A large pharma organization was looking to develop resource forecasting algorithms using Planisware Enterprise for new modalities in gene and cell therapies. However, due to the varying complexities for each project and the numerous drivers influencing them, the organization wanted to test the algorithms before they invested time and resources in developing them in Planisware enterprise. The testing was imperative because the accuracy of the resource forecasts would determine the course of action for projects that span across 10-15 years.

Testing the algorithms would allow the project managers to examine alternative scenarios by adjusting the drivers and checking the complexities for each project. It would also give them an opportunity to test the logic and the accuracy of the forecasts, make changes, accordingly, saving time and resource investment in modifying the algorithms once they are built.

The organization was looking for a PPM expert who is also well versed in life sciences resource management to design a suitable solution for their business needs.

To address the customer’s need for flexibility in testing resource demand forecasts for new modalities, we proposed an Excel-based Resource Forecasting Model. This solution was designed as a complementary tool to their existing enterprise PPM system, enabling the organization to experiment with new forecasting models without disrupting their established processes. The model served as a sandbox environment for testing resource demand scenarios, refining forecasting rules, and validating assumptions before integrating them into their enterprise solution.

The Excel-based model was built on the rules and conditions, where resource demand is calculated using predefined drivers such as project complexity, size, timelines, milestones, and critical activities. By tweaking these project drivers, the customer could simulate various scenarios and assess the impact of different rules on resource forecasts.

The model used a standardized resource pool with attributes like roles, and associated functions, departments and so on. It also used project data inputs that includes milestones, activities, timelines, and other project-specific data. As an outcome the model provided month-by-month resource forecasts for multiple projects, broken down by role and activity.

The model allowed the customer to quickly test and adjust rules, base allocations and assumptions for new modalities. Changes to drivers or project timelines were reflected instantly, giving them a clear understanding of how different factors influenced resource demand.

Though the model is standalone, it aligned seamlessly with the customer’s existing enterprise PPM solution. Once the forecasting model was validated in Excel, it could be translated into resource algorithms within the enterprise system, ensuring continuity and minimizing disruption.