Statistical engineering: Synergies with established engineering disciplines

IF 1.3 4区 工程技术 Q4 ENGINEERING, INDUSTRIAL
Susan O. Schall
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引用次数: 0

Abstract

Abstract This article explores the similarities and synergies between statistical engineering and established engineering disciplines. Statistical engineering is compared to the focus, process, and knowledge of established engineering disciplines and areas where synergies lie to the benefit of all engineering disciplines identified. Statistical engineering has the potential to help solve large unstructured engineering challenges to improve the planet. SUMMARY In theory, and also in my experience, statistical engineering has much in common with the definition and processes of engineering. Both focus on solving problems with similar frameworks and overlapping tool sets. Statistical engineering brings more emphasis, methods, and tools for data analysis that would benefit the engineering design process in attacking the large unstructured challenges of our world (the 14 grand challenges). It also defines problems at a broader level, including the political and social elements of the problem that could lead to better, more sustainable (long-term) solutions. Statistical engineering can also help all types of engineering enhance their problem-solving skills by incorporating empirical approaches into problem-solving efforts.
统计工程:与已建立的工程学科协同作用
摘要:本文探讨了统计工程与已建立的工程学科之间的相似性和协同作用。统计工程与已建立的工程学科和领域的重点、过程和知识相比较,其中协同作用在于所有确定的工程学科的利益。统计工程具有帮助解决大型非结构化工程挑战以改善地球的潜力。在理论上,以及在我的经验中,统计工程与工程的定义和过程有很多共同之处。两者都侧重于使用相似的框架和重叠的工具集来解决问题。统计工程为数据分析带来了更多的重点、方法和工具,这将有利于工程设计过程,以应对我们世界的大型非结构化挑战(14大挑战)。它还在更广泛的层面上定义问题,包括问题的政治和社会因素,这些因素可能导致更好、更可持续的(长期)解决方案。统计工程还可以帮助所有类型的工程通过将经验方法纳入解决问题的努力来提高他们解决问题的技能。
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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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