Strategy for Data-Based Problem Solving

IF 1.5 4区数学 Q3 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Applied Stochastic Models in Business and Industry Pub Date : 2026-10-09 DOI:10.1002/asmb.70134

Ronald D. Snee

引用次数: 0

Abstract

A strategy for data-based problem solving is presented. Strategy, a plan for being successful, is essential for any endeavor to be successful. Unfortunately, the statistical community has not placed a great deal of focus on strategy in the past. In this article, strategy is defined and its importance is delineated. The strategy presented has been developed over six decades, integrating the author's experiences and methods and those of other professionals working in the field. The overall strategy involves two interacting sub-strategies—Problem Solving and Data Analysis. The critical elements of each strategy are identified, illustrated, and referenced.

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基于数据的问题解决策略

提出了一种基于数据的问题解决策略。战略，一个成功的计划，是任何努力成功的必要条件。不幸的是，过去统计界并没有把重点放在战略上。本文对战略进行了界定，并阐述了战略的重要性。所提出的策略已经发展了60多年，整合了作者的经验和方法以及在该领域工作的其他专业人员的经验和方法。整体战略包括两个相互作用的子战略——问题解决和数据分析。每个策略的关键要素都被识别、说明和引用。

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

Applied Stochastic Models in Business and Industry 数学-数学跨学科应用

CiteScore

2.70

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： ASMBI - Applied Stochastic Models in Business and Industry (formerly Applied Stochastic Models and Data Analysis) was first published in 1985, publishing contributions in the interface between stochastic modelling, data analysis and their applications in business, finance, insurance, management and production. In 2007 ASMBI became the official journal of the International Society for Business and Industrial Statistics (www.isbis.org). The main objective is to publish papers, both technical and practical, presenting new results which solve real-life problems or have great potential in doing so. Mathematical rigour, innovative stochastic modelling and sound applications are the key ingredients of papers to be published, after a very selective review process. The journal is very open to new ideas, like Data Science and Big Data stemming from problems in business and industry or uncertainty quantification in engineering, as well as more traditional ones, like reliability, quality control, design of experiments, managerial processes, supply chains and inventories, insurance, econometrics, financial modelling (provided the papers are related to real problems). The journal is interested also in papers addressing the effects of business and industrial decisions on the environment, healthcare, social life. State-of-the art computational methods are very welcome as well, when combined with sound applications and innovative models.