# of Developers x Avg. Productivity = Software Innovation

If you look around it is clear that software is a key determinate of a company’s valuation and financial success. Over the last 10 years it’s role has quietly shift from business-process enablement to revenue engine and competitive differentiation.

Given the strategic importance and huge investments that are being made, every executive wants to know why aren’t we faster? To answer this questions let’s unpack the equation below.

#1 Capacity model

The first component is about understanding your development capacity. Today you might measure this through a headcount plan or a contract you have in place with an outsourced service provider.

This is a good starting point, but in many ways it only gives you a rough swag. It doesn’t account for slips in your hiring plan, time for ramping developers or their vacations, team churn, bug fixing distractions, or shifts in priorities.

To get a real-time view, it is best to look for the footprints hidden in your code and monitor development capacity based on the number of person days spent touching the code.

This approach for measuring your development capacity works at a macro level, while also giving you the ability to easily drill down into specific business units, geographies, projects, applications, teams, and service providers to understand fluctuations over time. As an executive, you can ensure you’re investing in the company’s strategic priorities, and as a development team or a project manager, you could use this insight to advocate for resources.

#2 Developer productivity

The second part of the equation is about understanding developer productivity. To be clear, there are many variables that go into productivity, and many would argue that measuring and managing it is as much an art as it is a science. That said, a single metric that is directionally accurate can be a source of truth for all kinds of things — e.g. talent development, vendor performance, training effectiveness, impact of team churn, documentation completeness, development methodologies, and developer tool usage.

With any productivity metic it’s also important to be able to drill into individual factors that influence the score (e.g. time spent working in complex code, bug fixing ratios, team churn) to see how they are trending and what your biggest levers for improving productivity are.

If this posts sparks a thought, or if you’ve got a question, reach out to me on Twitter or LinkedIn. You can also visit the resource library on Seerene.com.