Case Study Banner

CASE STUDY

Optimizing resource forecasting for operational efficiency: Scaling with a centralized RMO

industry-iconCLIENT :Confidential
industry-iconINDUSTRY :Pharmaceutical
industry-iconDURATION :4 months
CLIENT :
Confidential
INDUSTRY :
Pharmaceutical
DURATION :
4 months

Business case

A leading pharma organization was making consistent efforts to scale the resource management journey. It had streamlined resource forecasting processes in the organization and had a specific focus on making these existing processes more efficient. Since the forecasting process in resource management is time consuming and needs meticulous detailing, manual efforts were failing. They wanted to digitalize activity planning cycles and entirely replace manual efforts.

Each of the planning cycles needed multiple iterations of functions and inputs. The senior management was not able to comprehend much from the pool of data available without details and clarity. The client’s team was aware of algorithms but needed solutions that would take the organization to the next level of resource management maturity.  

The emphasis was on making existing planning and forecasting processes more efficient and automated with detailed reports and statuses.  

Our Solution

Our solution experts carefully analyzed the needs of the client and set out to automate the entire resource forecasting process with algorithms. This involved using pre-defined calculations based on project schedules and project metadata (therapeutic area, phase of study etc.). Our solutioning experts were able to combine both these datasets to determine resource profile and full-time engagement details of employees over time.  

The client had a Planisware environment for managing its projects. Our team designed and implemented algorithms within this for setting up the resource forecasting processes. Since i2e has decades of algorithm implementation and maintenance experience, this was an easy feat.  

The client could then forecast resource needs without a thorough forecasting cycle based on real project data (clinical trial management data, system data etc.), combinations of individual project attributes, and the complexities each project team faced. 

Challenges overcome

  • The algorithms relied heavily on accurate data inputs that were incomplete and inconsistent. i2e partnered with clients to clean this data defining clear criteria for project complexity.
  • With mounting complexity one size to fit all model not possible so phase-specific resource allocation rules were incorporated. 

Benefits

  • All the key assumptions on projects were stored in a central repository.  
  • Each of these assumptions or planned number of subjects to be started in the future, documented in PowerPoint was captured in the database.
  • The process of forecasting became easy, and these assumptions enabled structured data to eventually feed AI solutions the organization wanted to implement 

Results

Results