Introduction to Markov Processes in Healthcare Supply Chains

Workshop

Who is the course for?

This course is intended for healthcare supply chain researchers, practitioners, and students who want to model uncertainty in logistical systems using Markov Chains. It assumes familiarity with basic probability and matrix manipulation in R.

Learning Objectives

• Understand the structure and assumptions of discrete-time Markov chains (DTMCs)
• Apply transition matrices to simulate system evolution over time
• Compute and interpret steady-state distributions
• Model brand switching and service reliability in healthcare supply chains
• Simulate long-run outcomes and interpret them visually in R
• Link model insights to supply chain policy decisions (e.g. stockouts, demand, brand promotion)

Prerequisites

• Comfortable with basic probability (random variables, distributions)
• Familiarity with R and tidyverse for matrix operations and plotting
• No prior knowledge of Markov chains is assumed

Course Topics

Markov Chains for Healthcare Supply Chains

Section 1: Foundations of Markov Chains

• State-based systems and probabilistic transitions
• The Markov property and memoryless dynamics
• Transition probability matrices and system trajectories

Section 2: Steady-State Analysis

• n-step transitions and convergence
• Existence and uniqueness of steady-state distributions
• Interpreting long-run behaviour in real systems

Section 3: Brand Switching Case Study

• Promote-local strategy for paracetamol brands
• Simulate switching behaviour and market share convergence
• Solve steady-state equations using R

Section 4: Applied Simulation in R

• Vector-matrix calculations
• Transition matrix exponentiation
• Plotting and comparing convergence paths