Högskolan i Skövde

This paper presents the Adaptive Social Manufacturing (ASM) as a strategic framework for integrating emerging technologies, such as artificial intelligence, into manufacturing in a socially responsible, economically viable, and practically feasible manner. Building on Industry 4.0 foundations, ASM addresses the need for a balanced, human-centric framework that aligns technological advancements with the ethical, social, and environmental values central to Industry 5.0. The framework introduces core enabling capabilities, such as Digitalization Strategic Management and Resource Readiness, to drive responsible technology adoption, while preventive capabilities, including Technology Governance and Sustainability Performance Management Systems, mitigate potential socio-environmental risks. To validate the feasibility and practicality of the framework, the study incorporates a longitudinal case study of a medium-sized healthcare product manufacturer. This case demonstrates how ASM facilitated real-time monitoring, process integration, and sustainable innovation, achieving measurable operational improvements while balancing economic, social, and environmental objectives. The findings highlight how ASM enables feasible, stepwise digital transformation, ensuring that economic gains are leveraged to promote social inclusion and environmental responsibility. ASM offers a structured pathway for manufacturers seeking to transition beyond efficiency-driven digitalisation toward value-driven innovation, and holds promise as a guiding framework for policy design, workforce development, and cross-sectoral collaboration in future industrial ecosystems. 

With the emergence of Industry 5.0 and an increasing focus on human-centric approaches in manufacturing, the analysis of workers in production systems has gathered significant interest among researchers and practitioners. Previous studies have explored the impact of various aspects, such as skills, fatigue, and circadian rhythms, on human performance. However, the cumulative effect of these aspects as disturbances on work performance has yet to be fully elucidated. This study introduces an approach using the Functional Resonance Analysis Method (FRAM) to investigate the impact of multiple disturbances on workers’ performance. Furthermore, this approach explored how the resilience-related skill aspects of workers affect their performance under multiple disturbances. A case study on engine test and repair processes was conducted, employing qualitative data collection and semi-quantitative simulation studies examining the impact of combined disturbances across 4,094 scenarios. The results show that a larger number of compounded variabilities expressed in Common Performance Conditions (CPCs) made it significantly challenging to recover work performance, and CPCs with particularly critical effects were identified. In addition, the FRAM model of skilled workers was shown to sustain higher performance across more scenarios. The approach of this study has demonstrated its ability to provide insights for effectively and safely managing production systems while considering complex disturbances.

The advent of Industry 5.0 and advancements in collaborative robot (cobot) technology have driven many industries to adopt human–robot collaboration (HRC) in their assembly lines. This collaborative approach, which combines human expertise with robotic precision, necessitates an optimized method for balancing and scheduling tasks and operators across stations. This study proposes various constraint programming (CP) models tailored to straight and U-shaped assembly layouts, with objectives such as minimizing the number of stations, reducing cycle time, and minimizing costs. To enhance real-world applicability, the models consider the presence of diverse humans and cobots with varying skills and energy requirements working collaboratively or concurrently on assembly tasks. Additionally, practical constraints are addressed, including robot tool changes, zoning, and technological needs. Computational results demonstrate the superior efficiency of the proposed CP models over state-of-the-art mixed-integer programming models, validated through a case study and a comprehensive set of test problems. The results indicate that U-shaped layouts offer greater flexibility than straight-line configurations, particularly in reducing cycle time. Furthermore, higher HRC levels, including more humans and cobots, can significantly improve the number of stations, cycle time, and cost by up to 50%, 29%, and 36%, respectively.

Purpose: Healthcare wastes (HCWs) present substantial environmental and societal risks, including infection and exposure to hazardous substances. The aim of this study is to present a new multi-criteria decision-making (MCDM) method, named the ELECTOR method, for selecting the best healthcare waste disposal method (HCWDM) based on a comprehensive list of criteria. The main research question of this study is: What is the prioritization of HCWDMs considering economic, environmental, technical and social criteria?

Design/methodology/approach: This research employs a novel hybrid MCDM method to evaluate and select suitable HCWDMs. Initially, a comprehensive set of criteria for assessing and prioritizing HCWDMs is established. Criteria weights are determined using the best-worst method. Subsequently, a hybrid MCDM method is introduced to rank the HCWDMs. Fuzzy numbers are applied to handle qualitative criteria uncertainties. The proposed method is applied to a real-world case study to prioritize HCWDMs.

Findings: A total of 24 criteria, including two novel criteria (“System process speed” and “System setup speed”), for evaluating and prioritizing the HCWDMs were identified from the literature review and case study analysis. The study showed that the key criteria influencing HCWDM selection were “Operation cost”, “Occupational hazards of human resources”, and “The impact of released substances on health”. Based on the results, the autoclave, encapsulation and hydroclave methods are identified as the most suitable HCWDMs for the studied case, respectively.

Originality/value: This study introduces a novel hybrid MCDM method tailored for HCWDM selection, enhancing the robustness of the decision-making. The inclusion of innovative criteria and the integration of fuzzy numbers to address qualitative ambiguities strengthen the originality of the findings. Specifically, introducing “System process speed” and “System setup speed” contributes to expanding the criteria landscape in HCWDM research.

Objectives: This research aims to enhance the quality of hospital services by utilizing Quality Function Deployment (QFD) with a novel Multi-Dimensional House of Quality (MD-HOQ) approach. This method integrates Service Quality (SERVQUAL) analysis and considers resource constraints, such as financial and workforce limitations, to select and prioritize technical requirements effectively.

Methods: The proposed MD-HOQ approach was applied to a private hospital in Tehran, Iran. Data were gathered from a sample of 8 experts and a sample of 386 patients, using 2 in-depth interviews and 4 questionnaires. The process included identifying hospital sections and determining their importance using the Analytic Hierarchy Process. Patients’ needs in each section were then identified and weighted through SERVQUAL analysis. Subsequently, technical requirements to meet these needs were listed and weighted using MD-HOQ. A mathematical model was employed to determine the optimal set of technical requirements under resource constraints.

Results: Application of the MD-HOQ approach resulted in the identification of 50 patient needs across 5 hospital sections. Additionally, 40 technical requirements were identified. The highest implementation priorities were assigned to “training practitioners and nurses,” “improving the staff’s sense of responsibility,” and “using experienced specialists, physicians, and surgeons.”

Conclusions: The integrated QFD approach, utilizing MD-HOQ and SERVQUAL analysis, provides a comprehensive framework for hospital managers to prioritize technical requirements effectively. By considering resource constraints and the gap between patient expectations and perceptions, this method ensures that resources are allocated to the most impactful technical requirements, leading to improved patient satisfaction and better overall hospital service quality. This approach not only enhances the quality of hospital services but also ensures efficient utilization of resources, ultimately benefiting patient satisfaction.

The rise of Digital Twin (DT) and Discrete Event Simulation (DES) technologies in manufacturing underscores the critical importance of accurate data. Without key data attributes, DT models become unreliable for system optimization and decision-making support. Through a case study, this research contributes to the knowledge domain by identifying essential data attributes for effective DES-based DT implementation and categorizing them according to their availability and relevance to various optimization objectives. To achieve this, a mixed method approach was employed, combining a literature review, semi-structured interviews, and consultations with industrial practitioners, including simulation specialists, manufacturing execution system experts, and shop floor managers. The study’s findings reveal significant data gaps when using DES-based DT in the manufacturing sector and, through Quality Function Deployment (QFD) analysis, provide industry practitioners with actionable insights for prioritizing data collection efforts. Ultimately, this research facilitates data-driven decision-making in large-scale manufacturing environments by offering a structured framework for identifying key data attributes necessary to enhance DES-based DT.

In the rapidly evolving landscape of hyperconnected digital manufacturing, known as Industry 4.0, achieving energy efficiency has become a critical priority. As manufacturers worldwide strive to meet sustainable development goals, enhancing energy efficiency is essential for reducing operational costs and minimizing environmental impact. In this context, line balancing is a pivotal strategy for optimizing energy consumption within manufacturing processes. This study presents a comprehensive literature review on the Line Balancing Problems (LBPs) focused on enhancing energy efficiency. The review aims to provide a holistic understanding of this domain by examining past, present, and future trends. A systematic literature review is conducted using the PRISMA method, incorporating both qualitative and quantitative analyses. The quantitative analysis identifies prevalent patterns and emerging trends in energy efficiency optimization within the LBP domain. Concurrently, the qualitative analysis explores various aspects of existing studies, including configurations of lines, managerial considerations, objectives, solution methodologies, and real-world applications. This review synthesizes current knowledge and highlights potential avenues for future research, underlining the importance of energy efficiency in driving sustainable practices in Industry 4.0 and the emerging Industry 5.0 paradigm.

This study addresses the Distributed Flexible Job-Shop Scheduling Problem (DFJSP) while considering the delivery of customer orders and transportation time. To tackle the problem, a mathematical model is presented, and a novel variant of the Genetic Algorithm (GA), called Time-Travel GA (TTGA), is developed. The optimization objectives include minimizing the total orders’ delivery time to the related customers, reducing transportation and production costs, reducing manufacturing pollution, and maximizing the quality of completed orders. The performance of TTGA is tested by implementing it in a real-life manufacturing setting. Additionally, TTGA results are compared with those of four other algorithms over a set of test problems. Furthermore, the solutions obtained by TTGA are compared with the optimal solutions obtained by a commercial solver for small-size problems. The results of these comparisons collectively demonstrate the promising performance of TTGA in addressing the DFJSP.

Transitioning from fossil fuels to renewable energy is essential for sustainable development. However, a wide range of economic, technical, regulatory, and social barriers hinder this transition. This study aims to identify these barriers and propose prioritized strategies to overcome them. A novel decision-making framework is developed, combining an adapted Quality Function Deployment (QFD) model with a new hybrid fuzzy Multi-Criteria Decision-Making (MCDM) method, named the Fuzzy ARASKOR (F-ARASKOR) method. Data collection involved one in-depth expert interview and four structured questionnaires, completed by a panel of domain experts. First, a comprehensive list of barriers to renewable energy development was identified from the literature. Their importance weights were assessed using the first questionnaire. Then, strategies to overcome the barriers were derived through expert interviews and analyzed via the second questionnaire using a QFD approach. Next, these strategies were evaluated against seven criteria: impact on barriers, implementation cost, duration, capability, risk, complexity, and acceptability. Criterion weights were obtained through the Analytic Hierarchy Process (AHP) using the third questionnaire, and strategy performance was assessed through the fourth. Final prioritization was conducted using the F-ARASKOR method. The results identified 75 barriers and 38 strategies. Among them, “Policy Stability and Long-Term Commitment,” “Promote Renewable Energy as a Climate Solution,” and “Develop Training and Certification Programs” emerged as top-priority strategies. This study offers practical guidance for governments and industries to formulate stable, targeted policies, make effective investments, and address the key barriers hindering renewable energy development.

Owing to the realization of advanced manufacturing systems, manufacturers have more flexibility in improving their processes through design decisions. Design decisions in production lines primarily involve two complex problems: buffer and resource allocation (B&RA). The main aim of B&RA is to determine the best location and size of buffers in the production line and optimally allocate production resources, such as operators and machines, to workstations. Inspired by a real-world case from the marine engine production industry, this study addresses B&RA in high-mix, low-volume hybrid flow shops (HFSs) with feed-forward quality inspection. These HFSs can be characterized by uncertainties in demand, material handling, processing times, and quality control. In this study, the production environment is modeled via discrete-event simulation, which reflects the features of the actual system without requiring unreasonable or restrictive assumptions. To replace the expensive simulation runs, five widely used regressor machine learning algorithms in manufacturing are trained on data sampled from the simulation model, and the best-performing algorithm is selected as the predictive model. To obtain high-quality solutions, the predictive model is coupled with an enhanced non-dominated sorting genetic algorithm (En-NSGA-II) that incorporates lifelong meta-learning and features a customized representation and a variable neighborhood search. Additionally, a post-optimality analysis using a pattern-mining algorithm is performed to generate knowledge for allocating buffers and operators based on the optimization results, thus providing promising managerial insights.