1.1 The foundations of supply chain
Supply chain is the quantitative yet street-smart mastery of optionality when facing variability and constraints related to the flow of physical goods.
Watch lectureSupply chain lectures
This lecture series presents the foundations of supply chain management: the challenges, the methodology, and the technologies. The perspective developed by Joannes Vermorel diverges from mainstream supply chain theory and is referred to as the Quantitative Supply Chain.
The series progresses from definitions and methodological principles to a set of fictional supply chain personae, then to the auxiliary sciences, forecasting, decision-making, and execution practices that support this perspective. It is intended to be read as a structured body of work, not as isolated talks.
1. Prologue
The opening lectures define the quantitative supply chain perspective and establish the conceptual foundations of the series.
1.2 The Quantitative Supply Chain in a nutshell
A concise manifesto of the quantitative supply chain and the principles that set it apart from mainstream supply chain theory.
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1.3 Product-oriented delivery
Why a quantitative supply chain initiative should be treated as a product-engineering effort rather than a spreadsheet-heavy operational patchwork.
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1.4 Programming Paradigms for the Supply Chain
A review of the programming paradigms and software design choices that make predictive optimization workable in real supply chains.
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1.5 XXIst century trends in supply chain
A survey of the major long-term trends that have reshaped supply chains, from complexity and competition to the growing role of software.
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1.6 Quantitative principles for supply chains
A look at the general observational and optimization principles that recur across most real-world supply chains.
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1.7 On Knowledge, Time and Work for Supply Chains
An exploration of how economics, systems thinking, and the passage of time shape sound supply chain decisions.
Watch lecture2. Methodology
These lectures cover the methods needed to study supply chain scientifically, from experimental practice to documentation and vendor analysis.
2.1 Supply Chain Personae
Why fictitious companies can be better teaching tools than biased real-world case studies when exploring supply chain problems.
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2.2 Experimental Optimization
An iterative approach to optimization focused on investigating insane decisions and correcting the economic drivers behind them.
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2.3 Negative Knowledge
How antipatterns and other forms of negative knowledge help practitioners remember what repeatedly fails in practice.
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2.4 Adversarial Market Research
Why software vendor evaluation needs to account for conflicts of interest, biased analysts, and marketing distortion.
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2.5 Writing for supply chains
Why written documentation is indispensable for scalable supply chain work and for long-term continuity across teams.
Watch lecture3. Personae
A series of fictional companies designed to highlight the operational realities and tradeoffs of different supply chain environments.
2.1.1 Paris - a fashion retail network
A fashion persona centered on novelty, collections, pricing, store allocation, and the operational consequences of rapid assortment turnover.
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3.1 Miami - an aviation MRO
A persona built around aviation maintenance, repair, and overhaul, where uptime and AOG avoidance dominate decision-making.
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3.2 Amsterdam - cheese brands
A dairy FMCG persona used to explore freshness, waste, promotions, and the competing constraints of branded food supply chains.
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3.3 San Jose - homeware ecommerce
A homeware ecommerce persona illustrating marketplace competition, service quality, and the complexity of online retail operations.
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3.4 Stuttgart - an automotive aftermarket company
An automotive aftermarket persona spanning branches, ecommerce, and used vehicles in a highly competitive spare-parts landscape.
Watch lecture4. Auxiliary Sciences
These crash courses bring in adjacent disciplines that materially improve supply chain practice, from computing and algorithms to cybersecurity.
4.1 Modern computers
Why computing hardware characteristics matter directly to the performance of supply chain systems and numerical recipes.
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4.2 Modern algorithms
How algorithmic design shapes computational efficiency and why supply chain problems benefit from algorithmic specialization.
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4.3 Mathematical optimization
Why mathematical optimization sits at the core of both forecasting systems and decision-making systems in modern supply chains.
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4.4 Machine learning
An overview of modern learning methods and the paradoxes practitioners must understand to make data-driven anticipation work.
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4.5 Languages and compilers
Why programmatic expressiveness, not just enterprise software feature lists, determines what supply chain teams can actually achieve.
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4.6 Software engineering
A software-engineering perspective on why complexity, reliability, and maintainability make or break supply chain initiatives.
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4.7 Cybersecurity
How cyber risk intersects with distributed operations, numerical recipes, and the broader software stack used by supply chain teams.
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4.21 Blockchains
A critical look at blockchain use cases for supply chain, separating the few real opportunities from the broader hype.
Watch lecture5. Forecasting
This chapter of the series focuses on predictive modeling, from forecasting competitions to structured modeling and lead times.
5.0 No1 at the SKU-level in the M5 forecasting competition
Lessons from Lokad's M5 competition performance and what the result reveals about practical forecasting for supply chain.
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5.1 Structured predictive modeling
An introduction to differentiable programming and why structured modeling supersedes much of the classic forecasting literature.
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5.2 Probabilistic forecasting
Why probabilistic forecasts outperform point forecasts when uncertainty is irreducible and supply chain decisions carry financial risk.
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5.3 Lead-time forecasting
How lead times can be forecast probabilistically and then combined with demand forecasts for stronger supply chain modeling.
Watch lecture6. Decision Making
The decision-making lectures move from anticipation to action, showing how forecasts are translated into economically sound choices.
6.1 Retail stock allocation with probabilistic forecasts
A practical illustration of risk-adjusted stock allocation in a retail network where stores compete for the same inventory.
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6.2 Pricing Optimization for the Automotive Aftermarket
A pricing case study showing why optimizing the right objective matters more than polishing the mathematics around the wrong one.
Watch lecture7. Tactical and Strategic Execution
The closing lectures focus on execution: getting initiatives started, bringing decisions into production, and defining the role of the supply chain scientist.
7.1 Getting started with a quantitative initiative
A review of the early mistakes that derail supply chain initiatives and the principles needed to avoid them.
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7.2 Bringing the decisions to production
How to turn numerical recipes into production-grade decision systems without relying on brittle waterfall-style delivery.
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7.3 The Supply Chain Scientist
An explanation of the supply chain scientist role and its responsibility for data, economics, KPIs, and automated decisions.
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