AHFE Open AccessIndustrial and Systems Engineering
Editors: Bhushan Lohar, Tareq Z. Ahram, Waldemar Karwowski
Topics: Systems Engineering
ISBN: 978-1-964867-95-3
DOI: 10.54941/ahfe1007238
Table of Contents
Operationalizing Shipbuilding 4.0 Technologies for Material Management Sustainability
Shipbuilding faces pressure to improve efficiency and effectiveness while meeting rising disclosure requirements and environmental targets. This article approaches sustainability through the operational and competitive lens, focusing on practical digital solutions that make material management both leaner and greener in shipbuilding 4.0 context. We conduct a design science study that combines empirical site observations, interviews, and co creation workshops to build practical problem themes, such as project design and change management, logistics planning, material visibility, warehousing and last mile operations, and quality and rework. Based on these findings, we operationalize Shipbuilding 4.0 design principles through four software concepts: a mobile app for deviation capture, material route tracking, hybrid simulation for material flow analysis, and a shared KPI and maturity platform integrating data from previous. These are expected to cut rework, improve delivery to installers, reduce inventory related waste, and estimate CO₂ impacts along routes.
Kimmo Tarkkanen, Janne Siivonen, Jari Hietaranta, Antti Laatikainen, Pertti Ranttila
Open Access
Article
Conference Proceedings
Measurement and quantification of quality characteristics in quality-in-use
Quality-in-use model which is one of software engineering quality standard series (SQuaRE) has been revised in 2023. As a result of this, ISO/IEC 25022 which described about the measurement of quality in use also be required to revise. As the concept of this standard is to deal with influence on stakeholder by use of product, system and service in specified context of use as quality, the target stakeholders were wider. According to this change, measurement methods for “Beneficialness” and “Acceptability” which are new quality characteristics and “usability” which is one of the sub-characteristics in “Beneficialness” are also required. This paper shows the new concept for measuring these quality characteristics, especially “Beneficialness”. "Beneficialness" is structured by "usability", "accessibility" and "suitability" as quality sub-characteristics. , By representing these sub-characteristics as measures of quantitatively, new quality models can be measured.
Shinichi Fukuzumi
Open Access
Article
Conference Proceedings
Shaping Future Work Systems: A Framework for the Integration of Humanoid Robots
Advances in humanoid robotics, artificial intelligence, and autonomous control are accelerating the integration of humanoid robots into industrial and service‑oriented work systems. Their anthropomorphic kinematics, human‑scale reachability, and multimodal sensing enable operation within infrastructures originally designed for human workers. Yet, structured, theory‑based methodologies for their systematic integration into socio‑technical environments remain limited. This paper presents a human‑factors‑oriented framework supporting the selection, design, implementation, and evaluation of humanoid‑robot‑enhanced work systems. It integrates socio‑technical systems theory, human-robot interaction, and ergonomic design across five domains: robot selection, contextual diagnostics, work allocation and interaction modelling, system integration, and multidimensional evaluation. Industrial cases in automotive assembly and intralogistics demonstrate how humanoid robots can assume physically demanding tasks while humans retain decision‑intensive roles. The framework positions humanoid robots as flexible, modular automation resources and highlights future research needs, including interoperability standards and scalable safety architectures for industrial deployment.
Ralph Hensel, Vera Hummel, Jan Schuhmacher
Open Access
Article
Conference Proceedings
Effects of Artificial Intelligence Decision Support Systems on Operator Trust and Workload
AI-based decision support systems (AI-DSS) used in production lines are being increasingly adopted due to their potential to improve operator performance in complex and uncertainty-laden operations. These systems support operators throughout the process in terms of algorithmic accuracy and computational capacity. Another reason for their preference is their direct relationship with operator trust and perceived workload over time. This study aims to examine the effects of AI-based decision support systems on operator trust and workload from the perspective of human factors and ergonomics. By addressing the operator’s interaction with artificial intelligence within the system, the study focuses on the relationship between operators’ trust in system output and cognitive workload. The system outputs were tested by operators within scenarios offering different levels of AI support and were evaluated using multidimensional measurement tools. Subjective and objective indicators reflecting operator workload, together with time-based measurements, were considered jointly. Trust in the system was examined through dimensions such as transparency, predictability, and behavior in the face of errors. The findings suggest that when the output provided to the operator by AI-based support systems are not designed appropriately, they may lead to either over-trust or excessive caution among operators. As a result, both the accuracy of the operator’s decisions and situational awareness may be weakened. It was observed that as the level of automation increases, cognitive workload decreases; however, the ability to respond quickly and accurately to unusual situations within the system also declines. From the operators’ perspective, it was determined that adaptive and highly explainable AI solutions support trust in the system and contribute to maintaining workload balance. Overall, this study demonstrates that ergonomic principles play a decisive role in influencing operator performance in the design of AI-supported decision systems. As a fundamental design criterion, maintaining the balance between sustained performance, operator trust, and cognitive workload is essential. In this regard, the research contributes to the development of evaluation and design approaches for AI-supported decision systems in industrial, healthcare, and safety-critical application areas within lean manufacturing environments. The findings are expected to guide design processes toward establishing a more robust and effective foundation for human–AI interaction.
Si̇nan Apak
Open Access
Article
Conference Proceedings
Integrating Human-factors into Enterprise Architecture Leveraging Ontologies and Metamodels
This paper builds on the author’s previous work regarding domain-specific ontologies (DSO) and its importance in the human-factors integration (HFI) space. Explicit term definitions captured by a DSO allow the HFI vocabulary to be mapped into a model-based enterprise architecture (MBEA). Integrating this terminology into the overall MBEA provides insight into the role that individuals play by considering personnel as a critical system component. Often considered external actors, human resources are typically not accounted for in the original solution design. However, MBEA promises to reverse this trend by implementing the Unified Architecture Framework (UAF). The UAF is composed of various domains and their aspects and is meant to graphically illustrate enterprise concepts such as strategy, operations, resources, personnel, and services in a digital environment. Capturing HFI information in a model improves the traceability of person(s) and organizational concerns, responsibilities, and competencies to highlight gaps that must be addressed. The incorporation of the HFI DSO into an MBEA enhances communication between disciplines and provides transparency for stakeholders. This research demonstrates the feasibility of constructing a DSO based on an HFI body of knowledge; leveraging the Web Ontology Language (OWL), the subject-predicate-object (SPO) approach, and the Protégé ontology editor. It also shows that by importing the OWL file into a concept model, understanding HFI terms facilitates MBEA while maintaining personnel as a critical part of a successful organization. This research identifies areas for improvement of the UAF domain-specific modeling language (DSML) to ensure that it adequately addresses HFI concerns by mapping like-terms.
Sarah Rudder
Open Access
Article
Conference Proceedings
Quantum-safety enabling cybersecurity reference infrastructure model for edge and access services
The emergence of large scale quantum computing threatens the long term security of widely deployed public key cryptography, creating an urgent need for quantum safe migration across heterogeneous edge and access infrastructures. We present a scalable reference architecture that supports Post Quantum Cryptography (PQC) adoption in resource constrained, multi vendor environments characteristic of edge and access services. The model integrates continuous cryptographic inventory, lightweight PQC for constrained devices, protocol level modernization, interoperability, and crypto agility, while addressing long term data protection and device integrity across distributed systems. Our experimental setup demonstrates PQC ready communication, secure cross domain interactions, and AI assisted monitoring under realistic edge network conditions. The results provide a practical and extensible reference model for organizations seeking to reduce cryptographic exposure and transition toward quantum safe security in complex, large scale edge and access services.
Reijo Savola
Open Access
Article
Conference Proceedings
Artificial Intelligence in Industrial Production: A Survey of concepts, technologies & Practical Application in an Intelligent Production Environment
The increasing digitalization of industrial production systems in the context of Industry 4.0 is leading to a growing use of data-driven and intelligent technologies in manufacturing and assembly environments. In particular, Artificial Intelligence (AI), machine learning, and deep learning methods based on neural networks open up new possibilities for the automation of complex decision-making and inspection processes. One of the central application areas in this context is AI-based image processing for visual inspection and quality control. This paper provides a structured and comprehensive overview of the fundamental concepts, technologies, and methods of Artificial Intelligence in the context of industrial production. Among other aspects, relevant neural network architectures with a focus on industrial image processing, typical training and optimization procedures, data preprocessing, and key challenges are addressed. In addition, common application areas of AI in Smart Factory environments are systematically presented. Finally, the paper presents current research at Hochschule Bochum, in which the practical implementation of these technologies using developed neural networks for automated visual quality control in a gearbox assembly process is demonstrated. Lastly, a comparative approach between the AI-based image processing solution and a conventional rule-based machine vision system is presented.
Haris Karic, Dirk Mohr, Arockia Selvakumar Arockiadoss, Daniel Schilberg
Open Access
Article
Conference Proceedings
Enhancing Design Flexibility in Electric Vehicles via Robotic extrusion-based Additive Manufacturing
This article focuses on design-driven development of light electric vehicles for sustainable urban mobility. In response to EU directives to reduce city traffic, light electric vehicles are increasingly relevant for personal and commercial use, requiring innovative design and production strategies.Additive manufacturing (AM), particularly large-scale robotic extrusion, was explored as a tool to expand design freedom and optimize structural components. User needs and mobility challenges informed product requirements, guiding conceptual design, detailed component development, material selection, numerical simulations, and prototyping.The resulting vehicle demonstrates over 80% of components produced via additive technologies, validating robotic extrusion as a sustainable method that enhances design flexibility. This approach positions design at the core of innovation in urban electric mobility solutions.
Álvaro M. Sampaio, Vitor Carneiro, António J. Pontes
Open Access
Article
Conference Proceedings
Employment initiatives for an ageing workforce: a case study
In this paper we assessed initiatives related to ageing workforce management in a selected manufacturing company. The study was based on a survey conducted among 50 production and administrative employees. The research covered five areas such as: organizational activities, health protection and promotion, training and educational initiatives, career and professional development, and pre-retirement activities. The results showed that age management practices are only partially implemented in the organization. Most respondents indicated that there is no person responsible for coordinating age-related initiatives and that monitoring of recruitment processes to prevent age discrimination is limited. Older employees are rarely recognized as experts, and age-diverse teams are not actively promoted. In addition, respondents reported insufficient health protection measures, limited access to medical services, and a lack of initiatives promoting healthy lifestyles. Many workplaces are also not adequately adapted to the needs of older employees. At the same time, training related to new technologies and equipment was positively assessed. The study suggests introducing a coordinated age management strategy, improving health promotion activities, and implementing flexible work arrangements for employees over 50.
Beata Mrugalska, Jakub Nawrocki
Open Access
Article
Conference Proceedings
Transformation from manual arc welding to collaborative robot welding: Comparison of ergonomic and process-related influencing factors and effects
The increasing use of collaborative robots (cobots) in welding for small-batch production is changing both technical processes and the requirements for employees. This paper examines the transition from manual to cobot-based gas metal arc welding (GMAW). The focus is on ergonomic aspects and their evaluation, changes in job profiles, including the required qualification level, as well as process-related aspects such as processing time, post-processing effort, weld seam quality, and shift output.For the ergonomic analysis, a marker-based virtual reality (VR) system is used to record the work process in both scenarios, manual welding and cobot-based welding, and to evaluate it using a scientifically established ergonomic assessment method. Differences in physical strain (posture) and movement profiles between the two welding methods are discussed in order to highlight the potential effects on the health and performance of employees. In addition, the impact of the introduction of cobots on qualification requirements is investigated, as well as the new skills required for planning, programming, and interaction in automated processes. Finally, process-related key indicators are compared to identify possible changes in effectiveness and quality. The discussion addresses the question of the extent to which collaborative robotics can contribute to reducing manual strain while at the same time posing new challenges for work design, work organization, and qualification. The aim is to gain practical insights for the design of future workplaces in the welding sector and to highlight the importance of integrated socio-technical approaches that equally take technology, ergonomics, and qualification into account.
Christian Schmidt, Christina Pietschmann, Leif Goldhahn, Julia Zähr, David Sauer, Matthias Schmidt
Open Access
Article
Conference Proceedings
Human-Centered Decision Support for Data Analytics in Production Systems
Manufacturing companies increasingly rely on production data to enable data-driven decisions, yet the feasibility and reliability of analytics are often constrained by insufficient data quality. To support practitioners in selecting suitable analytics methods under real-world constraints, this paper validates a human-centered decision-support approach that links a data-quality maturity assessment to a catalog of production- and quality-related analytics methods. Building on an ISO/IEC based process assessment logic and data-quality measures, the approach was evaluated through an industrial single-case study in a manufacturing plant. An exploratory analysis of an SAP quality dataset informed three semi-structured expert interviews spanning return-quality analytics, production-quality analytics, and data engineering. The scoped assessment focused on the data lifecycle phase Data Processing and selected measures for completeness, consistency, and currentness. Findings show that completeness and semantic accuracy are the most consequential limiting factors for downstream analytics, while duplicates and some inconsistencies can be mitigated effectively through automated, rule-based controls. Data quality is shown to be a socio-technical outcome shaped by enterprise systems, process design, and work practices. Documented processes may diverge from lived practice, and automation can both reduce input variability and introduce new failure dependencies. Based on the case evidence, the paper derives practical requirements for maturity-based decision support, including reduced implicit expertise demands, clearer separation of process and data documentation versus execution, and explicit checks for operational process adherence and automation context.
Lennart Frederik Müller-Stein, Kevin Schwarz, Hendrik Sandkühler, Roland Jochem
Open Access
Article
Conference Proceedings
Designing Error-Resilient Human-in-the-Loop Interfaces for Battery Passport Compliance
The implementation of the Digital Product Passport (DPP) mandated by the EU Battery Regulation (EU 2023/1542) requires the precise recording and continuous updating of technical and lifecycle data for batteries. While large manufacturers typically rely on automated machine-to-machine (M2M) interfaces, small and medium-sized enterprises (SMEs), including both producers placing batteries on the market and downstream actors involved in activities such as repair, repurposing, and dismantling, are more likely to depend on manual data entry to maintain passport records. These environments, characterized by heterogeneous processes, and varying levels of digital literacy, the risk of human error is substantial and directly affects regulatory compliance and operational safety. This paper investigates how data quality can be ensured in manual Human-in-the-Loop processes within the DPP ecosystem. Building on established taxonomies of human error that differentiate between execution slips and knowledge-based mistakes, it applies a multi-layered validation framework for DPP user interfaces. The framework introduces design principles for error-resilient data entry that extend beyond basic syntactic checks to include semantic plausibility controls, for example, cross-validating mass, chemistry, and application type as well as context-aware constraints tailored to specific lifecycle stages.
Dogan Efe, Elena Andrushchenko, Muhammad Muneeb Riaz, Roland Jochem
Open Access
Article
Conference Proceedings
Designing Human-Centric Interfaces for the Digital Product Passport in Small and Medium Enterprises
The mandatory implementation of the Digital Product Passport (DPP) under the EU Battery Regulation (EU 2023/1542) requires Economic Operators to exchange comprehensive lifecycle data in order to support transparency and circularity across complex value chains. Although current standardization efforts provide detailed specifications for semantic and technical interoperability and define machine-readable formats for data exchange, the design of the human-facing interaction layer remains insufficiently addressed. As a result, users with different levels of technical familiarity and varying operational objectives must work with highly technical information, although a single interface concept may not equally support the needs of all stakeholder groups.This paper proposes a human-centered design framework for DPP interfaces that translates abstract API outputs into user-specific and actionable information. The approach examines the information needs and cognitive constraints of different user roles within the DPP ecosystem, with particular consideration of small and medium-sized enterprises (SMEs), where personnel often lack specialized software engineering expertise but are still required to make operationally relevant decisions based on technically structured product and lifecycle data. The paper outlines how role-based adaptation, progressive disclosure, and targeted visualization strategies can help reduce information density, lower cognitive load, and decrease the risk of misinterpretation. In addition, a prototype implemented as a browser-based application is presented, and its design decisions are discussed in relation to established principles from Human-Computer Interaction and Cognitive Load Theory.
Elena Andrushchenko, Dogan Efe, Muhammad Muneeb Riaz, Roland Jochem
Open Access
Article
Conference Proceedings
METIS: A quality-oriented multi-stage decision framework for IT tool-stack optimization
Small and medium-sized enterprises (SMEs) frequently operate heterogeneous IT tool landscapes that evolve incrementally, resulting in fragmented digital work environments and limited support for integrated digital transformation. Existing tool selection approaches mainly evaluate isolated software solutions and rarely address system-level configuration or digital work quality. This paper proposes a human-centered Situation-Aware Digitalization Framework for systematic IT tool stack reconfiguration in collaborative product development. The framework combines CEAM-derived Engineering Collaboration Building Blocks (ECBBs) to standardize digital work activities, quality science–based prioritization to identify improvement needs, and a multi-stage multi-criteria decision-making (MCDM) mechanism to generate situation-aware tool stack recommendations. Functional capability, user-perceived quality derived from crowdsourced ratings, and SME-specific digitalization targets are integrated into a unified scoring model. Two iterative strategies support either compact reconfiguration or incremental transformation. Implemented in the METIS application and validated in industrial workshops, the framework demonstrates feasibility and practical decision-support value for aligning IT tool environments with SME-specific digitalization objectives.
Can Cagincan, Juliane Balder, Roland Jochem, Rainer Stark
Open Access
Article
Conference Proceedings
Design and Implementation of a Knowledge-Based Assistance System for Smart Failure Management in Manufacturing SMEs Using Large Language Models
Manufacturing small and medium-sized enterprises (SMEs) are under increasing pressure to detect, analyse, and eliminate quality-related failures faster and more systematically, while operating with limited personnel, fragmented information structures, and heterogeneous IT environments. Although standards such as ISO 9001 require structured corrective action and documented organizational learning, practical failure management in many SMEs remains reactive, media-discontinuous, and weakly connected to reusable knowledge. This paper presents the design and prototypical implementation of a modular, knowledge-based assistance system that combines established quality engineering methods with natural language processing (NLP), machine learning, and large language models (LLMs) to support the entire problem-solving cycle. The research follows a design science research approach. First, requirements were derived from normative sources, the state of research, and an empirical industry survey with 104 valid company responses. The resulting requirements were structured into seven functional domains. On this basis, a modular reference architecture was developed that integrates structured failure capture, historical document analysis, method-guided problem solving, an explainable knowledge base, and feedback-driven learning loops. The prototype was implemented as a containerized full-stack web application using open-source technologies, including Flask, PostgreSQL, Docker, HTML/CSS/Bootstrap/JavaScript, and Llama 3- and Rasa-based conversational services. Transformer-based subcomponents for root-cause classification and guided 5-Why questioning complement the LLM-supported retrieval-augmented generation (RAG) assistant. The system was prototypically validated using historical failure documentation from manufacturing case studies. Evaluation results indicate improvements in knowledge accessibility, reduction in analysis time, increased consistency in root cause identification, and enhanced standardization of corrective action documentation. The findings suggest that AI-enhanced assistance systems can significantly strengthen organizational learning capabilities in SMEs, provided that they are embedded within structured quality management frameworks. The paper contributes to research in digital quality management by (1) providing a structured requirement-based reference architecture for AI-supported failure management systems, (2) proposing a systematic mapping between data mining methods and problem-solving phases, and (3) demonstrating a practical integration approach for LLMs in industrial quality environments. It bridges the gap between classical quality engineering and modern AI-based knowledge systems, offering a scalable pathway toward intelligent, learning-oriented failure management in manufacturing.
Turgut Refik Caglar
Open Access
Article
Conference Proceedings
A Human-Centered Systems Approach to AI-Enhanced VR Training for Home-Based Peritoneal Dialysis
Peritoneal Dialysis (PD) is a home-based therapy for kidney failure that requires patients to independently perform detailed sterile procedures, often several times per day. Even minor deviations in technique can lead to serious complications, including peritonitis and catheter failure. Although structured education programs are typically available, variations in training quality, health literacy, home environments, and patient confidence continue to contribute to preventable harm. Immersive Virtual Reality (VR) and Artificial Intelligence (AI) present promising opportunities to enhance PD education. However, their implementation must be grounded in patient safety principles and Systems Engineering approaches rather than driven solely by technological advancement. This paper presents a patient-focused, systems-based framework for AI-enhanced VR training in home PD, informed by human-centereddesign. The framework integrates realistic procedural simulations with AI-driven feedback on sequencing and sterile technique, while modeling the complete PD workflow within the home as a safety-critical care environment. Core elements include co-design with patients and PD nurses, identification of high-risk procedural steps, adaptation to varying literacy levels, transparent AI feedback mechanisms, and structured processes for ongoing monitoring and evaluation. Interdisciplinary collaboration among clinicians, human factors experts, AI developers, and patient representatives is essential to ensure safe, effective, and scalable implementation aimed at reducing preventable complications and strengthening patient confidence.
Sarah Ahmed Alkindi, Saed Amer, Mecit Can Emre Simsekler, Zakia Dimassi, Siddiq Anwar
Open Access
Article
Conference Proceedings
Zero-Trust Access Control for IoT in Critical Infrastructure Environments
Static permissions in conventional access control systems for the Internet of Things (IoT) are often persistent even after a device has registered in a deployment. Therefore, a compromised device may retain long-lived privileges through a cloned identity, and this increases the likelihood of unauthorized activity and lateral movement in the context of critical infrastructure environments. This paper presents a user-centred access control model that combines zero-trust principles and short-lived capability tokens. Devices are not trusted by default; each service request explicitly carries verifiable authorization. The policy engine issues tokens that bind device identity, target service, permitted operation, validity window, and contextual constraints. Gateways and services validate tokens for each request and deny requests that are expired or out of scope. As a result, misuse is limited without requiring continuous connectivity to the policy engine. The proposed model is also protocol agnostic, and it transports tokens via application-layer message exchanges across heterogeneous IoT stacks. A simulation-based evaluation using a heterogeneous IoT model assesses credential cloning, unauthorized invocation, and compromised-node scenarios. At high compromise levels, unauthorized request success drops from 74% in the baseline to 6% under the proposed model. The operational cost remains moderate, with a mean end-to-end latency increase of about 20% and total communication overhead between 21.25% and 30.75% across the tested token lifetimes. Overhead is split into token carriage and issuance; issuance cost falls as token lifetime grows. The results show reduced unauthorized requests with bounded per-request verification cost and moderate overhead.
Osama Khashan, Samar Mouti, Nour Khafajah, Nachaat Mohamed, Waleed Alomoush
Open Access
Article
Conference Proceedings
A Digital Twin Framework for Uncrewed Systems (UxS): Uncrewed Ground Vehicle (UGV) Use Case
The rapid convergence of Artificial Intelligence, the Internet of Things, and high-capacity Cloud Computing has accelerated the implementation of the Digital Twin (DT) paradigm. However, the practical realization of high-fidelity, interoperable DTs within a Cyber-Physical System (CPS) context remains hindered by architectural deficiencies and the complexity of integrating formal predictive models with the dynamic state of physical assets. This paper addresses these constraints by advancing the previously proposed Digital Twin Enabled Artificial Intelligence Uncrewed System (DEAUS) framework transitioning from theoretical abstraction to utility through a Model-Based Systems Engineering (MBSE) application featuring the Boston Dynamics SPOT as a Uncrewed Ground Vehicle (UGV). By developing a formal system architecture using System Modeling Language (SysML), this work serves as the axiomatic foundation for the Virtual Twin. This high-fidelity digital representation moves beyond traditional “Digital Shadows” by enabling bi-directional, synchronous emulation. A critical advancement in this work is the modeling of low-level system Technical Performance Measures and constraint blocks. This level of precision allows DT to accurately predict stability failure, model actuator wear, and optimize performance in hazardous environments. This foundational framework facilitates the conceptual modularization of complex systems into tractable units while enforcing the Single Source of Truth principle. Furthermore, the architecture embeds digital traceability and data lineage, essential for rigorous Verification and Validation, and for maintaining a forensically sound Chain-of-Custody. Providing an unambiguous blueprint, this domain-agnostic CPS DT solution is designed for replication across diverse UxS platforms, significantly enhancing operational guidance and system resiliency in time-critical mission scenarios such as Disaster Response.
Sai Raghava Pathuri, Bhushan Lohar, Sudhanshu Tarale, Ninad Pandit
Open Access
Article
Conference Proceedings
Employee Perceptions of Lean Production System Implementation: Linking Method Integration and Perceived Success with Occupational Health and Safety Outcomes
Manufacturing companies operate in increasingly volatile and turbulent market environments, requiring the implementation of effective organizational and operational measures to remain competitive. Lean Production Systems (LPS) have evolved into a widely adopted management framework in manufacturing, providing structured principles and methods to enable continuous improvement across the entire value chain. In Germany, the technical guideline VDI 2870 serves as a reference framework for LPS implementation and reflects the current state of the art in terms of Lean principles and methods. Beyond efficiency gains, Lean offers significant advantages such as improved process stability, enhanced quality, waste reduction, and increased organizational adaptability. However, despite the central role of employees in manufacturing systems, the human dimension of LPS implementation is insufficiently addressed in existing integration approaches. To investigate this gap, a systematic literature review was conducted to analyze employee perceptions of LPS implementation. The findings reveal substantial variation in how employees experience Lean transformations. A key result is the strong relationship between the degree of systematic method integration and perceived implementation success. Organizations that implement LPS in a coherent and structured manner report more positive employee perceptions, higher engagement, and improved occupational health and safety outcomes. In contrast, fragmented or isolated application of Lean methods often fails to produce sustainable improvements and may increase psychosocial stressors. The results indicate that employee perception serves as an early and sensitive indicator of the quality and sustainability of Lean implementation, highlighting from a Human Factors perspective that long-term success depends not only on technical-methodological integration but also on social coherence and employee sensemaking.
Anne Gemeiner, Uwe Dombrowski, Tim Mielke
Open Access
Article
Conference Proceedings
Dynamic Driver Risk Management: Integrating AFDD and FMEA
Driving accidents continue to be a major global concern, with many fatalities linked to human-factor limitations such as cognitive strain, fatigue, distraction, and other behavioral or physiological impairments. Although tools like Intelligent Speed Adaptation (ISA), GPS alerts, and driver-behavior programs help manage external road risks, they often do not address the internal driver-state conditions that trigger unsafe behaviors, including speeding, drifting out of lane, delayed braking, or reduced hazard awareness. This paper introduces an integrated, intelligence-based safety framework that combines Automatic Fatigue and Distraction Detection (AFDD) with a Dynamic Failure Mode and Effects Analysis (FMEA) model to more effectively reduce accident risk. The AFDD system continuously monitors physiological, behavioral, and environmental cues to identify early signs of driver impairment. These real-time observations are then fed into the dynamic FMEA, which updates the Occurrence and Detection ratings and produces a more accurate, continuously refreshed Risk Priority Number (RPN). Experimental results show that this combined approach can lower RPN values by 42–62% across several driver-related failure modes, including those associated with fatigue, distraction, visibility challenges, cognitive workload, and unsafe driving behaviors. By replacing static risk assessments with a real-time predictive approach, the framework enables safety decisions to be made more rapidly and with improved accuracy. This approach not only enhances human-centered transportation safety but also sets the stage for future improvements, including integration with V2X networks and fleet-wide risk management.
Saed Amer, Hasan Al-Ali, Maitha Almarzooqi, Khalifa Almansoori, Aisha Alzahmi
Open Access
Article
Conference Proceedings