Artificial Intelligence in Business Simulation Analysis
DOI:
https://doi.org/10.47672/ejt.629Keywords:
Machine learning, Business Simulation, Algorithms, Business trends, Artificial IntelligenceAbstract
Purpose: Research on business simulation and machine learning has attracted immense interest in the last few years. The aim of this study was to provide a comprehensive view of machine learning in business simulation. To review the use of artificial intelligence in business simulation analysis. A review of the literature, however, shows little systematic reviews on the application of machine learning techniques to business simulation, yet systematic reviews have gained prominence in the academic jargon.
Methodology: Thus, this study does reviews systematically a total of 123 shortlisted articles that focus on the machine learning techniques in the business simulation process.
Findings: There are immense algorithms of machine learning which can be used in a business simulation, although this study was able to review ten machine learning algorithms in the business simulation process. As a whole, the machine learning algorithms have been deployed to yield lead-time production in the industry. In inventory and storage, machine learning has been applied to improve efficiency in identifying inventory patterns that would have never been revealed and thus saves on costs. Future direction also discussed.
Downloads
References
Aleksendri´c, D., & Carlone, P. (2015). Soft computing techniques. In D. Aleksendri´c, & P. Carlone (Eds.), Soft Computing in the Design and Manufacturing of Composite Materials, 4 pp. 39–60). Oxford: Woodhead Publishing. https://doi.org/10.1533/ 9781782421801.39.
Bravo, C., Castro, J. A., Saputelli, L., Ríos, A., Aguilar-Martin, J., & Rivas, F. (2011). An implementation of a distributed artificial intelligence architecture to the integrated production management. Journal of Natural Gas Science and Engineering, 3, 735–747. https://doi.org/10.1016/j.jngse.2011.08.002
Bryman, A. (2007). The Research Question in Social Research: What is its Role? International Journal of Social Research Methodology, 10, 5–20. https://doi.org/ 10.1080/13645570600655282
Bundy, A. (1997). Artificial Intelligence Techniques: A Comprehensive Catalogue (4th ed.). Berlin Heidelberg: Springer-Verlag.
Camarillo, A., Ríos, J., & Althoff, K.-D. (2018). Knowledge-based multi-agent system for manufacturing problem solving process in production plants. Journal of Manufacturing Systems, 47, 115–127. https://doi.org/10.1016/j.jmsy.2018.04.002 Canhoto, A. I., & Clear, F. (2020). Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential. Bus. Horiz. Artificial Intelligence and Machine Learning, 63, 183–193. https://doi.org/10.1016/j. bushor.2019.11.003
Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184, 1140–1154.
Cardoso, R. N., Pereira, B. L., Fonseca, J. P. S., Ferreira, M. V. M., & Tavares, J. J. P. Z. S. (2013). Automated planning integrated with linear programming applied in the container loading problem. IFAC Proceedings, 46, 153–158. https://doi.org/10.3182/
-3-BR-3021.00077
Chen, S. H., Jakeman, A. J., & Norton, J. P. (2008). Artificial Intelligence techniques: An introduction to their use for modelling environmental systems. Mathematics and Computers in Simulation, 78, 379–400. https://doi.org/10.1016/j. matcom.2008.01.028
Chong, A. Y.-L., & Bai, R. (2014). Predicting open IOS adoption in SMEs: An integrated SEM-neural network approach. Expert Systems with Applications, 41, 221–229. https://doi.org/10.1016/j.eswa.2013.07.023
Clifton, J. R., & Frohnsdorff, G. (2001). Applications of Computers and Information Technology. In V. S. Ramachandran, & J. J. Beaudoin (Eds.), Handbook of Analytical Techniques in Concrete Science and Technology, 18 pp. 765–799). Norwich, NY: William Andrew Publishing. https://doi.org/10.1016/B978-081551437-4.50021-7.
Dirican, C. (2015). The impacts of robotics, artificial intelligence on business and economics. Procedia Social and Behavioral Sciences, 195, 564–573. https://doi.org/ 10.1016/j.sbspro.2015.06.134
Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., … Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International Journal of Production Economics, 226, Article 107599. https://doi.org/10.1016/j. ijpe.2019.107599
Edelkamp, S., Schrodl, ¨ S. (2012). Chapter 14 - Selective Search, in: Edelkamp, S., Schrodl, ¨ S. (Eds.), Heuristic Search. Morgan Kaufmann, San Francisco, pp. 633–669. https://doi.org/10.1016/B978-0-12-372512-7.00014-6.
Efendigil, T., Onüt, ¨ S., & Kahraman, C. (2009). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems with Applications, 36, 6697–6707. https://doi.org/10.1016/j. eswa.2008.08.058
Feo, A., & Resende, T. M. (1995). Greedy Randomized Adaptive Search Procedures. Journal of Global Optimization 6, 109–133. https://doi.org/10.1007/BF01096763.
Frayret, J.-M., D’Amours, S., Rousseau, A., Harvey, S., & Gaudreault, J. (2007). Agent- based supply-chain planning in the forest products industry. International Journal of Flexible Manufacturing Systems, 19, 358–391.
García, F. T., Villalba, L. J. G., & Portela, J. (2012). Intelligent system for time series classification using support vector machines applied to supply-chain. Expert Systems with Applications, 39, 10590–10599.
Geem, Z. W., & Roper, W. E. (2009). Energy demand estimation of South Korea using artificial neural network. Energy Policy, Carbon in Motion: Fuel Economy, Vehicle Use, and Other Factors affecting CO2 Emissions From Transport, 37, 4049–4054. https://doi. org/10.1016/j.enpol.2009.04.049
Gholami, R., & Fakhari, N. (2017). Chapter 27 - Support Vector Machine: Principles, Parameters, and Applications. In P. Samui, S. Sekhar, & V. E. Balas (Eds.), Handbook of Neural Computation (pp. 515–535). Academic Press. https://doi.org/10.1016/ B978-0-12-811318-9.00027-2. Journal of the Operational Research Society, 59, 455–463.
Martínez-Lopez, ´ F. J., & Casillas, J. (2013). Artificial intelligence-based systems applied in industrial marketing: An historical overview, current and future insights. Industrial Marketing Management Special Issue on Applied Intelligent Systems in Business-to- Business Marketing, 42, 489–495. https://doi.org/10.1016/j.indmarman.2013.03.001
Martínez-Lopez, F. J., & Casillas, J. (2009). Marketing Intelligent Systems for consumer behaviour modelling by a descriptive induction approach based on Genetic Fuzzy Systems. Industrial Marketing Management., 38, 714–731. https://doi.org/10.1016/j. indmarman.2008.02.003
Mayr, A., Weigelt, M., Masuch, M., Meiners, M., Hüttel, F., & Franke, J. (2018). Application Scenarios of Artificial Intelligence in Electric Drives Production. Procedia Manufacturing, 24, 40–47. https://doi.org/10.1016/j.promfg.2018.06.006
Pino, R., Fernandez,´ I., de la Fuente, D., Parreno,˜ J., & Priore, P. (2010). Supply chain modelling using a multi-agent system. J. Adv. Manag. Res., 7, 149–162.
Pioro, M. & Medhi, D. (2004). Chapter 5 - General Optimization Methods for Network Design, in: Pioro, ´ M., Medhi, D. (Eds.), Routing, Flow, and Capacity Design in Communication and Computer Networks, The Morgan Kaufmann Series in Networking. Morgan Kaufmann, San Francisco, pp. 151–210. https://doi.org/ 10.1016/B978-012557189-0/50008-1.
Quin˜onez-G´ amez, ´ O. P., & Camacho-Velazquez, ´ R. G. (2011). Validation of production data by using an AI-based classification methodology; a case in the Gulf of Mexico. Journal of Natural Gas Science and Engineering, 3, 729–734. https://doi.org/10.1016/ j.jngse.2011.07.015
Ransbotham, S., Kiron, D., Gerbert, P., & Reeves, M. (2017). Reshaping business with artificial intelligence: Closing the gap between ambition and action (p. 59). Rev: MIT Sloan Manag.
Redding, S. J., & Turner, M. A. (2015). Chapter 20 - Transportation Costs and the Spatial Organization of Economic Activity. In G. Duranton, J. V. Henderson, & W. C. Strange (Eds.), Handbook of Regional and Urban Economics, Handbook of Regional and Urban Economics (pp. 1339–1398). Elsevier. https://doi.org/10.1016/B978-0-444-59531- 7.00020-X.
Regal, T., & Pereira, C. E. (2018). Ontology for Conceptual Modelling of Intelligent Maintenance Systems and Spare Parts Supply Chain Integration. IFAC-Pap., 51, 1511–1516. https://doi.org/10.1016/j.ifacol.2018.08.285
Rekha, A. G., Abdulla, M. S., & Asharaf, S. (2016). Artificial Intelligence Marketing: An application of a novel Lightly Trained Support Vector Data Description. J. Inf. Optim. Sci., 37, 681–691. https://doi.org/10.1080/02522667.2016.1191186 Rowley, J., & Slack, F. (2004). Conducting a literature review. Manag. Res. News, 27, 31–39.
Saka, M. P., Dogan, ˘ E., & Aydogdu, I. (2013). Analysis of Swarm Intelligence-Based Algorithms for Constrained Optimization. In X.-. S. Yang, Z. Cui, R. Xiao, A. H. Gandomi, & M. Karamanoglu (Eds.), Swarm Intelligence and Bio-Inspired Computation, 2 pp. 25–48). Oxford: Elsevier. https://doi.org/10.1016/B978-0-12- 405163-8.00002-8.
Downloads
Published
How to Cite
Issue
Section
License
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
