Business Process Clustering in Small and Medium Manufacturing Industries Based on Enterprise Resource Planning Concepts: A Systematic Literature Review

• Autorzy: Fathoni Muhammad Zainuddin , Sri-Asih Anna Maria , Wibisono Muhammad Arif

Identyfikatory

DOI

10.2478/mspe-2024-0050

• Języki publikacji: EN

Abstrakty

EN

Business process management has become increasingly important in the manufacturing sector, playing a vital role in fostering productivity and facilitating organizational adaptability to technological advancements. Although each company’s business process models vary in uniqueness and complexity, certain similarities can be identified from these differences. This study employs a systematic literature review to aggregate and summarize findings on business process models, modeling languages, clustering techniques, and foundational clustering principles. Results demonstrate the dearth of research on the grouping of business process models in the manufacturing industry. Several studies have focused on sectors such as services, trading, and insurance. Research specifically addressing clustering in the manufacturing sector is limited. Existing clustering efforts in manufacturing revolve around groupings related to product defects, industrial locations, business ecosystems, and similar factors. Analysis of the methods, scope, and criteria used in grouping business process models in the manufacturing industry indicates that most approaches rely on structural or graphic similarities. Follow-up research is lacking once these business process clusters are identified. This study proposes a novel approach to grouping, integrating business process modeling with the implementation of a management information system. Business process management relies on integrating departments within the company through an enterprise resource planning (ERP) system. The next step involves proposing a conceptual framework to categorize business processes and assess the comparability of models in the manufacturing sector. Future research directions are also delineated.

Słowa kluczowe

EN

business process; clustering; ERP concept; systematic literature review

• Wydawca: STE GROUP
• Czasopismo: Management Systems in Production Engineering
• Rocznik: 2024
• Tom: nr 4 (32)
• Strony: 525--536
• Opis fizyczny: Bibliogr. 32 poz., rys., tab.

Twórcy

autor

Fathoni Muhammad Zainuddin

• Industrial Engineering Program Department of Mechanical and Industrial Engineering Faculty of Engineering Universitas Gadjah Mada Yogyakarta 55281, Indonesia
• Industrial Engineering Program Faculty of Engineering Universitas Muhammadiyah Gresik Gresik 61121, Indonesia

• https://orcid.org/0000-0002-5930-1743

autor

Sri-Asih Anna Maria

• Industrial Engineering Program Department of Mechanical and Industrial Engineering Faculty of Engineering Universitas Gadjah Mada Yogyakarta 55281, Indonesia

• https://orcid.org/0000-0002-1172-4528

autor

Wibisono Muhammad Arif

• Industrial Engineering Program Department of Mechanical and Industrial Engineering Faculty of Engineering Universitas Gadjah Mada Yogyakarta 55281, Indonesia

• https://orcid.org/0009-0002-9580-1988

Bibliografia

• [1] J. Jeston and J. Nelis, Business Process Management, Practical guidelines to successful implementations, First Edit. Elsevier Ltd., 2006.
• [2] J. Y. Jung, J. Bae, and L. Liu, “Hierarchical clustering of business process models,” Int. J. Innov. Comput. Inf. Control, vol. 5, no. 12, pp. 4501-4511, 2009.
• [3] S. Mamoghli, V. Goepp, and V. Botta-Genoulaz, “An approach for the management of the risk factors impacting the model-based engineering methods in ERP projects,” IFAC-PapersOnLine, vol. 51, no. 11, pp. 1206-1211, 2018, doi: 10.1016/j.ifacol.2018.08.426.
• [4] P. Ruivo, B. Johansson, S. Sarker, and T. Oliveira, “The relationship between ERP capabilities, use, and value,” Comput. Ind., vol. 117, 2020, doi: 10.1016/j.compind.2020.103209.
• [5] S. Chopra, Supply chain Management; strategy, planning, and operation, Fifth ed. Pearson, 2013.
• [6] R. Sarno, H. Ginardi, E. W. Pamungkas, and D. Sunaryono, “Clustering of ERP business process fragments,” Proceeding – 2013 Int. Conf. Comput. Control. Informatics Its Appl. “Recent Challenges Comput. Control Informatics”, IC3INA 2013, pp. 319-324, 2013, doi: 10.1109/IC3INA.2013.6819194.
• [7] H. Ordonez, J. Torres-jimenez, C.C. Id, A. Ordonez, E. Herrera-viedma, and G. Maldonado-martinez, “A business process clustering algorithm using incremental covering arrays to explore search space and balanced Bayesian information criterion to evaluate quality of solutions,” pp. 1-27, 2019.
• [8] S. Israilova, A. Mukhanova, and T. Yesikova, “Business Process Verification With Integrated Simulation Methods : Focus On ‘Customer Engagement,’” vol. 99, no. 21, pp. 5112-5124, 2021.
• [9] J. A. Fitzsimmons and M. J. Fitzsimmons, Service management; operation, strategy, information technology, Seventh. McGraw-hill, 2011.
• [10] E. Prasteyo, Data Mining: Mengolah Data Menjadi Informasi Menggunakan Matlab, Pertama. ANDI Yogyakarta, 2014.
• [11] Suyanto, Data Mining untuk Klasifikasi dan Klasterisasi Data, Revisi. Informatika Bandung, 2019.
• [12] A. Liberati et al., The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration, vol. 62, no. 10. 2009. doi: 10.1016/j.jclinepi.2009.06.006.
• [13] Vos viewer, “Center for Science and Technology Studies.” Leiden University, Leiden, The Netherland, 2023. [Online]. Available: https://www.vosviewer.com/.
• [14] H. Ahn and T. W. Chang, “A similarity-based hierarchical clustering method for manufacturing process models,” Sustain., vol. 11, no. 9, 2019, doi: 10.3390/su11092560.
• [15] M.K. Chen, C.M. Wu, L.S. Chen, and Y.P. Huang, “The influential factors of taiwan smes’ clustering keystone business strategy – the perspective of business ecosystem using fahp,” Sustain., vol. 13, no. 18, 2021, doi: 10.3390/su131810304.
• [16] J.M. Müller, O. Buliga, and K.I. Voigt, “Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0,” Technol. Forecast. Soc. Change, vol. 132, no. December 2017, pp. 2-17, 2018, doi: 10.1016/j.techfore.2017.12.019.
• [17] P. Holzmann, R.J. Breitenecker, E.J. Schwarz, and P. Gregori, “Business model design for novel technologies in nascent industries: An investigation of 3D printing service providers,” Technol. Forecast. Soc. Change, vol. 159, no. July, p. 120193, 2020, doi: 10.1016/j.techfore.2020.120193.
• [18] E. Hariyanti, A. Djunaidy, and D. Siahaan, “Information security vulnerability prediction based on business process model using machine learning approach,” Comput. Secur., vol. 110, p. 102422, 2021, doi: 10.1016/j.cose.2021.102422.
• [19] M.R.C.I, “Process modeling: technological innovation to control the risk for perioperative positioning injury,” vol. 74, no. Suppl 6, pp. 4-7, 2021.
• [20] B.A. Tama and M. Comuzzi, “An empirical comparison of classification techniques for next event prediction using business process event logs,” Expert Syst. Appl., vol. 129, pp. 233-245, 2019, doi: 10.1016/j.eswa.2019.04.016.
• [21] A. Haeri, K. Rezaie, and M.S. Amalnick, “Developing a novel approach to assess the efficiency of resource utilisation in organisations: A case study for an automotive supplier,” Int. J. Prod. Res., vol. 52, no. 10, pp. 2815-2833, 2014, doi: 10.1080/00207543.2013.839891.
• [22] A. Haeri and S.H. Iranmanesh, “Using classification techniques to recognize patterns of resource utilization in organizations,” 2015, doi: 10.1177/0954405415584897.
• [23] V.S.W. Lam, “Equivalences of BPMN processes,” pp. 189-204, 2009, doi: 10.1007/s11761-009-0048-5.
• [24] J. Koszela, “Methods of structural analysis of business processes,” vol. 04016, pp. 1-8, 2018.
• [25] M.M. Hassan, M.S. Alenezi, and R.Z. Good, “Spatial pattern analysis of manufacturing industries in Keraniganj, Dhaka, Bangladesh,” GeoJournal, vol. 85, no. 1, pp. 269-283, 2020, doi: 10.1007/s10708-018-9961-5.
• [26] T. Wuest, C. Irgens, and K.D. Thoben, “An approach to monitoring quality in manufacturing using supervised machine learning on product state data,” J. Intell. Manuf., vol. 25, no. 5, pp. 1167-1180, 2014, doi: 10.1007/s10845-013-0761-y.
• [27] G. San-Payo, J.C. Ferreira, P. Santos, and A.L. Martins, “Machine learning for quality control system,” J. Ambient Intell. Humaniz. Comput., vol. 11, no. 11, pp. 4491-4500, 2020, doi: 10.1007/s12652-019-01640-4.
• [28] Y. Jin, S. Ji, L. Liu, and W. Wang, “Business model innovation canvas: a visual business model innovation model,” Eur. J. Innov. Manag., vol. 25, no. 5, pp. 1469-1493, 2022, doi: 10.1108/EJIM-02-2021-0079.
• [29] A.L. Rodrigues, F.B.G. Torres, E.A.P. Santos, and M.R. Cubas, “Process-modeling-technological-innovation-to-control-the-risk-for-perioperative-positioning-injury – Modelagem-de-processos-inovao-tecnolgica-paracontrole-do-risco-de-leso-por-posicionamentoperioperatrioRevista-Bra.pdf.” 2021.
• [30] L. Sneller, A Guide to ERP: Benefits, implementation & trends. 2014.
• [31] Badan Pusat Statistik, Klasifikasi Baku Lapangan Usaha Indonesia (KBLI) 2020. 2020.
• [32] S. Sinulingga, Perencanaan dan Pengendalian Produksi, 2nd ed. 2013.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).