Building an Integrative Community Platform to Alleviate the Workforce Aging Crisis
The United States is in the midst of a workforce aging crisis. According to labor force projections, workers aged 55 and older will not only be the fastest growing segment of the labor market in the coming decade, but will also make up 25.2% (compared to 13.1% in 2000) of the total workforce by 2020, with approximately 41 million workers will be over the age of 55 by 2024. Although automation has become widespread in many industries, some workplaces such as utility industries still rely heavily on individuals to perform critical tasks. These individuals have been performing critical tasks over a long period of time and have accumulated crucial experience and domain knowledge. This intellectual capital not only improves productivity and efficacy under normal conditions, but more importantly, it enables quick identification of anomalies and swift action to unexpected events and situations. We may soon lose the accrued essential knowledge as skilled workers retire. This planning project will serve as a vital starting point to build a community platform to alleviate the fast-growing workforce aging crisis that builds upon integrative research spanning technical and social dimensions.
The main objective of this project is to form a task force to develop a road map to combat the imminent workforce aging challenge in collaboration with the San Diego community. In particular, we will form a team of researchers (both in social sciences and STEM), community and industry partners (including skilled workers, members of management teams, industrial and community representatives, human resource representatives, and policy makers) to develop a set of computer technologies and tools to accurately capture and retain the critical experiences and non-conventional knowledge. This team will work to effectively transfer that knowledge to the new workforce, and develop a set of policies, protocols, and processes to alleviate and/or even prevent future workforce aging crisis. This project includes multidisciplinary efforts from computer science and engineering, policy making and social science, and participation of community agencies, stakeholders, and skilled workers. This integrative approach will lead to a sustainable technology transfer platform for the community and stakeholders to address the workforce aging issue, and a smarter and more connected community where critical experiences and unconventional knowledge can be accurately retained and effectively transferred to the next generation workforce.
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Performance PeriodMay 2020 - April 2023
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San Diego State University Foundation
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Award Number1952225
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Lead PIShangping Ren
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Co-PIBaris Aksanli
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Co-PIAudrey Beck
Project Material
- PMDG: Privacy for Multi-perspective Process Mining Through Data Generalization
- STEWART: STacking Ensemble for White-Box AdversaRial Attacks Towards more resilient data-driven predictive maintenance
- Process scenario discovery from event logs based on activity and timing information
- DOWELL: Diversity-Induced Optimally Weighted Ensemble Learner for Predictive Maintenance of Industrial Internet of Things Devices
- Empirical Studies of Three Commonly Used Process Mining Algorithms
- ENFES: ENsemble FEw-Shot Learning For Intelligent Fault Diagnosis with Limited Data
- OPELRUL: OPtimally Weighted Ensemble Learner for Remaining Useful Life Prediction
Shangping Ren works for the Department of Computer Science within the College of Sciences at the San Diego campus as a Department Chair, and Professor.