — basic familiarity is sufficient; many concepts are introduced in Excel before being scaled in R.

● No R or coding experience required — Sections 3 and 4 teach R installation and programming from absolute scratch.

● No data science or machine learning background required — all methods are introduced step by step using supply chain data.

● Motivation to move beyond Excel and make supply chain decisions at scale — the program rewards 4–5 hours of commitment per week.

● R and RStudio are both free — full installation guidance is provided in Section 3. Inventorize is free and open-source.

WHAT IS INCLUDED

● 40 hours of on-demand video across 27 sections and 399 lectures

● 165 downloadable resources: R project files, datasets, Excel workbooks, Inventorize notebooks, and templates for every section

● Graded assignments in most sections — all on real supply chain use cases, not synthetic or textbook data

● Inventorize library taught in full depth across Sections 9, 13, 15, 16, 17, 18, 19, 20, and 21 — exclusively by its creator

● Machine learning forecasting with Tidymodels (Section 25) — added August 2023 by student request

● Bonus: one-hour machine learning webinar with Haytham as panellist for Noble Prog, including Orange Data Mining demo

● Lifetime access to all content and any future curriculum updates

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

● Certificate of completion upon finishing the program

YOUR INSTRUCTOR

Haytham Omar, Ph.D.

Supply Chain & Business Intelligence Consultant · Developer · Trainer — UAE & France · Founder, Rescale Analytics

Haytham is a practising supply chain and data science consultant whose clients include Sephora France (omni-channel optimisation, Ph.D. collaboration), Sharaf Group Adventure HQ (replenishment and revenue maximisation algorithms deployed since 2019), Aster Group, DNO, Qarar, PWC Training Academy, and the Higher College of Technology. He has trained over 70,000 professionals across 70+ workshops in the UAE in R, Python, and applied supply chain analytics.

He holds a Ph.D. in Supply Chain from the University of Bordeaux and a Master of Science in Global Supply Chain Management from Bordeaux École de Management. He is the creator of the Inventorize package for R — downloaded over 60,000 times — which this course teaches in full depth, from its creator.

This R edition of the program is the original — the course that earned the Highest Rated Supply Chain Course badge on Udemy for four consecutive years and gave birth to the Python bestseller. Haytham built it because no comprehensive program existed that tackled supply chains using data science. It still does not exist anywhere else.

Stop doing supply chain in spreadsheets. Start doing it with data science.

40 hours · 165 resources · 27 sections · R + Inventorize · #1 Rated 4 years · Lifetime access