July 23, 2026
The client is a leading global e-commerce marketplace platform that operates multiple authentication hubs and processes large volumes of inbound products. To improve workforce planning and ensure timely processing and delivery, the organization sought a more accurate and scalable demand forecasting solution that could account for seasonal and operational fluctuations.
THE CHALLENGE
The client managed staffing across multiple authentication hubs using an Excel-based process that tracked inbound product volumes and workforce requirements. While this provided a basic estimate of staffing needs, it relied heavily on historical trends and manual adjustments, without accounting for holidays, shipping disruptions, logistics delays, or other operational factors that significantly influenced incoming volumes.
As product volumes fluctuated throughout the year, the lack of accurate forecasting led to capacity shortages in some hubs and overstaffing in others, resulting in inefficient resource utilization, processing delays, and limited visibility into demand versus available capacity. To address these challenges, the client needed a scalable demand forecasting solution that could accurately predict inbound volumes, provide a rolling view of future demand, and enable data-driven staffing decisions through centralized dashboards and optimized workforce planning.
THE SOLUTION
The Prescience team analyzed the client’s existing staffing process and implemented a time-series forecasting solution using Facebook Prophet to accurately predict inbound product volumes and improve workforce planning. The solution introduced a rolling 12-month forecasting model that incorporated business factors such as hub holidays, shipping schedules, logistics delays, and other operational variations to generate more accurate demand forecasts.
To improve planning and visibility, centralized demand-versus-capacity dashboards were developed, enabling teams to monitor staffing requirements across all authentication hubs. The solution also automated workforce forecasts, supported controlled staffing adjustments with audit tracking, and replaced the manual Excel-based process with a scalable, centralized forecasting framework. This enabled the client to make proactive, data-driven staffing decisions, optimize resource allocation, and improve overall operational efficiency.
The different technologies used for this engagement included:
- Facebook Prophet
- Python
- Pandas
- NumPy
- SQL
- Power BI
- Microsoft Excel
- Time-Series Forecasting Analytics
THE IMPACT
With the new demand forecasting solution, the company achieved 95% forecasting accuracy, enabling more reliable workforce planning across authentication hubs. By replacing the manual excel-based process with automated forecasting and dashboards, the organization improved alignment between staffing levels and demand, reduced delays in product authentication and delivery, and enhanced operational visibility. The solution also enabled more consistent, data-driven staffing decisions across hubs, resulting in greater operational efficiency.
If you would like to know more about A/B testing and how Prescience Decision Solutions, a Movate company can assist you on this A/B testing journey, do reach out to us on through our company website. One of our senior executives will reach out and guide you through the process.
