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CASE STUDY

Enhancing resource forecasting and productivity: Achieving granular time-tracking and data integration

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

Business case

A pharma organization had reached maturity in resource management activities with algorithms and set processes for operational efficiency. After making consistent and steady efforts to scale the resource management journey, the team was ready to take it to the next level. They now wanted to clear existing doubts about forecast accuracy within the planning cycle.  

Despite having a robust time-tracking process they still wanted to refine algorithms. There was a misalignment between time tracking data and algorithm outputs, which needed to be fixed. They were looking for a PPM and Planisware expert to help them seamlessly transform to the next level of resource management. 

Our Solution

Our PPM experts concurred that the time tracking data was at the indication level with forecasts broken down at the work packages level. These forecasts were both for the study and non-study packages. Our team at i2e combined the time tracking details with forecasts. They built visualizations to represent time tracking into buckets based on critical milestones within project plans. This enabled better comparison with algorithm outputs.  

This comparison enabled teams and senior management to get insights into how much effort was put in to deliver individual work packages. Resource productivity was also measured by analyzing how much time each resource spent on non-project work.  

Instead of analyzing full time engagement or FTE for individual functions, the client now had visibility of FTE expectation for each role within a function. There was more granularity in data collation. Existing reporting techniques were enhanced to include details about forecast demand and resource capacity within teams. Remaining resource forecasts were refined based on effort to date.  

Challenges overcome

  •  Developed reports and dashboards to track the impact of algorithm optimization on forecast accuracy, helping justify the client’s investment.
  • Creation of time-tracking buckets achieved granularity in data management

Benefits

  • Better decision-making with the help of enhanced forecasting methods
  • Enhanced resource mobility and strategic planning for long-term
  • Better availability of resource productivity insights
  • Large volumes of timesheet data handled seamlessly with data processing methods