| Affiliations: | STEM Research Leadership |
| Project Leader: | Ashrant Aryal, Ph.D. ashrantaryal@tamu.edu Construction Science |
| Meeting Times: | TBA |
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Team Size:
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4 |
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Open Spots:
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0
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| Special Opportunities: | Student will be considered for co-authors on publications depending on their contributions. |
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Team Needs:
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Experience in data analysis, Machine learning and/or Web development
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| Project Description: | Research has shown that absenteeism rates between 6-10% can result in a 24% loss in productivity and a 10% absenteeism rate can lead to a 15% increase in direct labor costs due to last-minute disruptions and decreased productivity. To address this challenge, this research proposes developing a data-driven predictive model to forecast absenteeism, enabling proactive workforce management and mitigating the negative impacts on electrical contractors’ performance. This research will leverage advanced data analytics and machine learning to identify key indicators of absenteeism, develop predictive models and develop a user-friendly web based tool for electrical contractors to utilize the model. By understanding the factors that influence absenteeism, electrical contractors can develop targeted interventions and proactive employee engagement strategies to foster a more present and productive workforce. |