A Multilayered Manufacturing Workforce Productivity Analytics Model

Current manufacturing productivity tools do not provide comprehensive insights, resulting in lowered operational efficiency, increased costs, and decreased competitiveness. Though the market and the body of knowledge both have existing productivity tools and models, these tools and models tend to focus mainly on machine efficiency or optimizing the production chain. Key human aspects, such as engagement, satisfaction, and well-being are not factored in. This research implements a productivity analytics model tailored to the unique operational nature of the manufacturing industry to enhance worker productivity, optimize time management, and improve employee wellbeing. Three interconnected elements, engagement, efficiency, and well-being form the proposed overall productivity score (OPS). A real-world case study, which concentrated on a medium-sized firm that manufactured bottled water, was performed, demonstrating how the OPS could be practically applied. Exploratory factor analysis, regression modeling, structural equation modeling, correlation analysis, and cross-validation were performed to assist in validating the associations between each of the components of the framework. The EF component showed the strongest predictive power, while WB displayed burnout risks through interaction terms and workload clustering. The integrated OPS model showed good fit and predictive validity. Not only could the technical output of employees at manufacturing firms be determined, but also the human factors that contribute to the degree of manufacturing output would be identified, hence offering more interpretable and actionable perspectives.

Author

• Al Humaidy, Abdullah A.

Language

• en

Keyword

• Workforce
• Manufacturing
• Productivity

Date created

• 2026

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Engineering Management

Degree

• D.Engr.

Advisor

• Oliver, Rhett

Committee Member(s)

• Fossaceca, John
• Blackford, Joseph

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/sb397927h