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Case Study
  • Manufacturing

A Better Framework

A technique for analyzing random variables

Publication:
Quality Progress
Date:
December 2022
Issue:
Volume 55 Issue 12
Pages:
pp. 74-81
Author(s):
Fiedeldey, Mark; Harkins, Ray
Organization(s):
Ohio Star Forge Co., Warren, OH

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.

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