A methodology to boost data-driven decision-making process for a modern maintenance practice

IF 6.1 3区管理学 Q1 ENGINEERING, INDUSTRIAL

Production Planning & Control Pub Date : 2021-12-08 DOI:10.1080/09537287.2021.2010823

A. Polenghi, I. Roda, M. Macchi, A. Pozzetti

引用次数: 4

Abstract

Abstract Maintenance is evolving due to the double-sided influence of the Asset Management paradigm and digitalization. In this evolution, assessing the maintenance management process status in terms of process completeness, information and data completeness and integration is paramount to boost reliable data-driven decision-making. Grounding on Design Science Research, a methodology is realized to favour the comparison of two data models, a reference one and a company-specific one, used as a means to evaluate the process status. In particular, the methodology embeds a reference data model for the maintenance management process. Both methodology and data model are artefacts tested and refined during action research in an automotive company willing to improve the maintenance management process. The application of both artefacts demonstrates that the company is facilitated in planning improvement actions for various time horizons to foster a modern maintenance practice whose decision-making is more data-driven.

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一种促进现代维护实践的数据驱动决策过程的方法

由于资产管理模式和数字化的双重影响，维护正在不断发展。在这种演变中，根据过程完整性、信息和数据完整性以及集成来评估维护管理过程状态对于促进可靠的数据驱动决策至关重要。在设计科学研究的基础上，实现了一种有利于比较两种数据模型的方法，一种参考模型和一种公司特定模型，用作评估过程状态的手段。特别地，该方法为维护管理过程嵌入了一个参考数据模型。在一家愿意改进维护管理流程的汽车公司的行动研究期间，方法和数据模型都是经过测试和改进的工件。这两种人工制品的应用表明，公司在规划不同时间范围的改进行动方面得到了促进，以促进现代维护实践，其决策更多地由数据驱动。

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

Production Planning & Control 管理科学-工程：工业

CiteScore

19.30

自引率

9.60%

发文量

审稿时长

6-12 weeks

期刊介绍： Production Planning & Control is an international journal that focuses on research papers concerning operations management across industries. It emphasizes research originating from industrial needs that can provide guidance to managers and future researchers. Papers accepted by "Production Planning & Control" should address emerging industrial needs, clearly outlining the nature of the industrial problem. Any suitable research methods may be employed, and each paper should justify the method used. Case studies illustrating international significance are encouraged. Authors are encouraged to relate their work to existing knowledge in the field, particularly regarding its implications for management practice and future research agendas.