A Real-world Case Study of Process and Data Driven Predictive Analytics for Manufacturing Workflows

Petros Papapanagiotou; James Vaughan; Filip Smola; Jacques D Fleuriot

doi:10.24251/HICSS.2021.122

A Real-world Case Study of Process and Data Driven Predictive Analytics for Manufacturing Workflows

Petros Papapanagiotou, James Vaughan, Filip Smola, Jacques D Fleuriot

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We present a novel application of business process modelling and simulation of manufacturing workflows. Using formal methods, we produce correct-by-construction executable models that can be simulated in an interleaved way. The simulation draws advanced analytics from live IoT monitoring as well as an ERP system to provide predictive business intelligence. We describe our process and resource modelling efforts in the context of a collaborative project with two manufacturing partners. We evaluate our results based on the improvement of the scheduling accuracy for real production flows.

Original language	English
Title of host publication	Proceedings of the 54th Hawaii International Conference on System Sciences 2021
Pages	1001 - 1010
Number of pages	10
DOIs	https://doi.org/10.24251/HICSS.2021.122
Publication status	Published - 5 Jan 2021
Event	54th Hawaii International Conference on System Sciences - Kauai, United States Duration: 5 Jan 2021 → 8 Jan 2021 https://hicss.hawaii.edu/

Conference

Conference	54th Hawaii International Conference on System Sciences
Abbreviated title	HICSS-54
Country/Territory	United States
City	Kauai
Period	5/01/21 → 8/01/21
Internet address	https://hicss.hawaii.edu/

Access to Document

10.24251/HICSS.2021.122Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND)

A Real-world Case_PAPAPANAGIOTOU_DOA12102020_VOR_CC-BY-NC-NDFinal published version, 648 KBLicence: Creative Commons: Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND)

http://hdl.handle.net/10125/70734Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND)

EIT Digital follow-on funding
Fleuriot, J. (Principal Investigator)
EU government bodies
1/01/19 → 31/12/19
Project: Research
Fleuriot EIT Digital: Digitizing industrial workflows
Fleuriot, J. (Principal Investigator)
EU government bodies
1/01/18 → 31/12/18
Project: Research

Cite this

@inproceedings{f569f8b4c3794db0aae7d9ea1f956ed6,

title = "A Real-world Case Study of Process and Data Driven Predictive Analytics for Manufacturing Workflows",

abstract = "We present a novel application of business process modelling and simulation of manufacturing workflows. Using formal methods, we produce correct-by-construction executable models that can be simulated in an interleaved way. The simulation draws advanced analytics from live IoT monitoring as well as an ERP system to provide predictive business intelligence. We describe our process and resource modelling efforts in the context of a collaborative project with two manufacturing partners. We evaluate our results based on the improvement of the scheduling accuracy for real production flows.",

author = "Petros Papapanagiotou and James Vaughan and Filip Smola and Fleuriot, \{Jacques D\}",

year = "2021",

month = jan,

day = "5",

doi = "10.24251/HICSS.2021.122",

language = "English",

isbn = "978-0-9981331-4-0",

pages = "1001 -- 1010",

booktitle = "Proceedings of the 54th Hawaii International Conference on System Sciences 2021",

note = "54th Hawaii International Conference on System Sciences, HICSS-54 ; Conference date: 05-01-2021 Through 08-01-2021",

url = "https://hicss.hawaii.edu/",

}

Papapanagiotou, P, Vaughan, J, Smola, F & Fleuriot, JD 2021, A Real-world Case Study of Process and Data Driven Predictive Analytics for Manufacturing Workflows. in Proceedings of the 54th Hawaii International Conference on System Sciences 2021. pp. 1001 - 1010, 54th Hawaii International Conference on System Sciences, Kauai, Hawaii, United States, 5/01/21. https://doi.org/10.24251/HICSS.2021.122

TY - GEN

T1 - A Real-world Case Study of Process and Data Driven Predictive Analytics for Manufacturing Workflows

AU - Papapanagiotou, Petros

AU - Vaughan, James

AU - Smola, Filip

AU - Fleuriot, Jacques D

PY - 2021/1/5

Y1 - 2021/1/5

N2 - We present a novel application of business process modelling and simulation of manufacturing workflows. Using formal methods, we produce correct-by-construction executable models that can be simulated in an interleaved way. The simulation draws advanced analytics from live IoT monitoring as well as an ERP system to provide predictive business intelligence. We describe our process and resource modelling efforts in the context of a collaborative project with two manufacturing partners. We evaluate our results based on the improvement of the scheduling accuracy for real production flows.

AB - We present a novel application of business process modelling and simulation of manufacturing workflows. Using formal methods, we produce correct-by-construction executable models that can be simulated in an interleaved way. The simulation draws advanced analytics from live IoT monitoring as well as an ERP system to provide predictive business intelligence. We describe our process and resource modelling efforts in the context of a collaborative project with two manufacturing partners. We evaluate our results based on the improvement of the scheduling accuracy for real production flows.

U2 - 10.24251/HICSS.2021.122

DO - 10.24251/HICSS.2021.122

M3 - Conference contribution

SN - 978-0-9981331-4-0

SP - 1001

EP - 1010

BT - Proceedings of the 54th Hawaii International Conference on System Sciences 2021

T2 - 54th Hawaii International Conference on System Sciences

Y2 - 5 January 2021 through 8 January 2021

ER -

A Real-world Case Study of Process and Data Driven Predictive Analytics for Manufacturing Workflows

Abstract

Conference

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Projects

EIT Digital follow-on funding

Fleuriot EIT Digital: Digitizing industrial workflows

Cite this