Progress in Machines and Systems Olaniyi Olawale Omoyajowo, Ezendu Ariwa and Yue Yong 15 2 2026 https://doi.org/10.6025/pms/2026/15/2/53-67 https://www.dline.info/pms/fulltext/v15n2/pmsv15n2_1.pdf 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.