Industry 4.0 Based Business Process Re-Engineering Framework for Manufacturing Industry Setup Incorporating Evolutionary Multi-Objective Optimization
Summary :
Manufacturers are coping with increasing pressures in quality, cost and efficiency as more and more industries are moving from traditional setup to industry 4.0 based digitally transformed setup due to its numerous playbacks. Within the manufacturing domain organizational structures and processes are complex, therefore adopting industry 4.0 and finding an optimized re-engineered business process is difficult without using a systematic methodology. Authors have developed Business Process Re-engineering (BPR) and Business Process Optimization (BPO) methods but no consolidated methodology have been seen in the literature that is based on industry 4.0 and incorporates both the BPR and BPO. We have presented a consolidated and systematic re-engineering and optimization framework for a manufacturing industry setup. The proposed framework performs Evolutionary Multi-Objective Combinatorial Optimization using Multi-Objective Genetic Algorithm (MOGA). An example process from an aircraft manufacturing factory has been optimized and re-engineered with available set of technologies from industry 4.0 based on the criteria of lower cost, reduced processing time and reduced error rate. At the end to validate the proposed framework Business Process Model and Notation (BPMN) is used for simulations and perform comparison between AS-IS and TO-BE processes as it is widely used standard for business process specification. The proposed framework will be used in converting an industry from traditional setup to industry 4.0 resulting in cost reduction, increased performance and quality.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.7 pp.1283-1295
- Publication Date
- 2022/07/01
- Publicized
- 2022/04/08
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022EDP7021
- Type of Manuscript
- PAPER
- Category
- Software Engineering
Authors
Anum TARIQ
NUST College of Electrical and Mechanical Engineering
Shoab AHMED KHAN
NUST College of Electrical and Mechanical Engineering
Keyword
Latest Issue
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Anum TARIQ, Shoab AHMED KHAN, "Industry 4.0 Based Business Process Re-Engineering Framework for Manufacturing Industry Setup Incorporating Evolutionary Multi-Objective Optimization" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 7, pp. 1283-1295, July 2022, doi: 10.1587/transinf.2022EDP7021.
Abstract: Manufacturers are coping with increasing pressures in quality, cost and efficiency as more and more industries are moving from traditional setup to industry 4.0 based digitally transformed setup due to its numerous playbacks. Within the manufacturing domain organizational structures and processes are complex, therefore adopting industry 4.0 and finding an optimized re-engineered business process is difficult without using a systematic methodology. Authors have developed Business Process Re-engineering (BPR) and Business Process Optimization (BPO) methods but no consolidated methodology have been seen in the literature that is based on industry 4.0 and incorporates both the BPR and BPO. We have presented a consolidated and systematic re-engineering and optimization framework for a manufacturing industry setup. The proposed framework performs Evolutionary Multi-Objective Combinatorial Optimization using Multi-Objective Genetic Algorithm (MOGA). An example process from an aircraft manufacturing factory has been optimized and re-engineered with available set of technologies from industry 4.0 based on the criteria of lower cost, reduced processing time and reduced error rate. At the end to validate the proposed framework Business Process Model and Notation (BPMN) is used for simulations and perform comparison between AS-IS and TO-BE processes as it is widely used standard for business process specification. The proposed framework will be used in converting an industry from traditional setup to industry 4.0 resulting in cost reduction, increased performance and quality.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDP7021/_p
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@ARTICLE{e105-d_7_1283,
author={Anum TARIQ, Shoab AHMED KHAN, },
journal={IEICE TRANSACTIONS on Information},
title={Industry 4.0 Based Business Process Re-Engineering Framework for Manufacturing Industry Setup Incorporating Evolutionary Multi-Objective Optimization},
year={2022},
volume={E105-D},
number={7},
pages={1283-1295},
abstract={Manufacturers are coping with increasing pressures in quality, cost and efficiency as more and more industries are moving from traditional setup to industry 4.0 based digitally transformed setup due to its numerous playbacks. Within the manufacturing domain organizational structures and processes are complex, therefore adopting industry 4.0 and finding an optimized re-engineered business process is difficult without using a systematic methodology. Authors have developed Business Process Re-engineering (BPR) and Business Process Optimization (BPO) methods but no consolidated methodology have been seen in the literature that is based on industry 4.0 and incorporates both the BPR and BPO. We have presented a consolidated and systematic re-engineering and optimization framework for a manufacturing industry setup. The proposed framework performs Evolutionary Multi-Objective Combinatorial Optimization using Multi-Objective Genetic Algorithm (MOGA). An example process from an aircraft manufacturing factory has been optimized and re-engineered with available set of technologies from industry 4.0 based on the criteria of lower cost, reduced processing time and reduced error rate. At the end to validate the proposed framework Business Process Model and Notation (BPMN) is used for simulations and perform comparison between AS-IS and TO-BE processes as it is widely used standard for business process specification. The proposed framework will be used in converting an industry from traditional setup to industry 4.0 resulting in cost reduction, increased performance and quality.},
keywords={},
doi={10.1587/transinf.2022EDP7021},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - Industry 4.0 Based Business Process Re-Engineering Framework for Manufacturing Industry Setup Incorporating Evolutionary Multi-Objective Optimization
T2 - IEICE TRANSACTIONS on Information
SP - 1283
EP - 1295
AU - Anum TARIQ
AU - Shoab AHMED KHAN
PY - 2022
DO - 10.1587/transinf.2022EDP7021
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2022
AB - Manufacturers are coping with increasing pressures in quality, cost and efficiency as more and more industries are moving from traditional setup to industry 4.0 based digitally transformed setup due to its numerous playbacks. Within the manufacturing domain organizational structures and processes are complex, therefore adopting industry 4.0 and finding an optimized re-engineered business process is difficult without using a systematic methodology. Authors have developed Business Process Re-engineering (BPR) and Business Process Optimization (BPO) methods but no consolidated methodology have been seen in the literature that is based on industry 4.0 and incorporates both the BPR and BPO. We have presented a consolidated and systematic re-engineering and optimization framework for a manufacturing industry setup. The proposed framework performs Evolutionary Multi-Objective Combinatorial Optimization using Multi-Objective Genetic Algorithm (MOGA). An example process from an aircraft manufacturing factory has been optimized and re-engineered with available set of technologies from industry 4.0 based on the criteria of lower cost, reduced processing time and reduced error rate. At the end to validate the proposed framework Business Process Model and Notation (BPMN) is used for simulations and perform comparison between AS-IS and TO-BE processes as it is widely used standard for business process specification. The proposed framework will be used in converting an industry from traditional setup to industry 4.0 resulting in cost reduction, increased performance and quality.
ER -
English
