Supply Chain Design and Planning with Excel & Python.

SUPPLY CHAIN OPTIMIZATION · NETWORK DESIGN · LINEAR PROGRAMMING · PYTHON · PULP · OR-TOOLS · FACILITY LOCATION · VEHICLE ROUTING · LAST MILE DELIVERY · PRODUCTION PLANNING

★ Included in Udemy for Business — Chosen by Companies for Corporate Supply Chain Training

This course is part of the Udemy for Business catalogue — the platform used by companies like Nasdaq, Volkswagen, NetApp, and Eventbrite to train their teams. When organisations are searching for supply chain optimisation training for their planning and logistics professionals, this course is what they find. That institutional endorsement reflects the depth and professional relevance of the content.

★ PuLP + Google OR-Tools — The Two Most Powerful Open-Source Optimisation Libraries in Python

Most supply chain courses stop at Excel Solver. This course goes further — teaching PuLP for linear programming formulation and Google OR-Tools for advanced routing, vehicle scheduling, and capacity optimisation. These are the same libraries used by engineers at Google, Uber, DHL, FedEx, and Amazon to solve real-world logistics problems at scale. You will use both, from scratch, applied to supply chain problems.

★ 11.5 Hours of Real Optimisation Content — Not a 2-Hour Overview

The average supply chain design and optimisation course on Udemy is 2–3 hours long. This course is 11.5 hours across 9 sections and 126 lectures — built by a Ph.D. holder and active supply chain consultant who has deployed these exact models for clients. It covers strategic supply chain design, production planning, facility allocation, network flow optimisation, route scheduling, vehicle routing with time windows, and last-mile delivery. Nothing is skipped.

★ Real-World Cases from Ikea, Zara, Walmart, Dell, Uber, FedEx, Amazon, and DHL

This course does not use synthetic data or made-up scenarios. Strategic supply chain design is studied through the real network decisions of Ikea, Zara, Walmart, and Dell. Route optimisation and last-mile delivery are studied through the real scheduling logic of Uber, FedEx, Amazon, and DHL. Every case was selected because it illustrates a genuine supply chain optimisation decision that practitioners face — and that these companies solved analytically.

★ Built by a Ph.D. Supply Chain Consultant — Not a Textbook Instructor

Haytham holds a Ph.D. in Supply Chain from the University of Bordeaux and is an active consultant whose optimisation models have been deployed by Sharaf Group, Sephora France, and Aster Group. The facility allocation models, network flow formulations, and routing algorithms in this course come from live client projects — not adapted textbook exercises. What you learn here, practitioners use today.

Supply chain optimisation is not a data science topic. It is a business survival skill. Where you place your warehouses, how you schedule your production, which routes your vehicles take, and how you balance service level against cost — these are decisions that determine whether your supply chain makes or loses money. This course gives you the analytical framework and the Python tools to make those decisions with precision.

You will work through the complete supply chain optimisation decision hierarchy: strategic network design (where to locate facilities, how to allocate demand across warehouses), tactical production planning (how to schedule production to minimise cost under capacity constraints), and operational route optimisation (vehicle routing, time windows, last-mile delivery). Every level of the supply chain planning hierarchy is covered, using the same open-source Python libraries — PuLP and Google OR-Tools — that Uber, FedEx, DHL, and Amazon use to solve these problems at industrial scale.

The course begins with Excel — building intuition for linear programming, resource allocation, and production scheduling in the tool you already know. Then it moves to Python, with a complete crash course included, before applying PuLP to manufacturing and production problems and OR-Tools to supply chain network design and vehicle routing with time windows.

This course is included in the Udemy for Business catalogue — chosen by companies for corporate supply chain and logistics team training. It has 21,000+ enrolled students and is taught by a practising Ph.D. supply chain consultant. Most supply chain design courses on Udemy are 2–3 hours. This is 11.5 hours of real optimisation content. There is no comparison.

WHAT MAKES THIS COURSE DIFFERENT:

[ OPT ]

PuLP + OR-Tools — industry-grade optimisation

Most courses stop at Excel Solver. This one teaches the two most widely-used open-source Python optimisation libraries — the same tools Uber, DHL, FedEx, and Amazon use to solve real logistics problems.

[ REAL ]

Real cases from real companies

Ikea, Zara, Walmart, Dell, Uber, Amazon — every strategic decision is studied through a real network design choice. No synthetic examples. No made-up data.

[ FULL ]

End-to-end: strategy to last mile

From strategic facility location all the way to vehicle routing with time windows — this course covers the full supply chain design and planning decision hierarchy in one program.

TOOLS COVERED IN THIS COURSE

Microsoft Excel | Python | PuLP | Google OR-Tools | Jupyter Notebook / Anaconda

WHAT YOU WILL LEARN

✓ Understand linear programming and formulate supply chain optimisation problems mathematically

✓ Build production scheduling models in Excel and Python that minimise cost under opening, capacity, and resource constraints

✓ Use PuLP to solve manufacturing and production planning problems in Python from scratch

✓ Design supply chain networks: allocate demand across warehouses, optimise facility locations, and minimise total network cost

✓ Build facility allocation and network flow models in Python with PuLP — including sensitivity analysis and binding constraint identification

✓ Integrate service level into network design decisions — setting distance constraints, relaxing DC constraints, and evaluating direct delivery options

✓ Solve the Travelling Salesman Problem and route optimisation problems using OR-Tools

✓ Build Vehicle Routing Problem (VRP) models with capacity constraints using OR-Tools

✓ Implement Vehicle Routing with Time Windows (VRPTW) — the model used by FedEx, Uber, and Amazon for last-mile scheduling

✓ Map and visualise routing solutions across a real delivery network

✓ Understand sensitivity analysis and identify binding constraints in your supply chain optimisation models

✓ Apply optimisation to real-world cases: restaurant routing, SportStuff distribution, CoolWipes network design

COURSE CONTENT — 9 SECTIONS · 126 LECTURES · 11.5 HOURS · 2 PRACTICE TESTS

SECTION 1: Supply chain design and optimisation fundamentals

What is supply chain optimisation and why does it matter? Study how Ikea, Zara, Walmart, and Dell design their supply chains strategically — with real cases that illustrate the trade-offs between efficiency and responsiveness. Understand the types of optimisation problems in supply chain, mathematical formulation, transportation problems, resource utilisation, and the manufacturing model. Build your first optimisation model in Excel. Includes an assignment and graded quiz.

Excel

SECTION 2: Production planning and scheduling

How do you decide what to produce, when, and at what cost? Build production scheduling models from scratch: understand the problem structure, formulate opening and capacity constraints, and solve for the minimum-cost production plan in Excel. Then tackle a global production scheduling assignment that spans multiple facilities. Includes two graded assignments.

Excel

SECTION 3: Python crash course for supply chain optimisation

No Python experience? No problem. Install Anaconda, set up Jupyter Notebook and Spyder, and learn Python with a supply chain mindset: dataframes, arithmetic, lists, dictionaries, arrays, data import, subsetting, conditions, writing functions, mapping, and for loops. Includes a two-part graded assignment on real supply chain data.

Python Jupyter/Anaconda

SECTION 4: Linear programming with PuLP in Python

Translate the optimisation models from Sections 1 and 2 into Python using the PuLP library. Build the manufacturing model in PuLP, solve production scheduling problems, define objective functions and constraints in code, and validate solutions. Then extend to production scheduling orientation and complete a graded assignment with full solution.

Python PuLP

SECTION 5: Supply chain network design: facility allocation

Strategic supply chain design made computational. Understand network design components, build warehouse allocation models in Python, define decision variables and objective functions for facility allocation, formulate and solve constraints, run sensitivity analysis, and visualise the cost-distance trade-off across different numbers of warehouses. Introduces the DC extended model and linking constraints.

Python PuLP

SECTION 6: Network design with service level integration

Network design is not just about cost — it is about cost at a given service level. Formulate the full multi-echelon network design problem with service level constraints: develop costs, formulate demand and flow constraints, solve, and interpret results. Analyse direct delivery options, relax DC constraints, set binding service level constraints, and complete a full network design assignment. Includes a PuLP Network Design practice exam with solved notebook.

Python PuLP

SECTION 7: Real case: SportStuff distribution network

Apply everything from Sections 5 and 6 to a complete, real-scale distribution network design problem. Import model data, construct variables, build costs, formulate the objective function, define demand and flow constraints, solve, display results, set service level requirements, relax DC constraints, apply service level constraints, and identify binding constraints. Two-part graded assignment included.

Python PuLP

SECTION 8: Route optimisation with OR-Tools

Move from strategic network design to operational route planning. Solve the Travelling Salesman Problem. Apply ML-Rose for route optimisation. Use Google OR-Tools to build route optimisation models, add distance and capacity constraints, loop solutions over vehicle fleets, implement capacity dimensions, and display routing results. A complete real-world routing example for a restaurant delivery network is included.

Python OR-Tools

SECTION 9: Vehicle Routing with Time Windows (VRPTW)

The most operationally complex section of the course — and the most directly applicable to last-mile delivery. Build a complete VRPTW model: add the time dimension to OR-Tools, set time window ranges, instantiate route start and end points, extract routes and time windows, map the full solution, and validate total distance and load calculations. This is the exact model structure used by FedEx, Uber, DHL, and Amazon for daily last-mile scheduling.

Python OR-Tools

THIS COURSE IS NOT FOR YOU IF...

✗ You are looking for a supply chain overview or theory-only course — this course builds working optimisation models in Excel and Python from the first section

✗ You want a warehouse management or ERP implementation guide — this course focuses on quantitative network design and operational planning models, not software configuration

✗ You have no interest in Python — Sections 3 through 9 are Python-based (a complete crash course is included for absolute beginners)

✗ You are looking for a forecasting or inventory management course — this course covers supply chain design, production planning, and route optimisation; separate courses in the instructor’s catalogue cover inventory and forecasting

WHAT STUDENTS AND CLIENTS SAY

“I attended this course with high expectations. And I was not disappointed. 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

“In Q4 2018, I attended a Supply Chain Forecasting & Demand Planning Masterclass conducted by Haytham and the possibilities seemed endless. We requested Haytham to conduct a 5-day workshop in our office to train 8 staff members, which opened us up to deeper data analysis. We have gone further and retained Haytham as a consultant to implement inventory guidelines for our business.”

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 planners and analysts

You manage production plans, network flows, or distribution schedules and want to move from instinct-based decisions to quantified, model-driven optimisation using Python.

Logistics and transport professionals

You plan routes, manage fleets, or design distribution networks and want the analytical tools to minimise cost, maximise vehicle utilisation, and hit service level targets.

Operations and supply chain managers

You are responsible for strategic decisions — where to place facilities, how to allocate production, how to structure your network — and want a rigorous, quantitative framework to make them.

Data scientists entering supply chain

You know Python and want to apply optimisation and linear programming to real supply chain problems — facility location, production scheduling, route optimisation, and vehicle routing.

Production planners and schedulers

You build production schedules in Excel and want to formalise them as optimisation models that minimise cost, respect capacity constraints, and can be re-solved automatically as conditions change.

Students and supply chain career changers

You want a portfolio of working Python optimisation models — network design, facility allocation, VRP with time windows — to stand out in supply chain and logistics job applications.

REQUIREMENTS

● Microsoft Excel — basic formulas and functions. No advanced Excel knowledge required.

● Basic understanding of supply chain concepts is helpful — what a supply chain is, what distribution networks look like, what production planning involves.

● No Python experience required — Section 3 is a complete Python crash course built for supply chain professionals. Anaconda installation is guided step by step.

● PuLP and Google OR-Tools are both free and open-source Python libraries — installation instructions are provided inside the course.

● A computer capable of running Anaconda and Jupyter Notebook — setup is fully guided within the course.

WHAT IS INCLUDED

● 9 sections, 126 lectures, and 11.5 hours of on-demand content covering the complete supply chain design and planning optimisation workflow

● 56 downloadable resources: Excel workbooks, Python notebooks, and optimisation model files for every section

● 2 practice tests: the PuLP Network Design exam (Section 6) and the Production Scheduling exam (Section 2), each with a solved notebook

● Multiple graded assignments across every section — each assessed on real supply chain cases, not synthetic data

● Real-world cases: Ikea, Zara, Walmart, Dell (strategic design), SportStuff and CoolWipes (network flow), restaurant routing and FedEx/Uber/Amazon scheduling models (VRPTW)

● 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 · Founder, Rescale Analytics

Haytham 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 an active supply chain optimisation consultant whose models have been deployed by Sharaf Group Adventure HQ (replenishment and revenue maximisation algorithms since 2019), Sephora France (omni-channel optimisation), and Aster Pharmacy group.

He has trained over 70,000 supply chain professionals across 70+ workshops in the UAE in Python, R, and applied supply chain analytics. Additional clients include DNO, Qarar, PWC Training Academy, Lamprell, and the Higher College of Technology. He is also the creator of the Inventorize package — used by over 90,000 supply chain professionals worldwide.

The optimisation models in this course — facility allocation, network flow, production scheduling, vehicle routing with time windows — are not adapted from textbooks. They are the same analytical frameworks Haytham builds for clients. In supply chain optimisation, the difference between a consultant’s model and a textbook example is everything.

Stop managing supply chains by spreadsheet. Start optimising them by model.

9 sections · 11.5 hours · PuLP + OR-Tools · Excel · Udemy for Business · Ph.D. instructor · Lifetime access