Linear programming for resource allocation, queuing theory and Little's Law, decision analysis under uncertainty, and where these techniques get applied in real industrial engineering problems — worked with real numbers.
Operations research applies rigorous mathematical modeling — an explicit objective function and constraints — to decisions that intuition alone handles poorly. This module works through a full linear programming resource-allocation example solved to its optimal vertex, queuing theory's arrival-rate/service-rate/utilization relationship and its sharply nonlinear behavior near full utilization, Little's Law relating work-in-process, throughput, and lead time, and decision analysis under uncertainty (EMV, maximax, maximin, minimax regret).
By the end of this module you should be able to explain why capping work-in-process is a direct, quantifiable lever on lead time through Little's Law — the actual mathematical justification behind the kanban pull systems covered back in Module 2, not an arbitrary lean ritual.