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A statistical engineering approach to problem-solving
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AI
DOI：10.1080/08982112.2026.2635741.png)
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Abstract
En 中文
The design, monitoring, improvement, and control of processes of all types creates a continual flow of problems that must be solved for processes to perform as designed, and effectively and efficiently serve customers. As a result, various types of problems arise, and numerous problem-solving methods have been developed to address these problems. Using the principles of statistical engineering, this research develops a framework that integrates problem types and problem-solving strategies. The proposed framework introduces a structured decision logic based on several dimensions, including: the fundamental intent of intervention (fixing, improving, or creating), whether the solution direction is known or must be discovered, and the availability of sufficient problem-relevant data. The framework is designed to help practitioners choose the most effective problem-solving methodology for each unique challenge. This work emphasizes that the problem and its characteristics should drive the selection of tools, not the other way around. While this framework can be useful in practice, it should serve only as a guide to problem-solving, not the dictator of the approach. That is, the framework should work for the practitioner, not the other way around. The framework is illustrated using four real problems from our collective experience.
Keywords:
Agile
CRISP-DM
8D
Lean
Six Sigma
Journal
Q
IF:
2.2
Papers:
40
Citations:
1.4K