- A company in recent years had experienced a wide variation in productivity and were alarmed about a downward trend. They needed help understanding the drivers of productivity and the actions they could take to improve it.
- Drawing from four operational databases, including timesheet data, we compiled a dataset for ad hoc and modeling analysis.
- We proposed new ways of measuring productivity and built statistical models to quantify the impact of controllable and uncontrollable factors.
- First, we determined that organization-wide monitoring and visibility into productivity made a big difference in performance.
- Second, the models were able to produce expected baseline productivity levels, based on the task. These metrics were added to monthly reports, giving stakeholders more information to act on, effectively upgrading the reports from traditional, backward-looking status reporting.
- Finally, the budget process was expanded to include information from the models and their workload drivers, to better budget for staffing.