Financial impact of data-driven workforce decisions --- MIND THE GAP!

To illustrate the bottom line business impact of making optimal workforce decisions based on properly analyzing data --- and data relationships, here's one example that most companies face all the time:

For every gap in workforce capacity ... either in terms of numbers, skills, competencies and/or physical location, there are 5 viable options:

(1) train an incumbent to address the gap
(2) re-deploy another employee ...
(3) promote another employee ...
(4) hire a regular employee ...
(5) hire a contractor ...

Taking a recent benchmark from Staffing.org on the average fully-loaded cost of hiring an exempt (non-executive) employee (= approximately $12,000) ...

Consider an organization with 500 exempt-level 'workforce gaps' to address in the course of a year that is not in a position to -- or by force of habit doesn't -- make data-driven workforce decisions.

Let's assume for this example that the 500 workforce gaps are addressed as follows (excluding the cost of labor):

- 80% or 400 gaps were addressed by an external hire (400 x $12k = $4.8 million)
- 20% or 100 gaps were addressed by an internal redeployment, half of which (or 50)created other (cascading) gaps to fill (50 x $12k = $600k)
- None of the gaps were addressed by training as it was not considered an option
- None of the gaps were addressed by hiring more costly contractors

So in this fairly typical example, the total cost of addressing the 500 workforce gaps is $5.4 million -- excluding the cost of labor itself.

Now let's further assume that IF the appropriate data -- and data relationships -- were analyzed so as to optimize each individual workforce decision, the breakdown would have looked like this:

- 40% or 200 gaps were addressed by an external hire (200 x $12k = $2.4 million)
- 40% or 200 gaps were addressed by an internal redeployment, half of which (or 100)created other (cascading) gaps to fill (100 x $12k = $1.2 million)
- 20% or 100 of the gaps were addressed by training an incumbent at an average cost per training instance of $2,000 (100 x $2,000 = $200k)

So in this decision-optimized example, the total cost of addressing the 500 workforce gaps would be $3.8 million -- excluding the cost of labor itself.

The difference -- $1.6 million ---- for EVERY 500 workforce gaps to fill.

A large organization of 10,000 employees would likely have at least 1,500 workforce gaps to fill annually ... so they would enjoy a cost savings of approximately $5 million!!!

Based on many years in and around HR functions, I believe this example is quite plausible and realistic in highlighting the benefits of a data-driven decision process in HR.

As they say in London --- "Mind the Gap!"