How to Measure—and Improve—Labor Productivity

Key methods to maximize worker productivity in a tight labor market

Published: Monday, August 15, 2022 - 12:02

As labor becomes more costly and emerges as a major bottleneck for many manufacturing and service industries, improving labor productivity is an obvious priority. Whether it’s the preparation time it takes for a restaurant worker to cook a meal, the time for an autoworker to install a component, the call duration for a customer service representative to resolve a problem, or the service time it takes a healthcare worker to administer a vaccine, when labor is scarce, time is critical. In such cases, improving labor productivity isn’t just a matter of reducing costs. Higher labor productivity in capacity-constrained operations has a direct effect on service level, revenue, and growth.

Accurately measuring the productivity of workers has become something of a lost art. Benjamin Franklin famously said, “Lost time is never found again.” So how are we supposed to keep track of how much labor time it takes to perform a particular task? Here are three ways to measure it.

1. Using a stopwatch
Although the approach might look archaic, manually measuring processing times of employees performing a repetitive task is still the most common approach to measuring labor productivity. When dealing with processing times that are in the range of seconds to minutes, an observer simply “peeks over the shoulder” of the employee to create a sample of observations. For example, many processing times for assembling standardized products such as cars or computers range between 30 seconds to 600 seconds. Using a stopwatch to measure labor productivity is easy and can be done quickly at low costs.

2. Output tracking
Many modern workflow systems leave a trail of data behind as a unit goes through the process. Time stamps stored in the cash register of a retail checkout, connection times for customer requests in a contact center, bar code scans and GPS information for a delivery driver, or physical flows in a manufacturing facility that are tracked via RFID tags—all of these examples leave a trail of data behind. Such “digital exhaust” is a true treasure chest for those interested in analyzing worker productivity.¹ Output tracking happens automatically as a byproduct of the ongoing operations. Data collection, therefore, is cheap and doesn’t have to be limited to a sample of observations. It can be performed for every worker and every single task performed.

3. Visual tracking
Recent advances in computer vision and artificial intelligence have shown promising results in combining the best elements of the methods above. Nonobstructive data collection in real time for all workers, and spotting specific opportunities for improving the process, are two examples. Cameras capture activity on the front lines, be it the kitchen of a fast-food restaurant or the assembly line of an automotive supplier. Deep learning methods help read the video data and identify objects, like a cup of coffee or an operator in a production plant. The software then uses a set of rules to determine processing times. This enables real-time reports to be generated for management. Real-time analysis can also be used to ensure adherence to predefined business rules and quality standards.

From measuring productivity to driving performance

Collecting accurate data about how workers spends their time is the foundation for any active measurement of labor productivity. High-performing operations take these data as a starting point to launch the following steps.

Coach, don’t punish
Observing workers can easily be regarded as a “big brother” approach that encroaches on the worker’s privacy and creates a culture of fear and anxiety. However, when such concerns arise, this typically isn’t a reflection on measuring productivity specifically, but rather an indication of a negative organizational culture and a lack of trust. So, first and foremost, the organization must create a psychologically safe work environment in which workers trust that whatever data about their productivity are collected won’t lead to negative consequences.

Study how your workers differ
We once conducted a labor productivity study of nurses in an emergency department, tracking the treatment times of a group of 189 care providers working in emergency care.² The difference between the top-performing providers (the 10% with the highest productivity) and the bottom-performing providers (the 10% with the lowest productivity) was enormous. After controlling for all external variables, such as patient conditions and time of day, we found that the top performers were able to see twice as many patients per shift than the bottom performers. We have seen similar levels of variation in other settings, including loan processing tasks, call durations, and assembly operations. Variation across workers is the norm, not the exception, and should always trigger further analysis.

Find the KPIs and focus on what matters
As advances in technology make measuring labor productivity easier, we face the risk of drowning in data generated by automated reports. This can leave us confused about what actions to take. Just as a medical doctor should start with careful diagnosis and only order necessary tests for a patient, we should start implementing measurement systems where they matter the most. For this, we first need to go through the traditional steps of process analysis and improvement, including mapping out the workflows, identifying bottlenecks, and spotting inefficiencies. Such efforts generate the key performance indicators (KPIs) that spotlight the biggest levers for improving the system’s performance.

Build a learning organization

Monitoring labor productivity alone won’t lead to improvement. Instead, we have to think about the process leading from data collection to process improvement. Any form of learning—in school, sports, or on the job—is built on the principle of providing feedback. We find it helpful to articulate three feedback loops:
• Workers should get feedback about their current performance. Such a direct and immediate feedback loop works best if fully automated and the feedback is provided in real time. This makes output and visual tracking the most useful technology to use. Automated feedback allows workers to correct errors themselves or seek help from their supervisor if needed.
• Share worker data with “kaizen circles,” or groups of workers who are empowered to improve the process without explicit involvement of management. This equips workers with the data needed to come up with good ideas for improvement.
• Keep in mind that some problems are more structural in nature and might not be solvable at the worker or the team level. This includes problems of coordination among multiple processes or with suppliers and customers. More radical process changes, such as introducing new technologies, can’t be done at the worker or team level, and instead require high-quality data to be analyzed by management.

Thanks to modern accounting systems, companies can track and record every dollar, euro, or yuan that goes through their operation. It’s hard to waste money without triggering some form of an alert.

The same level of rigor is often lacking when measuring and analyzing labor productivity, even though one could argue that employee time is one of the most valuable resources in an organization.

A new generation of measurement tools holds great promise in improving labor productivity. These tools have the potential to improve workforce skills, reduce labor shortage problems, and ensure that labor time is never lost again.

References
Terwiesch, Christian. “OM forum—empirical research in operations management: From field studies to analyzing digital exhaust.” Manufacturing & Service Operations Management, 21 (4), 2012, p. 713–722.

McCarthy, M.L.; Ding, R; Pines, J.M.; Terwiesch, C.; Sattarian, M.; Hilton, J.A. “Provider variation in fast track treatment time.” Medical Care, 2012, p. 43–49.

First published July 25, 2022, on Knowledge at Wharton.

About The Author

Christian Terwiesch

Professor Christian Terwiesch is chair of Wharton’s operations, informations, and decisions department, and co-director of the University of Pennsylvania’s Mack Institute for Innovation Management. Terwiesch is also a professor of health policy at the Perelman School of Medicine.