KPIs

A Key Performance Indicator (KPI) Measures the result of an activity in an organization so that you can compare it to operational or strategic goals and attempt to improve performance. (KPI Key Performance Indicator measures the result of an activity in an organization so that you can compare it to operational or strategic goals and attempt to improve performance.) is a metric that requires some form of calculation. It can be the combination of metrics Measures and tracks workforce performance by comparing planned with actual workload or coverage, or by showing variances at any organizational level. with operators - for example, Metric A/Metric B or a more complex formula that uses conditional logic.

Analytics defines a large number of standard KPIs, such as Actual Hours, Late In Exception Count, and Scheduled Productive Costs. These KPIs are configurable in the sense that they use standard metrics that can be configured through their associated mapping categories.

You can also create an unlimited number of custom KPIs using the standard metrics or custom metrics that you built on your own. For example, you could create a KPI named Late In and Early Out Exceptions as a % of All Exceptions by using the formula ([Late In & Early Out Exceptions]/[All Exceptions]). You could create a KPI named Late In and Early Out Exceptions per Worked Day by using the formula ([Late In & Early Out Exceptions]/[Worked Hours]/8)).

The basis for considering creating a KPI is some sort of calculation with a metric and other metrics that will return more interesting data than the metrics alone. From an analytics perspective, what is most valuable or useful always involves using ratios as a way of performing comparisons between employees and organizational units. However, you must understand the data sufficiently to avoid invalid comparisons. For example, if Employee A is absent 8 hours and Employee B is also absent 8 hours, what does that mean if 8 hours is 50% of Employee A's scheduled hours for the week but only 10% of Employee B's scheduled hours? Making valid comparisons requires you to understand the magnitude of what is being compared and its impact. This can be particularly challenging when you are aggregating the data at higher levels.

KPIs provide an additional level of modification of metrics over and above the mapping categories that are applied to their data sources. For example, assume that you used the Actual Hours data source and a mapping category called Overtime Hours to create the Overtime Hours metric. Now assume that you want only those overtime hours related to certain employees with certain attributes, such as a specific pay rule or worker type. This would require you to add conditions to the metric to return only the data for which these conditions are true for the employee. For example, given a configuration with a worker type of full time, you can use this worker type when creating a KPI that returns overtime hours for full time workers. Now there is a dual filter on it, which makes it possible to return very refined data, so that when you look at that data from an org perspective, you see totals for full-time workers.

As another example, assume that you want only those overtime hours to be returned where the employee has a hire date before the first of this year. Limiting the employees that are returned also limits the transactions for which data is being pulled. This is only really useful when data is being aggregated above the employee level, perhaps comparing overtime hours for this type of employee in this region with those for the same type of employee in another region.

You may want to show scheduled hours for employees who have a base wage of x. You could set up columns for base wage ranges, such as $11-15 and $16-20, so that you could easily compare the results. The total of these columns would represent the total for the organization as a whole.

Such KPIs can be hard to conceptualize and may be hard to see. KPI Builder, however, gives you the flexibility to create them.