ABSTRACT Nowadays, the adoption of integrated product-service solutions has become imperative for companies dealing with ever-increasing, sophisticated customer requests. Advanced informatics tools can support companies in collecting and interpreting a large amount of information to provide more customer-tailored solutions. However, few studies have investigated the use of Artificial Intelligence (AI) tools in multiple criteria decision-making (MCDM) for the development of integrated product-service offerings. Using a case study from the green energy sector, this study explores the integration of AI tools with Quality Function Deployment for Product-Service Systems (QFDforPSS) to refine customer-centric PSS development by leveraging data from customer care services and customer relationship management systems. Results obtained by AI tools were compared with those of experts. Research findings underscore AI’s capability to extract detailed insights from customer data, enabling a more holistic and concurrent development of PSS characteristics. This research introduces a novel data-driven framework for PSS design, demonstrating AI’s potential to transform customer service data into actionable specifications and providing a more thorough analysis of PSS features. The study not only suggests several implications that can assist management in improving business offerings in the photovoltaic industry but also augments knowledge on the capabilities of QFD powered by AI tools.

Utility poles are critical in supporting various electrical and communication infrastructure systems, including power transmission lines, streetlights, telephone networks, and cable services. Each type of pole whether steel, aluminum, or fiber-reinforced polymer (FRP) is designed with specific applications and performance characteristics in mind. This study presents a Quality Function Deployment (QFD) framework tailored for industrial applications, focusing on enhancing information integration to guide the selection of the most suitable pole type. The research examines advancements in utility pole technologies and management practices over the past two decades. Through market surveys, focus group discussions and individual interviews, ten KPIs were identified: service life, safety performance, overall cost, color retention, conductivity resistance, weight, production duration, transportability, installation approach, and wind resistance. Based on these KPIs, decision-makers outlined nine functional requirements that, when met, would enhance user satisfaction. The proposed framework was developed to support analytical evaluation and selection of the optimal pole type by aligning client needs with technical specifications. Using the QFD approach, the FRP pole emerged as the top-performing alternative, receiving a score of 4.12 out of 5. This framework provides a structured method for decision-makers to evaluate electrical pole options based on project-specific criteria, enabling informed and client-focused choices in early design phases.

Manufacturing decarbonization and sustainability improvement require decision-support methods that can prioritise actions across multiple, often conflicting dimensions, including product quality, process stability, resource efficiency, and environmental performance. In industrial practice, such decisions are further complicated by stochastic variability and the presence of dominant drivers, which limit the usefulness of conventional linear, weighted-sum scoring approaches. This paper proposes a non-compensatory decision framework with explicit stochastic uncertainty propagation that integrates quality function deployment (QFD) with life cycle assessment (LCA) to support robust, value-driven prioritisation of manufacturing improvement actions under uncertainty. The approach combines QFD-style influence factor modelling with LCA-based environmental indicators and employs a nonlinear, non-compensatory aggregation scheme to reduce sensitivity to arbitrary weighting and to better capture dominant and tail-risk effects. Uncertainty is propagated using Monte Carlo simulation, and the stability of prioritisation outcomes is analysed using sensitivity measures. The framework is demonstrated on an industrial old corrugated container (OCC) paper machine line using operational data from plant information systems, including quality, process control, laboratory, and maintenance databases. Results show that the proposed integration yields more stable and interpretable prioritisation of improvement actions than conventional compensatory scoring methods, particularly under variable operating conditions. The proposed approach enables practical, data-driven sustainability decision-making in complex manufacturing processes under variable operating conditions and alternative process configurations.

ABSTRACT Healthcare 4.0 can support SDG 3 and SDG 9 by using Industry 4.0 technologies to improve care delivery, operational efficiency, and environmental performance. In practice, hospitals face two persistent barriers: high uncertainty when prioritizing transformation initiatives and strict constraints related to data protection, interoperability, and legacy systems. This study addresses the need for a transparent, requirement‐driven way to identify and rank the critical success factors of sustainable Healthcare 4.0, and to translate those priorities into implementable technical actions. A hybrid framework combining Interval‐Valued Pythagorean Fuzzy AHP (IVPF‐AHP) and Quality Function Deployment (QFD) is proposed. IVPF‐AHP is used to weight sustainability‐oriented success factors under uncertainty, and QFD maps the weighted requirements to technical and managerial responses. The framework is demonstrated through a hospital case study in Istanbul with five Industry 4.0 experienced decision‐makers. Eleven sub‐factors across economic, environmental, and social dimensions are evaluated. The results show that the economic dimension carries the highest importance (48.0%), followed by the environmental (42.4%) and social (9.6%) dimensions. The highest‐priority sub‐factors are effective data source management and security (15.3%) and a digital strategy for sustainable transformation (14.6%), with circular waste management and enabling technological infrastructure also ranking among the leading requirements. The proposed approach supports decision‐makers by clarifying where to invest first to achieve a secure, measurable, and sustainability‐aligned Healthcare 4.0 transition.

Educators are challenged to keep courses relevant amid fast-changing technology and knowledge, yet systematic approaches for continuous course updates remain underdeveloped. To address this gap, we propose the Course Development-Quality Function Deployment (CD-QFD) framework, which adapts organizational QFD principles that center on creating value for customers as a core objective. Our objective is to provide educators with a streamlined, student-centered framework that can guide the continuous alignment of courses with evolving competency requirements. Methodologically, CD-QFD integrates insights from internal and external sources to identify learner competencies and map them onto interconnected matrices that align with the QFD principles. A case study of digital literacy course development for healthcare workers illustrates the practical deployment of CD-QFD. The contribution of this work lies in offering a systematic, discipline-neutral process for curriculum development that enables educators to refine competencies into specific subject areas, topics, modules, sections, and, finally, lesson objectives.

Projects integrating Science, Technology, Engineering, and Mathematics (STEM) are essential to interdisciplinary research. This study presents a STEM (Science, Technology, Engineering, and Mathematics) methodology with the primary objective of designing, constructing, and validating a functional cannabinoid extraction device. To inform the device’s drying parameters, the dehydration kinetics of female hemp buds or flowering buds (FHB) were first analyzed using infrared drying at 100 °C for different durations. The plants were cultivated and harvested in accordance with good agricultural practices using Dinamed CBD Autoflowering seeds. The FHB were harvested and prepared by manually separating them from the stems and leaves. Six 5 g samples were prepared, each with a slab geometry of varying surface area and thickness. Two of these samples were ground: one into a fine powder and the other into a coarse powder. Mathematical fits were obtained for each resulting curve using either an exponential decay model or the logarithmic equation yt=Ae−kt+y0 calculate the equilibrium moisture (mE). The Moisture Rate (MR) was calculated, and by modelling with the logarithmic equation, the constant k and the effective diffusivity (Deff) were determined with the analytical solution of Fick’s second law. The Deff values (ranging from 10−7 to 10−5) were higher than previously reported. The coarsely ground powder sample yielded the highest k and Deff values and was selected for oil extraction. The device was then designed using Quality Function Deployment (QFD), specifically the House of Quality (HoQ) matrix, to systematically translate user requirements into technical specifications. A 200 g sample of coarsely ground, dehydrated FHB was prepared for ethanol extraction. Chemical results obtained by Liquid Chromatography coupled with Photodiode Array Detection (LC-PDA) revealed the presence of THC, CBN, CBC, and CBG. The extraction device design was validated using previous results showing the presence of CBD and CBDA. The constructed device successfully extracted cannabinoids, including Δ9-THC, CBG, CBC, and CBN, from coarsely ground FHB, validating the integrated STEM approach. This work demonstrates a practical framework for developing accessible agro-technical devices through interdisciplinary collaboration.

Service quality in aviation fuel supply operations is critical due to its direct impact on flight punctuality, safety, and overall airport performance. This study aims to systematically identify service quality gaps, prioritize customer requirements, and determine actionable technical responses to improve refueling services at DPPU Juanda. An integrated framework combining SERVQUAL, Importance Performance Analysis (IPA), Focus Group Discussion (FGD), Best Worst Method (BWM), and Quality Function Deployment (QFD) is employed to translate customer expectations into prioritized technical initiatives. The results indicate that schedule integration with stakeholders, real-time monitoring of refueling operations, and strengthening apron audit systems emerge as the most critical technical priorities for service quality improvement. The proposed framework provides a structured decision-support approach that supports managerial decision-making and offers practical guidance for enhancing service reliability in aviation fuel supply operations.

This study aims to improve the quality of gallon water products and strengthen the competitiveness of CV. Anugerah Gemilang through the implementation of Quality Function Deployment (QFD) using the House of Quality (HoQ). The QFD approach is employed to translate the Voice of Customer (VoC) into technical product characteristics as well as service quality attributes that influence customer satisfaction. The research method applied is quantitative-descriptive with the support of qualitative data. Quantitative data were collected through questionnaires distributed to customers to measure the importance and satisfaction levels regarding product and service attributes, while qualitative data were obtained through in-depth interviews to reinforce and validate the questionnaire results. Subsequently, the VoC data were analyzed using the HoQ matrix to determine quality improvement priorities based on customer importance weights. This study is expected to produce strategic recommendations in the form of prioritized product and service quality enhancements focused on customer satisfaction, thereby supporting the sustainable improvement of the company’s competitiveness.

Human-centered artificial intelligence (AI) is increasingly recognized as important to advancing quality, safety, and trust in healthcare analytics, yet most validation frameworks continue to prioritize technical metrics over interpretability and stakeholder experience. Quality Function Deployment (QFD) offers a systematic method for translating customer requirements into technical design, but its application within healthcare AI remains limited. This study adapts QFD methodology to systematically align user feedback with prioritized technical requirements in healthcare AI systems. The analysis encompassed 14,938 patient reviews from 53 hospitals, from which 1,279 negative reviews were extracted for thematic analysis using large language model-driven coding (Cohen's Kappa = 0.81) and empirical factor structure, mapping multidimensional patient needs to technical specifications through a House of Quality matrix. Sensitivity analysis revealed that Granular Categorization demonstrated the highest improvement potential, achieving 21.9% advantage over LLM Coding Accuracy. This framework offers a potential approach for integrating technical validation and human-centered quality assessment, and may provide guidance for developing trustworthy, interpretable, and equitable digital medicine. While validation is limited to Malaysian private hospitals, the methodology offers a potentially scalable approach for healthcare AI development that warrants further validation. Future directions include real-world deployment across diverse populations and dynamic regulatory contexts.

Stroke has become one of the leading causes of death and long-term disability worldwide. Effective rehabilitation training is crucial for alleviating post-stroke sequelae and promoting functional recovery. However, traditional rehabilitation equipment is primarily designed for hospitals and rehabilitation centers, requiring patients to rely on medical staff for assistance and guidance, which results in high costs and prolonged treatment durations. Consequently, many already disadvantaged patients, due to mobility limitations or inadequate medical resources, miss the optimal rehabilitation period, potentially leading to permanent functional impairment. This study aims to design a home-based rehabilitation device that enables stroke patients to perform efficient and continuous upper limb rehabilitation training at home. First, the study employs the Kawakita Jiro Method (KJ) to identify user needs and utilizes the Analytic Hierarchy Process (AHP) to quantify the weight of key design factors. Next, the Quality Function Deployment (QFD) method is applied to translate user needs into specific design features. Finally, the feasibility of the design is evaluated using Fuzzy Comprehensive Evaluation (FCE). The results indicate that, compared to traditional devices, the proposed product supports remote monitoring and guidance from medical professionals while dynamically adjusting training programs based on patient needs, thereby enhancing rehabilitation effectiveness and providing new insights for optimizing home-based rehabilitation equipment.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-32496-8.

The study presents a methodology that supports the development of the Information Technology Project Management Plan (PMP) and Scope Plan (SP) elements by formulating structured sentences from Quality Function Deployment (QFD) outputs produced through the House of Quality (HoQ) matrix. Rather than proposing QFD as a new planning tool, the novelty lies in systematically mapping HoQ results to newly structured PMP and SP elements based on established standards and then transforming these results into planning statements through an integrated fuzzy logic layer. Additionally, the introduced fuzzy logic component addresses the uncertainty, prioritization needs, and subjectivity inherent in stakeholder inputs. This enables more accurate and consistent assistance in formulating plan elements, while strengthening the alignment between customer needs and project deliverables. Finally, the usefulness of the proposed methodology is demonstrated through an applied IT project case study that evaluates selected elements and highlights the concrete benefits of improving planning efficiency.

This paper presents a systematic approach to developing and refining new services and service processes by integrating the voice of the customer (VOC) through SERVQUAL and Quality Function Deployment. By quantifying VOC data and prioritizing customer requirements, companies can identify significant factors that most influence perceived service quality and translate them into actionable improvement strategies. Focusing on the restaurant industry, the study uses pre- and post-service surveys to classify service winners and service qualifiers. The resulting framework provides service providers with the agility to respond to changing customer expectations, shorter service life cycles, and increasing competitive pressures driven by rapid technological advancements and the push for sustainable practices.

A dual-similarity based consensus reaching process in quality function deployment with heterogeneous linguistic preference relations

Green smart home design based on quality function deployment integrating customer requirements and life-cycle sustainability in Z-numbers environment

PT XYZ is one of the companies engaged in the business of developing Architectural Products. The main products produced by PT XYZ include aluminum extrusion finished goods such as windows, doors, curtain walls, and bar profiles. Production output throughout 2024 reached approximately 78.7% of the 85% target that had been set. Based on the criteria for dies that can be redesigned, the aluminum profile 9K-005 was prioritized in the process of new die redesign. The purpose of this research is to redesign dies using the rational method in order to increase the output of the extrusion machine. The application of the rational method is expected to provide an optimal die design that can enhance the productivity of extrusion machines at PT XYZ.The rational method consists of seven stages: clarifying objectives, establishing functions, setting requirements, determining characteristics using Quality Function Deployment (QFD), generating alternatives, evaluating alternatives, and product improvement. The redesign results are illustrated with the aid of CAD/CAM. The best alternative die design was selected with the highest score of 3.79. Implementation of the new design in cavity 2 resulted in a productivity increase of 23.1%. This approach has proven effective in improving process efficiency and production performance at PT XYZ.

Rice milling is a vital agribusiness subsystem that produces rice and by-products such as seeds. UD. Sri Rejeki, a small-scale mill, still relies on manual seed packing using plates, which is repetitive, inefficient, and ergonomically risky. This study developed a vacuum-based ergonomic packing tool for rice seeds. Quality Function Deployment (QFD) identified worker needs, Rapid Upper Limb Assessment (RULA) evaluated posture, usability testing assessed tool performance, and the Mann-Whitney test compared workload before and after implementation. Design attributes were based on worker requirements, posture, and benchmarking. Usability testing showed three workers completed five tasks with a System Usability Scale (SUS) score of 70.8 (Grade C, “Good”). RULA indicated reduced posture risk from 7 (high) to 4 (low). The Mann-Whitney test confirmed a significant workload reduction after tool adoption. In conclusion, the vacuum-based ergonomic packing aid improved efficiency, reduced workload, and enhanced worker safety in small-scale rice seed packaging.

Magie Broom is an innovative cleaning tool designed by considering several principles such as ergonomics and anthropometry. This study aims to determine the optimal ergonomics design that can minimize the risk of musculoskeletal disorders (MSDs) that often arise from the use of traditional cleaning tools. The development of this tool involves several stages: literature review, field observations, and detailed design using the Quality Function Deployment (QFD) method, followed by testing. Anthropometric principles, especially for determining the optimal length and grip, are carefully considered to ensure the design meets user needs for ease of use, comfort, and cleaning effectiveness. The design The Magie Broom prototype was tested by various user groups such as housewives and students to obtain input used in refining the design. The test results showed that this tool was able to increase cleaning efficiency by provide 3in1 function and modularity. Its also provide comfort for users and reduce the potential for MSDs by decrease REBA score from 11 to 3. The design is very easy to carry, modular, and equipped with a rechargeable battery. The Magie Broom serves as a promising model for ergonomic product development especially in houshold tools, illustrating how thoughtful design can minimize physical strain and injury risk. Magie Broom offers a practical solution for everyday cleaning needs and has great potential to be further developed and marketed widely.

ABSTRACT Sustainable UAV adoption requires aligning the identification and priorities of user needs with the objective to mitigate flight noise. To link the two, we identify UAV user needs and estimate their baseline importance weights, then guide and re‐estimate these weights through a video‐based information intervention, enabling manufacturers to adopt the guided weights in low‐noise product design while meeting user demand. This study has two objectives that can be jointly operationalised in product design: (1) to identify UAV user demands and estimate their baseline weights via a two‐stage quality function deployment (QFD) and fuzzy best–worst method (F‐BWM) and (2) to guide the relative weighting of these demands through a video‐based information framework that encourages users to prioritise low‐noise related attributes when purchasing UAVs and to estimate the post‐guidance weights. The baseline analysis produced individual weights for six user demands and ranked ‘environmental and green design’ and ‘technical performance’ as the top two; although ‘environmental and green design’ was already highly weighted, the video intervention further increased its weight from 27.5% to 28.7%. The methodology provides guidance for manufacturers to optimise UAV design and reduce noise, promoting the sustainable development of the low‐altitude economy and the environment.

To address challenges in selecting analysis tools for product innovation design within smart manufacturing environments – such as difficulty in tool selection and high reliance on experts – this study proposes a multi-scenario-driven intelligent innovation tool recommendation method. A corresponding recommendation system was developed based on this approach. A standardised requirement expression format was established using Quality Function Deployment (QFD), alongside an ontology-based innovation tool knowledge base. Leveraging natural language processing and fine-tuning techniques, a domain-fine-tuned open-source large language model (DeepSeek-R1:7b) was applied to map the interrelationships among multiple demand scenarios and innovation tools within complex innovation chains. This led to the development of a self-feedback fine-tuning model for intelligent innovation tool recommendation. A software framework encompassing interaction, functionality, modelling, and data layers was constructed, leading to the development of a multi-scenario-driven intelligent innovation tool recommendation system. Comparative experiments validated the proposed method's effectiveness, showing that compared to raw natural language requirements and keyword-based matching, the recommendation accuracy increased from 68% and 45% to 89%, respectively. Finally, using the rapid disassembly structure innovation design of reactor fuel rods as a case study, the system's efficacy and practicality were demonstrated.

ABSTRACT In response to ecological sustainability and resource challenges, this study proposes an Eco-Service Design (ECO-SD) framework for university dormitories, as essential collective living spaces, with the goal of aligning human well-being and ecological transition. The framework consists of four stages: (1) Eco-Service Exploration to identify ecological values and user needs; (2) Eco-Service Visualization using service blueprints to identify barriers and potential impacts; (3) Eco-Service Ideation through KJ clustering, Analytic Hierarchy Process (AHP), Quality Function Deployment (QFD), Function Behavior Structure (FBS) modeling, and co-creation workshops to generate multifunctional solutions; and (4) Eco-Service Assessment using Heart Rate Variability (HRV), Pleasure-Arousal-Dominance (PAD) emotional analysis, Sustainability Design-Orienting (SDO) assessment, and ECO-SD evaluation. The experimental validation, combining HRV measurement and PAD emotional analysis, showed that the proposed Eco-Service-based design increased SDNN and RMSSD while reducing LF/HF, indicating greater emotional stability and relaxation. Participants reported a joyful emotional state, confirming the design’s positive impact on emotional well-being. The SDO and ECO-SD evaluation also demonstrated strong sustainability performance, validating the framework's ecological and service effectiveness. This interdisciplinary approach offers a replicable methodology for sustainable innovation in the design of collective living support systems and contributes to the broader discourse on ecological transition.