Proposing a Holistic Framework for the Assessment and Management of Manufacturing Complexity through Data-centric and Human-centric Approaches

Dominik Kohr, Mussawar Ahmad, Bugra Alkan, Malarvizhi Kaniappan Chinnathai, L. Budde, D. Vera, T. Friedli, R. Harrison
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引用次数: 6

Abstract

A multiplicity of factors including technological innovations, dynamic operating environments, and globalisation are all believed to contribute towards the ever-increasing complexity of manufacturing systems. Although complexity is necessary to meet functional needs, it is important to assess and monitor it to reduce life-cycle costs by simplifying designs and minimising failure modes. This research paper identifies and describes two key industrially relevant methods for assessing complexity, namely a data-centric approach using the information theoretic method and a human-centric approach based on surveys and questionnaires. The paper goes on to describe the benefits and shortcomings of each and contributes to the body of knowledge by proposing a holistic framework that combines both assessment methods.
通过以数据为中心和以人为中心的方法提出制造复杂性评估和管理的整体框架
包括技术创新、动态操作环境和全球化在内的多种因素都被认为是制造系统日益复杂的原因。虽然复杂性是满足功能需求所必需的,但重要的是评估和监控它,通过简化设计和最小化故障模式来降低生命周期成本。本文确定并描述了两种关键的工业相关复杂性评估方法,即使用信息理论方法的以数据为中心的方法和基于调查和问卷的以人为中心的方法。本文接着描述了每一种评估方法的优点和缺点,并通过提出结合这两种评估方法的整体框架来贡献知识体系。
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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