Data quality problems in discrete event simulation of manufacturing operations

Bokrantz, Jon; Skoogh, Anders; Lämkull, Dan; Hanna, Atieh; Perera, Terrence

doi:10.1177/0037549717742954

Cited by 30 publications

(11 citation statements)

References 36 publications

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“…Thus, the integration of a trade-off relation into the DSS must be considered. Even when it is substantially complex to analyse a considerable number of parameters to solve specific problems (Sant'Anna, 2015), fewer or wrong parameters will not permit decisions toward improvement to be made (Bokrantz et al, 2018). On a large scale, this condition will have critical impact on system balance and on its high sensitivity to problem-solving parameters, specifically in process control and analysis.…”

Section: Trade-off and Unbalanced Systems Relationshipmentioning

confidence: 99%

Time variability management and trade-off analysis of quality, productivity, and maintenance efficiency

Campos¹,

Ferreira²,

Freires³

2021

BJO&PM

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Goal: This research aims to examine the trade-off behaviour between productivity, quality and maintenance performance and evaluate the efficiency of the framework integrated to a time variability in operational management. Design / Methodology / Approach: The proposed framework integrates a stochastic analysis of process and TVM to manage process variability and analyse the trade-off behaviour between quality, productivity, and maintenance efficiency. The framework generates a control chart for each workstation and analyses the operator's time to execute an assembly process for six workstations. The processes are characterised and evaluated via stochastic simulation, resulting in a trade-off analysis with a multicriteria approach. Results: A relational graphic of performance dimensions shows that productivity and maintenance performance have a high dependency on time variability management, whereas quality has a minor impact over trade-off decisions. The proposed framework has a huge significance to operational management which allow to apply complex models to time variability analysis and organisation performance. Limitations of the investigation: The trade-off analysis in this study is limited to a constant trade-off model to validate the framework. Practical implications: This framework, used in association with traditional management philosophies, is a powerful tool that permits managers to develop a trade-off analysis and make decisions on workstations. Thus, a research field that correlates time variability with performance dimensions may be validated by this framework. Originality / Value: A framework that allows the use of MCDM to analyse the trade-off behaviour on organization performance and a trade-off analysis between performance dimensions and time variability management in operational performance.

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Section: Trade-off and Unbalanced Systems Relationshipmentioning

confidence: 99%

Time variability management and trade-off analysis of quality, productivity, and maintenance efficiency

Campos¹,

Ferreira²,

Freires³

2021

BJO&PM

View full text Add to dashboard Cite

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“…Table 1 lists the issues that were faced in the project and classifies them according to the proposed classification. Indeed, the subject of data quality problems, data issues, or dirty data is not new [47], [48], not even for the simulation community [49]. In fact, Bokrantz et al [49] presented a multiple-case study within the automotive industry to provide empirical descriptions of data quality problems in simulation projects.…”

Section:  Data Conflicts (F)mentioning

confidence: 99%

“…Indeed, the subject of data quality problems, data issues, or dirty data is not new [47], [48], not even for the simulation community [49]. In fact, Bokrantz et al [49] presented a multiple-case study within the automotive industry to provide empirical descriptions of data quality problems in simulation projects. As the authors postulated, simulation requires high-quality data and, often, extensible transformations to allow its utilization in simulation models, i.e., data issues must be bypassed, in order to produce a coherent simulation model.…”

Section:  Data Conflicts (F)mentioning

confidence: 99%

On the use of simulation as a Big Data semantic validator for supply chain management

Vieira

Dias

Santos

et al. 2020

Simulation Modelling Practice and Theory

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Simulation stands out as an appropriate method for the Supply Chain Management (SCM) field. Nevertheless, to produce accurate simulations of Supply Chains (SCs), several business processes must be considered. Thus, when using real data in these simulation models, Big Data concepts and technologies become necessary, as the involved data sources generate data at increasing volume, velocity and variety, in what is known as a Big Data context. While developing such solution, several data issues were found, with simulation proving to be more efficient than traditional data profiling techniques in identifying them. Thus, this paper proposes the use of simulation as a semantic validator of the data, proposed a classification for such issues and quantified their impact in the volume of data used in the final achieved solution. This paper concluded that, while SC simulations using Big Data concepts and technologies are within the grasp of organizations, their data models still require considerable improvements, in order to produce perfect mimics of their SCs. In fact, it was also found that simulation can help in identifying and bypassing some of these issues.

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“…In fact, the multiple problems that can be found when using real industrial data have already been analyzed by previous research, e.g. see the works of Bokrantz, Skoogh, Lämkull, Hanna, and Perera (2018) and Wang and Strong (1996). As such, the above list corresponds to the business processes (and the respective data sources) that could be obtained.…”

Section: Proposed Approachmentioning

confidence: 99%

“…On the other hand, it fosters the engagement of all stakeholders in the project and increases their interest and involvement in the tool. The need to use real data in the project may also result in finding data problems (Bokrantz et al, 2018), further heightening the need to and the importance of the engagement with stakeholders.…”

Section: Managerial Implicationsmentioning

confidence: 99%

Simulation of an automotive supply chain using big data

Vieira

Dias

Santos

et al. 2019

Computers & Industrial Engineering

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Supply Chains (SCs) are dynamic and complex networks that are exposed to disruption, which have consequences hard to quantify. Thus, simulation may be used, as it allows the uncertainty and dynamic nature of systems to be considered. Furthermore, the several systems used in SCs generate data with increasingly high volumes and velocities, paving the way for the development of simulation models in Big Data contexts. Hence, contrarily to traditional simulation approaches, which use statistical distributions to model specific SC problems, this paper proposed a Decision-Support System, supported by a Big Data Warehouse (BDW) and a simulation model. The first stores and integrates data from multiple sources and the second reproduces movements of materials and information from such data, while it also allows risk scenarios to be analyzed. The obtained results show the model being used to reproduce the historical data stored in the BDW and to assess the impact of events triggered during runtime to disrupt suppliers in a geographical range. This paper also analyzes the volume of data that was managed, hoping to serve as a milestone for future SC simulation studies in Big Data contexts. Further conclusions and future work are also discussed.

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Data quality problems in discrete event simulation of manufacturing operations

Cited by 30 publications

References 36 publications

Time variability management and trade-off analysis of quality, productivity, and maintenance efficiency

Time variability management and trade-off analysis of quality, productivity, and maintenance efficiency

On the use of simulation as a Big Data semantic validator for supply chain management

Simulation of an automotive supply chain using big data

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