Course at a glance
Seven decision questions, organized into three Parts that map to how operations leaders actually run a business: how we make things → where stuff comes from → how we optimize. Click any card to jump in.
Part 1 · How We Make Things
Process choice, layout, project management, and finding the bottleneck.
Part 2 · Where Stuff Comes From & How We Plan
Sourcing, lean logistics, forecasting, S&OP, inventory, and MRP.
Part 3 · How We Optimize
Quality, statistical control, scheduling, theory of constraints, and ERP.
🚀 What you walk away with
The fluency to diagnose a real operation — spot the bottleneck, defend a sourcing trade-off, forecast next quarter, size inventory, run a Six Sigma improvement, sequence the work, and explain any of it to a CFO in plain English. Six fictional-but-realistic capstone companies you can point a hiring manager at.
About this course
Self-paced. Free. Pick whichever sections matter to you.
Who this course is for
Anyone who wants to actually run things — new managers, founders, MBAs, operations analysts, supply-chain hires, engineers moving toward business, or curious learners. No prior operations background is assumed; basic Excel and a willingness to think in trade-offs gets you started.
What the course covers
A complete tour of operations and supply chain management. You'll work through process design and project management, find bottlenecks with Little's Law, evaluate suppliers and design lean networks, forecast demand and plan S&OP, size inventory with EOQ and run MRP, raise quality with DMAIC and statistical process control, sequence jobs with priority rules, and connect it all with ERP and IoT. Each topic ends with a hands-on activity using real company-style data.
Why "decision-based" learning?
This course organizes content around decisions a manager makes, not chapters in a textbook.
Why? Because a year from now, you'll remember “that's how I find the bottleneck” — not “that's Little's Law in Chapter 11.”
Each topic starts with a real decision question. We introduce the framework that answers it, walk through a hands-on case using realistic data, and reinforce with an integrative case at the end of the course.
What you'll learn
By the end you'll be able to:
- Diagnose an operation — identify process type, capacity, bottleneck, and the metric that matters most.
- Plan demand, capacity, and inventory using forecasting, S&OP, EOQ, and MRP.
- Improve quality and flow with DMAIC, the Seven Tools, statistical process control, and the Theory of Constraints.
- Decide sourcing, location, and scheduling trade-offs by weighing cost, quality, risk, and lead time.
- Communicate operational recommendations to executives in language they can act on.
Materials & tools (all free)
You can do this whole course with a spreadsheet. A few optional tools deepen the experience.
- Microsoft ExcelThe single most important operations tool. Every dataset in the course is a workbook.
- An AI assistant (Claude / ChatGPT / Gemini)Used as a coaching partner for analysis — see the AI policy below.
- Python (optional)A few notebooks use Python for route optimization and bullwhip simulation. Run them in Colab or Jupyter.
- IBM SkillsBuildFree credentials for Problem Solving & Process Controls and Six Sigma fundamentals.
- McGraw-Hill Connect — Practice OperationsA six-module business simulation (paid, optional). The topic pages mention which module pairs with each decision.
Going further with AI
AI is woven into every topic in this course — not as a side trip, but as the tool a working operations analyst actually uses. If you want to go deeper, this companion library walks through how AI is used across the discipline:
- AI in Operations & Supply Chain — Industry ReportWhere AI is actually being used in operations today, with examples.
- AI in OSCM — Part 1: BasicsFoundations: what AI does well, what it doesn't, where to start.
- AI in OSCM — Part 2: AdvancedOrchestration patterns, agents, multi-step workflows.
- AI Orchestrators Playbook for OSCMHow to chain AI agents through an operations workflow.
- AI Capabilities GuideA taxonomy of what current AI tools can and can't do for operations work.
- Prompt Engineering for OSCMPractical prompts you can paste into Claude or ChatGPT to analyze operations data.
- Marble AI — How-To GuideBuild a 3D facility model with AI — companion to the activity below.
- Marble 3D Facility ActivityStep-by-step build of an AI-generated facility model.
- Python Crash Course for OSCMEnough Python to read and tweak the notebooks in this course.
- AI Student GuideA self-guided activity: use AI to diagnose a failing operation.
- AI Lab InstructionsFive datasets, five AI-assisted analyses, one workflow.
How to use this course
Two ways to work through it:
- Follow the schedule. The 15-week plan is a suggested rhythm — go faster or slower as you like. Weeks are grouped into the three Parts.
- Browse by topic. If you have a specific question ("how do I size inventory?"), jump straight to that decision page. Each one is self-contained.
The integrative cases at the end are where you stitch decisions together against realistic company data. Midnight Bakery and BrewLine are the canonical end-to-end builds — pick one and work it.
Using AI tools responsibly
AI is a real operations tool now — demand sensing, supplier risk, root-cause analysis, scheduling. Use it. A few principles:
- Learn WITH AI, not FROM AI. Treat it as an analyst sitting next to you, not a replacement for your judgment.
- Own the decision. You should be able to defend every number, framework, and recommendation you ship.
- Verify before you trust. AI confidently produces wrong answers — especially on calculations. Always sanity-check.
If you're working on a real project at work or school, follow your organization's AI policy on top of these principles.