Learning from previous events represents a crucial element to improve the design and operations of industrial processes, especially considering the many variables characterizing the functioning of a plant. This learning process aims to reduce the frequency of incidents and/or mitigate their severity, which are both continuous and open challenges.

This paper is grounded on a large incident repository, i.e., the Major Hazard Incident Data Service (MHIDAS) database, which was developed in 1986 by the Health and Safety Executive (HSE) to provide a reliable source of data on major hazard incidents involving hazardous materials. The database includes more than 9000 reports collected over five decades (1950s–1990s). This paper aims to provide a novel understanding of the industrial incidents reported in MHIDAS and unveil possible ways of exploring occupational/operational incidents through descriptive and quantitative analyses. Consequently, this paper proposes the implementation of Business Intelligence (BI) tools to facilitate dynamic data visualization and Machine Learning (ML) algorithms for the extraction of knowledge from different data entries. Therefore, after engineering the MHIDAS data model, a set of BI dashboards was designed and complemented with a ML-driven categorization of incidents through representative key variables for occupational/operational incidents.

The manuscript describes the process necessary to create a BI model for safety data management in an industrial context, and its integration with ML solutions that may support an in-depth multi-variate investigation of reported data. The investigation provides evidence on the importance of a precise reporting of safety events, thus unveiling the potential for lessons learned in the process industry.

从以前的事件中学习是改进工业过程设计和操作的关键因素，特别是考虑到表征工厂功能的许多变量。这个学习过程旨在减少事件的频率和/或减轻其严重性，这是持续和公开的挑战。

本文基于一个大型事件存储库，即重大危害事件数据服务 (MHIDAS) 数据库，该数据库由健康与安全执行局 (HSE) 于 1986 年开发，旨在为涉及危险的重大危害事件提供可靠的数据来源。材料。该数据库包括过去五年（1950 年代至 1990 年代）收集的 9000 多份报告。本文旨在提供对 MHIDAS 报告的工业事故的全新理解，并通过描述性和定量分析揭示探索职业/运营事故的可能方法。因此，本文提出了商业智能 (BI) 工具的实施，以促进动态数据可视化和机器学习 (ML) 算法，以便从不同的数据条目中提取知识。所以，

该手稿描述了在工业环境中创建用于安全数据管理的 BI 模型所需的过程，以及它与 ML 解决方案的集成，这些解决方案可能支持对报告数据进行深入的多变量调查。该调查为准确报告安全事件的重要性提供了证据，从而揭示了在过程工业中汲取经验教训的潜力。