research-article

ProcessGAN: : Supporting the creation of business process improvement ideas through generative machine learning

Authors:

Christopher van Dun,

Wolfgang Kratsch,

Maximilian RöglingerAuthors Info & Claims

Volume 165, Issue C

https://doi.org/10.1016/j.dss.2022.113880

Published: 01 February 2023 Publication History

Abstract

Business processes are a key driver of organizational success, which is why business process improvement (BPI) is a central activity of business process management. Despite an abundance of approaches, BPI as a creative task is time-consuming and labour-intensive. Most importantly, its level of computational support is low. The few computational BPI approaches hardly leverage the opportunities brought about by computational creativity, neglect process data, and rely on rather rigid improvement patterns. Given the increasing amount of process data in the form of event logs and the uptake of generative machine learning for automating creative tasks in various domains, there is huge potential for BPI. Hence, following the design science research paradigm, we specified, implemented, and evaluated ProcessGAN, a novel computational BPI approach based on generative adversarial networks that supports the creation of BPI ideas. Our evaluation shows that ProcessGAN improves the creativity of process designers, particularly the originality of BPI ideas, and shapes up useful in real-world settings. Moreover, ProcessGAN is the first approach to combine BPI and computational creativity.

Highlights

•

Supported creation of business process improvement ideas.

•

Based on generative adversarial networks to leverage computational creativity.

•

Use of process deviance inherent in event data as inspiration for improvement.

•

Use of design science research methodology and evaluation frameworks for validation.

•

Prototype evaluated as useful and applicable in artificial and real-world settings.

References

[1]

M. Dumas, M. La Rosa, J. Mendling, H.A. Reijers, Fundamentals of Business Process Management, Springer, Berlin Heidelberg, Berlin, Heidelberg, 2018.

[2]

T. Grisold, S. Gross, K. Stelzl, J. vom Brocke, J. Mendling, M. Röglinger, M. Rosemann, The five diamond method for explorative business process management, Bus. Inf. Syst. Eng. 64 (2022) 149–166,.

[3]

M. Malinova, S. Gross, J. Mendling, A study into the contingencies of process improvement methods, Inf. Syst. 104 (2022) 101880,.

Digital Library

[4]

W. van der Aalst, Business process management: a comprehensive survey, ISRN Softw. Eng. 2013 (2013) 1–37,.

[5]

G. Zellner, A structured evaluation of business process improvement approaches, Bus. Process. Manag. J. 17 (2011) 203–237,.

[6]

D. Beverungen, J.C. Buijs, J. Becker, C. Di Ciccio, W.M.P. van der Aalst, C. Bartelheimer, J. vom Brocke, M. Comuzzi, K. Kraume, H. Leopold, Seven paradoxes of business process management in a hyper-connected world, Bus. Inf. Syst. Eng. 63 (2021) 145–156.

[7]

W. van der Aalst, Process mining, Commun. ACM 55 (2012) 76–83,.

Digital Library

[8]

W. Kratsch, J. Manderscheid, M. Röglinger, J. Seyfried, Machine learning in business process monitoring: a comparison of deep learning and classical approaches used for outcome prediction, Bus. Inf. Syst. Eng. (2020),.

[9]

W. Kratsch, J. Manderscheid, D. Reißner, M. Röglinger, Data-driven process prioritization in process networks, Decis. Support. Syst. 100 (2017) 27–40,.

[10]

R.J.B. Vanwersch, I. Vanderfeesten, E. Rietzschel, H.A. Reijers, Improving business processes: does anybody have an idea?, in: H.R. Motahari-Nezhad, J. Recker, M. Weidlich (Eds.), Business Process Management, Springer International, 2015, pp. 3–18.

[11]

T. Kreuzer, M. Röglinger, L. Rupprecht, Customer-centric prioritization of process improvement projects, Decis. Support. Syst. 133 (2020) 113286,.

Digital Library

[12]

S. Gross, M. Malinova, J. Mendling, Navigating through the maze of business process change methods, in: 52^nd HICSS, 2019.

[13]

S. Gross, K. Stelzl, T. Grisold, J. Mendling, M. Röglinger, J. vom Brocke, The business process design space for exploring process redesign alternatives, Bus. Process. Manag. J. (2020),.

[14]

M. Röglinger, C. van Dun, T. Fehrer, D.A. Fischer, L. Moder, W. Kratsch, Automated process (re-)design, in: Intl Workshop on BPM Problems ‘21 Co-Located with BPM 2021, Rome, Italy, 2021.

[15]

S. Limam Mansar, H.A. Reijers, F. Ounnar, Development of a decision-making strategy to improve the efficiency of BPR, Expert Syst. Appl. 36 (2009) 3248–3262,.

Digital Library

[16]

S.Y. Huang, C.H. Lee, A. Chiu, D. Yen, How business process reengineering affects information technology investment and employee performance under different performance measurement, Inf. Syst. Front. 17 (2015) 1133–1144,.

Digital Library

[17]

Y. Al-Anqoudi, A. Al-Hamdani, M. Al-Badawi, R. Hedjam, Using machine learning in business process re-engineering, BDCC 5 (2021) 61,.

[18]

W. Kratsch, F. König, M. Röglinger, Shedding light on blind spots – developing a reference architecture to leverage video data for process mining, Decis. Support. Syst. 158 (2022) 113794,.

Digital Library

[19]

T. Fehrer, D.A. Fischer, S.J. Leemans, M. Röglinger, M.T. Wynn, An assisted approach to business process redesign, Decis. Support. Syst. 156 (2022) 113749,.

Digital Library

[20]

P. Afflerbach, M. Hohendorf, J. Manderscheid, Design it like Darwin - a value-based application of evolutionary algorithms for proper and unambiguous business process redesign, Inf. Syst. Front. 19 (2017) 1101–1121,.

Digital Library

[21]

T.-M. Truong, L.-S. Le, On business process redesign and configuration: leveraging data mining classification & outliers and artifact-centric process modeling, in: 2016 IEEE ACOMP, Can Tho City, Vietnam, 2016, pp. 59–66.

[22]

A. Mustansir, K. Shahzad, M.K. Malik, Towards automatic business process redesign: an NLP based approach to extract redesign suggestions, Autom. Softw. Eng. 29 (2022),.

Digital Library

[23]

B. Zuhaira, N. Ahmad, Business process modeling, implementation, analysis, and management: the case of business process management tools, Bus. Process. Manag. J. 27 (2020) 145–183,.

[24]

N. Haefner, J. Wincent, V. Parida, O. Gassmann, Artificial intelligence and innovation management: a review, framework, and research agenda, Technol. Forecast. Soc. Chang. 162 (2021) 120392,.

[25]

X. Han, L. Hu, Y. Dang, S. Agarwal, L. Mei, S. Li, X. Zhou, Automatic Business Process Structure Discovery Using Ordered Neurons LSTM: A Preliminary Study, 2020.

[26]

F. Taymouri, M. La Rosa, M. Dumas, F.M. Maggi, Business process variant analysis: survey and classification, Knowl.-Based Syst. 211 (2021) 106557,.

[27]

K. Heinrich, P. Zschech, C. Janiesch, M. Bonin, Process data properties matter: introducing gated convolutional neural networks (GCNN) and key-value-predict attention networks (KVP) for next event prediction with deep learning, Decis. Support. Syst. 143 (2021) 113494,.

[28]

F. Taymouri, M. La Rosa, S. Erfani, Z.D. Bozorgi, I. Verenich, Predictive business process monitoring via generative adversarial nets: the case of next event prediction, in: D. Fahland, C. Ghidini, J. Becker, M. Dumas (Eds.), Business Process Management, Springer International, 2020, pp. 237–256.

[29]

Perri, L. (2022): What's New in the 2022 Gartner Hype Cycle for Emerging Technologies. https://www.gartner.com/en/articles/what-s-new-in-the-2022-gartner-hype-cycle-for-emerging-technologies?source=BLD-200123&utm_medium=social&utm_source=bambu&utm_campaign=SM_GB_YOY_GTR_SOC_BU1_SM-BA-SWG-OC-KA.

[30]

March Hevner, Ram Park, Design science in information systems research, Manag. Inf. Syst. Q. 28 (2004) 75–105,.

[31]

S.T. March, G.F. Smith, Design and natural science research on information technology, Decis. Support. Syst. 15 (1995) 251–266.

[32]

K. Peffers, T. Tuunanen, M.A. Rothenberger, S. Chatterjee, A design science research methodology for information systems research, J. Manag. Inf. Syst. 24 (2007) 45–77,.

Digital Library

[33]

S. Gregor, A.R. Hevner, Positioning and presenting design science research for maximum impact, Manag. Inf. Syst. Q. 37 (2013) 337–355,.

Digital Library

[34]

A. Bernstein, M. Klein, T. Malone, The process recombinator: a tool for generating new business process ideas, in: ICIS 1999 Proceedings, 1999, pp. 178–192.

[35]

F. Niedermann, S. Radeschutz, B. Mitschang, Design-time process optimization through optimization patterns and process model matching, in: In: 2010 IEEE 12^th Conference on Commerce and Enterprise Computing, IEEE, Shanghai, China, 2010, pp. 48–55.

[36]

M.A. Zemni, A. Mammar, N.B. Hadj-Alouane, An automated approach for merging business process fragments, Comput. Ind. 82 (2016) 104–118,.

Digital Library

[37]

Y. Borgianni, G. Cascini, F. Rotini, Business process reengineering driven by customer value: a support for undertaking decisions under uncertainty conditions, Comput. Ind. 68 (2015) 132–147,.

Digital Library

[38]

P. Koen, G. Ajamian, R. Burkart, A. Clamen, J. Davidson, R. D’Amore, C. Elkins, K. Herald, M. Incorvia, A. Johnson, R. Karol, R. Seibert, A. Slavejkov, K. Wagner, Providing clarity and a common language to the “fuzzy front end”, Res. Technol. Manag. 44 (2001) 46–55,.

[39]

W. Kettinger, J. Teng, S. Guha, Business process change: a study of methodologies, techniques, and tools, Manag. Inf. Syst. Q. 21 (1997).

[40]

K. Figl, J. Recker, Process innovation as creative problem solving: an experimental study of textual descriptions and diagrams, Inf. Manag. 53 (2016) 767–786,.

Digital Library

[41]

F.D. Davis, perceived usefulness, perceived ease of use, and user acceptance of information technology, Manag. Inf. Syst. Q. 13 (1989) 319,.

Digital Library

[42]

K. Wang, C. Gou, Y. Duan, Y. Lin, X. Zheng, F.-Y. Wang, Generative adversarial networks: introduction and outlook, IEEE/CAA J. Autom. Sin. 4 (2017) 588–598,.

[43]

Goasduff, L. (2020): 2 Megatrends Dominate the Gartner Hype Cycle for Artificial Intelligence, 2020. https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/ accessed 6 April 2021.

[44]

A. Rai, P. Constantinides, S. Sarker, Next-generation digital platforms: toward human-AI hybrids, Manag. Inf. Syst. Q. 43 (2019) iii–ix.

[45]

F. Chollet, Deep Learning with Python, Manning, Shelter Island, NY, 2018.

Digital Library

[46]

M. Cherti, B. Kegl, A. Kazakci, Out-of-class novelty generation an experimental foundation, in: 2017 IEEE ICTAI, Boston, MA, 2017, pp. 1312–1319.

[47]

O. Sbai, M. Elhoseiny, A. Bordes, Y. LeCun, C. Couprie, DesIGN: design inspiration from generative networks, in: L. Leal-Taixé, S. Roth (Eds.), Computer Vision – ECCV 2018 Workshops, Springer International, 2019, pp. 37–44.

[48]

A. Roberts, J. Engel, C. Raffel, C. Hawthorne, D. Eck, A hierarchical latent vector model for learning long-term structure in music, in: 35^th Intl Conf on Machine Learning, 2018, pp. 4364–4373.

[49]

S. Sengupta, S. Basak, P. Saikia, S. Paul, V. Tsalavoutis, F. Atiah, V. Ravi, A. Peters, A review of deep learning with special emphasis on architectures, applications and recent trends, Knowl.-Based Syst. 194 (2020) 105596,.

[50]

Y. Hong, U. Hwang, J. Yoo, S. Yoon, How generative adversarial networks and their variants work, ACM Comput. Surv. 52 (2019) 1–43,.

Digital Library

[51]

I.J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative Adversarial Networks, 2014.

Digital Library

[52]

P. Hofmann, T. Rückel, N. Urbach, Innovating with artificial intelligence: capturing the constructive functional capabilities of deep generative learning, in: 54^th HICSS, 2021.

[53]

I. Goodfellow, NIPS 2016 Tutorial: Generative Adversarial Networks, 2016.

[54]

A. Elgammal, B. Liu, M. Elhoseiny, M. Mazzone, CAN: Creative Adversarial Networks, Generating “Art” by Learning about Styles and Deviating from Style Norms, 2017.

[55]

R. Andrews, C.G.J. van Dun, M.T. Wynn, W. Kratsch, M.K. Röglinger, A.H. ter Hofstede, Quality-informed semi-automated event log generation for process mining, Decis. Support. Syst. 132 (2020) 113265,.

[56]

C. Sonnenberg, J. vom Brocke, Evaluation patterns for design science research artefacts, in: M. Helfert, B. Donnellan (Eds.), Practical Aspects of Design Science, Springer, Berlin Heidelberg, Berlin, Heidelberg, 2012, pp. 71–83.

[57]

D. Jung, M. Adam, V. Dorner, A. Hariharan, A practical guide for human lab experiments in information systems research, JSIT 19 (2017) 228–256,.

[58]

D.M. Oppenheimer, T. Meyvis, N. Davidenko, Instructional manipulation checks: detecting satisficing to increase statistical power, J. Exp. Soc. Psychol. 45 (2009) 867–872,.

[59]

D. Compeau, B. Marcolin, H. Kelley, C. Higgins, Research commentary —generalizability of information systems research using student subjects—a reflection on our practices and recommendations for future research, Inf. Syst. Res. 23 (2012) 1093–1109,.

Digital Library

[60]

P. Lohmann, M. zur Muehlen, Business process management skills and roles: an investigation of the demand and supply side of BPM professionals, in: H.R. Motahari-Nezhad, J. Recker, M. Weidlich (Eds.), Business Process Management, Springer International, 2015, pp. 317–332.

[61]

O. Müller, T. Schmiedel, E. Gorbacheva, J. vom Brocke, Towards a typology of business process management professionals: identifying patterns of competences through latent semantic analysis, Enterp, Inf. Syst. 10 (2016) 50–80,.

Digital Library

[62]

M. Polato, Dataset Belonging to the Help Desk Log of an Italian Company, University of Padova, 2017.

[63]

M. Dumas, J. Mendling, Business process event logs and visualization, in: S. Sakr, A.Y. Zomaya (Eds.), Encyclopedia of Big Data Technologies, Springer, 2019, pp. 398–409.

[64]

U.M. König, A. Linhart, M. Röglinger, Why do business processes deviate? Results from a Delphi study, Bus. Res. 12 (2019) 425–453,.

[65]

P. Delias, A positive deviance approach to eliminate wastes in business processes, IMDS 117 (2017) 1323–1339,.

[66]

Y.A. Effendi, R. Sarno, Modeling parallel business process using modified time-based alpha miner, IJICIC 14 (2018) 1565–1582,.

[67]

Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature 521 (2015) 436–444,.

[68]

F.M. Maggi, R.P.J.C. Bose, W.M.P. van der Aalst, Efficient discovery of understandable declarative process models from event logs, in: R. King (Ed.), Active Flow and Combustion Control 2018, Springer International, 2019, pp. 270–285.

[69]

H.A. Reijers, Business process management: the evolution of a discipline, Comput. Ind. 126 (2021) 103404,.

Digital Library

[70]

S. Gregor, L. Kruse, S. Seidel, Research perspectives: the anatomy of a design principle, JAIS 21 (2020) 1622–1652,.

[71]

T. Grisold, S. Gross, M. Röglinger, K. Stelzl, J. vom Brocke, Exploring explorative BPM - setting the ground for future research, in: T. Hildebrandt, B.F. van Dongen, M. Röglinger, J. Mendling (Eds.), Business Process Management, Springer, 2019, pp. 23–31.

Cited By

Schreiber CAbbad-Andaloussi AWeber B(2024)On the cognitive and behavioral effects of abstraction and fragmentation in modularized process modelsInformation Systems10.1016/j.is.2024.102424125:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.is.2024.102424
Maldonado AFrey CTavares GRehwald NSeidl T(2024)GEDI: Generating Event Data with Intentional Features for Benchmarking Process MiningBusiness Process Management10.1007/978-3-031-70396-6_13(221-237)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70396-6_13
Schmid SModer LHofmann PRöglinger M(2023)Everything at the proper timeInformation Systems10.1016/j.is.2023.102246118:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.is.2023.102246

Index Terms

ProcessGAN: Supporting the creation of business process improvement ideas through generative machine learning

Index terms have been assigned to the content through auto-classification.

Recommendations

An Agile Business Process and Practice Meta-model

Business Process Management (BPM) encompasses the discovery, modelling, monitoring, analysis and improvement of business processes. Limitations of traditional BPM approaches in addressing changes in business requirements have resulted in a number of ...
Towards Business Process Model Extension with Cost Perspective Based on Process Mining
ICEIS 2014: Proceedings of the 16th International Conference on Enterprise Information Systems - Volume 3

Business process improvement has ever been the major organizations concern to enhance efficiency, flexibility and competitiveness. Business process management is a contemporary approach which includes different techniques to support business process ...
Representation Type Preferences in Operational Business Process Redesign: A Quasi-Experimental Field Investigation

This paper reports on a quasi-experimental field study in which business process redesign groups in four different US organizations used two different business process representation types. One of the representation types emphasized an activity flow or ...

Comments

Information & Contributors

Information

Published In

cover image Decision Support Systems

Decision Support Systems Volume 165, Issue C

Feb 2023

108 pages

ISSN:0167-9236

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 February 2023

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 16 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Schreiber CAbbad-Andaloussi AWeber B(2024)On the cognitive and behavioral effects of abstraction and fragmentation in modularized process modelsInformation Systems10.1016/j.is.2024.102424125:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.is.2024.102424
Maldonado AFrey CTavares GRehwald NSeidl T(2024)GEDI: Generating Event Data with Intentional Features for Benchmarking Process MiningBusiness Process Management10.1007/978-3-031-70396-6_13(221-237)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70396-6_13
Schmid SModer LHofmann PRöglinger M(2023)Everything at the proper timeInformation Systems10.1016/j.is.2023.102246118:COnline publication date: 1-Sep-2023
https://dl.acm.org/doi/10.1016/j.is.2023.102246

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents