Product Development Process Research Articles

A Mixed-Methods Investigation of How Digital Immersion Affects Design for Additive Manufacturing Evaluations

Abstract Applications for additive manufacturing (AM) continue to increase as more industries adopt the technology within their product development processes. There is a growing demand for designers to acquire and hone their design for AM (DfAM) intuition and generate innovative solutions with AM. Resources that promote DfAM intuition, however, historically default to physical or digitally non-immersive modalities. Immersive virtual reality (VR) naturally supports 3D spatial perception and reasoning, suggesting its intuitive role in evaluating geometrically complex designs and fostering DfAM intuition. However, the effects of immersion on DfAM evaluations are not well-established in the literature. This study contributes to this gap in the literature by examining DfAM evaluations for a variety of designs across modalities using varying degrees of immersion. Specifically, it observes the effects on the outcomes of the DfAM evaluation, the effort required of evaluators, and their engagement with the designs. Findings indicate that the outcomes from DfAM evaluations in immersive and non-immersive modalities are similar without statistically observable differences in the cognitive load experienced during the evaluations. Active engagement with the designs, however, is observed to be significantly different between immersive and non-immersive modalities. By contrast, passive engagement remains similar across the modalities. These findings have interesting implications on how organizations train designers in DfAM, as well as on the role of immersive modalities in design processes. Organizations can provide DfAM resources across different levels of immersion, enabling designers to customize how they acquire DfAM intuition and solve complex engineering problems.

Sustainable business models in SMEs: The customer’s perspective


 
 
 The main purpose of the study is to identify the relationship between sustainability goals and measures of the degree of sustainable policy implementation, as well as the customer perspective as determined by the business model (BM) template of A. Osterwalder and Y. Pigneur. In the conducted research, the customer perspective takes into account five elements of the BM and each element was examined by several characteristics. The elements of the BM from the customer perspective are: customer segment, value proposition, distribution channels, customer relationships, and revenue. The study used the authors’ survey questionnaire, in which respondents, who were enterprises in the SME sector, answered questions about their own BM divided into three layers – business, social, and environmental. The results were statistically analysed using SPSS STATISTIC 28 software and a logit model was developed to verify the significance of the relationship between the binary variables studied. 303 enterprises participated in the survey, 59% of which were enterprises guided by sustainability goals (SGs) in their BMs. However, only 27% of those surveyed had measures of the degree of sustainable policy implementation.
 
 
 
 The conducted research confirmed that acting in accordance with SGs is strongly related to customer-oriented elements of the business model, such as (1) customer segregation by sex or by level of environmental awareness, (2) value proposition – an environmental orientation, (3) distri- bution channels – direct sales online, (4) customer relationships through dedicated personal assistance or automated service based on personal customer profiles, (5) revenue – sales of products and services. Slightly different results were obtained for using measures of the degree of the SD policy implementation, where the highest correlation occurred for: (1) main customers – manufacturing entrepreneurs, (2) customer segregation by end-user experience or by level of involvement in socially important issues, (3) value proposition – strengthening democracy, (4) distribution channels – wholesale stores are run by partners, (5) customer relationships through self-service or automated service based on personal customer profiles or involving customers in the product development process, (6) revenue – rental/lease/leasing or licenses.
 
 
 
 
 

A Fuzzy Ontology-Based Decision Tool for Concept Selection to Maintain Consistency Throughout Design Iterations

Abstract Concept selection is one of the most important activities in new product development processes in that it greatly influences the direction of subsequent design activities. As a complex multiple-criteria decision-making problem, it often requires iterations before reaching the final decision where each selection is based on previous selection results. Reusing key decision elements ensures decision consistency between iterations and improves decision efficiency. To support this reuse, this article proposes a fuzzy ontology-based decision tool for concept selection. It models the key decision elements and their relations in an ontological way and scores the concepts using weighted fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). By applying the tool to an example, this article demonstrates how the concepts, criteria, weights, and results generated for one decision can be reused in the next iteration.

Utilizing CNN to predict homogeneous thermo-mechanical properties of conductive layers for reliability numerical analysis in electronics

This study explores the application of Convolutional Neural Networks (CNN) in predicting the partitioned homogeneous properties (PHPs) of electronic product wiring structures, aiming to enhance the efficiency of reliability analysis through Finite Element Analysis (FEA). A systematic and novel method was developed to generate the input partitioned wiring diagram image sets that foster model generalization and universality. The performance of the CNN-based approach was assessed through regression analysis by employing a leave-one-out cross-validation (LOOCV) approach across three PCBs. Additionally, the predicted PHPs of one of the PCBs were applied in product-level FEA to validate their reliability, further demonstrating the practical applicability of the CNN-based method. The results demonstrate that a well-trained CNN model can accurately predict the properties of previously unencountered wiring structures, thereby facilitating direct application in product-level reliability FEA and improving the efficiency of reliability assessment. Furthermore, efficiency evaluation revealed that the CNN-based method offers significant advantages in terms of time and cost economy compared to the mesoscopic FEA method in determining, highlighting its potential for broader application in electronic product reliability analysis. The findings provide preliminary insights and propose strategies to enhance the applicability of CNN methods in this domain, ultimately aiming to improve the efficiency and reduce costs in reliability assessments, thereby streamlining the overall product development process.

Overcoming the semantic gap in the customer-to-manufacturer (C2M) platform: A soft prompts-based approach with pretrained language models

Customer-to-Manufacturer (C2M) is a strategy in smart manufacturing where customers collaborate with manufacturers for customized product development on an online platform. The platform enables the shift from the traditional manufacturing process, which is driven by research and marketing, toward a customer-centric product development process. However, a challenge arises as customers lack technical knowledge to communicate their product specifications effectively, creating a semantic gap. This paper proposes a soft prompt-based network structure that utilizes pretrained language models to bridge the semantic gap on the C2M platform. To address limited customer needs data and imbalanced classes, a large corpus of product review texts is used to establish a mapping between reviews and product specifications. A smaller set of customer needs text is then employed to adapt this mapping to the target customer needs-product specifications relationship, thereby closing the semantic gap. The experimental results demonstrate the effectiveness of the proposed model adaptation operation and the prompting structure. Additionally, the experiments highlight the robustness of the proposed method against variations in training data size, thereby mitigating the challenges posed by imbalanced classes. The proposed method could potentially bring innovation to product customization and C2M platform development. By bridging the semantic gap, companies can better integrate customers in the co-design process and effectively translate customer needs into actionable product specifications.

Forecasting the Mexican craft beer market: an integrated consumer perception conjoint analysis approach incorporating price and product attributes

PurposeThis paper aims to uncover patterns of Mexican craft beer consumers and guide companies’ decisions in the creation of new products, marketing strategies, advertising and promotion to increase craft beer sales and contribute to faster growth.Design/methodology/approachThis is a conjoint analysis with a selection of attributes for new or renewed products, marginal disposition to pay for particular characteristics through brand-specific choice-based design, and market simulation.FindingsThis paper clearly demonstrates consumers’ preferences and willingness to pay in Mexico, with a cutting-edge market research technique combining the prioritization of preferred craft beer characteristics, and the price consumers are willing to pay for such product characteristics.Research limitations/implicationsThe study's sample size of 501 responses is relatively small compared to the total number of craft beer consumers in Mexico. To enhance the validity and reliability of the findings, future studies should aim to obtain larger samples and compare their results with those of this study.Practical implicationsThis study has important implications for craft beer producers, allowing them to develop targeted craft beers with appealing attributes for Mexican consumers, such as color, aroma intensity, alcohol degree intensity, bitterness, foam level and price.Social implicationsThis study's market forecasting simulation technique is based on assumptions of consumer behavior and market dynamics. Although relevant variables were considered, unanticipated external factors or market changes could impact the forecasts' accuracy. This will allow for a more comprehensive understanding of craft beer consumer preferences in different markets and enhance the reliability of forecasting techniques.Originality/valueThis paper informs craft beer producers by providing valuable knowledge on customers’ preferences and willingness to pay to enhance craft beer companies’ product development processes.

TMM−Sim: A versatile tool for optical simulation of thin−film solar cells

The Transfer Matrix Method (TMM) has become a prominent tool for the optical simulation of thin−film solar cells, particularly among researchers specializing in organic semiconductors and perovskite materials. As the commercial viability of these solar cells continues to advance, driven by rapid developments in materials and production processes, the importance of optical simulation has grown significantly. By leveraging optical simulation, researchers can gain profound insights into photovoltaic phenomena, empowering the implementation of device optimization strategies to achieve enhanced performance. However, existing TMM−based packages exhibit limitations, such as requiring programming expertise, licensing fees, or lack of support for bilayer device simulation. In response to these gaps and challenges, we present the TMM Simulator (TMM−Sim), an intuitive and user−friendly tool to calculate essential photovoltaic parameters, including the optical electric field profile, exciton generation profile, fraction of light absorbed per layer, photocurrent, external quantum efficiency, internal quantum efficiency, and parasitic losses. An additional advantage of TMM−Sim lies in its capacity to generate outcomes suitable as input parameters for electro−optical device simulations. In this work, we offer a comprehensive guide, outlining a step−by−step process to use TMM−Sim, and provide a thorough analysis of the results. TMM−Sim is freely available, accessible through our web server (nanocalc.org), or downloadable from the TMM−Sim repository (for Unix, Windows, and macOS) on GitHub. With its user−friendly interface and powerful capabilities, TMM−Sim aims to facilitate and accelerate research in thin−film solar cells, fostering advancements in renewable energy technologies.

Integrating mid-infrared spectroscopy, machine learning, and graphical bias correction for fatty acid prediction in water buffalo milk.

Buffalo milk, constituting 15% of global production, has higher fatty acids content than Holstein milk. Fourier-transform mid-infrared (FT-MIR) spectroscopy is widely used for dairy analysis, but its application to buffalo milk, with larger fat globules, remains understudied. The ultimate goal of this study is to develop machine learning models based on FT-MIR spectroscopy for predicting fatty acids in buffalo milk and to assess the accuracy of commercial milk analyzers. This research provides a convenient, fast, and environmentally friendly method for detecting the fatty acid composition in buffalo milk. We employed six machine learning algorithms to establish a detection model for 34 fatty acids in buffalo milk. The predictive models demonstrated robust capabilities for high-content fatty acids [C14:0, C15:0, C16:0, C17:0, C18:0, C18:1, saturated fatty acid (SFA), monounsaturated fatty acid (MUFA)], with errors within a 15% range. Traditional FT6000 detection methods exhibited limitations in measuring SFAs and polyunsaturated fatty acids (PUFA). Implementing a mean difference correction of 0.21 for MUFAs and applying regression equations (SFA × 1.0639 + 0.0705; PUFA × 0.5472 + 0.0047) significantly improved measurement accuracy. This study successfully developed a predictive model for fatty acids in Mediterranean buffalo milk based on FT-MIR spectroscopy. Additionally, a correction was applied to the existing measurement device, FT6000, enabling more accurate measurements of fatty acids in buffalo milk. The findings have practical implications for the food industry, offering a faster and more reliable approach to assess and monitor fatty acid composition in buffalo milk, potentially influencing product development and quality control processes. © 2024 Society of Chemical Industry.

Copying and its place in product development: definitions and practices by fashion industry personnel

Having apparel similar to what others are wearing is a basic desire in the fashion world and results in copying. Copying is both easier with technology and more prevalent with the attraction of brands and logos; however, research in the fashion literature is limited in the exploration of copying. To address this gap, this study investigated the definition of copying, the integration of copying technologies into product development, and training within the industry relative to copying and counterfeiting. Ten product development personnel working in the fashion industry were interviewed using an online, structured survey technique. Findings indicate that copying is inherent in the product development process for fashion goods with important implications for education and training about copying and counterfeiting.

The psychological and behavioral consequences of customer empowerment in new product development: Situational framework, review, and research agenda

AbstractIn search of innovation and market success, firms have started to empower their customers in many ways, from customizing and self‐producing their own products (products made for one) to selecting and designing products for the broader marketplace products made for many. This power shift has important behavioral and psychological consequences for customers and, hence, has attracted considerable interest from academics and practitioners alike. However, the literature is scattered, provides inconsistent findings, and lacks both a comprehensive conceptualization and empirical overview. Specifically, extant literature neglects the situational nature of customer empowerment, equalizing inherently different customer empowerment activities while failing to consider the divergent effects on participating versus observing customers (i.e., customers who do not participate in the new product development process themselves). This limits advancement of the field, and impedes integration with the related fields of innovation, marketing, and consumer research. To facilitate a better understanding of the psychological and behavioral consequences of customer empowerment, we systematically review literature in the field and develop a conceptual framework that integrates different customer empowerment situations and their respective psychological (e.g., firm perceptions and feelings of empowerment) and behavioral (e.g., product preferences and willingness‐to‐pay) consequences. Using this framework, we structure previous research, highlight similarities and differences across customer empowerment situations, and set the stage for future research. By taking a customer perspective, this research advances our understanding of why some customer empowerment strategies are more successful than others (and under which circumstances). On a broader level, we show that adopting a behavioral and psychological perspective may be a promising way to study innovation.

Glutamate production from aerial nitrogen using the nitrogen-fixing bacterium Klebsiella oxytoca

Glutamate is an essential biological compound produced for various therapeutic and nutritional applications. The current glutamate production process requires a large amount of ammonium, which is generated through the energy-consuming and CO2-emitting Haber–Bosch process; therefore, the development of bio-economical glutamate production processes is required. We herein developed a strategy for glutamate production from aerial nitrogen using the nitrogen-fixing bacterium Klebsiella oxytoca. We showed that a simultaneous supply of glucose and citrate as carbon sources enhanced the nitrogenase activity of K. oxytoca. In the presence of glucose and citrate, K. oxytoca strain that was genetically engineered to increase the supply of 2-oxoglutarate, a precursor of glutamate synthesis, produced glutamate extracellularly more than 1 g L−1 from aerial nitrogen. This strategy offers a sustainable and eco-friendly manufacturing process to produce various nitrogen-containing compounds using aerial nitrogen.

Aesthetic Improvement: Innovating the Design and Development Framework for Engineering Products

One of the primary issues with many product design and development processes today is to have a good balance between the elements of form and function. While all products must be able to perform their intended functions, considerations of aesthetic features are also necessary for them to be accepted as a good quality design. However, most available design and development processes of engineering products have been more focused on achieving the functional aspects and tend to treat the aesthetic aspects as less crucial features. This approach has led to many failed functional products in the market as their physical design lacks appealing factors to the targeted customers. To improve the situation, a new product design and development framework is proposed in this study to better facilitate designers or engineers in creating an all-rounded quality product design. This new method is developed based on the findings from a conducted survey among engineering students, who are future product design engineers, to identify and resolve issues with current methods that they use for their design tasks. All in all, the new proposed method is essentially tailored to offer a comprehensive guide for developing beautiful and useful products based on a semantics design approach.

DESIGNING AN INTERDISCIPLINARY COURSE: CASE STUDY OF AN INTERDISCIPLINARY PROJECT COMBINING MARKETING RESEARCH, ENGINEERING, AND MEDIA ARTS

ABSTRACT Interdisciplinary courses and activities are becoming more popular as the benefits of collaboration have become apparent. For example, collaboration between different disciplines in organizations, such as engineering and marketing, has been shown to significantly enhance the product development process. Various strategies have been used to encourage this collaboration. One strategy is to provide instruction in collaboration across disciplines at the undergraduate collegiate level. This case study discusses an interdisciplinary project between Marketing Research, Engineering, and Media Arts. The basic design of the initial course, strategies used to enhance collaboration, student learning outcomes, identification of key issues affecting the interdisciplinary project, and a revised design for teaching future interdisciplinary projects are included. Also included are some of the major challenges and design issues with interdisciplinary work.

A Review on the Recent Advancements and Artificial Intelligence in Tablet Technology.

Tablet formulation could be revolutionized by the integration of modern technology and established pharmaceutical sciences. The pharmaceutical sector can develop tablet formulations that are not only more efficient and stable but also patient-friendly by utilizing artificial intelligence (AI), machine learning (ML), and materials science. The primary objective of this review is to explore the advancements in tablet technology, focusing on the integration of modern technologies like artificial intelligence (AI), machine learning (ML), and materials science to enhance the efficiency, cost-effectiveness, and quality of tablet formulation processes. This review delves into the utilization of AI and ML techniques within pharmaceutical research and development. The review also discusses various ML methodologies employed, including artificial neural networks, an ensemble of regression trees, support vector machines, and multivariate data analysis techniques. Recent studies showcased in this review demonstrate the feasibility and effectiveness of ML approaches in pharmaceutical research. The application of AI and ML in pharmaceutical research has shown promising results, offering a potential avenue for significant improvements in the product development process. The integration of nanotechnology, AI, ML, and materials science with traditional pharmaceutical sciences presents a remarkable opportunity for enhancing tablet formulation processes. This review collectively underscores the transformative role that AI and ML can play in advancing pharmaceutical research and development, ultimately leading to more efficient, reliable and patient-centric tablet formulations.

The Impact of Transformational Leadership Climate on Business Sustainability and New Product Development Process: Moderating Influence of Strategic Flexibility

Transformational leadership is believed to be crucial factor behind an organization’s success. Studies have been conducted on leaders’ transformational leadership style, but there has been limited research on transformational leadership climate (TLC) in an organization and its effect on new product development process (NPDP) and business sustainability (BS). This study fulfills this gap and it also contributes to the literature by testing the moderating role of strategic flexibility (SF) between TLC and NPDP & BS. The study draws its hypothesis on the basis of contingency theory, upper echelons theory and transformational leadership theory. The authors collected the data from the top managers of private educational institutes affiliated with BISE Multan and used SmartPLS for analyses of data. Based on the analyses of 145 questionnaires, we report that TLC enhances NPDP and BS whereas SF has a significant moderation impact on the relationship between TLC and BS. For the educational institutes, we suggest top management to acknowledge and implement TLC in their organizations for sustaining their performance and for successful launching of new programs/services. Additionally, the importance of resource and coordination flexibility is emphasized for sustainability.

Improvement of Process Conditions for H2 Production by Chemical Looping Reforming

A syngas production process was studied cyclically, exploiting the redox properties of Ce-based oxygen carriers. The two steps of the looping cycle were investigated through thermogravimetric analysis and fixed bed experiments. While TGA experiments were focused on the identification of the optimal temperatures ranges for methane partial oxidation (900–1000 °C) and carrier regeneration (400–900 °C), fixed bed testing was performed isothermally (at 900 or 950 °C), with a 10% CH4 feed stream in N2 to investigate material stability and cyclic performance reproducibility. The effect of the process times on carbon deposition, specific syngas yields, and selectivity was inspected, together with the investigation of best conditions to fully regenerate the carrier, adjust the syngas final ratio, and to ensure stable performances. The obtained results ensured the possibility to work in fully isothermal operations, with CH4 conversion of up to 38% and specific yields of syngas per mass of O2 carrier between 4.0–6.8 mmol∙g−1, preserved even across cycles, thus paving the path to the development of alternative and effective processes for syngas production. Under the operating conditions of the lab-scale experiment, an effective reforming time was 20 min, corresponding to 1.16 times of the characteristic time of reaction kinetics at 950 °C.

Implementation of the User Centered Design (UCD) Method for MSME E-commerce

Web and mobile-based application development for MSMEs is very necessary to help marketing and increase revenue. A user-centered design (UCD) approach that actively involves users at every stage of the design process for product development becomes more effective and efficient, resulting in E-commerce applications that are designed together with users to produce applications that suit the needs of local MSME users

Experimental investigation and optimization of the effect garnet vibratory tumbling as a post-process on the surface quality of 3D printed PLA parts

The method known as additive manufacturing causes high surface roughness between layers depending on the technique used at the end of the product development process. This can be an important problem in three-dimensional (3D) manufacturing depending on the usage area. To solve this problem, in this experimental study, the effect of vibratory tumbling (VT) on surface roughness in 3D printing was investigated using garnet abrasive particles. Optimization with the best parameters was also performed and the results were analyzed. This experimental study investigated the effect of vibratory tumbling on surface roughness in 3D printing produced from Polylactic acid (PLA) material using garnet abrasive particles. The surface roughness (Ra) values were measured at different vibration durations for each mesh size. The results provide insights into the impact of vibratory tumbling on surface roughness in 3D-printed parts. The study involved subjecting the printed parts to vibratory tumbling using garnet abrasive particles of various mesh sizes (80, 90, 100, 120, 150, 180, and 220 mesh). Surface roughness measurements were taken at different vibration durations (2, 4, 6, 8, 10, and 12 hours) for each mesh size. A surface roughness measuring device was used to obtain the roughness values. The findings reveal that vibratory tumbling with garnet abrasive particles effectively reduces surface roughness in 3D printed parts. As the vibration duration increased, smoother surfaces were achieved. The data collected for each mesh size and vibration duration offer valuable insights into the relationship between vibratory tumbling and surface roughness in 3D printing. The surface roughness of the printed samples was reduced by 60% on average by using the optimum values after post-process. This research highlights the potential of vibratory tumbling as a viable method for improving surface roughness in 3D printing applications. Emphasis is placed on optimizing the vibration duration and selecting the appropriate mesh size to achieve the desired surface quality. Overall, this study contributes to our understanding of the effect of vibratory tumbling on surface roughness in 3D printing and provides considerable insights for enhancing surface quality in additive manufacturing processes.

A Co-Simulation Model Integrating a Musculoskeletal Human Model with Exoskeleton and Power Tool Model

Working at and above head height with a power tool represents a common activity in craft and assembly applications. To assist and protect the user from overload and injuries in these situations, the development and use of application-specific support systems, such as exoskeletons and power tools, have greatly increased in recent years. Thus, the integration of aspects of the user-centered product development of support systems in the early phases of product development process has high potentials. A common approach to integrate the user early in the product development process is the use of musculoskeletal human models, which allow the evaluation of effects on the human body. This could also be applicable in the mentioned use case to enable the evaluation of the interactions for the user. Therefore, a co-simulation model for virtual modelling and simulating human–machine interactions is presented. The co-simulation model is made up of a musculoskeletal human model and the models of the technical systems (exoskeleton and power tool). By applying the co-simulation model, the impact of technical systems on the human body can be taken into account to derive design alternatives for the technical system due to the requirements of the user. The paper describes the design of the co-simulation model and particularly, the interaction of the submodels. The evaluation of the co-simulation model is carried out with the help of a subject study for the selected use case working at and above head height. The results show plausible results for the muscle loads considering the support by an exoskeleton. Furthermore, the comparison of simulated results to measured muscle activations via surface electromyography shows a good agreement. Thus, the co-simulation model passes the test for functionality and seems to be applicable for the derivation of design alternatives of technical systems regarding the user needs. In future, the co-simulation model will be further validated with a higher number of subjects and to implement design alterations in the technical systems.

Implementation of discovery learning-based STEM education with hands-on activities in distance education

ABSTRACT The main purpose of this study is to reveal the opinions of science teacher candidates regarding the implementation of Discovery Learning-Based STEM Education in distance education with Applied Activities. The study was designed as a case study. Participants were first given basic chemistry education and introduced to the concepts of STEM, learning by discovery and applied activities. Participants were then asked to design an activity related to the chemistry topic of their choice. Three events were designed and delivered online. After the presentation, two academics who participated in the process evaluated the activities. At the end of the research, a semi-structured interview was conducted with each participant. Participants designed products through invention. As a result of expert evaluations, it has been determined that the product development process is suitable for learning through discovery.