Search Results (184)

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. Full article

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 $y\left(t\right)=A\left(e^{-kt}\right)+y_0$ calculate the equilibrium moisture (m_E). 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. Full article

Forests play a strategic role in global sustainability, and restoration is essential to meet ESG targets. Seedling quality strongly influences reforestation success, but standardized evaluation protocols are often lacking. This study aimed to identify and prioritize critical technical parameters of forest seedlings and determine the highest-priority factor affecting field performance. A total of 100 seedlings of Handroanthus impetiginosus and Sparattosperma leucanthum were evaluated using Quality Function Deployment (QFD), considering reforestation as the client to translate field performance requirements into nursery-level technical parameters. Seedling characteristics were compared to standards based on the literature and nursery best practices. QFD analysis revealed that stem thickness and integrity, absence of borers, well-developed and firm roots, and complete and healthy leaves were the most critical attributes. Hardiness, combining structural robustness, disease resistance, and vigor, emerged as the central factor. Observed non-conformities included disease (15%), stem bifurcations (10%), and substrate deficiencies (12%). These results demonstrate that QFD is an effective tool for systematically identifying and prioritizing seedling attributes. The study provides a structured approach for nursery evaluation and quality control, supporting informed decision-making to enhance the success of forest restoration projects. Full article

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. Full article

The symmetrical relationship between affective semantics and form bionics creates new possibilities for tea packaging. This study proposes a biologically inspired workflow for tea packaging design, effectively integrating natural forms, affective semantics, and sustainability assessment. First, ten natural forms suitable for bionic design were collected. The Affinity Diagram (AD) method was adopted based on evaluations from 20 consumers and tea merchants, yielding nine effective semantic and sustainability evaluation systems. Then, 10 domain experts scored the affective semantics, and the indicator weights were determined via the Precedence Chart (PC) method. The Quality Function Deployment (QFD) method was used to construct a relationship matrix between natural forms and affective semantics, identifying prioritized natural forms. Three biomimetic tea packaging designs were developed based on the three selected priority forms. Subsequently, the Criteria Importance Through Intercriteria Correlation (CRITIC) method calculated the objective weights of sustainability indicators. These weights were combined with Grey Relational Analysis (GRA) for comprehensive ranking to determine the optimal packaging scheme. The results show that stylish design (P1) has the highest weight among affective semantics, while low resource consumption (Q1) ranks first in sustainability evaluation indicators. Bamboo joint packaging was selected as the optimal design solution in the comprehensive ranking. This design process provides a methodological framework for tea packaging design, integrates biological bionics with affective semantics, and demonstrates potential for cross-category applications. Full article

The objective of this study is to construct a “Double-Circulation Double-Diamond” model integrating AHP, QFD, and TRIZ. This will enable the resolution of contradictions between user requirements and technical solutions in the design of highway maintenance unmanned vehicles. The construction of an efficient, safe, and iterative systematic design framework will be achieved by following these steps. The model incorporates both internal and external feedback loops into the conventional Double-Diamond framework, thereby establishing a dynamic closed-loop process of “requirement identification—technical transformation—contradiction resolution—feedback optimization.” AHP is employed to conduct a hierarchical analysis of user requirements; QFD is utilized to map these requirements to technical characteristics; and TRIZ is integrated to facilitate innovative problem-solving and solution generation. The proposed model has been demonstrated to be an effective means of achieving requirement hierarchy decomposition, technical translation, and resolution of key contradictions. MATLAB R2025b (version 25.2.0) simulations were employed to verify the role of the external feedback loop in scheme iteration and optimization. This confirmed the A* algorithm as the optimal path planning approach, which achieves a balance between efficiency and safety. The fuzzy comprehensive evaluation yielded a score of 3.142, indicating performance between “well achieved” and “fully achieved”. In comparison with conventional linear development methodologies, the “Double-Circulation Double-Diamond” model has been shown to markedly enhance the systematicness and dynamic adaptability of complex equipment design through the utilization of cross-phase feedback and methodological coupling. This approach provides a transferable design framework applicable to highway maintenance, unmanned vehicles, and other complex engineering systems. Full article

The cosmetics industry faces mounting environmental pressure due to significant carbon emissions and pollution from daily product consumption, necessitating the systematic integration of sustainability into product development processes. This study develops an integrated decision-support framework combining Analytic Network Process (ANP) and Quality Function Deployment (QFD) with sustainability dimensions to guide cosmetics companies toward environmentally responsible operations. Using facial moisturizer development as a case study, the methodology transforms customer ecological expectations and technical requirements into prioritized design requirements through interdependent matrices (W_C and W_A) and integrated weighting, incorporating both classical ANP priorities (α = 0.70) and sustainability E-Vector scores (β = 0.30). Statistical analysis confirms the independence of technical and sustainability dimensions (r = 0.127, p = 0.743), validating the additive integration approach. Results reveal that hybrid criteria combining regulatory compliance with environmental performance achieve top priority rankings, with the integrated model demonstrating 75–80% concordance with industry R&D priorities from leading cosmetic companies and parametric robustness across realistic weighting scenarios. The framework enables the systematic translation of consumer sustainability demands into operational strategies while preserving safety primacy. This ANP-QFD approach provides cosmetics managers with a quantitative tool for balancing environmental responsibility with market competitiveness, positioning sustainability as a strategic advantage in an evolving regulatory landscape. Full article

The growth of electronic word-of-mouth (eWOM) on digital platforms has heightened the need to distinguish authentic user-generated content from covert promotional material. This study proposes an integrated framework combining Natural Language Processing (NLP), machine learning, and Latent Dirichlet Allocation (LDA) to classify sentiment and detect advertising features in online game reviews. Reviews from the Steam platform were analyzed using Support Vector Machine (SVM), Decision Tree, and Naïve Bayes classifiers, with class imbalance addressed through SMOTE and SMOTE–Tomek techniques. The SMOTE-augmented SVM achieved the highest performance, with 98.18% overall accuracy and 97.52% negative sentiment detection. LDA and Quality Function Deployment (QFD) further uncovered latent promotional themes, providing insights into how advertising elements manifest in positive reviews and how negative feedback reflects genuine user concerns. The framework assists platform managers in enhancing eWOM credibility and supports marketers in designing data-driven advertising strategies. By bridging sentiment analysis with covert marketing detection, this research contributes a novel methodological approach for assessing review trustworthiness, improving transparency, and fostering consumer trust in digital information environments. Full article

Shading system design is a complex, multi-objective optimization problem that requires balancing interdependent economic, environmental, social, energy, architectural, and daylighting factors, while also integrating decision-makers’ preferences and user satisfaction. This study aims to develop and validate a hybrid decision-support framework that addresses both quantitative and qualitative data under uncertainty to improve shading system performance. This paper proposes a novel framework that integrates fuzzy logic with multi-criteria decision-making (MCDM) methods. The Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) is employed for criteria prioritization, whereas the Fuzzy Quality Function Deployment (Fuzzy-QFD) translates customer needs into technical requirements. Two evolutionary algorithms, the Single-Objective Genetic Algorithm (SOGA) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), were implemented and compared. The framework was validated through its application to an existing educational building in Mansoura, Egypt, evaluating both fixed and dynamic shading solutions. The results indicate that the proposed framework effectively translates customer requirements into design criteria and accurately identifies optimal shading solutions, with SOGA outperforming NSGA-II in optimization performance, while dynamic shading systems significantly enhance glare control and visual comfort, thereby confirming the framework’s efficiency in managing interdependent objectives under uncertain conditions. Overall, the framework provides a robust and systematic methodology for incorporating customer satisfaction into shading design and advancing sustainable building performance. Full article

Dynamic technological changes and the variability of market requirements pose significant challenges for modern manufacturing companies in the effective development and implementation of new technological solutions. The aim of the research was to develop an integrated approach covering all key stages of implementation—from formulating technological solutions, through selecting and evaluating variants, to preparing and managing production processes—under the conditions of a medium-sized manufacturing company specializing in the batch production of steel constructions. The analysis was based on an interdisciplinary approach, combining methods of creative design of new technological solutions, including Blue Ocean Strategy, value proposition design, and QFD methodology, with analytical approaches that include multi-criteria evaluation of solution variants, technical preparation of production, as well as the organization and management of production processes in modified organizational conditions. This approach enabled a comprehensive assessment of the developed solutions, taking into account both their operational potential and practical feasibility in realistic implementation conditions, through the use of case studies and simulations to validate the results. The results of the research indicate that integrating methods for creating new solutions with analytical assessment and simulation tools leads to a more precise and data-driven approach to process design, enabling better decision-making based on thorough analysis and predictive modeling. Furthermore, this approach allows for a significant reduction in the risk of implementation failure through early identification of potential problems. The conclusion of the study confirms that a comprehensive and interdisciplinary approach to the implementation of new technologies ensures better alignment with customer demands, reduces production downtime, and enhances product optimization and resource utilization, which are critical factors in building a sustainable competitive advantage for manufacturing companies. The proposed approach enables more deliberate design and organization of manufacturing processes, supporting their flexible adaptation to changing market and technological conditions. Full article

In the context of deep AI–design integration, traditional methods struggle to translate multi-source requirements into sustainable engineering solutions while balancing innovation with practicality. This study proposes AQTA, an intelligent design framework that integrates Analytic Hierarchy Process (AHP), Quality Function Deployment (QFD), Theory of Inventive Problem Solving (TRIZ), and AI-Generated Content (AIGC) to enable sustainable product development. AQTA employs a four-stage closed-loop process: requirement analysis, contradiction resolution, solution generation, and validation. QFD and AHP quantify user and sustainability requirements to identify key contradictions, TRIZ resolves technical conflicts and stimulates innovative solutions, while AIGC generates eco-efficient visual concepts through prompt engineering. Multi-criteria decision-making supports evaluation and optimization based on environmental and economic indicators. Empirical studies demonstrate that AQTA significantly enhances innovation quality, design efficiency, and sustainability performance. The framework provides a replicable, hybrid ‘theory-driven + AI-generated’ methodology, which is validated through the case study of urban fire trucks, contributing to sustainable manufacturing practices in the intelligent era. Full article

High uncertainty in new product development is primarily driven by multidimensional risks arising from dynamic interactions among factors including customer requirements (CRs), design characteristics (DCs), and solution decisions. To effectively address decision-making risks in uncertain environments, an integrative framework is proposed incorporating the Best–Worst Method (BWM), Interval-Valued Intuitionistic Fuzzy Quality Function Deployment (IVIF-QFD), and the IVIF-VlseKriterijumska Optimizacija I Kompromisno Resenje (IVIF-VIKOR) approach. Initially, CRs are identified through market research and focus group interviews, with weights determined by the BWM to enhance consensus and efficiency in judgment. Subsequently, an IVIF-QFD model is constructed. This model effectively addresses the fuzziness in expert judgments during the translation of CRs into DCs, strengthening its expressive capability in uncertain environments. Finally, candidate solutions are generated for critical DCs, and the IVIF-VIKOR method is employed to rank these solutions, identifying the Pareto-optimal solution. The framework’s effectiveness is validated by a steering wheel design, in addition, sensitivity analysis and comparative experiments are employed to quantify the robustness of the framework against parameter variations. This paper not only theoretically establishes a collaborative decision-making paradigm for uncertain environments but also provides an operational end-to-end decision support toolchain. Full article

High-overload testing equipment is a key platform for evaluating mechanical performance under extreme conditions, requiring specialized functional design to meet stringent operational demands. The current design process faces numerous challenges, including overreliance on designers’ experience, incomplete requirement analysis, and insufficient automation, resulting in suboptimal solutions. To address these issues, in this study, we propose an integrated method based on axiomatic design (AD) and the Theory of Inventive Problem Solving (TRIZ). This method systematically decomposes technical specifications, maps requirements to a structural framework, and resolves design conflicts using inventive principles. The method employs a comprehensive evaluation framework combining the analytic hierarchy process (AHP) and quality function deployment (QFD) to quantitatively assess candidate designs. It facilitates the development of efficient, standardized high-load testing equipment solutions, enhancing design reliability and innovation capabilities. Full article

The rapid development of contemporary artificial intelligence and Internet of Things (IoT) technologies has given rise to the emerging paradigm of the AIoT (Artificial Intelligence of Things), which is profoundly impacting human life and driving the digital transformation of industries and society. The AIoT not only enhances product functionality and convenience but also accelerates the achievement of the United Nations Sustainable Development Goals (SDGs). However, the widespread adoption of these technologies still poses challenges related to social inclusivity, particularly regarding insufficient accessibility for elderly users, which may exacerbate the digital divide and social inequality, contradicting SDG 10 (reducing inequality). This study integrates AIoT product development processes based on sustainability and universal design principles using methods such as Quality Function Deployment, the Analytic Hierarchy Process, the Scenario Method, the Entropy Weight Method, and Fuzzy Comprehensive Evaluation. The features of this process include ease of operation and high flexibility, making it suitable for cross-departmental product development while prioritizing the needs of diverse age groups throughout the development process. The research findings indicate that the AIoT product concepts proposed can meet the needs of diverse users, contributing to the realization of age-friendly products. This study provides a reference point for future AIoT product development, promoting the inclusive and sustainable development of smart technology. Full article

Lean construction is considered a new methodology for minimizing the causes of waste that hinder the achievement of green building (GB) goals. The main aim of this study is to develop a lean model using fuzzy logic technique to mitigate causes of waste effect in GB projects and to determine the most appropriate lean tools affecting these causes. The inputs of this model include GB waste and four lean tools, comprising Quality Function Deployment (QFD), Last Planner System (LPS), Value Stream Mapping (VSM), and 5S, while the outputs include four improvement level indices based on the lean tools. The model uses various logical rules to achieve several relations among the inputs and outputs, and it is applied and verified using data related to several causes of waste categorized under five groups. The strongest correlation is found between VSM and 5S indices, while an adverse relationship is observed between QFD and 5S indices. The results indicate that a cause of waste that refers to poor assessment of site conditions is considered the most substantial one due to its high improvement level indices across all lean tools. The most significant waste group is related to GB stakeholders, which contains 38% of key causes of waste. The improvement using QFD increases by 10% compared to VSM and 28.20% compared to 5S. QFD and LPS are measured as the most suitable lean tools to mitigate the causes of waste effects due to their high impact and high improvement level indices. Full article