Understanding and addressing complexity in problem solving

Abstract

Complexity manifests itself in many ways when attempting to solve different problems, and different tools are needed to deal with the different dimensions underlying that complexity. Not all complexity is created equal. We find that most treatments of complexity in problem solving within both the statistical and quality literature focus narrowly on technical complexity, which includes complexity of subject matter knowledge as well as complexity in the data access and analysis of that data. The literature lacks an understanding of how political complexity or organizational complexity interferes with good technical solutions when trying to deploy a solution. Therefore, people trained in statistical problem solving are ill-prepared for the situations they are likely to face on real projects. We propose a framework that illustrates examples of complexity from our own experiences, and the literature. This framework highlights the need for more holistic problem-solving approaches and a broader view of complexity. We also propose approaches to successfully navigate complexity.

Keywords:

• Decision making
• problem solving
• Six Sigma
• statistical engineering
• statistics

Additional information

Notes on contributors

Roger Hoerl

Roger Hoerl is Associate Professor at Union College in Schenectady, NY. Previously, he led the Applied Statistics Lab at GE Global Research.

Willis Jensen

Willis Jensen is a member of the HR Analytics team at Gore, where he supports people analytics across the globe. He previously led the Global Statistics team at Gore.

Jeroen de Mast

Jeroen de Mast is Professor of Statistics at the University of Waterloo, Canada, and Academic Director of Professional Education at the Jheronimus Academy of Data Science in The Netherlands.