Yenilikçi Endüstri 4.0 Paradigması Kapsamında Kurumsal Kaynak Planlaması ve Yönetim Bilişim Sistemlerinde Yapay Zeka

Year 2021, Volume: 8 Issue: 1, 186 - 214, 30.06.2021

Ahmet Efe

https://doi.org/10.47097/piar.913441

Abstract

Yapay zekâ (YZ), günümüzde iş zorluklarının çoğuna etkililiğin, ekonomikliğin ve verimliliğin şaşırtıcı düzeyde arttırılması noktasında otomatik ve otonom çözümler sunabilmektedir. Örneğin, imalat endüstrisinde YZ tarafından desteklenen otomasyon, otomobil üretim şirketlerini sürücüsüz otomobilleri ve diğer endüstri 4.0 fırsatlarıyla piyasa zirvesine taşıdı. Buna benzer şekilde özellikle kurumsal kaynak planlaması (KKP), akıllı şehir, akıllı bina, yeşil enerji uygulamaları ile ev otomasyon sistemleri inanılmaz hızlı bir şekilde ilerleme göstermiştir. YZ'nin farklı ekonomik, kurumsal süreçler ve teknik alanlarında daha ne kadar ilerleme sağlayabileceğini değerlendirmek ve anlamak önem arz etmektedir. Literatür taraması, endüstriyel raporlar ile kilit sektör aktörlerine ait internet verilerinin analizine dayanan bu çalışma, YZ uygulamasının son on yıl içinde bilişim sistemleri otomasyonunu geliştirip geliştirmediğine, nasıl dönüştüğüne ve tüm yenilikçi YBS ile KKP uygulama ve türlerinde yakın zamanda daha ileriye gitme potansiyeli için genel bir bakış ve öneriler sunmaktadır.

Keywords

Yapay zeka,, Akıllı yönetim bilgi sistemleri,, Bilgi sistemleri otomasyonu, Otonom karar destek sistemleri, Bilgi tabanlı sistemler, Endüstri 4.0.

Artificial Intelligence in the Enterprise Resource Planning and Management Information Systems Under the Innovative Industry 4.0 Paradigm

Abstract

Artificial Intelligence (AI) can offer automated and autonomous solutions to most business challenges today, surprisingly increasing efficiency, affordability, and productivity. For example, automation in the manufacturing industry, powered by artificial intelligence, has brought auto manufacturing companies to the top of the market with driverless cars and other industry 4.0 opportunities. Similarly, especially with enterprise resource planning (ERP), smart city, smart building, green energy applications, home automation systems have progressed incredibly rapidly. It is essential to evaluate and understand how much progress can be achieved using artificial intelligence effectively in the economy, corporate processes, and technical aspects. This study depends on a literature survey, analysis of industrial reports, and websites of key actors to provide an overview of whether and how AI implementation has improved and transformed information systems automation over the past decade. Furthermore, it gives suggestions for the potential of going further in all innovative MIS and ERP implementations.

Keywords

Artificial intelligence,, intelligent management information systems,, Information systems automation,, Autonomous decision support systems, Knowledge based systems, Industry 4.0.

References

• Agrawal A., Gans J., Goldfarb A., (2018) “Prediction Machines: The Simple Economics of Artificial Intelligence” (Boston: Harvard Business Review Press,).
• Alhayani B., Mohammed H. J., Chaloob I. Z., Ahmed J. S., (2021) Effectiveness of artificial intelligence techniques against cyber security risks apply of IT industry, Materials Today: Proceedings, ISSN 2214-7853, https://doi.org/10.1016/j.matpr.2021.02.531
• Al-Mashari, M. & Zairi, M. (2000) Supply-chain re-engineering using enterprise resource planning (ERP) systems: an analysis of an SAP R/3 implementation case. International Journal of Physical Distribution & Logistics Management, 30 (3/4), 296–313.
• Anaçoğlu, E . (2019). Effects of Information Technology Usage on Business Performance. Pamukkale İşletme ve Bilişim Yönetimi Dergisi, 5 (1), 22-29. https://dergipark.org.tr/ tr/pub/pibyd/issue/42368/427860
• Azizi A. (2019) Hybrid Artificial Intelligence Optimization Technique. In: Applications of Artificial Intelligence Techniques in Industry 4.0. SpringerBriefs in Applied Sciences and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-13-2640-0_4
• Baxter, N., Collings, D., & Adjali, I. (2003). Agent-Based Modelling Intelligent Customer Relationship Management. BT Technology Journal, 21(2), 126-132.
• Beaumaster, S. (2002) Local government IT implementation issues: a challenge for public administration. Paper presented at the 35th Hawaii International Conference on System Sciences, Hawaii, USA.
• Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends®in Machine Learning, 2(1), 1-127.
• Bocij, P., Greasley, A., & Hickie, S. (2008). Business Information Systems: Technology, Development & Management. Harlow, England: Prentice Hall.
• Bruce G., Buchanan (2005) A. (very) Brief History of Artificial Intelligence. AI Magazine Vol. 26 Number 4 (AAAI).
• Brunette E.S., R.C. Flemmer, C.L. Flemmer, (2009) School of Engineering and Advanced Technology Massey University.
• Brynjolfsson E., Mitchell T., & Rock D., (2018) “What Can Machines Learn, and What Does It Mean for Occupations and the Economy?” AEA Papers and Proceedings 108 (May): 43-47.
• Brynjolfsson E., Rock D., & Syverson C., (2020) “The Productivity J-Curve: How Intangibles Complement General Purpose Technologies,” American Economic Journal: Macroeconomics, forthcoming.
• China Daily. (2018) AI seen as driving force in industry 4.0. http://www.chinadaily.com.cn /a/201804/27/WS5ae29547a3105cdcf651ae80.html
• Cornwell C., Schmutte I.M., & Scur D., (2019) “Building a Productive Workforce: The Role of Structured Management Practices,” discussion paper no. 1644, Centre for Economic Performance, London, August.
• Cowgill B. & Tucker C.E., (2019) “Algorithmic Fairness and Economics,” Journal of Economic Perspectives, forthcoming; and A. Lambrecht and C. Tucker, “Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads,” Management Science 65, no. 7: 2966-2981.
• Davenport, T. H. (1998) Putting the enterprise into the enterprise system. Harvard Business Review, July-August, 121–131.
• Dhaliwal, J. S., and Benbasat, I. (1996) “The Use and Effects of Knowledge-Based System Explanations: Theoretical Foundations and a Framework for Empirical Evaluation,” Information Systems Research (17:3), , pp. 342-362.
• Efe, A. & Isık, A. (2020) “A General View of Industry 4.0 Revolution From Cybersecurity Perspective”, IJISAE, vol. 8, no. 1, pp. 11-20, Mar.
• Ein-dor, P. & Segev, E. (1978) Organizational context and the success of management information systems. Management Science, 24 (10), 1064–1077.
• EU, Committee on Legal Affairs. (2017). REPORT with recommendations to the Commission on Civil Law Rules on Robotics. http://www.europarl.europa.eu/sides/getDoc.do?pubRef=-
• Fordyce, K. (1985). To the Editor: [Editor's Comment: Decision Support Systems, Artificial Intelligence, and Expert Systems].
• Gargeya, V. B. & Brady, C. (2005) Success and failure factors of adopting SAP in ERP system implementation. Business Process Management Journal, 11 (5), 501–516.
• Garret, O. (2017). 10 Million Self-Driving Cars Will Hit The Road By 2020 --Here's How To Profit. Forbes. Forbes Magazine. https://www.forbes.com/sites/oliviergarret/2017/03/03/10-million-self-driving-cars-will-hit-the-road-by-2020-heres-how-to-profit/#5f741bf57e50
• Gil Press: (2017) A Very Short History of Artificial Intelligence(AI) –, Forbes.com https://www.forbes.com/sites/gilpress/2016/12/30/a-veryvery-short-history-of-artificial-intelligence-ai/#4ba811d66fba
• Goldfarb A., Taska B., & Teodoridis F., (2020) “Artificial Intelligence in Health Care? Evidence From Online Job Postings,” AEA Papers and Proceedings 110 (May): 400-404.
• Gregor, S., and Benbasat, I. “Explanations from Intelligent Systems: Theoretical Foundations and Implications for Practice,” MIS Quarterly (23:4), 1999, pp. 497-530
• Harris, M. (2016). Google reports self-driving car mistakes: 272 failures and 13 near misses. The Guardian. Guardian News and Media. https://www.theguardian.com/technology/ 2016/jan/12/google-self-driving-cars-mistakes-data-reports
• Helper S., Martins R., & Seamans R., (2019 ) “Who Profits From Industry 4.0? Theory and Evidence From the Automotive Industry,” NYU Stern School of Business, New York, Jan. 31.
• Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for industry 4.0 scenarios. Proceedings of the Hawaii International Conference on System Sciences, IEEE, Koloa, HI, USA, 49. https://doi.org/10.1109/HICSS.2016.488
• Herschel, G. (2017). Develop Your Artificial Intelligence Strategy Expecting These Three Trends to Shape Its Future, (April).
• Hevner, A. R., March, S. T., Park, J. & Ram, S. (2004) Design science in information system research. MIS Quarterly, 28 (1), 75–105.
• Hong, K. K. & Kim, Y. G. (2002) The critical success factors for ERP implementation: an organizational fit perspective. Information & Management, 40, 25–40.
• Jantan, H. A., Hamdan, R. & Othman, Z. A. (2010). Intelligent Techniques for Decision Support System in Human Resource Management, Decision Support Systems, Advances in, Ger Devlin, IntechOpen, DOI: 10.5772/39401. https://www.intechopen.com/books/decision-support-systems-advances-in/intelligent-techniques-for-decision-support-system-in-human-resource-management#B31
• Jenab K., Staub S., Moslehpour S. & Wu C. (2019) Company Performance Improvement by Quality Based Intelligent-ERP, Decision Science Letters, vol. 8 (2019), pp. 151–162, https://scholarworks.moreheadstate.edu/msu_faculty_research/902/
• JIJI, A. F. P. (2017). Robots to be "scattered" about Haneda airport to help visitors to 2020 Tokyo Olympics. The Japan Times. The Japan Times. https://www.japantimes.co.jp /news/2017/12/13/national/robots-scattered-haneda-airport-helpvisitors-2020-tokyo-olympics/#.WufGTIjwbIU
• Khan, M. Z., Al-Mushayt, O., Alam, J., & Ahmad, J. (2010). Intelligent Supply Chain Management. Journal of Software Engineering and Applications, 3(4), 404-408.
• Küçük, D , Arıcı, N . (2018). Doğal Dil İşlemede Derin Öğrenme Uygulamaları Üzerine Bir Literatür Çalışması. Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi, 2 (2), 76-86. https://dergipark.org.tr/tr/pub/uybisbbd/issue/41787/443574
• Laudon, K., & Laudon, J.P., (2011). Essentials of Management Information Systems. Boston: Prentice hall.
• Laudon K.C., Laudon J.P., (2019) Management Information Systems—Managing the Digital Firm, 9edn Chapter 2.1 p. 475
• Lee, J., Davari, H., Singh, J., & Pandhare, V. (2018). Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters, 18, 20-23. https://doi.org/10.1016/j.mfglet.2018.09.002
• Martin L. (2017) MIS: Defintion and How It Works. https://www.cleverism.com/management-information-systems-mis
• Martínez-López, 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(7), 714-731.
• Meystel, A. M., & Albus, J. S. (2002). Intelligent System: Architecture, Design and Control. New York: John Wiley & Son.Inc.
• Milis, K. & Mercken, R. (2002) Success factors regarding the implementation of ICT investment projects. International Journal of Production Economics, 80, 105–117.
• Murthy, C. S. V. (2006) Management information systems. Mumbai, Himalaya Publishing House.
• Negnevitsky, M. (2005). Artificial Intelligence: A guide to Intelligent Systems: Addison Wesley, England.
• Perrault R., Shoham Y., Brynjolfsson E., et al., (2019) “Artificial Intelligence Index 2019 Annual Report,” Human-Centered Artificial Intelligence Institute (Stanford, California: Stanford University, December).
• Rock D., (2019) “Engineering Value: The Returns to Technological Talent and Investments in Artificial Intelligence,” unpublished working paper, MIT Sloan School of Management, Cambridge, Massachusetts, May.
• Russell, S., & Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd Edition). Upper Saddle River: Prentice Hall.
• Sadaf S., Rana A., Pathak A., (2021) Robotic Process Automation, EasyChair Preprint № 5504, https://login.easychair.org/publications/preprint_download/6J4l
• Schalkoff, R. J. (2011). Intelligent Systems: Principles, Paradigms, and Pragmatics. Boston: Jones and Bartlett Publishers.
• Schwab, K. (2015). The fourth industrial revolution: What it means and what to respond. Foreign Affairs. https://www.foreignaffairs.com/articles/2015-12-12/fourth-industrial-revolution
• Shi Z, Wang G., (2018) Integration of big-data ERP and business analytics (BA), The Journal of High Technology Management Research, Volume 29, Issue 2, Pages 141-150, ISSN 1047-8310, https://doi.org/10.1016/j.hitech.2018.09.004.
• Silver D., Huang A., Maddison C.J., et al., (2020) “Mastering the Game of Go With Deep Neural Networks and Tree Search,” Nature 529, no. 7587 (Jan. 28, 2016): 484-489;
• Simon H.A., (1995) Artificial Intelligence Artificial Intelligence : An Empirical Science
• Spalding, J. O. (1998) Transportation industry takes the right-of-way in the supply chain. ILE Solutons, 30 (7), 24–28.
• Spathis, C. & Constantinides, S. (2003) The usefulness of ERP systems for effective management. Industrial Management & Data systems, 103 (9), 677–685.
• Sun, Z., & Firmin, S. (2012). A strategic perspective on management intelligent systems. In J. Casillas et al, Management Intelligent Systems, AISC 171 (pp. 3-14). Springer
• Takaoğlu, M , Özer, Ç . (2019). Saldırı Tespit Sistemlerine Makine Öğrenme Etkisi. Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi, 3 (1), 11-22. https://10.33461/uybisbbd.558192
• Tambe P., (2014) “Big Data Investment, Skills, and Firm Value,” Management Science 60, no. 6: 1452-1469.
• Tenfold, (2017) How AI will change Decision Making for Business. https://becominghuman.ai/how-artificial-intelligence-will-change-decision-making-for-businesses-96d47cde98df
• Theiruf, R. J. (1994) Effective management and evaluation of information technology. New York, Quorum Books.
• Tripathi, K. P. (2011) Role of management information system (MIS) in human resource. International Journal of Computer Science and Technology, 2 (1), 58–62.
• Tucci, L. (2020) Artificial Intelligence Definition, Ultimate guide to artificial intelligence in the enterprise, https://searchenterpriseai.techtarget.com/definition/AI-Artificial-Intelligence
• Turban, E., Aronson, J. E., Liang, T.-P., & Sharda, R. (2007). Decision Support and Business Intelligence Systems (Eighth ed.). New Jersey: Pearson Education International.
• Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J.R. & Tchatchouang Wanko, C.E. (2020), "Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects", Business Process Management Journal, Vol. 26 No. 7, pp. 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411