Statistical engineering – Part 2: Future

IF 1.3 4区工程技术 Q4 ENGINEERING, INDUSTRIAL

Quality Engineering Pub Date : 2022-08-18 DOI:10.1080/08982112.2022.2106440

C. Anderson‐Cook, Lu Lu, William Brenneman, J. De Mast, F. Faltin, Laura Freeman, W. Guthrie, R. Hoerl, Willis A. Jensen, Allison Jones-Farmer, Dennis Leber, Angela Patterson, M. Perry, S. Steiner, Nathaniel T. Stevens

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引用次数: 1

Abstract

Abstract In the second of two panel discussion articles focused on the evolution of statistical engineering (SE) as introduced by Roger Hoerl and Ronald Snee, a group of leading applied statisticians from academia, industry, and government present their perspectives on what the future might hold for this important movement. The invited panelists discuss the challenges and opportunities presented by the emergence of data science and the abundance of large amounts of data. They also consider the possible paths forward for SE, and the roles for statisticians in academia, industry, and government. The final question addresses what additional skills would be helpful to increase the effectiveness of the practice and advance SE. As with the first article, the format of the article follows the order of a posed question, a summary of key ideas, and then the detailed individual panelist answers. The article seeks to inspire statisticians to consider their possible role to leverage the potential of SE to solve important problems.

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统计工程——第2部分：未来

摘要在Roger Hoerl和Ronald Snee介绍的两篇关于统计工程（SE）演变的小组讨论文章中的第二篇中，来自学术界、工业界和政府的一群领先的应用统计学家就这一重要运动的未来提出了他们的观点。受邀的小组成员讨论了数据科学的出现和大量数据的丰富所带来的挑战和机遇。他们还考虑了SE可能的前进道路，以及统计学家在学术界、工业界和政府中的作用。最后一个问题是，哪些额外的技能有助于提高实践的有效性和提高SE。与第一篇文章一样，文章的格式遵循提出的问题的顺序、关键想法的摘要，然后是详细的小组成员回答。这篇文章试图激励统计学家考虑他们可能发挥的作用，利用SE的潜力来解决重要问题。

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 ENGINEERING, INDUSTRIAL-STATISTICS & PROBABILITY

CiteScore

3.90

自引率

10.00%

发文量

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.