Process mining in logistics: The need for rule-based data abstraction

Ruud  van Cruchten; Hans Weigand

doi:10.1109/RCIS.2018.8406653

Process mining in logistics: The need for rule-based data abstraction

Ruud van Cruchten, Hans Weigand

Information Systems & Oper. Mgt.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

13 Citations (Scopus)

Abstract

Organizations struggle to gain insight in how their business processes are conducted in reality. Process mining enables organizations to extract this knowledge by analyzing business events recorded in their information systems. However, the business events recorded in these systems do not always reflect the same level of abstraction as the desired process model that is used by the business. Current process mining approaches give insufficient attention to this gap. This paper proposes several data preparation methods that apply logistic domain knowledge for process mining the material movements within an organization. In addition, an adapted process mining project methodology is presented that explicitly includes these preparation methods.

Original language	English
Title of host publication	Proceedings of the 12th International Conference on Research Challenges in Information Science (RCIS 2018)
Place of Publication	Brussels
Publisher	IEEE
Pages	1-9
ISBN (Print)	9781538665176
DOIs	https://doi.org/10.1109/RCIS.2018.8406653
Publication status	Published - May 2018
Event	13th International Conference on Research Challenges in Information Science - Brussels, Belgium Duration: 29 May 2018 → 31 May 2018

Conference

Conference	13th International Conference on Research Challenges in Information Science
Abbreviated title	RCIS 2018
Country/Territory	Belgium
City	Brussels
Period	29/05/18 → 31/05/18

Keywords

process mining
event mapping
abstraction levels
logistics
business rules

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10.1109/RCIS.2018.8406653

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@inproceedings{097f2d58790344708912e26b2d09f3aa,

title = "Process mining in logistics: The need for rule-based data abstraction",

abstract = "Organizations struggle to gain insight in how their business processes are conducted in reality. Process mining enables organizations to extract this knowledge by analyzing business events recorded in their information systems. However, the business events recorded in these systems do not always reflect the same level of abstraction as the desired process model that is used by the business. Current process mining approaches give insufficient attention to this gap. This paper proposes several data preparation methods that apply logistic domain knowledge for process mining the material movements within an organization. In addition, an adapted process mining project methodology is presented that explicitly includes these preparation methods.",

keywords = "process mining, event mapping, abstraction levels, logistics, business rules",

author = "{van Cruchten}, Ruud and Hans Weigand",

year = "2018",

month = may,

doi = "10.1109/RCIS.2018.8406653",

language = "English",

isbn = "9781538665176",

pages = "1--9",

booktitle = "Proceedings of the 12th International Conference on Research Challenges in Information Science (RCIS 2018)",

publisher = "IEEE",

note = "13th International Conference on Research Challenges in Information Science, RCIS 2018 ; Conference date: 29-05-2018 Through 31-05-2018",

}

van Cruchten, R & Weigand, H 2018, Process mining in logistics: The need for rule-based data abstraction. in Proceedings of the 12th International Conference on Research Challenges in Information Science (RCIS 2018). IEEE, Brussels, pp. 1-9, 13th International Conference on Research Challenges in Information Science, Brussels, Belgium, 29/05/18. https://doi.org/10.1109/RCIS.2018.8406653

TY - GEN

T1 - Process mining in logistics

T2 - 13th International Conference on Research Challenges in Information Science

AU - van Cruchten, Ruud

AU - Weigand, Hans

PY - 2018/5

Y1 - 2018/5

N2 - Organizations struggle to gain insight in how their business processes are conducted in reality. Process mining enables organizations to extract this knowledge by analyzing business events recorded in their information systems. However, the business events recorded in these systems do not always reflect the same level of abstraction as the desired process model that is used by the business. Current process mining approaches give insufficient attention to this gap. This paper proposes several data preparation methods that apply logistic domain knowledge for process mining the material movements within an organization. In addition, an adapted process mining project methodology is presented that explicitly includes these preparation methods.

AB - Organizations struggle to gain insight in how their business processes are conducted in reality. Process mining enables organizations to extract this knowledge by analyzing business events recorded in their information systems. However, the business events recorded in these systems do not always reflect the same level of abstraction as the desired process model that is used by the business. Current process mining approaches give insufficient attention to this gap. This paper proposes several data preparation methods that apply logistic domain knowledge for process mining the material movements within an organization. In addition, an adapted process mining project methodology is presented that explicitly includes these preparation methods.

KW - process mining

KW - event mapping

KW - abstraction levels

KW - logistics

KW - business rules

U2 - 10.1109/RCIS.2018.8406653

DO - 10.1109/RCIS.2018.8406653

M3 - Conference contribution

SN - 9781538665176

SP - 1

EP - 9

BT - Proceedings of the 12th International Conference on Research Challenges in Information Science (RCIS 2018)

PB - IEEE

CY - Brussels

Y2 - 29 May 2018 through 31 May 2018

ER -

Process mining in logistics: The need for rule-based data abstraction

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Keywords

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