量化复杂性成本时的数据质量问题

A. Staskiewicz, L. Hvam, Anders Haug
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摘要

对产品和服务多样性的需求增加意味着许多制造公司面临产品和工艺复杂性增加的问题。复杂性管理方面的文献在量化产品复杂性成本的基础上提供了降低产品和过程复杂性的方法,但在确定产品复杂性成本时,很少关注数据质量挑战。本文的目的是通过阐明数据质量的作用来扩展量化产品复杂性成本的文献。本文以一家世界领先的医疗保健产品制造商的案例研究为基础,对降低产品复杂性进行了研究。案例研究表明,数据质量差导致在寻找所需数据时额外使用资源,这意味着必须大大缩小项目的范围。在此基础上,本文认为降低产品复杂性的方法需要更多地纳入数据质量视角。
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Data Quality Issues When Quantifying Costs of Complexity
Increased demand for product and service variety has meant that many manufacturing companies face problems of increasing product and process complexity. Literature on complexity management provides means for reducing product and process complexity based on quantifying product complexity costs, but when determining product complexity cost, little attention is paid to data quality challenges. The purpose of this paper is to expand the literature on quantifying product complexity costs by clarifying the role of data quality. This is done based on a case study at a world-leading healthcare product manufacturer, where reducing product complexity was investigated. The case study showed that poor data quality resulted in extra use of resources for finding the needed data and implied that the scope of the project had to be significantly reduced. On this basis, this paper argues that methods for reducing product complexity need to incorporate data quality perspectives more.
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