Customer–oriented product design using an integrated neutrosophic AHP & DEMATEL & QFD methodology

Research on product design of FAHP bone marrow aspiration needle

Bone marrow aspiration is a crucial medical procedure to obtain bone marrow samples for diagnosis and treatment. However, traditional bone marrow aspiration needles face several challenges such as operational difficulties, inadequate sample acquisition, and patient discomfort. To address these issues, we aimed to design a bone marrow aspiration needle product by using fuzzy analytic hierarchical process (FAHP). The FAHP method was used to identify key factors in the design of the bone marrow aspiration needle, including technicality, usage, and application characteristics. The importance weights and priorities of each factor were determined through questionnaires and interviews with experts. A new bone marrow aspiration needle product was developed based on the results of the FAHP. The new product design considers the weights and priorities assigned to key factors, resulting in improved convenience during operation and a higher success rate of sample acquisition. This was achieved by optimising the structure and material selection of the needle. This study presents a novel bone marrow puncture needle product that effectively integrates the importance and priority of the key factors. It successfully enhances operational performance and patient experience, thereby offering an innovative solution to improve the success rate and therapeutic effect of bone marrow punctures.

Promoting active aging through assistive product design innovation: a preference-based integrated design framework

Background

With the accelerating trend of global aging, over one billion people need to use one or more types of assistive products. However, the high abandonment rate of current assistive products is affecting the quality of life of the older adults, posing challenges to public health. Accurately capturing the preference factors of the older adults in the design process is an important way to improve the acceptance of assistive products. In addition, a systematic approach is needed to translate these preference factors into innovative product solutions. These two issues are less addressed in existing research.

Methods

First, the evaluation grid method was used to conduct in-depth interviews with users and extract the structure of preference factors for assistive products. Quantification theory type I was used to calculate the weight of each factor. Secondly, universal design principles, TRIZ theory's contradiction analysis techniques, and invention principles were used to translate the preference factors into design guidelines. Then, finite structure method (FSM), morphological chart, and CAD techniques were used to visualize the design guidelines as alternatives. Finally, Analytic Hierarchy Process (AHP) was used to evaluate and rank the alternatives.

Results

A Preference-based Assistive Product Design Model (PAPDM) was proposed. The model includes three stages: definition, ideation, and evaluation. A case study on walking aid demonstrated the execution of PAPDM. The results show that 28 preference factors influence the four psychological needs of the older adults: sense of security, sense of independence, self-esteem, and sense of participation. These psychological needs were reflected in the shape, color, material, universality, user-friendly, reliability, and smart functions of assistive products. The preference factors were transformed into five design guidelines, and three alternatives were generated. Finally, the evaluation concludes that solution C was the optimal solution.

Conclusion

The PAPDM framework provides designers with a transparent, progressive approach to designing assistive products that meet unique needs and preferences of older adults. This enhances objectivity and scientific rigor in assistive product development, avoiding blind design and production. By considering the perspective of older adults from the outset, we can avoid high abandonment rates of assistive products and contribute to promoting active aging.

Improving the elicitation of critical customer requirements through an understanding of their sensitivity

Customer requirements (CRs) are the essential driven forces of product development. Constrained by the rigid budget and time allocated to product development, much attentions and resources should be paid on critical customer requirements (CCRs). Product design occurs with an increasingly frenetic pace of change in today's competitive market, and the changes of external environment will lead to the changes of CRs. Thus, involving the sensitivity of CRs toward influence factors to identify CCRs is of great significance to grasp the directions of product evolution and enhance market competitiveness. To fill this gap, this study proposes a CCRs identification method integrated Kano model and structural equation model (SEM). First, the Kano model is adopted to determine the category of each CR. Second, based on CRs' categorization, an SEM model is established to measure the sensitivity of CRs toward the turbulence of influence factors. Then the importance of each CR is calculated, and by integrating the sensitivity and importance, a four-quadrant diagram is constructed to identify the CCRs. Finally, the identification of CCRs for smartphone is implemented as an example to demonstrate the feasibility and additional value of the proposed method.

Comparative study between GRA and MEREC technique on an agricultural-based MCGDM problem in pentagonal neutrosophic environment

In this research article, an improved Multi-criteria group decision-making (MCGDM) strategy has been developed in pentagonal neutrosophic environment incorporating grey relational analysis and method on the removal effects of criteria (MEREC) techniques to address the relative advantages and disadvantages of these aspects in MCGDM. The aim of the study is to improve MCGDM technique which can capture the underlying uncertainties in robust way and can produce consistent results in a more rigorous way. Here, the conception of Hamming distance between two pentagonal neutrosophic number (PNN)s is introduced and the weighted arithmetic and geometric averaging operators in PNN arena are deployed to craft our computational technique more progressive and robust. An agriculture-based numerical problem is illustrated to demonstrate the ranking results of the alternatives by both of the techniques. After evaluating the problem by two aggregation operators, it is found that "plantation crop" is the best alternative under certain circumstances. Lastly, the sensitivity investigation is performed which reveals that with the appliance of arithmetic and geometric aggregation operators the best ranked alternative preserves its position by both of the ranking methods, which definitely exhibit the consistency and robustness of our executed methodology.

An integrated rough-fuzzy WINGS-ISM method with an application in ASSCM

Environmental deterioration, the COVID-19 pandemic and the Russian-Ukrainian conflict had brought chronic and dramatic impacts on agricultural supply chain around the world, resulting in high inflation rates and unavoidable costs. In order to reduce the adverse impacts and achieve sustainability in agricultural supply chain, it's necessary to scientifically explore composite indicators interlinked with agricultural sustainable supply chain management (ASSCM). The current study developed an integrated rough-fuzzy WINGS-ISM method to reveal the hierarchal and causal structure of indicators. It is found that environmental legislation, regulation, licensing, and government subsidies are the main drivers of ASSCM. Specifically, the government can guide the sustainable development of ASSCM by regulating the business environment. The financial support needs to be enlarged to optimize the structure in science and technology of ASSCM. Moreover, corporates and organizations are highly motivated by the increasing awareness of social responsibility and sustainability consciousness to improve the economic performance and achieve the ASSCM goals. A comparative analysis is proposed to illustrate the practicality and reliability of the results obtained from the proposed method, which can be utilized as a reference in ASSCM.

Investigation of the pharmaceutical warehouse locations under COVID-19-A case study for Duzce, Turkey

Pharmaceutical warehouses are among the centers that play a critical role in the delivery of medicines from the producers to the consumers. Especially with the new drugs and vaccines added during the pandemic period to the supply chain, the importance of the regions they are located in has increased critically. Since the selection of pharmaceutical warehouse location is a strategic decision, it should be handled in detail and a comprehensive analysis should be made for the location selection process. Considering all these, in this study, a real-case application by taking the problem of selecting the best location for a pharmaceutical warehouse is carried out for a city that can be seen as critical in drug distribution in Turkey. For this aim, two effective multi-criteria decision-making (MCDM) methodologies, namely Analytic Hierarchy Process (AHP) and Evaluation based on Distance from Average Solution (EDAS), are integrated under spherical fuzzy environment to reflect fuzziness and indeterminacy better in the decision-making process and the pharmaceutical warehouse location selection problem is discussed by the proposed fuzzy integrated methodology for the first time. Finally, the best region is found for the pharmaceutical warehouse and the results are discussed under the determined criteria. A detailed robustness analysis is also conducted to measure the validity, sensibility and effectiveness of the proposed methodology. With this study, it can be claimed that literature has initiated to be revealed for the pharmaceutical warehouse location problem and a guide has been put forward for those who are willing to study this area.

Selection of Production Reliability Indicators for Project Simulation Model

Due to technological enhancements, traditional, qualitative decision-making methods are usually replaced by data-driven decision-making even in smaller companies. Process simulation is one of these solutions, which can help companies avoid costly failures as well as evaluate positive or negative effects. The reason for this paper is twofold: first, authors conducted a Quality Function Deployment analysis to find the most vital reliability indicators in the field of production scheduling. The importance was acquired from the meta-analysis of papers published in major journals. The authors found 3 indicators to be the most important: mean time between failure (MTBF), mean repair time and mean downtime. The second part of the research is for the implementation of these indicators to the stochastic environment: possible means of application are proposed, confirming the finding with a case study in which 100 products must be produced. The database created from the simulation is analyzed in terms of major production KPIs, such as production quantity, total process time and efficiency of the production. The results of the study show that calculating with reliability issues in production during the negotiation of a production deadline supports business excellence.