Problem framing: Essential to successful statistical engineering applications

IF 1.3 4区 工程技术 Q4 ENGINEERING, INDUSTRIAL
R. Hoerl, D. Kuonen, T. Redman
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引用次数: 1

Abstract

Abstract The first two phases of the statistical engineering process are to identify the problem, and to properly structure it. These steps relate to work that is often referred to elsewhere as framing of the problem. While these are obviously critical steps, we have found that problem-solving teams often “underwhelm” these phases, perhaps being over-anxious to get to the analytics. This approach typically leads to projects that are “dead on arrival” because different parties have different understandings of what problem they are actually trying to solve. In this expository article, we point out evidence for a consistent and perplexing lack of emphasis on these first two phases in practice, review some highlights of previous research on the problem, offer tangible advice for teams on how to properly frame problems to maximize the probability for success, and share some real examples of framing challenging problems.
问题框架:对成功的统计工程应用至关重要
摘要统计工程过程的前两个阶段是识别问题,并正确构建问题。这些步骤与其他地方通常称为问题框架的工作有关。虽然这些显然是关键步骤,但我们发现,解决问题的团队往往在这些阶段“缺乏领导力”,可能过于急于进行分析。这种方法通常会导致项目“一到即死”,因为不同的各方对他们实际试图解决的问题有不同的理解。在这篇解释性的文章中,我们指出了实践中一贯且令人困惑的对前两个阶段缺乏重视的证据,回顾了以前对该问题研究的一些亮点,就如何正确地构建问题以最大限度地提高成功概率为团队提供了切实的建议,并分享了一些构建具有挑战性问题的真实例子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Quality Engineering
Quality Engineering ENGINEERING, INDUSTRIAL-STATISTICS & PROBABILITY
CiteScore
3.90
自引率
10.00%
发文量
52
审稿时长
>12 weeks
期刊介绍: Quality Engineering aims to promote a rich exchange among the quality engineering community by publishing papers that describe new engineering methods ready for immediate industrial application or examples of techniques uniquely employed. You are invited to submit manuscripts and application experiences that explore: Experimental engineering design and analysis Measurement system analysis in engineering Engineering process modelling Product and process optimization in engineering Quality control and process monitoring in engineering Engineering regression Reliability in engineering Response surface methodology in engineering Robust engineering parameter design Six Sigma method enhancement in engineering Statistical engineering Engineering test and evaluation techniques.
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