A Technique for Analyzing Random Variables

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.

Read more in “A Better Framework.”