Data-based description of process performance in end-to-end order processing

Schuh, Günther; Gützlaff, Andreas; Schmitz, Seth; Aalst, Wil M. P. van der

doi:10.1016/j.cirp.2020.03.013

Cited by 16 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, Wil M.P. van der Aalst, the father of process mining, shared in only one study [18] about the application of process mining in logistics from the 15 studies analyzed in this paper.…”

Section: Resultsmentioning

confidence: 99%

“…Although the search included studies from 2004, all studies that were relevant to the topic of process mining and logistics of manufacturing companies began from the year 2017. As shown in Table 2, two studies of [4,13] were conducted in 2017, three studies of [2,8,16] were published in 2018, five studies of [1,3,11,14,17] were conducted in 2019, two studies of [6,18] were published in 2020, and three studies of [12,15,19] were published in 2021. Figure 2 is a chart representing the years searched and the number of publications in each of these years.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Application of Process Mining in Logistic Processes of Manufacturing Organizations: A Systematic Review

Alnahas

2023

Sustainability

View full text Add to dashboard Cite

With the continuous development in technology and changes in the logistic systems, organizations should review their logistic processes that evolve over time. To attain a good insight into the conformance of these processes with the designed process model, constant detection and monitoring is required. The main objective of this systematic review is to investigate the state of the art in process mining applications in logistics specifically related to manufacturing organizations. The review aims to analyze and assess the use of process mining techniques and models in the logistics domain, based on the selected studies. In this review, literature was searched between the years 2004 and 2022 using several inclusion and exclusion criteria. Fifteen published studies were selected and analyzed on the use of process mining in the logistics domain based on the process mining techniques and models used. All of the selected studies used models and thirteen of them used real case studies. All of the fifteen studies used one or more algorithms. Only three of these studies did not mention the modeling language used to represent the process. Moreover, seven studies focused on discussing the process discovery alone and five more addressed process discovery in addition to other types of process mining. Eight studies mentioned the process mining tools used that included DISCO and several versions of ProM.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Application of Process Mining in Logistic Processes of Manufacturing Organizations: A Systematic Review

Alnahas

2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…This is non-trivial and a current field of research for the further development of process mining algorithms (cf. Raichelson, Soffer, and Verbeek 2017;Schuh et al 2020b;van der Aalst 2019). This is particularly challenging for manufacturing processes, where the level of the analysed entity changes -for instance, when a manufacturing order consists of multiple assembly orders for multiple products, or when parts are looping back to previous processes.…”

Section: Challenges Related To Implementationmentioning

confidence: 99%

Using process mining to improve productivity in make-to-stock manufacturing

Lorenz

Senoner

Sihn

et al. 2021

International Journal of Production Research

View full text Add to dashboard Cite

This paper proposes a data-driven procedure to improve productivity in make-to-stock manufacturing. By leveraging recent developments in information systems research, the paper addresses manufacturing systems with high process complexity and variety. Specifically, the proposed procedure draws upon process mining to dynamically map and analyse manufacturing processes in an automated manner. This way, manufacturers can leverage data to overcome the limitations of existing process mapping methods, which only provide static snapshots of process flows. By bridging data and process science, process mining can exploit hitherto untapped potential for productivity improvement. The proposed procedure is empirically validated at a leading manufacturer of sanitary products. The field test leads to three concrete improvement suggestions for the company. This research contributes to the literature on production research by demonstrating a novel use of process mining in manufacturing and by guiding practitioners in its implementation.

show abstract