A Workforce Capacity Optimization Model for Lean Environments: Applying Statistical Analytics to Predict Labor Demand and Enhance Efficiency

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@article{1704408,
      author = {Joseph Oluwasegun Shiyanbola, Julius Olatunde Omisola, Grace Omotunde Osho},
      title = {A Workforce Capacity Optimization Model for Lean Environments: Applying Statistical Analytics to Predict Labor Demand and Enhance Efficiency},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {11},
      pages = {975-992},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704408.pdf},
      abstract = {In lean environments, optimizing workforce capacity is crucial for enhancing operational efficiency, reducing waste, and maintaining consistent productivity. This proposes a workforce capacity optimization model that leverages statistical analytics to predict labor demand accurately and improve workforce allocation in lean environments. The model integrates key statistical techniques, including regression analysis, time series forecasting, and machine learning algorithms, to forecast labor requirements based on historical data, production schedules, and seasonal trends. By predicting labor demand with precision, the model ensures that workforce levels are aligned with operational needs, reducing overstaffing or understaffing and minimizing labor-related inefficiencies. The application of statistical analytics in workforce planning enables dynamic adjustments to staffing levels, improving scheduling accuracy and supporting lean principles such as waste reduction, continuous improvement, and value stream optimization. The model?s ability to integrate with existing workforce management systems ensures seamless operation, enabling real-time adjustments based on predicted demand. As a result, companies can achieve enhanced labor efficiency, reduce unnecessary costs, and better align their workforce with production goals. The implementation of the proposed model provides operational benefits, including improved productivity, optimized resource use, and reduced downtime. Moreover, by enhancing labor allocation accuracy, organizations can achieve economic benefits such as lower labor costs, higher profitability, and improved customer satisfaction. However, the review also discusses the challenges associated with the model?s implementation, including data quality concerns, system integration complexities, and resistance to change. Finally, it emphasizes the potential for future research in improving model accuracy, integrating real-time data, and exploring AI-driven solutions for further enhancing workforce optimization in lean environments.},
      keywords = {Workforce capacity, Optimization model, Lean environments, Statistical analytics, Labor demand, Efficiency},
      month = {May},
  }