How to Develop Engineered Labor Standards with Best Practices (2022)

Posted On : Dec 6, 2021

When measuring your processes and operations teams performance, you’re looking for efficiency, productivity, quality, and on-time delivery—among other things. But how are you defining those terms? 

What are your metrics for success in each of those areas? How do you determine which processes are effective, and which ones need improvement? These questions extend to your workforce as well; who is highly performant, and who needs coaching or training? The answers to these questions can be found using labor standards – also known as engineered labor standards, or engineered performance standards.

Watch the video on how to get up to date and defensible labor standards.

To effectively manage the performance of your production or distribution operation, you need to have steady and reasonable baseline KPIs so you can communicate expectations with your team, and monitor performance. This leads to the creation of labor standards (a.k.a. performance standards) and mapping those KPIs to a Labor Management System for reporting. Most people think this means investing in time and motion studies, and a costly engineering budget. Here, we are going to address the best practices in developing labor standards at every budget—with or without engineers. 

Many companies have taken their engineered labor standards to such extremes of analysis that they have lost sight of the big picture. When you drown yourself in overwhelming amounts of engineering data it’s easy to lose sight of the forest for the trees. There is a simpler way to improve labor efficiency: by measuring and acting on the most important metrics and ignoring the rest.


Download the PDF: Setting Accurate and Defensible Labor Standards


Where many companies begin to go wrong is during the creation of engineered labor standards. It’s not as simple as drawing a line on a graph and saying “this is the standard we expect for our processes”. That’s a good way to set yourself up for failure.

Setting labor standards used to be the domain of engineers performing time and motion studies. Traditional engineered labor standards were done with stop watches and spreadsheets, measuring each process one at a time over a set time period. Now fast forward to the digital era. With modern analytics, and data capture systems that can integrate data sources from multiple systems, there are more options. Now, labor standards have moved far beyond manual spreadsheets, and with the click of a button can be created by machine learning.

Determining the metrics for your labor standards, and determining the process with which you set them, are key parts of setting and maintaining performance standards that are accurate and defensible. Unfortunately, the creation of performance standards have traditionally been nuanced and difficult, but today are made much easier with purpose-built tools. 

Four Ways to Set Labor Standards

When implementing your labor standards, you have four general options: existing KPIs, engineered standards, data driven standards, or a hybrid approach. Each has its benefits and downsides, but one has been repeatedly shown to be a stronger basis for analysis, people management, and growth. 

Existing KPIs & Benchmarks

Maintaining your existing KPIs is cheap, because they already exist, and they set a goal for your team. Simple enough—until you add in any human element or nuance. Because these KPIs are often based on single metrics, you aren’t getting the comprehensive view of your processes that fair standards based on multiple metrics would provide. Single-metric KPIs, due to their limited scope, will often lead to unfair performance judgements that are ultimately useless for holding employees accountable on a daily basis. 

Warehouse operations are not factory assembly lines; the work changes in quantity, travel time, scale, and urgency. No two orders are going to be exactly the same, and so the data shouldn’t be used as if they are. Using existing KPIs is a simple solution to a complex problem, and falls short in the critical areas of accuracy, flexibility, and scalability. They will create labor performance standards that are incongruent with actual results. 

If you are using existing KPIs, consider this: employees who work the process every day have figured out ways to do it better and faster than what an engineer may develop from industry baselines.  Every operation is unique and that uniqueness is very difficult to capture through traditional labor standard development. Your employees own input and data can help create better labor standards through process benchmarks. Benchmarking gets supercharged with your team’s own data (see Data Driven Standards section below) will tell you the correlation of each metric to actual performance and can be done quickly and for a fraction of the cost of having a team of industrial engineers analyze your operation. As well, benchmarking models that incorporate your team’s actual data are more consistent than engineered standards because these models take into account all information that occurs in the process. But statistical modeling tells you what is actually being done and how it correlates to each metric, process and employee.

Engineered Labor Standards

Labor-standards-noise-process

Engineered labor standards are standards designed by industrial engineers (IEs), and are the industry norm for setting standards. The results of these standards will be as accurate as the IE’s measurements to create them, at the time they observed them. Typically they measure each defined process separately. They are also the method most generally accepted by workers’ unions. 

While there are positives to engineered labor standards, there are also downsides. 

Engineered standards are both expensive and time consuming to create. Standards set in this way are neither scalable nor easily maintainable. The IE’s measurements will be taken based upon the current process flow during a specific period of time. Changing a process, workflow, product mix, equipment, or location fundamentally requires a new set of standards to be created. Finally, they are built on the limited sampling the IE had access to at the time they measured the process, and they do not factor in changing, dynamic workflow typical in today’s operations.

Another potential problem is that there are many metrics Industrial Engineers may want to bundle into a set of engineered standards that don’t actually impact the time taken to do the job. It is possible for there to be too many metrics within one labor standard, which generates noise and doesn’t positively impact performance in any way. Where single-metric KPIs are ineffective and unfair due to their very limited scope, “noisy” standards add unnecessary complexity without improving the accuracy of the standard. Finding that balance is key for creating fair and effective standards. 

Thankfully, today’s IE’s are supercharged with the advancement of cloud technologies that take the grunt work out of creating labor standards, so that the IEs can make a larger business impact and focus on process improvement and innovation. Solutions like Easy Metrics have purpose built features for IEs that eliminate the need for stopwatches and data wrangling, and that do all the data mapping and measuring with software.

Measuring What’s Important – An Example

Distribution Center Labor Standards Example

These numbers come from measurements made by an Industrial Engineer on-site. In this case, the deceleration and acceleration of the forklift have such a low correlation to labor that they’re irrelevant. Therefore those metrics are signal noise and should be ignored. 

In this example, “number of cases” is one of the metrics that impact labor the most, because it requires human labor to hand stack the cases onto the pallet. Secondary metrics like travel distance, case numbers, and pallet numbers are the key metrics in this situation. Other metrics like acceleration and deceleration can be ignored. Focus on what is important, don’t get lost in the details. 

Data Driven Standards

The tools to develop data driven standards are built into Easy Metrics. We use data regression technology to leverage multiple metrics across each individual process, allowing them to adapt to the unique details within each process and adjust to potential changes. When data driven standards are created they will be set at a level of productivity designed to allow for a stretch goal. We make the option of data driven standards accessible without denying the option of engineered labor standards. Ideally, they could be applied together. 

The data driven solution is cost effective, because it’s done by computing, rather than manual efforts. This solution can be implemented quickly, is scalable, easily maintained, and takes all historical data into account. Data driven standards are the easiest and most efficient to re-calibrate when a process or facility changes.

Data driven standards have two primary problems: they are not yet the industry-accepted method for setting standards, and the data by itself is not able to incorporate the insight or long-term goals that an IE or Operations Manager could bring to the table. 

The Hybrid Approach: Machine + Human Engineer

The hybrid approach aims to balance the accuracy and flexibility of data driven standards with the meticulous and expertly-designed engineered labor standards. We’ve seen that this approach is preferred by our customers that have IEs on staff, and has two primary advantages: 

  • The amount of process data that Easy Metrics can capture and store dwarfs the amount of data an Industrial Engineer can gather via floor observation, which saves time and reduces the chance of neglected fringe/low-frequency data sets.
  • Since data is already stored within Easy Metrics, the IE can develop standards far quicker and more accurately, and easily revise or re-calibrate them in the future.

This hybrid approach utilizes the expertise of an IE familiar with the floor processes and who is familiar with the Easy Metrics analytics. With their understanding of each process, the IE can set ranges and limits for each metric in the system, forcing the data to optimize along reasonable paths. The end result of this is a set of multi-metric labor standards that utilized the extent of the available data and the expertise of the Industrial Engineer. 

Of the three, Easy Metrics highly recommends the hybrid approach. The power and flexibility of this method is unmatched, and its low cost is a strong selling point. The largest flaw in the data-driven method—the lack of human insight and planning—is corrected by the expertise of an engineer. The largest flaw in the purely Engineered approach—the problems with cost and scalability—are addressed by the constant stream of fresh and accurate data from the systems of the facility. It is the best of both worlds, and has the fewest downsides. 

The Advanced Analytics Toolset for Industrial Engineers

Industrial engineers (IEs) are no longer restricted by technology in how they develop their standards. Instead of timing employees on their tasks with stopwatches, they can focus their efforts on real and positive change to the business. They can offer solutions and act on their insights collected from an ocean of connected data and analytics. This allows them to save an extraordinary amount of time creating and maintaining labor and production standards—which frees them to focus on long-term innovation and improvements for your company. 

Easy Metrics automates the drudgery of manual data gathering and spreadsheet wrangling, allowing IEs to deliver a better customer experience by driving out waste and continually optimizing the operation.

Shifting to a Data driven standards model requires integrating your WSM data with other systems to transform the collected data into rigorous algorithm-powered analytics. This is where Easy Metrics can help. Easy Metrics has built-in functionality that creates data driven performance standards. Keeping them up to date as your operation or workflow changes is a five-minute task finished with a click of a button. Easy Metrics allows you to leverage the totality of your data to determine your outliers and deviations, optimize your processes and metrics, and determine your labor standards with ease. 

Easy Metrics leverages your production data to help you develop fair and accurate labor standards. These standards can be easily updated as needed to maintain fair standards in dynamic environments. This modeling can recur as many times as necessary to maintain fair standards as your company grows, expands, and improves its operations. Throughout the data analysis process, we work with you to maintain the integrity of your data and ensure you end up with the best, most accurate labor standards for your operation.

__________________________________

Easy Metrics for Labor Standards

Create and refresh labor standards with accuracy. Supercharge your IEs efforts or use data-driven standards on their own.

Fair labor standards are the first step to your success, and your success is our success. Work with us to define, design, refine, and implement the labor standards for your workforce.


Learn more about how you can manage your operation with fair labor standards, and see examples on our website. If you’re interested in seeing how Easy Metrics creates and reports on labor standards and team performance, or want to learn more about creating the best labor standards in your operation, schedule a call with our sales team.

Cookies are important to the proper functioning of a site. We take your privacy very seriously. To improve your experience, we use cookies to collect statistics to optimize site functionality, and deliver content tailored to your interests. Click Agree and Proceed to accept cookies and go directly to the site or see our privacy policy for more detail.

OK