Analytic Hierarchy Process-Based Decision Making for Selecting Yearly Vaccine Production Capacity Under Demand Uncertainty

Pharmaceutical industry face challenges due to high research and development costs. These costs push companies to market products globally. However, global marketing faces strict, varying regulations per country. This is because products like vaccines are widely used in government-funded healthcare systems. Several governments regulate product pricing, creating further challenges. PT. XYZ, Indonesia’s primary vaccine manufacturer, plans to increase capabilities by building a new facility to serve global Hepatitis B vaccine demand. They face challenges in determining optimal capacity due to recent changes and perceived demand volatility. A new hexavalent vaccine product entering the market in 2027 adds complexity. Capacity changes involve significant risks, such as long validation processes. Root cause analysis shows the company lacks robust forecasting methods and depends heavily on Indonesian government demand. The thesis develops a decision-making framework using Monte Carlo simulation for demand forecasts and Analytic Hierarchy Process (AHP) for capacity decisions. Demand includes national and global markets for Hepatitis B vaccines: single-dose adult, pediatric, pentavalent, and hexavalent. Forecasts span 15 years with pessimistic, forecasted, and optimistic scenarios. Six alternatives are generated, ranging from 55 million to 80 million doses per year. Metrics are benchmarked from financial feasibility reports and interviews. Alternatives are evaluated using three criteria: absorbed demand, capacity aspects, and financial feasibility. Subcriteria include utilization rate, cost per unit, net present value, internal rate of return, and payback period. AHP assesses the relative importance of each criteria to determine the best alternative.