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vikas_ohri2
Explorer
Employee Turonver is  when an employee is replaced with a new employee in any organization.

Can a attrition or turnover Risk Score , be assigned to each employee & use it as indicator for prediction score of any employee leaving ?  what is making this employee, likely to leave & on the contrary what is making this employee stay ?Not only know, How likey is this employee going to leave ? Can we determine when will this employee leave ? and most importantly , What can be done to avoid or prevent it. and what is the impact on organization if this employee leaves ?

With Readily avaliable Artificial Intelligence, Machine & Deep Learning technologies , can computer be used to address these questions ? Importantly, Does data support the answers to these questions ? Can Use of Deep Learning Artificial Intelligence Exercise predict and explain turnover in a way that managers could make better decisions and executives would see desired results ?

A while back i had attempted to get answers to employee turnover questions using predictive analytics library in SAP HANA, using random forest classification techniques.

Thought of getting answers again now with available Machine learning & Deep learning libraries like TensorFlow and with SAP Analytics on Cloud solution for digital boardrooms being available. It is a perfect way to get Employee Turnover Insights and assess its risk impact not only in Human Capital Management Functions but to overall Business.

Shared here are is a point of view, While working on SAP Qualified Package Solution by RenewHR , using sample data  made available by IBM

More details and a working demo are available on vikasohri/EmployeeTurnover

 



 

In essence, indvidualized attrition risk and factors or dominant features contributing towards attrition for each employee can be predicted using deep learning models.

Machine Model Training can be done on HANA or HANA External  Machine Learning Interface , in the example shown it's done externally and the predictive results brought in SAC to explore these with other organizational data elements for detailed explanations.

The top contributing features towards attrition can be further explored in overall organizational context of how well training, compensation, succession, benefits etc. are doing as well as see its impact on core line of business impacting sales, productivity, customer service etc.
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