Abstract Effective workforce capacity planning is critical for organizational resilience, yet traditional sales-forecasting models often fail to translate demand projections into actionable, quantitative staffing decisions. This study addresses this operational gap by developing an integrated analytical pipeline that links classical timeseries decomposition and Holt-Winters forecasting directly to workforce capacity planning and what-if staffing scenarios. Analyzing a 48-month synthetic retail and B2B sales dataset (January 2014–December 2017),... Effective workforce capacity planning is critical for organizational resilience, yet traditional sales-forecasting models often fail to translate demand projections into actionable, quantitative staffing decisions. This study addresses this operational gap by developing an integrated analytical pipeline that links classical timeseries decomposition and Holt-Winters forecasting directly to workforce capacity planning and what-if staffing scenarios. Analyzing a 48-month synthetic retail and B2B sales dataset (January 2014–December 2017), we decomposed historical demand into trend, seasonal, and residual components. Results revealed a clear upward trajectory (51.4% growth) and strong annual seasonality, with peak demand in autumn and troughs in early winter. An additive Holt Winters model produced robust six month ahead projections with approximate 95% confidence intervals. Volatility assessment indicated a raw coefficient of variation of 52.6%, which reduced to a manageable 16.5% after seasonal adjustment, thereby informing a recommended operational buffer of ±25-50%. Furthermore, discrete what-if scenarios modeling ±15% and ±30% demand shocks successfully mapped forecast deviations to concrete workload percentages and corresponding HR or operations responses, such as overtime utilization, temporary staffing, or hiring freezes. While reliance on synthetic data and a univariate approach limits external validity, this research shows that chaining transparent forecasting methods with scenario based capacity estimation provides a practical, reproducible framework. Ultimately, this approach equips managers with the quantitative anchors necessary to proactively balance labor costs against service level requirements. Read More Read Less

Abstract The rapid expansion of artificial intelligence (AI) has drastically increased the electricity demand of hyperscale data centers, making their carbon footprint a critical sustainability concern. However, the relative contributions of geographic, infrastructural, and operational factors to these emissions remain poorly quantified. This study systematically identifies and ranks the primary drivers of carbon emissions in AIoriented hyperscale data centers. Using a large scale synthetic telemetry... The rapid expansion of artificial intelligence (AI) has drastically increased the electricity demand of hyperscale data centers, making their carbon footprint a critical sustainability concern. However, the relative contributions of geographic, infrastructural, and operational factors to these emissions remain poorly quantified. This study systematically identifies and ranks the primary drivers of carbon emissions in AIoriented hyperscale data centers. Using a large scale synthetic telemetry dataset comprising over 71,000 complete observations, we employed analysis of variance (ANOVA), bivariate correlations, and multivariate ordinary least squares (OLS) regression with heteroscedasticity robust standard errors to evaluate the independent effects of region, energy source, cooling strategy, and workload intensity. Results reveal that geographic region and primary energy source overwhelmingly dominate the carbon footprint, collectively explaining nearly 90% of the adjusted variance. Transitioning from conventional grid power to renewables reduced mean emissions by approximately 84%, while siting in the EU versus the APAC region yielded a similar magnitude of reduction. Cooling strategy (liquid versus air) and hardware architecture (GPU versus standard compute servers) emerged as significant secondary levers. Surprisingly, computational workload intensity exhibited only a marginal association with total emissions once infrastructural variables were controlled. These findings demonstrate that decarbonizing AI infrastructure requires structural interventions such as strategic geographic siting, 24/7 renewable power purchase agreements, and liquid cooling mandates rather than relying solely on computational efficiency or workload optimization. Read More Read Less

Abstract Background: Global health systems face unprecedented workforce shortages, driving an increased reliance on international health worker migration. While the migration of physicians has long been a structural feature of many health systems, the dynamics governing nursing migration are rapidly evolving. This study examines the temporal trajectories of reliance on internationally trained nurses and physicians across OECD and partner economies between 2021 and 2025. Methods:... Background: Global health systems face unprecedented workforce shortages, driving an increased reliance on international health worker migration. While the migration of physicians has long been a structural feature of many health systems, the dynamics governing nursing migration are rapidly evolving. This study examines the temporal trajectories of reliance on internationally trained nurses and physicians across OECD and partner economies between 2021 and 2025. Methods: We analyzed 269 country year profession observations from the OECD Health Workforce Migration database, covering 36 economies. The primary outcome was the percentage of the domestic workforce trained abroad. We employed descriptive statistics, ordinary least-squares (OLS) linear trends, and non-parametric Mann-Kendall and Sen's slope analyses to quantify the extent and direction of change, accounting for unbalanced panel structures and data quality flags. Results: Dependence on foreign-trained physicians remained high but relatively stable, with aggregate means hovering around 22-23% and a non-significant OLS slope (+0.18 percentage points/year, p=0.55). In contrast, reliance on internationally trained nurses exhibited a sharp acceleration. After remaining stable at approximately 7.8% from 2021 to 2023, the cross-country mean for nurses surged to 9.6% in 2024 and 13.2% in 2025, yielding a borderline-significant positive OLS slope (+1.26 percentage points/year, p=0.07). This acceleration was heavily concentrated in a subset of high-income destination countries, notably the United Kingdom, Ireland, and New Zealand. Conclusion: The 2021-2025 period marks a critical inflection point in global health workforce dynamics, characterized by an accelerating reliance on internationally trained nurses to fill acute staffing gaps. While international recruitment provides a vital stopgap for destination countries, the rapid intensification of nurse migration underscores urgent needs for domestic retention strategies and ethical, proportionate co-investment frameworks to mitigate workforce depletion in source countries. Read More Read Less