What is it about?
In light of the Covid-19 pandemic, corporate decision-making has become more rigorous, especially when it comes to financial decisions. This research aims to help companies to plan their hiring decision for interns. This research will produce a simple interface that can be used by the head of a department in a company in estimating the recommended number of interns that is necessary to be assigned to complete a project. The logic for the interface will be built from historical log data of the workers which will be classified into levels of complexity.
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Why is it important?
This research consists of an attempt to optimize the K-Means Clustering Algorithm and calculating the Full Time Equivalent (FTE) of each cluster based on intern's daily work log data. The calculated FTE will be used to estimate the workload for the current workforce. This estimation is hoped to help companies decide in their hiring decision.
Perspectives
I hope this article can help companies decide in their hiring decision. The Full Time Equivalent (FTE) is very effective to estimate the workload for the current workforce. More than anything else, and if nothing else, I hope you find this article thought-provoking.
Ahmad Fuad Zainuddin
Universitas Prasetiya Mulya
Read the Original
This page is a summary of: WORKFORCE GROUPING IN COMPLETING PROJECTS WITH INTERN WORK ACTIVITY LOG DATA, VARIANCE Journal of Statistics and Its Applications, October 2023, Universitas Pattimura,
DOI: 10.30598/variancevol5iss2page131-138.
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