Towards an Efficient, Comprehensive Value Stream Planning in Production Networks

Kroeger, Sebastian; Zaeh, Michael F.

doi:10.1016/j.procir.2022.05.062

Cited by 6 publications

(14 citation statements)

References 23 publications

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“…This linkage of the value stream of an intermediate product to the value stream of a finished product is called a comprehensive value stream. In strategic network design, network planners aim to design an efficient, comprehensive value stream [2].…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the impact of design parameters on network performance is unknown, leading to ambiguous decision situations. To address these challenges, Kroeger and Zaeh [2] propose a data-farming-based planning approach. Data farming refers to a concept where a simulation model is used to "farm" synthetic data when real data are not available in sufficient quantity [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…The value stream data structure stores the simulation experiment results in a specific format to build a simulation database. Finally, the third part describes the analysis and evaluation of the simulation database based on a comprehensive digital value stream analysis using process mining [2].…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1. Data-farming-based planning approach and corresponding application methodology following [2]. This paper describes part I of the approach in detail, as the black outline indicates.…”

Section: Introductionmentioning

confidence: 99%

“…The solution space management acts as the foundation to enable the data-farmingbased planning approach proposed by Kroeger and Zaeh [2], which is why this paper focuses on a detailed elaboration of Part I and the evaluation of its applicability and validity.…”

Section: Introductionmentioning

confidence: 99%

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Solution Space Management to Enable Data Farming in Strategic Network Design

et al. 2023

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During strategic network design, not only strategic but also operational decisions must be made long before a production network is put into operation. These include determining the location and size of inventories within the production network and setting operational parameters for production lines, such as the shift model. However, the large solution space comprising a high number of highly uncertain design parameters makes these decisions challenging without decision support. Therefore, data farming offers a potential solution, as synthetic data can be generated via the execution of multiple simulation experiments spanning the solution space and then analyzed using data mining techniques to provide data-based decision support. However, data farming has not yet been applied to strategic network design due to the lack of adequate solution space management. To address this shortcoming, this paper presents a structured solution space management approach that integrates production network-specific requirements and Design of Experiment (DoE) methods. The approach enables converting the solution space in strategic network design into individual input data sets for simulation experiments, generating a comprehensive database that can be mined for data-based decision support. The applicability and validity of the comprehensive approach are ensured via a case study in the automotive industry.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Solution Space Management to Enable Data Farming in Strategic Network Design

et al. 2023

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Data model to enable multidimensional process mining for data farming based value stream planning in production networks

Kroeger,

Rafles,

Jordan

et al. 2024

Prod. Eng. Res. Devel.

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International industrial companies operate complex value streams within production networks. Therefore, strategic network design aims to identify an efficient value stream from several value stream scenarios. For this purpose, Value Stream Mapping (VSM) is a well-established methodology from Lean Management. However, the complexity and variety of value streams in production networks can lead to high manual effort when using pen-and-paper-based VSM. Therefore, data-driven VSM based on process mining has to be applied. To create a comprehensive data-driven VSM, it is necessary to transparently understand the correlations between different dimensions, such as the material flow, the information flow, and the inventory, which requires a multidimensional process mining approach. Simulation experiments can generate the necessary data for each value stream scenario using a data farming based planning approach to conduct a data-driven VSM in strategic network design. However, no data model currently supports storing comprehensive datasets for multiple scenarios to enable multidimensional process mining. To overcome this shortcoming, this article presents a data model for applying multidimensional process mining that is scalable to multiple dimensions and scenarios. The data model is constructed based on the theoretical principles of data cubes and multidimensional process mining. The applicability is demonstrated by a case study of a production network from the automotive industry.

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