Workload and Productivity Assessment

A manager for a benefit administration team wants to assess the workload and productivity of her team. The team has data on the volume of transactions it processes. However, there has been no tracking of the time spent on various tasks. The manager gathers her team of six to discuss how best to produce the information.

There are two approaches:

1. Estimate the time spent on the key categories of work performed—this is a relatively quick exercise. Each team member lists at a high level how much time he spends each day on the main work categories. For instance, a team member might spend three hours each day on processing benefit claims, two hours on following up with outstanding claims, etc. When the total time is compiled for the team, gross estimates of the time allocation for the main work categories and an average time per claim submission are computed.
2. Track the actual time spent on the tasks performed—this takes more effort as each team member is asked to track the actual work time. The manager starts with listing the tasks involved in benefit claims processing and requests each team member to log the time and volume for a month. By the end of the month, the data for the team are compiled and averages are computed.

The first approach produces fairly crude estimates. The manager has an idea on time allocation but there is little information on the productivity of her team. Unproductive time is buried in the high-level estimates.

The second approach might be tedious but the data are more revealing. The time tracking at the task level allows the manager to understand the complexity of the tasks and gain a more comprehensive view of the team’s work. She is able to identify unproductive time, compare performances, and gauge the reasonableness of the workload. In addition, inefficiencies and bottlenecks surface, providing the ammunition to take action.

In most evaluation work, it is worthwhile to spend the time to gather the data necessary to do the assessment properly. Otherwise, poor data could be inconclusive and might lead to inappropriate decisions.