PREDICTIVE ANALYTICS ON SAP DATABASE (HANA) BY USING ARTIFICIAL INTELLIGENCE (AI) AND AUTOMATED MACHINE LEARNING CAPABILITIES
Keywords:
Business Intelligence, SAP Cloud, Predictive Analytics, SAP Analytics Cloud, Data IntelligenceAbstract
The essential necessity of the hour is for predictive analytics! Human lives have been affected, either deliberately or unknowingly, by it. One does not often recognize how deeply artificial intelligence has penetrated society and the effects it has had on it. In accordance with the preferences and browsing patterns, the shopping list is indexed. Because of the length of time that a person spends looking at a picture or a video, they are compelled to examine adverts. The watering schedule is adjusted accordingly when the sprinkler is able to detect the temperature. Just a few short months from now, there will be smart kitchens available in every single home. Many companies have decided to permanently automate their procedures in order to stay ahead of the competition in an augmented analytics-based industry. These groups have benefited from this decision's outcomes. Finding and implementing cutting-edge AI and ML technology is a top enterprise priority. Because of this rush, the market has become more open to creative expression, and a new period of rivalry has begun in the direction of developing and bringing the greatest items in their respective categories. Additionally, it encourages an atmosphere of innovative construction inside the sector, in addition to undergoing continuous renovations within itself. The reliability of the information conveyed by this technology will determine how well it performs.That being said, companies must have a firm grasp on the what, why, and how of deploying AI and ML technologies if they want to succeed in this area.
References
Riahi, Y., Saikouk, T., Gunasekaran, A. and Badraoui, I. (2021), “Artificial intelligence applications in supply chain: a descriptive bibliometric analysis and future research directions”, Expert Systems with Applications, Vol. 173, pp. 1-19.
Singh, L.P. and Challa, R.T. (2016), “Integrated forecasting using the discrete wavelet theory and artificial intelligence techniques to reduce the bullwhip effect in a supply chain”, Global Journal of Flexible Systems Management, Vol. 17 No. 2, pp. 157-169
Soleimani, S. (2018), “A perfect triangle with: Artificial intelligence, supply chain management, and financial technology”, Archives of Business Research, Vol. 6 No. 11, pp. 85-94.
Sun, H. (2019), Sourcing Risk Detection and Prediction with Online Public Data: An Application of Machine Learning Techniques in Supply Chain Risk Management, PhD thesis, NC.
Mohammed Bin Zayed University of Artificial Intelligence (2020), available at: https://mbzuai.ac.ae, (accessed 19 November 2020)
Klumpp, M. and Ruiner, C. (2018), “Regulation for artificial intelligence and robotics in transportation, logistics, and supply chain management: background and developments”, Network Industries Quarterly, Vol. 20 No. 2, pp. 1-7.
Hartmann, F. (2018), “Evolving digitisation: chances and risks of robotic process automation and artificial intelligence for process optimisation within the supply chain”, Bachelor thesis, Berlin School of Economics and Law, Germany.
Gupta, S. and Saxena, A. (2020), “Operations-based classification of the bullwhip effect”, Journal of Modelling in Management, doi: 10.1108/JM2-01-2020-0029.
Di Vaio, A., Boccia, F., Landriani, L. and Palladino, R. (2020), “Artificial intelligence in the Agri-food system: Rethinking sustainable business models in the COVID-19 scenario”, Sustainability, Vol. 12 No. 12, p. 4851.
Grover, P., Kar, A.K. and Dwivedi, Y.K. (2020), “Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions”, Annals of Operations Research, pp. 1-37
Thierry_Brunet. [Online]. Available: https://blogs.sap.com/2020/06/19/classification-in-sap-analytics-cloud-in-detail/
Thierry_Brunet. [Online]. Available: https://blogs.sap.com/2020/08/13/regression-in-sap-analytics-cloud-in-detail/
Bubicz, M.E., Moreira, C., Marques, L., Barbosa-Povoa, A. and Carvalho, A. (2020), “The relationship between NGOs and the improvement on social sustainability in the global apparel supply chain: a model based on artificial intelligence”, 7th EurOMA Sustainability Forum, Nottingham, UK, February 10-12, 2020.
Ramya Manikyam, J. Todd McDonald, William R. Mahoney, Todd R. Andel, and Samuel H. Russ. 2016.Comparing the effectiveness of commercial obfuscators against MATE attacks. In Proceedings of the 6th Workshop on Software Security, Protection, and Reverse Engineering (SSPREW’16)
R. Manikyam. 2019.Program protection using software based hardware abstraction.Ph.D. Dissertation.University of South Alabama.
GPB GRADXS, N RAO, Behaviour Based Credit Card Fraud Detection Design And Analysis By Using Deep Stacked Autoencoder Based Harris Grey Wolf (Hgw) Method, Scandinavian Journal of Information Systems 35 (1), 1-8.
R Pulimamidi, GP Buddha, Applications of Artificial Intelligence Based Technologies in The Healthcare Industry, Tuijin Jishu/Journal of Propulsion Technology 44 (3), 4513-4519.
R Pulimamidi, GP Buddha, AI-Enabled Health Systems: Transforming Personalized Medicine And Wellness, Tuijin Jishu/Journal of Propulsion Technology 44 (3), 4520-4526.
GP Buddha, SP Kumar, CMR Reddy, Electronic system for authorization and use of cross-linked resource instruments, US Patent App. 17/203,879.
Nadella, G. S. (2023). Validating the Overall Impact of IS on Educators in U.S. High Schools Using IS-Impact Model – A Quantitative PLS-SEM Approach, DAI-A 85/7(E), Dissertation Abstracts International, Ann Arbor, ISBN 9798381388480, 189, 2023.
Gonaygunta, Hari, Factors Influencing the Adoption of Machine Learning Algorithms to Detect Cyber Threats in the Banking Industry, DAI-A 85/7(E), Dissertation Abstracts International, Ann Arbor, United States, ISBN 9798381387865, 142, 2023.
Hari Gonaygunta (2023) Machine Learning Algorithms for Detection of Cyber Threats using Logistic Regression, 10.47893/ijssan.2023.1229.
Hari Gonaygunta, Pawankumar Sharma, (2021) Role of AI in product management automation and effectiveness, https://doi.org/10.2139/ssrn.4637857.
Sri Charan Yarlagadda, Role of Artificial Intelligence, Automation, and Machine Learning in Sustainable Plastics Packaging markets: Progress, Trends, and Directions, International Journal on Recent and Innovation Trends in Computing and Communication, Vol:11, Issue 9s, Pages: 818–828, 2023.
Sri Charan Yarlagadda, The Use of Artificial Intelligence and Machine Learning in Creating a Roadmap Towards a Circular Economy for Plastics, International Journal on Recent and Innovation Trends in Computing and Communication, Vol:11, Issue 9s, Pages: 829-836, 2023.
B. Nagaraj, A. Kalaivani, S. B. R, S. Akila, H. K. Sachdev, and S. K. N, “The Emerging Role of Artificial intelligence in STEM Higher Education: A Critical review,” International Research Journal of Multidisciplinary Technovation, pp. 1–19, Aug. 2023, doi: 10.54392/irjmt2351.
D. Sivabalaselvamani, K. Nanthini, Bharath Kumar Nagaraj, K. H. Gokul Kannan, K. Hariharan, M. Mallingeshwaran, Healthcare Monitoring and Analysis Using ThingSpeak IoT Platform: Capturing and Analyzing Sensor Data for Enhanced Patient Care, IGI Global eEditorial Discovery, Pages: 25, 2024. DOI: 10.4018/979-8-3693-1694-8.ch008.
Amol Kulkarni, Amazon Athena Serverless Architecture and Troubleshooting, International Journal of Computer Trends and Technology, Vol, 71, issue, 5, pages 57-61, 2023.
Amazon Redshift Performance Tuning and Optimization,International Journal of Computer Trends and Technology, vol, 71, issue, 2, pages, 40-44, 2023.
