Integrating Human Factors into Operations Management Education in the Industry 5.0 era

Summary

This study presents a framework for integrating human factors into operations management education in the context of Industry 5.0. The protocol applies concurrent engineering to integrate ergonomic evaluation during workplace design, including production data collection, problem analysis, digital factory establishment, model optimization, and validation.

Abstract

The era of Industry 5.0 emphasizes human centrality, sustainability, and resilience, posing new requirements for the direction of talent cultivation in modern higher education. Engineering education is also adapting and leading the development of Industry 5.0. This study proposes an innovative framework integrating human factors into the modern operations management education system in higher education. Operations management involves the management of the entire process, including supply chain, inventory, quality control, etc. With the evolution from Industry 4.0 to 5.0, operations management is centered on humans, emphasizing human-machine collaboration and employee empowerment. Therefore, the content of teaching needs to be adjusted to help students learn how to use human factors principles to optimize work processes, reduce employee fatigue, and mitigate risk. This paper proposes the application of human factors in operations management by creating a virtual, interconnected production environment that simulates real assembly lines, detects human factors issues, and reduces deviations through numerical analysis. This protocol enables the identification and evaluation of ergonomic risks at the design stage, providing targeted improvement measures. This protocol helps students understand how to reduce the incidence of accidents, improve work efficiency, and reduce the additional costs associated with corrective interventions during the teaching of operations management. Through digital ergonomics, students can comprehend and become proficient in ergonomic risk evaluation methods. Therefore, this paper analyzes how to better integrate human factors into operations management, attaining the integration of engineering technology and human factors, and proposing new ideas and directions for education and practice in the field of operations management. In future applications, combining human factors with human digital twin (HDT) and Artificial intelligence-generated content (AIGC) will empower smart manufacturing and enable human-centric smart manufacturing in engineering practice.

Introduction

Industry 4.0 has fundamentally transformed products, services, and processes through the integration of advanced technologies such as the Internet of Things, Big Data, Cyber Security, Cloud Computing, Additive Manufacturing, and Advanced Robotics^1,2. With the rise of Artificial Intelligence (AI), Industry 4.0 integrates AI technology with various advanced technologies, reshaping the manufacturing and operations landscapes³. However, while Industry 4.0 significantly improves productivity and creates new job opportunities for workers, it also brings challenges such as increased cognitive demands, higher work risks, and the need for continuous skill enhancement⁴. To address the issue of neglecting human factors during the rapid development of Industry 4.0, the European Commission proposed the concept of Industry 5.0, emphasizing three core elements: human centrality, sustainability, and resilience^5,6. Industry 5.0 emphasizes the collaboration between humans and AI, improving productivity while ensuring human health and environmental sustainability, thus achieving sustainable economic growth⁷. Horvat et al. investigated data from 1334 manufacturing companies, and the study results indicate that human-centric Industry 5.0 enhances the manufacturer's ability to innovate in products, with employee participation being an important factor, reflecting the importance of human-centric Industry 5.0⁸.

Industry 5.0 emphasizes the combination of human creativity and technological precision, giving rise to the concept of Operator 5.0⁹. Operator 5.0 places the operators within human-centered production systems, involving sociotechnical systems, social sustainability, and resilience engineering^10. Mattsson and Kurdve propose that Operator 5.0 includes 9 factors: cognitive/physical ability to perform task, overall digital skills, universal design, minimize unexpected events, productivity and quality, safety, standards, instructions, and training materials¹¹. Peruzzini et al., based on Operator 5.0, proposed the Augmented Digital Twin (ADT) that integrates machines, robots, environments, interfaces, and people, thereby developing human-centric smart manufacturing systems¹². Yaqot et al. proposed the enhanced human-automation symbiosis (EHAS) framework, emphasizing achieving human-automation symbiosis through physical and sensory enhancement, cognitive and emotional enhancement, communication and collaboration enhancement, digital ethics, digital citizenship, privacy, and human rights enhancement¹³.

The transition from Industry 4.0 to Industry 5.0 represents a shift from technology-centric manufacturing to value-centric manufacturing¹⁴. Higher education must confront the challenges brought by Industry 5.0 and reform existing curricula to train the future workforce. University programs must consider the competencies required for Operator 5.0 to prepare students receiving an engineering education for their future career development.

The implementation of Operator 5.0 education has led to changes in the teaching objectives of operations management courses. As a core function of modern enterprise management, operations management encompasses the entire process from raw material procurement to the delivery of final products or services. It covers multiple dimensions such as supply chain management, production planning and scheduling, work study, inventory management, and quality control. The teaching goal of operations management courses is to enable students to understand how to optimize resource allocation, improve efficiency, reduce costs, and ensure that the quality and delivery speed of products or services meet market demands. In particular, the work-study component helps students deeply understand and master theoretical knowledge of operators in operations management, analyze complex case studies, and propose innovative and practical solutions through hands-on learning.

The assembly line, as the most typical production situation in manufacturing, is the major application field for Operator 5.0-related technology. Industry 5.0 requires assembly lines to handle specific production needs. Operator 5.0, through the merging of human creativity and technology empowerment, enables the shift of assembly lines from mass manufacturing to mass customization. Operator 5.0 can swiftly alter assembly line procedures utilizing digital twins and simulation tools to satisfy unique product requirements, achieving the flexibility and adaptability of the assembly line¹⁵. Operator 5.0 can simulate the movements and weariness of assembly line workers through digital twins, optimizing workstation design, representing the concept of Human-in-the-loop in smart manufacturing¹⁶. Therefore, in the education of Operator 5.0, taking the improvement of the assembly line as an example, Figure 1A illustrates the main steps currently involved in identifying and addressing issues in an assembly line. Current practices do not adequately consider work-related musculoskeletal disorders (WMSDs) during the early stages of engineering design. Physical ergonomic risk factors include awkward postures, force exertion, material handling, stationary positions, and repetition¹⁷. When these risks are present in actual production operations, they not only cause WMSDs among workers but also prolong production cycles and increase operational costs for enterprises when corrective measures are implemented. Boysen et al. provided a systematic review of assembly line balancing issues, highlighting human factors as a critical factor in improving assembly line performance¹⁸. Therefore, in the era of Industry 5.0, the importance of human factors in operations management has become even more pronounced, playing a significant role in contemporary business strategies^19,20.

Caputo et al. introduced the theory of concurrent engineering into workplace design, proposing that ergonomic evaluations be considered at the stage of process design, thereby potentially preventing ergonomic risks in the design phase²¹. In the era of Industry 4.0, there is insufficient attention to the core role of ergonomics in production operations, making it difficult to fully adapt to the requirements of ergonomics in Industry 5.0²². Therefore, guided by the concept of Industry 5.0, the teaching practice of operations management needs to incorporate more ergonomics content. Taking the assembly line improvement issue as an example, Figure 1B shows that in the case of clear improvement objectives and targets, the measurement of time and ergonomic risks in each process is carried out simultaneously, and ergonomic evaluations are considered at the stage of process design in parallel with concurrent engineering thinking for assembly line improvement.

Figure 1: Two types of assembly line improvement processes. (A) The traditional assembly line improvement process, and (B) the one integrating human factors. Please click here to view a larger version of this figure.

In the Operator 5.0 framework and evolving demands of Industry 5.0, the workforce needs to acquire relevant digital skills to effectively engage in new work and tasks. 62% of manufacturing companies reported that the lack of employees with appropriate skills is a major obstacle to the success of digital transformation²³. Hansen et al. surveyed 30 companies pursuing digital transformation and found that the success of digital transformation during the transition from Industry 4.0 to Industry 5.0 depends on the cultivation of worker capabilities²⁴. Future professionals need to master advanced digital capabilities to effectively interact with intelligent systems²⁵. Hermawati et al. assessed the current state of ergonomics education and found that there is a gap between current ergonomics education and the requirements of Industry 5.0²⁶. Therefore, it is crucial to train new skills according to the requirements of Industry 5.0, and it is necessary to develop students' digital competencies⁷.

The emergence of digital twins incorporating ergonomic risk evaluation has enabled the integrated design of workplace design and ergonomic evaluation through digital simulation, applying the concept of concurrent engineering. This achieves human-centric digital design in the context of Industry 5.0. Caputo et al. proposed a digital twin model that includes ergonomic evaluation for workplace design, which was validated on a Fiat Chrysler Automobiles (FCA) assembly line using both Ergonomic Assessment Worksheet (EAWS) and Ovako Working Posture Analysing System (OWAS) assessment methods^21,27. The studies mentioned above utilized the Methods-Time Measurement (MTM) process language in their analyses. MTM is a type of Predetermined Time System (PTS) that decomposes manual operational procedures into basic elements of motions, taking work conditions into account to determine the time required for a task. Breznik et al. employed the MTM Universal Analysis System (MTM-UAS) along with simulation software to address the assembly line balancing problem (ALBP)²⁸.

In response to the aforementioned requirements in operations management—integrating physical ergonomic risk factors during the initial engineering design phase and developing students' digital competency—this study uses the assembly line improvement problem as an example. It incorporates ergonomic evaluation into a digital twin model to demonstrate how engineering education can adapt to the demands of Industry 5.0, fostering the development of Operator 5.0 through human-centric design and digital capability building.

In summary, against the backdrop of the transition from Industry 4.0 to human-centric Industry 5.0, this paper explores the integration of human factors into operations management education. Using the example of guiding students in assembly line design within teaching practices, it incorporates ergonomic risk evaluation to help students understand the essence of Operator 5.0. By demonstrating the process of integrating ergonomics into operations management education, this study aims to provide new insights and directions for both education and practice in the field of operations management in the era of Industry 5.0.

This protocol presents experimental procedures for measuring and improving time and ergonomic risks in assembly line design. The procedure can be summarized in five steps: 1) Collection of production status, 2) Problem analysis in the production process, 3) Establishment of the digital factory, 4) Model optimization, and 5) Effectiveness verification of the digital factory. If any one of these requirements in the five steps is not met, the process is incomplete and insufficient to prove effectiveness.

Protocol

This study was conducted in accordance with the principles of the Declaration of Helsinki and was approved by the Institutional Review Board of Jiangsu University of Science and Technology. We obtained the informed consent of the participants in the experiment to use and release their data.

1. Collection of production status

NOTE: The production status collection phase requires time measurement of the production site and filming on-site videos to prepare for subsequent problem analysis. Determine the production process flow, and collect worker information, as well as 3D models of equipment, products, and materials at the production site for subsequent digital factory construction.

1. Collect the working conditions of assembly lines that need improvement. This includes the assembly line's pace, assembly line scale and environmental layout, the area of the production area analyzed, production-related equipment information and floor space, the number of positions involved, human-machine collaboration processes, material supply types, and methods²⁹.
2. Measure man-hours using the stopwatch method to obtain time data for each work type.
   1. Press the handwheel of the mechanical stopwatch for the first time when the worker starts cyclic actions.
   2. Press the handwheel for the second time when the worker ends cyclic actions.
   3. Read the time.
   4. Press the handwheel for the third time to reset the stopwatch.
3. Film on-site production videos for subsequent problem analysis.
4. Collect worker information from the production site. This includes demographics: age, gender, nationality, and anthropometric data (the unit is cm): (1) Stature, (2) Eye height, (3) Shoulder height, (4) Elbow height, (5) Knuckle height, (6) Height, sitting, (7) Eye height, sitting, (8) Elbow rest height, sitting, (9) Thigh clearance height, (10) Knee height, sitting, (11) Buttock-knee distance, sitting, (12) Popliteal height, sitting, (13) Chest depth, (14) Elbow-elbow breadth, (15) Hip breadth, sitting³⁰.
5. Collect 3D models of products, materials, and equipment, including: 3D assembly drawings of products, 3D models of materials, 3D models of machinery and equipment, and tools.
   1. Collect the quality of components and tools involved in manual operation and handling (the unit is kilograms). Collect the key forces necessary in the workflow, such as the force needed to lift objects or the torque required to tighten bolts (the unit is Newtons).

2. Problem analysis in the production process

NOTE: Based on the collected production status data, use behavior analysis software to analyze on-site production videos, and then identify problems during the digital factory construction phase. The basic steps of the behavior analysis software include software basic settings, software coding settings, and video analysis. The software web accessibility is https://noldus.com/observer-xt-human.

1. Set basic settings.
   ​NOTE: This phase involves setting sample parameters for the software. Select an appropriate sample type (continuous or discontinuous sample) based on the filmed video. After completing the basic settings, the video can be imported.
   1. Double-click to open the behavior analysis software.
   2. Click Create a New Project and then New.
   3. Enter the file name in Name of New Project.
   4. Click Set up project in the pop-up new window.
   5. Select Combine Continuous and Instantaneous Sampling in the Observation Method since the existing production video is a sample of discontinuous video combinations.
2. Set up software coding.
   NOTE: This phase involves setting up software coding. During subsequent video analysis, rapid coding of the video is required to count problem points, and this step sets up the coding for this purpose. After completing this step, the video can be imported and analyzed.
   1. Set the coding method in the coding scheme under Setup. Click coding scheme, then Instantaneous Sampling and Subject.
   2. Click Add Behavior Group in the Behaviors interface.
   3. Enter ergonomic risk in the Group name.
   4. Enter stoop, turn-back, and sideways in Behaviors below, select No in Duration, since these are all instantaneous actions. Select Behaviors in this group type can overlap, as these phenomena can occur simultaneously with other possible situations, then click OK after completion.
      NOTE: This step adds all ergonomic risks that may appear in the video for subsequent analysis.
   5. Modify the coding in the Behaviors interface, setting the start and end keys for stoop, turn-back, and sideways as 1, 2, and 3, respectively.
      NOTE: Pressing 1 for the first time indicates the occurrence of a stoop behavior, and pressing 1 again indicates the stop of the stoop behavior. Coding behaviors help analysts quickly identify problem points in the video and use them for subsequent analysis.
3. Perform video analysis.
   ​NOTE: This step involves video import, coding, and problem statistics.
   1. Analyze the video in Observations of Project Explorer. Click Observations, select Create a New Observation in the pop-up window, enter the desired file name in Observation name, and click OK.
   2. Locate the video to be analyzed in the pop-up window and click Open.
   3. Click the Start Observation button on the interface to initiate video analysis, and press the preset code number when identifying a problem point, such as hitting 1 on the keyboard upon observing a bending motion. Click Save under File at the top of the page upon completion to store the data, enabling quick documentation of all problem points appearing in the video.
   4. View analysis data in the Analyses of Project Explorer. Click Data Profile to view the time-axis data of the coding. Check whether high-frequency physiological load actions appear in the video, including stooping, turning, and sideways movements.
   5. Count the frequency of high-frequency physiological load actions.

3. Establishment of the digital factory

NOTE: The assembly line has excessive job allocation, long working hours, uneven workload among stations, accumulation of work-in-process between positions, and frequent stooping, turning, and sideways movements among assembly line workers, according to the problem point analysis mentioned above. To solve these issues, solutions are required. This paper proposes simulating human operation processes and adjusting the assembly line in the digital factory by establishing a digital factory. Compared with traditional on-site improvements, establishing a digital factory ensures that production process data measurement is unaffected by the state of on-site personnel. Additionally, all improvement protocols can be verified and optimized in the software without repeated on-site adjustments, significantly reducing time and cost waste during the improvement process. The software web accessibility is https://imk-ema.com/en/ema-work-designer.

1. Simulate the assembly line using digital industrial engineering software.
   1. Open the digital industrial engineering software. Click Create New Project in the pop-up window.
   2. Set basic project information, cycle time, etc., in the Simulation Project at the top of the page.
   3. Use the Add Object(s) From File, Add Manikin, and Add Item From Library buttons in the Objects tab at the top of the page to import a human model that matches the worker information obtained in section 1, along with 3D models of shelves, parts, and other components.
   4. Layout the models in the 3D view at the bottom of the page.
      1. First, switch the control mode in the left drop-down menu to Layout, then select models in the 3D view to move, rotate, etc., complete the layout of the digital assembly line, and by dragging and moving the object in the structure list, double-clicking to edit the object's name, color, and annotations, or defining the parent object, merging the object structure, and generating references for the object through the right-click submenu, group the objects, establish subordination relationships, set reference objects, and modify parameters such as color, object mass, and grip posture for subsequent dynamic simulation.
   5. Assign assembly tasks to the corresponding digital human models according to the assembly sequence of the process flow in the Tasks tab at the top of the software page. Drag the required assembly tasks from the left list in the tasks page to the action axis of the digital human model.
      1. Find assembly tasks in the left list of the Tasks page, including object handling tasks (pick object, place object, join objects, etc.), body movement tasks (walk, kneel down, etc.), and other common industrial production-related action tasks.
      2. Then, fill in the corresponding parameters according to the simulation and process requirements of the current task, such as specifying the object to be operated, the target position where the object should be placed, selecting body posture restrictions during action execution, and task process description (reach level, placement accuracy, finger and arm force requirements, etc.).
2. Click the simulate icon to run the simulation, and the software automatically generates a simulation animation and analysis results. If an error message appears or the simulation results do not meet requirements, adjust the simulation project according to step 3.1.
3. Click the Results button at the top of the page to view model operation results. View evaluation results such as time, human ergonomics, and walking path from each left tab, and click Ergonomics to obtain the worker's EAWS score.
   ​NOTE: EAWS is a biomechanical load risk assessment method developed by an international expert team, used to evaluate the WMSDs risk to which operators may be exposed during work.
4. Confirm the workstations requiring improvement according to the EAWS assessment grade table (Figure 2). Meanwhile, view detailed evaluation data in the software's ergonomics results to further identify the most critical operational tasks requiring improvement.

Figure 2: EAWS assessment level. The standard for classifying ergonomic risk levels based on EAWS scores. Please click here to view a larger version of this figure.

4. Model optimization

NOTE: The purpose of this step is to reduce the EAWS risk value of production workers to a low-risk level.

1. Observe the EAWS scores of workers at the workstations identified for improvement in the previous phase. Switch between different workers and evaluation modules (body posture, hand force, load handling, etc.) in the ergonomics results page to identify the highest-risk contributors.
2. Identify related actions from the highest-risk contributors to prepare for subsequent improvements.
3. Develop improvement strategies based on risk points, adjust the model in the software, and re-run the simulation assessment.
   1. Click Tasks, locate the workbench of the worker with the highest-risk contribution in the tree diagram. Click Add Object(s) From File in the upper left corner, import the new design device model, and add the device under the workbench directory to simplify worker actions.
   2. Click Tasks, click the action axis of worker2_human, locate the changed operational task, and cancel redundant actions in this improvement scheme. Click the hollow dots on the left of the move hands to target and actuate until they turn into blue background right-arrow dots to cancel the current action simulation.
   3. Click Simulate to run the model, and check for anomalies after completion to prevent logical errors caused by adding new devices.
      1. If anomalies exist, modify the content of related actions according to the added equipment and changes.
   4. Run the model again, repeating the above steps until no anomalies exist in the action axis.
   5. Click Results, select Ergonomics, and observe whether the worker's EAWS risk value has dropped to green.
      1. If it has not dropped to green, consider increasing rest duration in the Common column of the Ergonomics page, or continue adjusting material supply positions, operational sequences, and tooling design.
   6. Repeat the above steps until the EAWS risk drops to green.
4. Run the model, and view whether the worker's EAWS Whole Body and EAWS Upper Limbs in the results are both within the safety threshold. The safety threshold requires the EAWS score to be less than 50 points, as seen in Figure 2, and the color is either green or yellow.
5. Click MTM-UAS Analysis to check whether improved man-hours are reduced compared to previous man-hours, achieving the goal of reducing man-hours and keeping ergonomic risks within the safety threshold during assembly line improvement.

5. Effectiveness verification of the digital factory

NOTE: The above steps have realized the design and optimization of the assembly line in the digital factory. The purpose of this verification stage is to ensure that the software simulation results match the actual measured condition of the assembly line following optimization implementation. If consistent, this experimental operation method can be adopted for future assembly line design optimization.

1. Verify the man-hour results.
   1. Measure man-hours at the improved production site as described in step 1.2.
   2. Compare the man-hours results with the software output.
2. Verify the ergonomic risk results.
   1. Measure ergonomic risks at the improved site as described in steps 2.2 and 2.3.
   2. Compare the results of ergonomic risks from software output.

Results

Based on the above experimental operations, the representative results of this paper take the assembly of a typical mechanical product—the valve body component—as an example to demonstrate the typical results of integrating human factors into operations management for work improvement. This experiment represents the practical teaching component of the already established Human Factors course, and its teaching outcomes have been demonstrated at the 2025 MTM China Forum: Industrial Engineering in the Age of Digital Intelli...

Discussion

This study incorporates not only time efficiency but also the measurement of ergonomic risks in assembly line design. By integrating ergonomic risk measurement, potential hidden safety risks can be identified during the design phase, ensuring that assembly line layouts account for ergonomic risk factors. This protocol enhances employee productivity, reduces the probability and impact of accidents, and contributes to the realization of employee well-being under Industry 5.0. There are three crucial steps within the protoc...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
ema Software Suite	imk Industrial Intelligence GmbH	V2.2.0.3	Establishment of the digital factory
Observer XT	Noldus Information Technology	V12.0	Problem analysis in the production process