Fix a process on the floor
24 case studies from my own projects, in four areas: warehousing, transport, Lean Six Sigma and inventory.
Everything below comes from problems I met on the floor: a warehouse that could not ship on time, trucks leaving half empty, a process nobody could measure, a stock that kept breaking. Each case study starts from the operational issue, builds a model in Python and shares the code, so you can reproduce it on your own data.
Pick the area closest to your problem. Read two or three case studies, then take the matching quiz in the Supply Science app to check what you keep.
Layout, picking, packing, workforce: the processes I designed and ran before writing about them.
Improve Warehouse Productivity using Order Batching with Python
Reduce Warehouse Space with the Pareto Principle using Python
Supply Chain Process Design using the Queueing TheoryRouting, container loading and network design, to cut lead times and freight costs.
Containers Loading Optimization with Python
Transportation Network Analysis with Graph Theory
Optimize E-Commerce Last-Mile Delivery with PythonThe statistical tests behind process improvement, done in Python instead of Minitab.
Lean Six Sigma with Python — Logistic Regression
Central Limit Theorem for Process Improvement with Python
Statistical Sampling for Process Improvement using PythonForecasting, safety stocks and replenishment rules, from the theory to the simulation.
Machine Learning for Retail Sales Forecasting — Feature Engineering
Inventory Management for Retail — Stochastic Demand
Inventory Management for Retail — Deterministic Demand