Building Predictive Systems for Workforce Compliance with Regulatory Mandates
Keywords:
Predictive Systems, Workforce Compliance, Regulatory Mandates, Machine Learning, Risk Management, Data-Driven Solutions.Abstract
Conformity with legal requirements within the workforce is an equally important factor of organizational leadership. Machine learning and predictive modeling based solutions identify new ways to improve compliance with the help of predictive systems. These systems use factors of the workforce data to predict patterns of non-compliance and indices for the decision-makers. For the success of compliance monitoring, predictive models can be useful, as it allows for the prevention of non-adherence to regulatory guidance and minimizes business process waste. This paper discusses the ways data acquisition and data cleaning and feature extraction and transformation are done, and outlines and compares decision tree, logistic regression, and random forest models in compliance prediction. Main research findings demonstrate the efficiency of these models in identifying the risks and improving compliance management approaches.
References
- Billal, M.M. and Hossain, M.M., 2018. Multi-objective optimization for multi-product multi-period four echelon supply chain problems under uncertainty. Journal of Optimization in Industrial Engineering, 10(2), pp.1-17.
- Deng, C. and Liu, Y., 2018. A deep learning-based inventory management and demand prediction optimization method for anomaly detection. Wireless Communications and Mobile Computing, 2018, Article ID 1234567.
- Falatouri, T., Darbanian, F., Brandtner, P. and Udokwu, C., 2018. Predictive analytics for demand forecasting – a comparison of SARIMA and LSTM in retail SCM. Procedia Computer Science, 130, pp.993-1003.
- Feizabadi, J., 2018. Machine learning demand forecasting and supply chain performance. International Journal of Logistics Research and Applications, 21(3), pp.255-276.
- Granillo-Macías, R., 2017. Inventory management and logistics optimization: a data mining practical approach. LogForum, 13(4), pp.457-467.
- Khan, M.A., Saqib, S., Alyas, T., Rehman, A.U., Saeed, Y., Zeb, A., Zareei, M. and Mohamed, E.M., 2018. Effective demand forecasting model using business intelligence empowered with machine learning. IEEE Access, 6, pp.116013-116023.
- Mishra, A. and Mohapatro, M., 2018. Real-time RFID-based item tracking using IoT and efficient inventory management using Machine Learning. In 2018 IEEE 4th Conference on Information and Communication Technology (CICT). IEEE, pp.1-6.
- Mitra, A., Jain, A., Kishore, A. and Kumar, P., 2018. A comparative study of demand forecasting models for a multi-channel retail company: a novel hybrid machine learning approach. Operations Research Forum, Springer, pp.58-72.
- Praveen, K.B., Kumar, P., Prateek, J., Pragathi, G. and Madhuri, J., 2018. Inventory management using machine learning. International Journal of Engineering Research & Technology (IJERT), 7(6), pp.866-869.
- Punia, S. and Shankar, S., 2018. Predictive analytics for demand forecasting: A deep learning-based decision support system. Knowledge-Based Systems, 158, pp.104-112.
- Ranjitha, P. and Spandana, M., 2018. Predictive analysis for big mart sales using machine learning algorithms. In 2018 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, pp.1416-1421.
- Zohdi, M., Rafiee, M., Kayvanfar, V. and Salamiraad, A., 2018. Demand forecasting based on machine learning algorithms on customer information: an applied approach. International Journal of Information Technology, 10(4), pp.1937-1947.
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