Employees Attrition Detection using PSONN

Authors

  • Yogesh yaduvanshi M.Tech. Scholar, Department of ME Sagar Institute of Research and Technology Excellence, Bhopal, India Author
  • Sudhir Shrivastava Assistant professor& Dean, Department of ME, Sagar Institute of Research and Technology Excellence, Bhopal, India. Author

Keywords:

Raw materials, Inventories, Inventory Management, Artificial Intelligence

Abstract

Raw materials, intermediate goods and finished  goods are termed as inventories while considering it as portion of  business’s assets which can be considered as prepared or are  prepared for sale. One of the suitable solutions is to design  optimal inventory model. Major concern of industry is to design  suitable inventory model. Some of the existing inventory  management research works are discussed in literature. But this  field is still a big area of interest. Many research works uses  artificial intelligence models for inventory management. One amongst the area for inventory management is worker behavior  in a company. So, employees are taken into account to be as an  inventory that contributes in growth of an organization.  Employee Attrition may be a big issue for the organizations  specially once trained, technical and key staff leave for a far  better chance from the organization. This leads to loss to  interchange a trained worker. Therefore, we use the present and  past worker data to analyze attrition behavior of employees. 

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References

Ford Whitman Harris, “Economic Order Quantity Model”, Institute for Operations Research and the Management Sciences (INFORMS), 24 December 2018.

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Y., & Shrivastava, S., “Analysis of Inventory Level Optimization Using Artificial Intelligence Approach.” IJOSTHE, 7(2), 8, 2019. Retrieved from https://ijosthe.com/index.php/ojssports/article/view/90

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Published

2019-11-01

How to Cite

Employees Attrition Detection using PSONN. (2019). International Journal of Innovative Research in Computer Science & Technology, 7(5), 139–142. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/13215