In our previous article, we showed why many companies live under the impression that their production is running at full capacity. Machines are running, people are working, orders are being fulfilled. But underneath, there are often downtimes, underused capacities, and small inefficiencies that add up to reduce both efficiency and profitability. Without accurately measuring performance, it’s impossible to identify or eliminate these losses. And that’s exactly where calculating labor productivity comes in—not as a formal obligation, but as a tool that helps you make better, data-driven decisions.
Why Do You Need to Calculate Labor Productivity?
Labor productivity is one of the key indicators that reveals the ratio between inputs and outputs—that is, between what a company puts into production and what it gets out. Calculating labor productivity enables you to compare the actual performance of individual workers, machines, production lines, work shifts, or entire departments. Without this overview, it’s impossible to identify weak points, set realistic goals, or evaluate the effectiveness of implemented changes.
What Does Labor Productivity Calculation Look Like?
Labor productivity can be measured in various ways, depending on the measurement goal, production type, and level of detail desired. The formula for calculating labor productivity must always be based on what you consider a relevant output (e.g., number of units produced / value added / machine performance) and which inputs you want to track (time / labor / technologies). Only then will the result provide relevant and comparable information with real value.
1️⃣ Labor Productivity per Worker or per Hour Worked
The simplest way to calculate labor productivity is to compare output with input. In practice, this might mean dividing the number of units produced by the number of workers or hours worked.
Example:
If five workers produce 2,000 units in an eight-hour shift:
Productivity per worker = Units produced / Number of workers
= 2,000 units / 5 workers = 400 units per worker
You can also calculate productivity per hour:
Productivity per hour = Units produced / Total hours worked
= 2,000 units / (5 workers × 8 hours) = 2,000 / 40 person-hours = 50 units per hour
This type of productivity calculation is more suitable for basic comparisons, especially in labor-intensive production where the human factor dominates. A drawback is that it doesn’t take into account technological factors, quality losses, or the efficiency of the machines themselves.
2️⃣ GDP per Employee or per Hour Worked
At the macro level, two indicators are commonly used: GDP per employee and GDP per hour worked. Both express the economic value created by one worker, but each takes a slightly different angle.
GDP per employee shows how much value, on average, one employee creates over a given period. It’s a widely recognized metric used for comparing countries, sectors, or regions, but can also be applied within a company.
Example:
If a company creates added value of €3,000,000 per year and employs 60 people:
GDP per employee = Gross Domestic Product / Number of employees
= €3,000,000 / 60 employees = €50,000 per employee annually
For a more precise view, GDP per hour worked is better. It accounts for part-time contracts, vacation, and inefficiently used time, providing a more accurate reflection of actual work performance.
Example:
If employees worked a total of 96,000 hours in the same company:
GDP per hour worked = Gross Domestic Product / Total hours worked
= €3,000,000 / 96,000 hours = €31.25 per hour
3️⃣ Machine Utilization Efficiency – OEE
In industrial production, it’s not enough to track only what people produce. It’s equally important to know how well machine capacities are utilized. That’s what OEE (Overall Equipment Effectiveness) is for.
OEE is calculated as the product of three factors: availability, performance, and quality. Each one represents a potential source of losses.
- Availability shows how much of the planned time the machine actually ran.
- Performance compares the real operating speed of the machine with its ideal cycle time.
- Quality expresses the proportion of defect-free units to total output.
Each factor is expressed as a percentage, and multiplying them gives a percentage indicating how much of the machine’s full potential is being used.
Example:
A machine should run for 8 hours per day (480 minutes). During the day, it was down for 30 minutes.
Availability = Actual run time / Planned production time
= (480 min. – 30 min. downtime) / 480 min. = 93.75%
If the machine produced 900 units in 450 minutes, with an ideal capacity of 2 units per minute (i.e., 900 units in 450 minutes = ideal), then:
Performance = Actual units produced / Ideal units
= 900 pieces / (450 min. / 0.5 min. (ideal cycle time)) = 900 / 900 = 100%
If 870 out of those 900 units were defect-free:
Quality = Good units / Total units
= 870 good pieces / 900 total pieces = 96.67%
OEE = Availability × Performance × Quality
= 93.75% × 100% × 96.67% = 90.6%
This means the machine is using 90.6% of its maximum potential. In industrial practice, OEE above 85% is considered excellent. A lower value is a clear signal that there are losses—whether due to downtime, reduced speed, or quality issues.
OEE is considered one of the most comprehensive and practical indicators of productivity in manufacturing. Unlike macroeconomic indicators, it can be measured in real time, in a specific production segment, on a specific machine. It allows companies not only to track performance over time, but more importantly, to identify the exact causes of losses and assign precise goals for elimination.
What Next?
As you can see, calculating labor productivity isn’t about one universal formula. It’s a set of indicators a company must choose depending on what it wants to monitor and improve. For some, labor productivity per worker is key; for others, machine utilization efficiency is the priority. But the most important thing is that these calculations must be based on accurate and current data.
If you don’t have a reliable data collection system in place, you might be able to calculate productivity, but the result will be more of a guess than a fact. That’s why it’s beneficial to connect productivity calculations with tools that ensure automated data collection and real-time evaluation, such as SCADA or MES systems. This gives you not only accurate calculations, but also the ability to monitor productivity trends, identify hidden patterns, and make data-based decisions.
At the end of the day, it’s not just about knowing the formula for calculating labor productivity, but about being able to work with that data. The calculation only makes sense if you can evaluate it, compare it with your goals, and turn it into concrete measures to improve productivity.
A Comprehensive Tailored Solution from IoT Industries
Calculating labor productivity is not a goal in itself. It’s a tool that helps you uncover weak spots, assess the impact of changes, and continuously improve performance—without having to invest in expanding capacity. Instead, you learn how to fully utilize what you already have.
At IoT Industries, we’ll help you build a complete system—from automated machine data collection, through calculations of indicators like OEE, to clear real-time visualizations. Don’t hesitate to contact us. Together, we’ll identify your biggest opportunities for improvement and show you how to make the most of them.
Why Choose IoT/IIoT Implementation with IoT Industries?
Traditional companies typically specialize in OT (operational technologies, such as production lines and devices) or classic enterprise IT systems. However, we are able to connect both of these worlds. Our unique expertise in integrating OT and IT allows us to deliver innovative solutions in digital transformation, enhancing efficiency, reliability, and competitiveness for manufacturing companies.