Productivity — the art of efficiently doing more — creates a more fulfilling work environment for everyone. Of course, what separates industry leaders like Apple and Google from their peers is a deep focus on maximizing productivity. One influential analysis found that, despite comparable numbers of high performers, these top brands outproduced their competitors by up to 40%.
The takeaway from this shocking statistic? Businesses can either galvanize their employees to reach new heights of productivity — or get in their way.
In many cases, businesses unintentionally slow their workers with inefficient procedures, debilitating infrastructure, unnecessary duplication of efforts, and dated technology. A Bain study found “organizational drag” hobbled up to 20% of an average firm’s productive capacity.
If you’re looking to remove common obstacles to productivity, understanding your team’s current performance is a must.
As Peter Drucker, the doyen of management literature, pointed out years ago, measuring productivity in the knowledge economy isn’t easy, especially in remote work culture. Checking off the number of automobiles a factory produces by labor hours is uncontroversial; figuring out whether a graphic designer’s inviting new webpage layout was an effective use of time is more difficult.
The challenge, then, is knowing what’s worth measuring. With the array of analytics tools at our disposal these days, it’s important to separate the signal from the noise. Different industries may vary in their metrics, but experts agree these five key performance indicators (KPIs) are critical for ramping up productivity.
When evaluating productivity, it’s best to start with the simplest, most global measures.
Calculating this number is a cinch. Simply take the number of goods or services provided, and divide that sum by the labor hours invested to generate this output. You’ve now got a foundational measure of business productivity.
That can give you the broad-brush landscape of your company’s aggregate performance with its current human resources. Making even marginal improvements to this figure can raise companies to new heights of prosperity.
That said, this global measure outlines only the most basic contours of productivity. What’s missing is the kind of granular detail that allows you to diagnose which parts of your business are humming along efficiently and which parts need more streamlining. To fill in those blanks, you’ll need to supplement this total with other metrics.
Overall Labor Effectiveness
Measuring companywide productivity is like looking at your favorite college football team’s record over the last century: It’s a place to begin. But it’s not very useful in finding answers to more pressing questions, like how well its defense should perform this year. Nor does it offer much insight into how to improve productivity.
That’s why analysts resort to a more multifaceted approach to the data in the form of Overall Labor Effectiveness (OLE). OLE examines the interaction of three factors: availability, performance, and quality. Availability looks at how much time workers actually spend on a task. Factors such as absenteeism, equipment malfunction, and poor management all impact this number.
Here, performance is linked to quantifiable outputs. While quality can be assessed in different ways, it’s evaluated here in relation to production — how many goods and services aren’t saleable, deemed defective, or get returned.
Where measuring bare productivity offers little diagnostic insight, OLE can help identify root causes of lagging output. It might be a lack of scalable procedure for onboarding new clients, or maybe it’s the need for new tech solutions. Whatever the case, you’ll be able to see more readily where the problems lie. That can be crucial data for evaluating notoriously hard-t0-gauge stats, like ROI on training. It’s also a useful starting point for improvement and forecasting.
Revenue per Employee
Is your workforce worth its wages? Which workers should get promoted, and which ones should you let go? Should you hire new employees? If so, in which departments?
These are the big questions managers and human resources professionals ask themselves every day. After all, businesses can only pay the employees they can afford.
While these decisions depend on multiple factors and can be hard to make, it’s best to look at the hard data on how much revenue each employee is responsible for.
Nowhere is this measure as important as it is in sales. How much a salesperson closes directly reflects her productive value to the company. There are other relevant metrics to consider in assessing performance, of course, but this is a nonnegotiable baseline.
In other sectors, this metric becomes a crude way to assess value. Instructional designers or customer service representatives typically don’t directly generate lots of money but try to succeed without them.
Units per Hour, Function Points, and Cycle Time
In some industries, such as warehousing or agriculture, units per hour still make sense as a basic measure of productivity.
In service-oriented sectors of the economy, however, this can be a lot harder to get at. This is particularly challenging when a team undertakes long-term, multi-step processes.
In completing complex projects, the best way to evaluate productivity is to divide them into discrete tasks, measuring the cycle time it takes for teams (or individuals) to cross off function points. That offers insight into just how efficiently the team is working, as well as demonstrating progress toward long-term goals.
To track this effectively — particularly while your team collaborates remotely or your stakeholders have spread outlook into a customizable work OS like monday.com. According to monday.com, work operating systems are an asset to any team, especially for remote teams. Effectively collaborating is much more challenging from afar, and a working OS ensures that everyone is aligned on tasks and goals while providing visibility into progress and productivity.
Another invaluable virtue of workflow optimization solutions is the insight they provide into project completion habits. Do you have an employee who over commits and underperforms? Do some of your employees have more capacity for work but aren’t volunteering? Is it better to have a true multitasker or a slow-and-steady operator who reliably bangs out one job at a time?
If you don’t have hard data to back up your intuitions about these questions, you need to collect it. Assessing planned-to-done ratios can reveal a lot about how agile your team is — and what can be reasonably expected to look forward.
Because productivity is so closely associated with revenue growth and profit margins, businesses need to measure it carefully and use this data well. To get a big picture snapshot, start with gross estimates of overall output relative to overall input. Then, drill down to specifics, using metrics like cycle time and revenue per employee. Home in on specific instances of organizational drag to improve them.
With advances in workflow optimization, managing tedious analytics has become much easier. That’s good news for companies looking to follow the productivity paths blazed by companies like Apple, Google, Amazon, and General Electric.