Industrial analytics – An overview

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

IT-Information Technology Pub Date : 2022-01-11 DOI:10.1515/itit-2021-0066

Christoph Gröger

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引用次数: 3

Abstract

Abstract The digital transformation generates huge amounts of heterogeneous data across the industrial value chain, from simulation data in engineering, over sensor data in manufacturing to telemetry data on product use. Extracting insights from these data constitutes a critical success factor for industrial enterprises, e. g., to optimize processes and enhance product features. This is referred to as industrial analytics, i. e., data analytics for industrial value creation. Industrial analytics is an interdisciplinary subject area between data science and industrial engineering and is at the core of Industry 4.0. Yet, existing literature on industrial analytics is fragmented and specialized. To address this issue, this paper presents a holistic overview of the field of industrial analytics integrating both current research as well as industry experiences on real-world industrial analytics projects. We define key terms, describe typical use cases and discuss characteristics of industrial analytics. Moreover, we present a conceptual framework for industrial analytics that structures essential elements, e. g., data platforms and data roles. Finally, we conclude and highlight future research directions.

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工业分析-概述

摘要数字化转型在整个产业价值链上产生了大量异构数据，从工程中的模拟数据、制造中的传感器数据到产品使用的遥测数据。从这些数据中提取见解是工业企业成功的关键因素。 g.优化工艺并增强产品特性。这被称为工业分析。 e.工业价值创造的数据分析。工业分析是数据科学和工业工程之间的一个跨学科学科领域，是工业4.0的核心。然而，现有的工业分析文献是零散和专业的。为了解决这个问题，本文对工业分析领域进行了全面的概述，结合了当前的研究以及现实世界工业分析项目的行业经验。我们定义了关键术语，描述了典型的用例，并讨论了工业分析的特点。此外，我们还提出了一个工业分析的概念框架，该框架构建了基本要素。 g.数据平台和数据角色。最后，我们总结并强调了未来的研究方向。

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

IT-Information Technology COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

3.80

自引率

0.00%

发文量