Homogenization and Clustering as a Non-Statistical Methodology to Assess Multi-Parametrical Chain Problems

TL;DR

This paper introduces a novel non-statistical methodology based on homogenization and clustering to efficiently analyze complex multi-parametrical chain problems, reducing computational effort and enabling analytical insights.

Contribution

The authors develop a new homogenization and clustering approach that replaces complex chains with simplified models, facilitating faster and more analytical problem-solving.

Findings

Reduces computational time significantly

Enables derivation of analytical formulas

Applicable to various business chain problems

Abstract

We present a new theoretical and numerical assessment methodology for a one-dimensional process chain with general applicability to management problems such as the optimization of decision chains or production chains. The process is thereby seen as a chain of subsequently arranged units with random parameters influencing the objective function. For solving such complex chain problems, analytical methods usually fail and statistical methods only provide approximate solutions while requiring massive computing power. We took insights from physics to develop a new methodology based on homogenization and clustering. The core idea is to replace the complex real chain with a virtual chain that homogenizes the involved parameters and clusters the working units into global units to facilitate computation. This methodology drastically reduces computing time, allows for the derivation of analytical formulas, and provides fast and objective insights about the optimization problem under investigation. We illustrate the analytical potency of this methodology by applying it to the production problem of selecting the economically superior quality maintenance strategy. It can further be applied to all sequential multi-parametrical chain problems commonly found in business.