DOI: https://doi.org/10.14710/jbs.29.2.89-100

Isu Proses Bisnis Berbasis Artificial Intelligence untuk Menyosong Era Industri 4.0

*Herbert Siregar  -  Universitas Pendidikan Indonesia, Indonesia
Wawan Setiawan  -  Universitas Pendidikan Indonesia, Indonesia
Puspo Dewi Dirgantari  -  Universitas Pendidikan Indonesia, Indonesia

Citation Format:
Abstract

Changes in business processes that are running very fast in the industrial era 4.0 have positive and negative impacts on the business world. For those who want to continue to progress and develop, then these changes become a necessity. This study aims to uncover various types of business process models that are currently developing. We conduct investigations from various international journal articles and classify articles according to the field of study. The results of the investigation can provide an overview of the issues that are currently developing in the international world and become one of the references for those engaged in the business world and academia.

Fulltext View|Download
Keywords: Business processes; artificial intelligence; industry 4.0; business process management

Article Metrics:

Article Info
Section: Articles
Language : ID
Recent articles
Larry Page’s Styles of Leadership Determining Consumers’ Willingness to Buy Halal Meat Peran Local Celebrity Endorsement Pada Iklan di Media Sosial Terhadap Pembelian Produk Kuliner di Provinsi Bali Pada Masa Stay Home Akibat Pandemi Covid-19 More recent articles
Most cited articles
CORPORATE GOVERNANCE: STATE OF THE ART ANALISIS FAKTOR - FAKTOR YANG MEMPENGARUHI KEBIJAKAN PENYALURAN KREDIT PERBANKAN (Studi pada Bank Umum di Indonesia Periode Tahun 2005 - 2009) PENGARUH KEADILAN PROSEDURAL DAN DISTRIBUTIF TERHADAP KEPUASAN KERJA DAN KOMITMEN AFEKTIF PENGARUH MOTIVASI KERJA, KOMUNIKASI ANTAR PRIBADI DAN KOMITMEN ORGANISASI TERHADAP KINERJA PEJABAT STRUKTURAL DAN DOSEN TETAP DI BEBERAPA PERGURUAN TINGGI SWASTA DI JAKARTA PERHITUNGAN NILAI BUDAYA DENGAN MENGGUNAKAN VALUE SURVEY MODULE 2008: SEBUAH TELAAH KRITIS More cited articles
  1. Amazon. (2016). Introducing Amazon Go and the world’s most advanced shopping technology - YouTube. Retrieved from www.youtube.com/watch?v=NrmMk1Myrxc
  2. Ashrafian, H. (2015). Artificial intelligence and robot responsibilities: Innovating beyond rights. Science and Engineering Ethics, 21(2), 317–326
  3. Aydiner, A. S., Tatoglu, E., Bayraktar, E., & Zaim, S. (2019). Information system capabilities and firm performance: Opening the black box through decision-making performance and business-process performance. International Journal of Information Management, 47, 168–182
  4. Charbonnier, S., Garcia-Beltan, C., Cadet, C., & Gentil, S. (2005). Trends extraction and analysis for complex system monitoring and decision support. Engineering Applications of Artificial Intelligence, 18(1), 21–36
  5. Chatterjee, S., Ghosh, S. K., Chaudhuri, R., & Nguyen, B. (2019). Are CRM systems ready for AI integration? The Bottom Line
  6. Chelliah, J. (2017). Will artificial intelligence usurp white collar jobs? Human Resource Management International Digest
  7. Cheung, C. F., Lee, W. B., Wang, W. M., Chu, K. F., & To, S. (2003). A multi-perspective knowledge-based system for customer service management. Expert Systems with Applications, 24(4), 457–470
  8. Day, M. (2018). Amazon Go Cashierless Convenience Store opening to the public in Seattle. Seattle Times
  9. Evans, N., & Price, J. (2017). Managing information in law firms: changes and challenges. Information Research: An International Electronic Journal, 22(1), n1
  10. Foster, K., Smith, G., Ariyachandra, T., & Frolick, M. N. (2015). Business intelligence competency center: Improving data and decisions. Information Systems Management, 32(3), 229–233
  11. Gawin, B., & Marcinkowski, B. (2017). Business intelligence in facility management: Determinants and benchmarking scenarios for improving energy efficiency. Information Systems Management, 34(4), 347–358
  12. Gupta, G. K. (2014). Introduction to data mining with case studies. PHI Learning Pvt. Ltd
  13. Höpken, W., Fuchs, M., Keil, D., & Lexhagen, M. (2015). Business intelligence for cross-process knowledge extraction at tourism destinations. Information Technology & Tourism, 15(2), 101–130
  14. Le, M., Gabrys, B., & Nauck, D. (2017). A hybrid model for business process event and outcome prediction. Expert Systems, 34(5), e12079
  15. Lee, J., Davari, H., Singh, J., & Pandhare, V. (2018). Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters, 18, 20–23
  16. Liao, S., & Tasi, Y.-S. (2019). Big data analysis on the business process and management for the store layout and bundling sales. Business Process Management Journal, 27(7), 1783-
  17. Liu, L., Li, W., Aljohani, N. R., Lytras, M. D., Hassan, S.-U., & Nawaz, R. (2020). A framework to evaluate the interoperability of0 information systems--Measuring the maturity of the business process alignment. International Journal of Information Management, 54, 102153
  18. Lohrmann, M., & Reichert, M. (2016). Effective application of process improvement patterns to business processes. Software & Systems Modeling, 15(2), 353–375
  19. Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60
  20. Markić, B., Bijakšić, S., & Šantić, M. (2015). Artificial intelligence in determination of marketing customer strategy. Informatologia, 48(1–2), 39–47
  21. Metzger, A., Leitner, P., Ivanović, D., Schmieders, E., Franklin, R., Carro, M., … Pohl, K. (2014). Comparing and combining predictive business process monitoring techniques. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(2), 276–290
  22. Muntean, M., & Mircea, G. (2007). Business intelligence solutions for gaining competitive advantage. Revista Informatica Economic{u{a}}, 3(43), 22–25
  23. Nyulásziová, M., & Pal’ová, D. (2020). Implementing a decision support system in the transport process management of a small Slovak transport company. Journal of Entrepreneurship, Management and Innovation, 16(1), 75–106
  24. Pereira, M. T., Silva, A., Ferreira, L. P., Sá, J. C., & Silva, F. J. G. (2019). A DMS to Support Industrial Process Decision-Making: a contribution under Industry 4.0. Procedia Manufacturing, 38, 613–620
  25. Polacco, A., & Backes, K. (2018). The amazon go concept: Implications, applications, and sustainability. Journal of Business and Management, 24(1), 79–92
  26. Remagnino, P., Shihab, A. I., & Jones, G. A. (2004). Distributed intelligence for multi-camera visual surveillance. Pattern Recognition, 37(4), 675–689
  27. Sid, I., Reichert, M., & Ghomari, A. R. (2019). Enabling flexible task compositions, orders and granularities for knowledge-intensive business processes. Enterprise Information Systems, 13(3), 376–423
  28. Vugec, D. S., Stjepić, A.-M., & Sušac, L. (2019). Business Process Management Software Functionality Analysis: Supporting Social Computing and Digital Transformation. ISSN 2671-132X Vol. 1 No. 1 Pp. 1-876 June 2019, Zagreb, 547
  29. Wang, J. (2003). Data mining: opportunities and challenges. Idea Group Pub
  30. Yao, X., Jin, H., & Zhang, J. (2015). Towards a wisdom manufacturing vision. International Journal of Computer Integrated Manufacturing, 28(12), 1291–1312
  31. Zhu, Z., Zhao, J., & Bush, A. A. (2020). The effects of e-business processes in supply chain operations: Process component and value creation mechanisms. International Journal of Information Management, 50, 273–285

Last update:

No citation recorded.

Last update: 2024-04-23 16:09:47

No citation recorded.