Data quality problems in discrete event simulation of manufacturing operations. (November 2018)

• Record Type: Journal Article
• Authors: Bokrantz, Jon; Skoogh, Anders; Lämkull, Dan; Hanna, Atieh; Perera, Terrence
• Is Part Of: Simulation. Volume 94:Number 11(2018)
• Page Start: 1009
• Page End: 1025
• Publication Date: 2018-11
• Subjects: Discrete Event Simulation -- data quality -- data collection -- input data management -- manufacturing -- maintenance; Computer simulation -- Periodicals; 003.3
• DOI: 10.1177/0037549717742954
• Languages: English
• ISSNs: 0037-5497

Abstract

High-quality input data are a necessity for successful discrete event simulation (DES) applications, and there are available methodologies for data collection in DES projects. However, in contrast to standalone projects, using DES as a daily manufacturing engineering tool requires high-quality production data to be constantly available. In fact, there has been a major shift in the application of DES in manufacturing from production system design to daily operations, accompanied by a stream of research on automation of input data management and interoperability between data sources and simulation models. Unfortunately, this research stream rests on the assumption that the collected data are already of high quality, and there is a lack of in-depth understanding of simulation data quality problems from a practitioners' perspective. Therefore, a multiple-case study within the automotive industry was used to provide empirical descriptions of simulation data quality problems, data production processes, and relations between these processes and simulation data quality problems. These empirical descriptions are necessary to extend the present knowledge on data quality in DES in a practical real-world manufacturing context, which is a prerequisite for developing practical solutions for solving data quality problems such as limited accessibility, lack of data on minor stoppages, and data sources not being designed for simulation. Further, the empirical and theoretical knowledge gained throughout the study was used to propose a set of practical guidelines that can support manufacturing companies in improving data quality in DES.