January 2017 |   Volume: 49 |   Number: 1
The member magazine of the Institute of Industrial and Systems Engineers
Paying attention to mental workloads can improve productivity and safety
By Sharon Claxton Bommer
The demands of manufacturing processing and design are changing rapidly. Production facilities must be able to reconfigure processes and adapt quickly to the changing production demands of the market in order to remain globally competitive and profitable. As a result, production employees are required to do more. But more complex procedures and automation can increase the cognitive load placed on the process operator.
The typical manufacturing process design focuses on traditional ergonomics. This approach may not be adequate for modern manufacturing processes because traditional ergonomics emphasizes reducing the operator's physical fatigue and discomfort to improve throughput and reduce safety hazards. The modern approach to designing manufacturing processes must include cognitive evaluations for effective implementation of system ergonomics. Therefore, the next generation of system design for the modern manufacturing process should consider not only traditional physical ergonomics but cognitive ergonomics.
Cognitive ergonomics examines how work affects people relative to their attention distribution, decision-making, cognitive aspects of mental load, stress and human errors. This is important for manufacturing processes because cognitive load can have an immediate impact on operator performance by slowing task performance and increasing errors.
Manufacturing processes are moving from requiring more physical strength and endurance to a greater need for problem-solving and reasoning skills. With the growing demand of these cognitive changes in manufacturing systems, practitioners must consider this new approach to the human system design.
Despite a lot of talk about ergonomics, at least 89 percent of the workforce does not have access to the latest technology and work-life balance forces 22 percent of employees to change jobs.
Employee Benefit Adviser magazine reported those numbers from the Staples Business Advantage's 2016 Workplace Index report. But progressive employers are learning to approach wellness from an ergonomics point of view, addressing how its workforce uses its space. This can take wellness programs far beyond nascent walking and nutrition initiatives, according to the magazine.
Part-time telecommuting, ergonomic workstations and sit-stand desks can help increase employee retention, Jeff Smith, a workplace consultant at The Standard, told the magazine.
"This is helping them become more comfortable at the position," Smith said. "It's helping them become more engaged in what they are doing, and it is helping them become, overall, more productive as well. What we have found after being able to make these accommodations to specific employees, the employer is finding that they can utilize these accommodations to help their other employees."
Other workplaces are expanding areas previously reserved for nursing mothers into wellness rooms. The worker who has a headache and needs a few minutes of shut-eye can use the room instead of heading home and losing an entire workday, said Jenya Adler, director of workspace strategy at Staples Business Advantage.
And working from home can offer your enterprise the added advantage of a decreased office footprint, saving in real estate or rental costs. After all, that 100-cubicle office might be operating at a capacity of only 30 percent to 40 percent.
"Maybe they don't need a permanent location, maybe they just need a touchdown location or just a place to come in and work that doesn't even need to be an official desk," Adler told Employee Benefit Adviser.
Mental workload builds from cognitive demands, which form from the interaction between operators and their assigned tasks. Mental workload is an important measurement because it provides awareness about where increased task demands could hinder human performance.
In an attempt to address the changing demands of the manufacturing industry, the author developed a strategy that presents a framework for assessing mental workload in manufacturing processes. There are five essential steps to the framework, as shown in Figure 1: Study the manufacturing system, identify the cognitive elements, model the process, measure the mental workload (MWL) and mitigate work overload.
In step one, an ergonomist or process improvement team studies the manufacturing process to gain knowledge and insight about the system. For this, the evaluator completes a hierarchical task analysis to understand the process task activity.
Next, in step two, the evaluator uses an applied cognitive task analysis to identify the cognitive task elements in the process.
The hierarchical task analysis and applied cognitive task analysis collectively define the process steps and cognitive elements, which are employed as discrete events in modeling the process, which takes place in step three.
This study used the Improved Performance Research Integration Tool (IMPRINT) as the human performance modeling tool to predict mental workload. IMPRINT was developed by the Human Research and Engineering Directorate of the United States Army Research Laboratory to assess human-system function allocation, human performance and mental workload estimation.
The discrete events, with their estimated task times and multiple resource theory ratings for their associated mental resources, were the primary simulation inputs for this strategy.
Multiple resource theory, as developed by Christopher Wickens, is a predictive model that supports understanding how well an operator performs while multitasking. According to multiple resource theory, when the human mind receives task demands (inputs), it can distribute its resources to handle these task demands either individually or collectively. Resources can come in various forms, including visual, auditory, cognitive, motor and speech.
Task demands that overlap leave the mind with fewer available resources. Multiple resource theory predicts that human performance will decline when multiple tasks require competing resources, which could decrease system safety and the effectiveness of your manufacturing process. IMPRINT applies a scale to the mental resources identified by multiple resource theory to assess the mental resource utilization for completing a process task's discrete events. This step determines the multiple resource theory ratings for each discrete event identified in steps one and two.
With this data, IMPRINT measures MWL in step four, outputs the workload predictions and provides a workload profile for the task under analysis. If there is an MWL overload, ergonomists should use multiple resource theory to conduct an assessment to attribute mental resources that correlate to the mental overload. (If a mental overload task element is not measured, this terminates the process.)
In the event of a mental overload condition in step five, the ergonomics team should modify the manufacturing system based on multiple resource theory principles to mitigate the overload. This is done by decreasing the mental resources that are creating the overload and applying the multiple resource theory ratings again to compare the modified system resources to the baseline process evaluation.
Once again, the MWL is measured using IMPRINT. The process will recommence until an optimal MWL range is established. Once an optimal range is attained, the altered job element sequence can be tested and validated on the production floor.
To test the feasibility of the strategy, a pilot study was conducted in the medical device domain.
It was applied in the production of a medical surgical implant for humans. Production parts are laser welded and go through a post-processing procedure that cleans the parts and checks them according to quality specifications. The process is repetitive, such that the same procedure must be completed each cycle.
This study analyzed the parts cleaning portion of the process. Process steps for parts cleaning include mixing acids for proper formulation, dipping parts in chemicals and monitoring equipment settings to track proper processing times.
Experienced real-world process operators who regularly work the parts cleaning process were used in on-site interviews and process walk-throughs to analyze and study the manufacturing process for data collection in order to test the strategy.
The process strategy indicates that the operators did not experience an overload condition while performing the cleaning task under review. Therefore, according to our model in Figure 1, this ends the process analysis because there was not an overload to mitigate.
So an additional evaluation was performed; the process was simulated under time compression by combining cleaning inspection tasks. The simulation results predicted that overlapping more than two inspection tasks during the cleaning process would be unmanageable by the operator because three-task overlap exceeded the proposed MWL threshold. Exceeding the threshold value means the operator could experience a state of high cognitive load, which could keep the operator from accomplishing the task efficiently.
The proposed strategy, which presents a system framework made up of various human factors methods, can be useful. During design and setup stages of a manufacturing process, ergonomists and improvement teams can build better processes to reduce mental overload for the process operator.
This strategy can help evaluators understand operator MWL resources in human-machine design for capacity planning of process setup for a system in the design phase. The strategy also can help enterprises modify an existing system to improve manufacturing processes that require increased cognitive demands.
As global competition continues to grow, practitioners and engineers must continue to improve upon the human system design process for manufacturing. The proposed strategy provides a baseline to build upon for future work in process development of repetitive task processes.
Since the development of this strategy, the framework has been expanded to validate the simulation modeling tool in the manufacturing setting. This was done by conducting laboratory experiments to test the simulation predictions to other MWL measures with evaluation tools from the various MWL categories of subjective, physiological and performance. The strategy has proven to be capable and effective in predicting overload conditions for processes with inspection and supervisory control (automation) tasks.
However, an MWL threshold for manufacturing needs to be systematically established for effective process design.
Sharon Claxton Bommer has more than 15 years of automotive manufacturing experience. She is an adjunct professor at Wright State University in the Department of Biomedical, Industrial and Human Factors Engineering. She has a doctoral degree from Wright State University in engineering with concentration in industrial and human systems. The pilot study presented in this article was performed as part of her dissertation research while working in the Human Performance and Cognition Laboratory at Wright State. Her research focus is human systems integration with a concentration in human performance and cognition for manufacturing.