@article{4817,
  author = {Olaniyi Olawale Omoyajowo, Ezendu Ariwa and Yue Yong},
  title = {Sales Demand Forecasting for Workforce Capacity Planning: Trend-Seasonality Decomposition, Holt-Winters Projections, and What-If Staffing Scenarios},
  journal = {Progress in Machines and Systems},
  year = {2026},
  volume = {15},
  number = {2},
  doi = {https://doi.org/10.6025/pms/2026/15/2/53-67},
  url = {https://www.dline.info/pms/fulltext/v15n2/pmsv15n2_1.pdf},
  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), 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.},
}