Global Best Practices for Measuring Workloads to Improve Workforce Classification at the Environment Authority in the Sultanate of Oman

Abstract

Measuring workload is one of the modern human resources development and management, essential for achieving organizational efficiency and workforce stability. The Environment Authority in Oman faces a significant challenge in the distribution and classification of its environmental compliance staff across the province. This study aims to develop an innovative model based on mathematical principles and algorithms to estimate the optimal number of employees for environmental compliance departments at the provincial level. The methodology relies on a scientific approach combining workload and risk indicators. The proposed formula is designed to measure these indicators and provides a solution to address the inaccuracy of observed time measurements for calculating workload from end-user samples. It reflects the key factors influencing compliance workload: Administrative Spread (number of wilayat, N) Environmental Sensitivity (S) of the governorate and Geographical Area (A). The model also incorporates a baseline factor (B) representing fixed office/coordination duties or permanent shifts. The structure and coefficients of the formula are justified based on best international practices, including the World Health Organization's Workload Indicators of Staffing Need (WISN) methodology. The EU's Risk-Based Compliance and Enforcement Inspection Framework (RMCE/RAM) principles from the International Network for Environmental Compliance and Enforcement (INECE) and guidelines from the US Environmental Protection Agency (US EPA). Applied to the compliance departments across Oman's governorates as a case study, the model produced quantitative estimates of actual staffing needs. The results indicate an average optimal staff number of approximately 21.89, compared to a current average of 20.67, suggesting a slight overall deficit about 5.6%. This deficit is more acute in specific governorates like Al Dhahirah in 50% deficit and 29% deficit in Al Wusta. A model variant assigning higher weight to Environmental Sensitivity b=1.8 increased the average optimal staff to 22.56, deepening the overall deficit to 8.9%, particularly in high-sensitivity governorates like South Al Sharqiyah in 18% deficit. This model offers environmental authorities a practical and objective tool for the efficient planning and distribution of human resources within environmental compliance departments. It contributes to enhancing environmental monitoring capacities and achieving environmental sustainability. The contribution of this research lies in its conceptual methodology to facilitating a shift from impressionistic planning to evidence and data driven flow. The study recommends adopting this model as a dynamic planning tool with periodic reviews of its coefficients to ensure responsiveness to operational variables and performance indicators.