Data driven logistics-oriented value stream mapping 4.0: A guideline for practitioners

Muehlbauer, K.; Wuennenberg, Maximilian; Meißner, Sebastian; Fottner, Johannes

doi:10.1016/j.ifacol.2022.09.051

Cited by 6 publications

(7 citation statements)

References 11 publications

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“…Therefore, the user must follow several steps when implementing data sciencebased process analyses building on an existing database: First of all -for all existing ILS subprocesses -the existing data transfer interfaces need to be merged according to the respective layer of the automation pyramid. The data maturity must then be assessed [7]. One can then determine which subprocesses should be considered for data-based process analytics.…”

Section: Results Of the Applicationmentioning

confidence: 99%

“…To that end, the right choice of application is referred to as context-aware process mining [5]. However, in order for analyses to be beneficial from a practitioner's perspective, they rely largely on the data architecture, i.e., in terms of data maturity, but also in terms of the right choice of analyzed data fields and a wise choice of data science applications [6,7]. The main challenges faced when analyzing process data using digital methods is the heterogeneity of this data, and its high specification with regard to the respective process [8].…”

Section: Initial Situation and Motivation Of The Topicmentioning

confidence: 99%

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A Classification of Data Structures for Process Analysis in Internal Logistics

Wuennenberg,

Haid,

Fottner

2023

Communications in Computer and Information Science

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Data Science plays a crucial role in driving new approaches to process optimization. With the increasing complexity of internal logistics systems, dataoriented methods have become essential in addressing the challenges that arise. However, standardized process analytics frameworks are lacking due to the heterogeneity of the underlying processes and the resulting data. This article aims to address this complexity by presenting a categorization of internal logistics data, consolidating the current state of the art. The categorization takes into account both real-world and scientifically proposed data architectures, providing a comprehensive overview. It includes a classification of comparative data fields based on their importance, the associated internal logistics processes, and potential usage scenarios. This classification is designed to cater to different use cases, such as diagnostics or prescriptive analytics. By presenting this categorization, the article enables practitioners to effectively leverage generated process data in a more goal-oriented manner. It empowers them to conduct suitable analyses tailored to their specific needs and objectives, based on the provided data architectures. In summary, this article offers valuable insights into internal logistics data categorization, providing a framework for practitioners to make informed decisions and optimize processes using data-driven approaches.

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Section: Results Of the Applicationmentioning

confidence: 99%

Section: Initial Situation and Motivation Of The Topicmentioning

confidence: 99%

A Classification of Data Structures for Process Analysis in Internal Logistics

Wuennenberg,

Haid,

Fottner

2023

Communications in Computer and Information Science

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show abstract

“…Moreover, a specification of the method for the particular application scenario of ILS allows for even more objective-oriented analysis concepts [4]. The logistics-oriented VSM 4.0 is one such specification and is therefore able to deliver versatile process data from ILS [5]. In order to turn those results into actual benefits from the practitioner's perspective, it needs to be clarified which data can be analyzed in what manner and what results can thus be obtained.…”

Section: Initial Situation and Motivation Of The Topicmentioning

confidence: 99%

“…This helps practitioners with the choice of an appropriate data science method based on the existing data infrastructure. [5].…”

Section: State Of the Artmentioning

confidence: 99%

“…These four types of data analytics cover a range from visualization to the detection of correlations to the prediction of future insight as well as the derivation of recommendations for action based on predictions [23][24][25]. Apart from the complexity of the application, the required data maturity is also a decisive aspect for the application of the respective type of data analytics [5].…”

Section: Fundamentals Of Data Sciencementioning

confidence: 99%

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