The Ultimate Guide to Manufacturing Time Studies in the Connected Factory

A manufacturing time study is a structured process of directly observing and measuring human work using a timing device to establish the time required for completion of the work by a qualified worker when working at a defined level of performance.

Time studies are most appropriate for processes involving sequences of repetitive actions that recur in a cycle. When a process can be divided into multiple discrete tasks, time studies are a useful way for measuring how much time employees spend on each part of a process.

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For over a century, time studies have been a core method for gathering data on manufacturing processes. Since Frederic Winslow Taylor introduced time studies in the early 20th century as part of his system of scientific management, manufacturers have used time studies to optimize their operations.

Time studies are also one of the easiest forms of measurement to perform incorrectly. Despite their simplicity, there are several ways in which a researcher can introduce bias and inconsistency into her data. While it may seem insignificant, the cost of bad data is high. According to research by Experian PLC, bad data can cost an organization 15-25% of their revenue. This adds up. IBM estimates that bad data costs the U.S. economy upwards of $3 trillion a year.

The good news is that there are a few simple things you can do to get the most out of your time studies. For those interested in an Industry 4.0 digital transformation, there are ways new technologies can be leveraged to produce more accurate, insightful time studies.

For the engineers at the Industrial Time Studies Institute, there are five main objectives to time studies.

1. The improvement of processes and procedures
2. The improvement of plan, office, or service area layout
3. Economy in human effort and the reduction of unnecessary fatigue
4. Improvement in the use of materials, machines, and manpower
5. Development of better physical working environment

When done correctly, time studies provide a granular, normalized view of a multi-step process. They can be used to drive efficiency in processes, improve factory and process design, and improve the output and experience of workers.

• Some common uses for time studies include:
• Setting and standardizing step times
• Establishing KPIs for a manufacturer’s processes
• Locating and eliminating inefficiencies in processes
• Collecting data to help predict yearly output and revenue
• Tightening yearly resource and inventory planning
• Identifying skills gaps and creating targeted training initiatives.

Time studies can be broken down into three phases: analysis, measurement, and synthesis.

Analysis: Decide what you’d like to measure, and determine a concrete goal for the study (speed up process times, set standard times, identify steps that might require targeted training, etc.). When you know which process you’re interested in studying (and why), break it down into its constituent parts. Make sure each task is well defined, with a clearly established beginning and end. Ask multiple subject matter experts how long the process takes them to complete, and ask them to estimate the time they spend on each constituent task. This information will help you calibrate standard times.

Measurement: Using a stopwatch, or some other timing device, measure how long workers spend completing each step. At this stage, you’ll also want to account for allowances that might impede a workers ability to complete a task.

Synthesis: Using a template or a spreadsheet, enter your data. Once you have finished collecting data, perform the necessary analyses. These will change based on the goals and designs of your time study.

One of the defining traits of the Industry 4.0 factory is increased connectivity.

Digital platforms like Tulip let you automatically record granular time studies. Here you can see each operator’s time by step against the target.

IoT connections and cloud computing have allowed for the creation and storage of data on an unprecedented scale. Wearable sensors, computer vision, and no-code applications are able to collect real-time data from workers. Because the data collection is automated, it eliminates human bias from the sample. And AI can find patterns in data that humans alone can not–because they get better over time, predictive maintenance is an attainable goal.

This connectivity lets engineers (or algorithms) perform continuous, real-time studies of processes. A constant stream of data provides full visibility into the factory. And a larger sample size makes root cause analysis easier and more accurate than sparse measurements.

When these technologies are working together as part of a fully connected factory, the potential for focused continuous improvement is immense.

Not many factories, however, have begun a digital transformation yet. For many, a stopwatch and clipboard are still the best tools.

Use the largest sample size possible. While many small manufacturing businesses will not have hundreds or thousands of employees available to study, they should still strive for the largest possible data set. More data points will give a more nuanced account of the process and will help to identify outliers.

Take worker skill into consideration. Not all employees perform every task with the same proficiency. Many time study templates will give the researcher an opportunity to “rate” the skill of the worker being observed. The purpose of this rating is account for disparities in employee ability. Only studying veteran associates will yield unrealistic standard times. Oversampling new hires will cause you to underestimate production volumes. Neither will give an accurate picture of aggregate performance.

Try not to record while you observe. Recording during observation can lead to inaccurate observations. If possible, use the lap function on a stopwatch to store step times. This will prevent you from taking in accurate data. If no such timer is available, consider observing in teams, with one person recording while the other observes.

Beware the Hawthorne Effect.The Hawthorne Effect describes changes in a workers’ behavior when they know they’re being observed. Part of a larger set of “observer effects,” the discovery that observation is not a neutral activity has led to field-changing advances in disciplines as different as physics and cultural anthropology.

Researchers should be aware that the simple act of observation can change the phenomenon being studied. While some researchers avoid the Hawthorne Effect by taking data in secret, the best strategy is the be honest with your workers about the purpose and goals of the study.

At this point, you should be ready to start taking data on the shop floor. Here are a few more things to consider.

Don’t lose sight of the goal. Data is great, but time studies for their own sake can be a waste of valuable resources. Make sure you know exactly why you’re performing the study, and always keep sight of the business need behind the measurements.

Your people are your best asset. Workers are often skeptical of time studies, and for good reason. Time studies are part of a long history of scientific management that rarely had the worker’s best interest at heart. But your people are the key to establishing realistic standard times, providing you with accurate data, and ultimately creating value on the line. The more they feel invested in the process and included in the outcome, the better the study will be for all involved.

Time studies are best performed multiple times. Multiple samples provide a larger, more comprehensive data set.

Using the technology you have to assist you–One way to validate process and step times is to check observations against time-stamps in an ERP or MES. Another way is to consider investing in low-cost, IoT-ready technology that will collect process and step data in real-time.