The complexity of simple workload analysis
Every manager needs to assess their resource requirements, so they can have the right number of people they need to get work done while also being as productive as possible.Every manager needs to assess their resource requirements, so they can have the right number of people they need to get work done while also being as productive as possible.
One of the more difficult things about assessing workloads is trying to match static analysis with real-world requirements. For example, if you performed a workload analysis in an accounting area, for example, over the course of a month you would likely find that there were too many people employed, given the amount of work that needed to be done. That’s what the static analysis usually tells you. Unfortunately, a lot of work tends to spike in accounting areas during one or two weeks of the month. So if you staffed the area based on the static analysis, work would never be completed on time, causing problems elsewhere in the organization.
So the problem changes from being a simple mathematical equation of calculating and balancing work volume to resources to a more complex problem of matching dynamic volume and resources over periods of time. This also changes the way you think about how to improve productivity. Rather than simply re-engineering process steps to reduce the aggregate amount of work in the area, you need to zero in on the work that is done specifically during these peak periods — this is another form of constraint analysis, which we discussed in Observation #11, “Productivity Improvement with No Benefit”). To improve productivity in an area like this, you could:
- Lower the peak work requirements by eliminating waste
- Move work to non-peak periods
- Eliminate activities that aren’t essential (through technology or other means)
- Reduce staffing in non-peak periods
This dynamic complexity is very common in the workplace. Almost all service-based organizations (e.g., hospitals, restaurants, airlines, banks, hotels, etc.) have to manage this type of workload. So when trying to improve productivity in these environments, think analytically in terms of workloads during peak and non-peak periods.