Abstract

Many managers make projections based on single-point estimates, such as average revenue, average cost or average defect rate. But complex decisions with broad potential consequences require more in-depth estimates that consider all available data. The Markov Chain Monte Carlo (MCMC) method is a more thorough and accurate technique used to analyze data that allows you to extract more insights than with a single-point estimate technique. The authors present a theoretical case study in which a manufacturer uses MCMC to analyze the monthly incoming quality cost data for two suppliers to determine whether the manufacturer can save money by single sourcing its axles.