Improve Product Design Research Articles

An observational study of female body shape characteristics in multiracial Malaysia

Abstract The current study aims to explore the representative and typical female body shapes through in-depth observation and analysis of the diversity in Malaysian female body shape characteristics across different ethnic and age groups, which assists in facilitating the establishment of accurate apparel size standards. This study was conducted in Selangor, which is the most populous and developed Malaysian state. The data were collected via a highly systematic approach. The K-mean clustering method was applied to synthesise and analyse the collected data, which classified the body types into five major clusters. The results not only revealed significant differences in the distribution of body shapes across ethnic and age groups but also highlighted the limitations of the various sizing systems currently employed by the Malaysian apparel industry to fulfil the requirements of the diverse Malaysian market. The findings provided practical implications for apparel manufacturers and retailers with concrete evidence to support improved product design and enhance customer satisfaction. A deeper understanding and categorisation of Malaysian females’ body types could also assist the apparel industry in developing more tailored sizing systems. Resultantly, an improved system minimises return rates, optimises inventory management, and supports environmental and sustainability goals by reducing overproduction.

Redesign of Coffee Enema Basket Using DFMA for Improved Efficiency

The general background highlights the growing popularity of coffee enemas, necessitating improvements in product design for better usability and economic viability. The specific background addresses inefficiencies in existing enema basket designs, which often lack user-friendly features. The knowledge gap identified is the absence of systematic approaches to optimize both manufacturing and assembly processes for this product category. The aim of the research is to develop a more efficient and safer design by employing the DFMA methodology. Results indicate that the new design not only reduces the number of components by two but also achieves a 36% improvement in assembly time and a 13% reduction in assembly costs. The novelty of this study lies in the integration of additional safety features while maintaining component efficiency, enabling multiple uses through a removable filter design. The implications suggest that adopting DFMA in product development can significantly enhance both economic and functional aspects, providing a model for future designs in consumer health products. Further research is recommended to incorporate Quality Function Deployment (QFD) for gathering consumer feedback to refine the design according to user preferences. Highlights: The study uses DFMA to enhance the efficiency and safety of coffee enema baskets. The new design reduces assembly time by 36% and costs by 13%. Incorporation of safety features and a removable filter for multiple uses addresses existing usability issues. Keywords: Coffee Enema, DFMA, Product Design, User Safety, Assembly Efficiency

Innovative Applications of Intelligent Technology in Infant Care Products: A Study on the Design of Bottle Warmers and Milk Warmers

This paper delves into the innovative application of intelligent technology in the design of infant care products, with a particular focus on the design of smart bottle warmers and milk warmers, and their impact on user experience. The study begins by analyzing the current market demands and limitations of existing products in the field of infant care products, followed by a detailed discussion of the design features, technological advantages, and user experience of HAUTURE brand products. Through an in-depth analysis of the temperature control technology of the smart bottle warmer, the user interface design, and the dual-sealing system and vibration reduction and noise reduction technology of the milk warmer, this paper reveals how intelligent technology enhances product performance and user satisfaction. In addition, this paper examines the role of user feedback in product design improvement and proposes solutions to technical challenges. Finally, it provides an outlook on the future development trends of intelligent infant care products, predicting how intelligent technology will further shape innovation in this field.

Scenario-Based Analysis of High-End New Energy Vehicle User Demands

With the rise in global environmental awareness and the rapid development of new energy technologies, high-end new energy vehicles (NEV) have become an important direction for the automotive industry. Consumer demand is evolving from merely seeking a means of transportation to desiring a comprehensive experience across various scenarios, emphasizing high quality, high performance, and environmental friendliness. Understanding the characteristics and trends of high-end NEV users' demands is crucial for car manufacturers in formulating market strategies, product design, and service improvement. However, current focus is often on policies and product design, lacking analysis of actual usage scenarios. To address this gap, this article employs scenario research methods to delve into the needs of high-end NEV users, selecting typical scenarios such as urban commuting, long-distance travel, and business operations. The research indicates that high-end NEV users are primarily young, highly educated, middle-to-high income individuals who exhibit rational consumption habits. Their most common usage scenarios include commuting to work, transporting family members, and shopping. In these contexts, there is a pronounced demand for vehicle safety performance, ride comfort, and intelligent features, each showing differentiated characteristics across various user groups. For instance, female users demonstrate a particularly strong preference for comfort-related configurations, whereas married users with children have the highest demand for spacious and convenient features. Based on these insights, this paper offers targeted recommendations from the perspectives of product design and marketing.

Product Colour Variation Management with Artificial Intelligence

This research focuses on the topic of using AI in color variant management in products to enhance the appeal and performance of the products in the marketplace by incorporating artificial intelligence, deep learning, and neural network systems. Real-time consumer and product information, preferences, buying history, and sales history; I created an AI model to predict and change product colors in real-time. The complete workflow used comprises data gathering, processing, and feature extraction, model training, integration of the color adjustment software tools, and finally, testing and validation. The efficiency of such AI-driven interventions was analyzed through the consumer satisfaction indices, the sales results, and the engagement data based on the consumption of digital platforms. This study demonstrates valuable potential of AI to improve product design application and development while providing valuable suggestions for Businesses adapting and improving market outcomes according to the changing consumer trends. Such an application of AI implements a new best practice in ways of enhancing futuristic consumer-oriented marketing approaches. The paper was first completed in 2021 and later I have modified the paper with latest updates till date 2024.

Integration of Design for Manufacture and Assembly with Product Design in Product Design Improvement: A Systematic Literature Review

This paper aims to investigate and analyze some previous literature relevant to the Design for Manufacture and Assembly and Design for Safety methods in international journals. An extensive review and analysis of the body of literature was conducted to discuss the findings in a few chosen papers pertaining to the application of both methods, using a survey based on descriptive and qualitative surveys published in international journals with publication period limitations from 2011 to 2023 published in proceedings and electronic journals in English. So far, there has never been any research or literature review that discusses the Integration of Design for Manufacture and Assembly with Design for Safety.

Leveraging digital data to facilitate circular control in the aftermarket – experiences from an international manufacturing firm

PurposeGrowing research attention has been given to both the circular economy and digitalisation in accounting research in recent years, but there are few studies exploring how digital tools are used to develop, analyse and respond to information for circular decision-making in industrial organisations. Therefore, this paper addresses how the data from digital technologies are leveraged in the aftermarket of an industrial firm for circular control.Design/methodology/approachThe paper develops an analytical framework that is then used to frame the findings through a single case study of an international heavy equipment manufacturer for circular control.FindingsThe case provides examples of how digital technologies are used for circular control, framed within the analytical model as the key contribution. The study illustrates the different ways through which the accounting information from such technologies supports the service marketing function through circular control and the types of controls needed for this.Practical implicationsManagers in large industrial organisations should ensure customer-facing staff have adequate digital competences and knowledge of circular products and services for marketing, product design improvements and material recovery that can help decrease costs and improve customer satisfaction. The digital systems need to be integrated with upstream and downstream partners.Social implicationsUnderstanding the transition towards increasingly circular product-service systems in industrial firms is important for current and future generations.Originality/valueThe originality lies in providing an empirical example of how digital technologies can be used to facilitate circular control and support the service marketing function in the aftermarket of an industrial firm.

Integration of Principal Component Analysis with AHP-QFD for Improved Product Design Decision-Making

The complexity of quality function deployment (QFD) matrices often hinders efficient decision-making in product design, leading to missed opportunities and extended development times. This study explores the integration of principal component analysis (PCA) with analytic hierarchy process-QFD (AHP-QFD) to address these challenges. PCA, a machine learning technique, was applied to QFD matrices from product design research to reduce complexity and enhance prioritization efficiency. The integrated method was tested with a product design team across various industries, including logistics, healthcare, and consumer electronics. The analysis demonstrated that PCA effectively reduced matrix complexity, optimizing feature prioritization. In the logistics sector, PCA explained 99.2% of the variance with the first five components, while in consumer electronics, it accounted for 86.9% with the first four components. However, PCA showed limitations in the healthcare sector due to evenly distributed variance among components. Expert feedback highlighted the practical benefits of the integrated approach: 75% of logistics experts and 62.5% of consumer electronics experts found the method clearer. For speed, 100% of logistics and 87.5% of consumer electronics experts preferred the method for quicker evaluations. For accuracy, 75% of logistics and 62.5% of consumer electronics experts deemed the method more accurate. Overall, the PCA-AHP-QFD method simplifies decision-making processes and reduces development time, particularly in industries where feature prioritization is crucial. These findings underscore the potential of the integrated approach to enhance product development efficiency and feature prioritization, with suitability varying based on industry characteristics.

Bibliometric analysis of model-based systems engineering in advanced manufacturing

PurposeModel-based systems engineering (MBSE) is an important approach for the transforming process from “document-centered” to “model centered” systems engineering mode in equipment development, which can effectively shorten the equipment development cycle and improve product design quality. This paper aims to understand if MBSE enables to support manufacturing and equipment development.Design/methodology/approachThe paper opted a bibliometric analysis of MBSE in domain of advanced manufacturing from different perspectives such as publication volume, research team, sources and keyword co-occurrence.FindingsFirstly, the application of MBSE in advanced manufacturing can be roughly divided into three stages. And MBSE has been widely implemented globally and has gradually formed several noteworthy teams. Secondly, this article has identified some high-quality sources, with a large number of publications and citations, the most influential publications focus on the practice or guidance of digital twins and intelligent manufacturing. Thirdly, research can be divided into six categories, including systems engineering, digitalization, intelligent manufacturing, product design, model and architecture and MBSE applications.Research limitations/implicationsBecause of the chosen research approach, the visualized network tends to lose certain information such as a few keywords may be inaccurately categorized.Practical implicationsThis paper comprehensively study the research status of MBSE in advanced manufacturing and forecasts future research trends, emphasizing the combination of intelligent manufacturing and digitization.Originality/valueThis paper fulfills an identified need to understand the current application status and future development trends of MBSE.

Embedded components design strategy framework for fused deposition modeling system

This paper proposed a framework for 3D printing of embedded objects specifically for the Fused Deposition Modeling (FDM) system. The main problem revolves around the difficulty of the additive manufacturing process to fully manufacture a product that can be used immediately after the printing process, thus creating the need for embedded component design to be utilized in the process. The framework was utilized to reduce components and assemblies to improve product design. To ensure environmental sustainability, the framework emphasizes materials used to make recyclable items. Design Process Flow, Design Knowledge, Key Enabling Technologies, and Design Application were used to create a four-layered design framework to tackle embedded product design challenges. The framework’s robustness and effectiveness were compared using three case studies: electric shaver, remote control and nintendo switch. The framework proposed a blade-interchangeable product for the electric shaver. In the second case study (remote control) the cover’s flexibility with similar sizes makes it reusable. In the third case study (nintendo switch), every part is interchangeable with numerous styles and designs, allowing the user to autonomously change to the desired pattern without external support. With its various features, the framework shows potential in reducing development time, cost, and resources to aid the embedded component design strategy and printing for the FDM process.

Identification of User Requirements and their Influencing Factors Based on Online Reviews and Operational Data

It is crucial for enterprises to clearly identify user needs during the process of formulating product design improvement plans. Therefore, it is essential to comprehensively and accurately identify user needs, explore the reasons behind the emergence of these needs, and incorporate user opinions into the process of product design improvement. A method is proposed to comprehensively and accurately capture user requirements and address the challenge of identifying the underlying causes of user requirements. This method utilizes online comments and operational data to identify user requirements and their influencing factors. First, text sentiment analysis techniques are employed to quantify the importance and performance values of product feature topics. Second, we construct a quadrant model to identify product features requiring improvement, and the original negative comments related to these features are traced. However, the quadrant model alone is insufficient to reflect specific product issues that users are concerned about. Therefore, a functional structure model based on product issues is designed to filter and identify factors that influence user requirements using operational data. Finally, a Bayesian network inference approach is utilized to identify the key influencing factors on user requirements, enabling analysis of the causes behind user requirements and the proposal of product design improvement strategies. The feasibility and effectiveness of the proposed method are validated through experiments conducted on heavy-duty truck data. By analyzing the original negative comments related to the power characteristics, specific user demands regarding the insufficient power of the product were identified, such as “obviously insufficient power when climbing slopes” and other issues. Based on the vehicle power system functional structure model, combined with expert knowledge and operational data, factors related to the state of parts and user behavior that may affect “insufficient vehicle power” were identified. Based on the analysis results, suggestions were made to improve the engine intake air temperature control strategy and to enhance vehicle performance by promoting correct user behavior through informational campaigns.

An investigation of the ensemble machine learning techniques for predicting mechanical properties of printed parts in additive manufacturing

This study investigates the ensemble machine learning models to predict the mechanical properties of the 3D-printed Polylactic Acid (PLA) specimens. We studied the effects of five process parameters, including the build orientation, infill angle, layer thickness, printing speed, and nozzle temperature, on the printed parts tensile strength and surface roughness. Machine learning models are developed using the experimental data collected from the printed 27 specimens. Gradient Boosting Regression, Extreme Gradient Boosting Regression, Adaptive Boosting Regression, Random Forest Regression, and Extremely Randomized Tree Regression models were developed during the machine learning modeling stage to predict the surface roughness and tensile strength of the printed parts. This research demonstrates the effectiveness of Extremely Randomized Tree Regression model in providing accurate tensile strength predictions with root mean square error (RMSE) of 1.03, mean absolute error (MAE) of 0.82, and mean absolute percentage error (MAPE) of 2.20%. Similarly, Random Forest Regression model shows better accuracy in predicting surface roughness having RMSE of 0.408, MAE of 0.31, and MAPE of 9.28%. Moreover, the comparative study confirms that ensemble machine learning techniques are more useful than the traditional support vector and k-nearest neighbor machine learning models for predicting the surface roughness and tensile strength of the printed parts. The results highlight a novel approach of using ensemble machine learning models in identifying complex correlations in the dataset, establishing the foundation for improved product design and mechanical property optimization through adjustment of the process parameters combination.

The role of heterogenous implementation on the uptake and long-term diffusion of agricultural insurance in a pastoral context

To make a difference in lower-income countries, agricultural innovations must be adopted and ultimately diffused across diverse local environments. This study contributes to the ongoing debate about the factors limiting the spread of agricultural innovations by considering the role of heterogenous supply in determining observed demand for the Index-Based Livestock Insurance (IBLI) product, which is a commercial insurance product serving historically uninsured pastoralists in the Horn of Africa. Analysis of sales data from 2010 to 2020 in Ethiopia and Kenya shows that local conditions can reduce the likelihood of supply channels reaching prospective clients, effectively excluding them from accessing insurance, while other factors can work towards increasing supply of insurance while also decreasing demand for it. Surveys collected from insurance sales agents reveals considerable heterogeneity in their ability to and effort in suppling IBLI. Discussions with IBLI’s providers confirms the role of supply constraints in observed demand; the firms consistently point towards the cost of last-mile extension and sales as their largest challenge to increasing sales, and emphasize that it is cost-prohibitive to provide equal access to well-trained insurance agents across the areas that they operate. These findings suggest that current investments aimed at increasing insurance coverage by increasing demand, for example through improved product design or by subsidizing premiums, should be accompanied by investments in developing more cost-effective marketing and distribution processes so that demand can be acted upon. On a broader level, the results highlight a need to consider non-random and incomplete supply as a factor when examining observed uptake of agricultural innovations.

THE ROLE OF TOTAL QUALITY MANAGEMENT AND TARGET COSTING IN REDUCING COSTS

In the contemporary business environment, organizations continuously seek strategies to enhance efficiency and reduce operational costs. This study explores the synergistic impact of Total Quality Management (TQM) and Target Costing (TC) in achieving cost reduction. Total Quality Management emphasizes a customer-focused approach, continuous improvement, and employee involvement, fostering a culture of quality throughout the organization. Target Costing, on the other hand, is a proactive cost management technique that sets cost targets based on market conditions and customer expectations, ensuring that products are profitable even before they reach the market. By integrating TQM and TC, organizations can achieve significant cost savings while maintaining high-quality standards. This research investigates the theoretical underpinnings and practical applications of both methodologies, examining their individual and combined effects on cost reduction. Through a comprehensive literature review and case study analysis, the study demonstrates how the alignment of TQM's quality-driven philosophy with TC's cost control mechanisms can lead to improved product design, efficient resource utilization, and enhanced customer satisfaction. The findings suggest that the implementation of TQM and TC not only drives down costs but also contributes to competitive advantage and long-term sustainability. The study concludes with recommendations for businesses seeking to leverage these strategies for optimal performance and cost efficiency.

A PTM-Based Framework for Enhanced User Requirement Classification in Product Design

Accurately identifying and classifying customer requirements is crucial for successful product design. However, traditional methods for requirement classification, such as Kano models based on questionnaires, can be time-consuming and may not capture all requirements accurately. Analyzing large volumes of user reviews using simple natural language processing techniques can also result in accuracy issues. To address these challenges, we propose a framework that combines pre-trained models (PTMs), Kano models, and the sentiment analysis technique. Our approach integrates an LDA-K-Means model enhanced by PTM ERNIE for pinpointing product feature topics within user reviews. Then, a sentiment analysis is performed using the fine-tuned PTM SKEP to assess user satisfaction with features. Finally, the Kano model is applied to perform requirement classification. We evaluate our framework quantitatively, demonstrating its superior performance compared to the baseline models. Our sentiment analysis model also outperforms the other baseline models. Moreover, a case study on smartphones illustrates the effectiveness of our framework. This research results suggest that leveraging a suitable PTM can better address the problem of requirement classification in user review analyses, leading to improved product design.

Research on the influence of vehicle durability vibration on gap flush of exterior trim parts

Maintaining the newness of a vehicle is currently significant concern for users and vehicle companies. The gap difference between interior and exterior trims plays a crucial role in the overall appearance of vehicles. Over time, the gap and flush parameters of these trims may change due to the movement of components, putting forward higher requirements for the assembly accuracy and reliability design of automotive products. This article presents a study where a test car is subjected to endurance vibration on a wheel-coupled vibration bench. Through measuring the changes in gaps and flushes between the exterior trim parts at different endurance mileage intervals, the variation patterns are obtained. Using dimensional engineering technology, the effects of durable vibration on the gaps and flushes of the vehicle's exterior trim parts were determined. This study provides a foundation for the design of new interior and exterior decoration parts in automobiles. It also offers valuable insights for product design, development, and quality improvement. Problems can be predicted in advance in the early stage of design, and closely coordinate various aspects of production and manufacturing to obtain products with higher standards, requirements, and quality.

Multi-modal online review driven product improvement design based on scientific effects knowledge graph

Online reviews serve as significant channels for users to express their preferences, constituting an essential data source for enterprises to identify product requirements. However, with the widespread adoption of smartphones, the act of capturing spontaneous photographs has become a habitual practice for the majority, resulting in the increasing prevalence of supplementary visual expressions within online reviews. Therefore, an important research question emerges: How can product requirements be effectively extracted from multimodal online reviews and subsequently translated into product design proposals? In this paper, we establish a framework, seamlessly integrating aspect-based sentiment analysis, product requirement identification, and requirement mapping based on a scientific effect knowledge graph. Firstly, we conduct aspect term extraction on the online reviews, followed by aspect sentiment classification. Subsequently, we delve deeper into the analyzed results obtained from aspect-based sentiment analysis to identify preferences in product requirements. Finally, we employ requirement mapping based on a scientific effect knowledge graph to generate proposals for product design improvements. To validate the efficacy of our approach, we conducted experiments and the results demonstrate that our method outperforms alternative approaches, while the requirement mapping based on a scientific effect knowledge graph efficiently facilitates the realisation of product design improvements.

Causal Artificial Intelligence Models of Food Quality Data.

Causal Artificial Intelligence Models of Food Quality Data.

Factors that Drive Customers towards E-Commerce in Bangladesh: A Perceptional Exploration of Click and Mortar Business

E-commerce has facilitated trade in many effective ways. With the reduction of physical contact, it has brought up to a whole new dimension with innumerable potentials in molding modern day trade. The concept of e-commerce is relatively novel in Bangladesh. A customer would go fore-commerce only when there is benefit inherent in the process. Commonly, benefit is measured in terms of time, cost, accessibility, convenience, value addition and post purchase services. In the study, the customers’ perception regarding click and mortar business showed that e-commerce is better than traditional commerce broadly in terms of time, accessibility and convenience, and psychosomatic features. Specifically, the findings show that, e-commerce saves purchase and trading time; does easy accessibility of sites; makes more savings, supports fast transaction, helps in hassle free shopping; provides necessary information, and demands physical shipping or delivery. But the respondents perceive to suffer from cognitive dissonance (negative feeling after purchase), unsatisfactory post purchase service, and inadequate change/money back for delivered products. The findings suggest further improvement in product design, marketing strategy, and especially, internet infrastructure.

Machine Learning Algorithms for Improved Product Design User Experience

Machine Learning Algorithms for Improved Product Design User Experience