Early Stages Of Product Development Research Articles

Industry 4.0 and Sustainability: a Study on the Applicability of Additive Manufacturing as a Tool for Minimizing Environmental Impacts on Production Processes

Objective: The present research sought to analyze the applicability of additive manufacturing as a tool for minimizing environmental damage in production processes. Methodology: The research was conducted through an integrative literature review. Academic platforms such as SciELO and Google Academic were consulted, using specific keywords related to the theme. Inclusion criteria were established to select complete articles, in Portuguese and Brazilian languages, directly associated with the research theme, published between 2020 and 2024. Results and data analysis: The revised studies emphasize the growing importance of additive manufacturing (AM) and 3D printing technology in transforming production processes towards more sustainable and efficient practices. MA's ability to produce complex parts accurately and personalized, significantly reducing raw material waste through layer-by-layer manufacturing, stands out as a crucial tool to minimize environmental impacts on production. In addition, the integration of MA in the early stages of product development promotes efficiency in production, although more in-depth studies on its environmental and energy impacts are needed. Conclusion: It is therefore concluded that additive manufacturing is an essential tool for more sustainable and innovative practices in the industry, promoting the conservation of natural resources, the reduction of waste and the creation of adaptable and customized solutions. It is therefore up to companies and society to exploit and develop these technologies in search of a more sustainable and competitive future.

Molecular farming- importance of stewarding food crops engineered to produce transferred food allergens and non-food substances

Developers are looking for new ways to produce proteins and other substances for food, pharmaceutical and industrial use by genetically engineering food crops to produce the desired substance of interest (i.e., molecular farming). Developers should be aware of the food safety concerns, legality and potential liability, and loss of consumer confidence that could arise if food or other plant materials from these crops were to inadvertently enter the food supply and compromise safety. At the earliest stages of product development, developers should consider whether it is feasible to consistently steward their crops and resulting plant materials from development through disposal to ensure they do not enter the food or feed supply in a way that would be unlawful. Developers should engage FDA’s foods program when considering their stewardship program. While molecular farming holds promise for the economical production of specific proteins and other substances at a large scale, it is important for developers to consider the efforts needed to protect the food supply from the crops used for molecular farming- particularly when the crop chosen for molecular farming is a crop traditionally used for human or animal food.

Residual stress computation in direct metal deposition using integrated artificial neural networks and finite element analysis

Direct Metal Deposition (DMD) as a part of additive manufacturing processes, utilizes a laser heat source to deposit metallic material to construct components layer by layer, allowing for controlled manufacturing. DMD processes inherently induce residual stresses and distortions in the build part. While finite element (FE) analysis is one of the approaches to predict the residual stress, the high computational time required for the FE analysis makes it undesirable for the early stages of product development. This study extends a previously developed approach to more complex structures to evaluate the accuracy and computational efficiency of predicting residual stresses. For the present study, six different metallic structures with various complex shapes made from AISI 304 L were considered. Comparing the residual stress fields predicted by the integrated ANN-FE with thermomechanical FE analysis, the results demonstrated consistent distributions for all stress components across structures. Although localized regions exhibited higher errors, these areas coincided with low-stress zones, posing no critical concerns. Despite higher errors in some areas, most structures displayed prediction errors below 15%. Moreover, the analysis achieved a substantial computational efficiency enhancement of approximately 6 times for all structures analyzed.

EcoSketch: promoting sustainable design through iterative environmental assessment during early-stage product development

Sustainability has long been a topic of substantial interest the design and human-centered computing communities. With industries increasingly prioritizing climate targets, there is a growing demand for sustainable product design. This paper addresses this need through EcoSketch, a digital tool designed to democratize environmental impact assessments for product designers. Shifting typically retrospective evaluations to the early stages of product development, EcoSketch enables proactive consideration and adoption of sustainable alternatives. Unlike software tailored to environmental scientists, it minimizes the need for specialized training or extensive data inputs. We delve into the development and evaluation of EcoSketch, highlighting its unique features and usability strengths. The paper concludes by discussing design implications and proposing future research avenues to strengthen the intersection of human-computer interaction and sustainable product design, advancing progress on environmental challenges at the systems level.

Surveying Quality Management Methodologies in Wooden Furniture Production

Furniture production is a specific industrial sector with a high human labor demand, a wide range of materials processed, and short production runs caused by high customization of end products. The difficulty of measuring the aesthetic requirements of customers is also specific to furniture. This review of academic papers identifies and explains effective quality management strategies in furniture production. The reviewed literature highlights a range of quality management methodologies, including concurrent engineering (CE), total quality management (TQM), lean manufacturing, lean six sigma, and kaizen. These strategies encompass a variety of pro-quality tools, such as 5S, statistical process control (SPC), quality function deployment (QFD), and failure mode and effects analysis (FMEA). The strengths of these quality management strategies lie in their ability to enhance efficiency, reduce waste, increase product diversity, and improve product quality. However, the weaknesses concern implementation challenges and the need for culture change within organizations. Successful quality management in furniture production requires tailoring strategies to the specific context of the furniture production industry. Additionally, the importance of sustainability in the furniture industry is emphasized, which entails incorporating circular economy principles and resource-efficient practices. The most important finding from the literature analysis is that early detection and correction of poor quality yields the most beneficial outcomes for the manufacturer. Therefore, it is essential to strengthen the rigor of quality testing and analysis during the early stages of product development. Consequently, a deep understanding of consumer perspectives on required furniture quality is crucial. The review identified two research gaps: (1) the impact of unnecessary product over-quality on the efficiency of furniture production and (2) the influence of replacing CAD drawings with a model-based definition (MBD) format on quality management in furniture production.

Eco-performance evaluation of product designs – an integrated fuzzy complex proportional assessment with life cycle assessment

ABSTRACT The growing public awareness has driven enterprises to develop environmentally friendly designs to fulfill customer satisfaction. Some eco-design tools have been introduced to designers to support greener product design and development qualitatively. However, most require qualitative judgement during the evaluation process. Although life cycle assessment (LCA) helps quantify a product system’s environmental loads, it requires much time and resources for data collection. Design selections are not preferred during the early product development stage, especially when the product development schedule is rather tight nowadays. This paper proposes a practical approach integrating a fast-track LCA, fuzzy-set, with COmplex PRoportional ASsessment (COPRAS). This paper proposes an approach by incorporating the fuzzy-set, COPRAS, and LCA for decision-makers to evaluate design alternatives at the early product development stage. The originality of the paper is to provide an efficient approach to evaluating various design alternatives by quantifying their corresponding environmental burdens using LCA and fuzzy-set. Three distinct features can be found in the proposed generic approach. This study brings two contributions to the related field. For the area related to environmental impact assessment, this study illustrates the applicability of LCA in conjunction with COPRAS and fuzzy-set for the design alternative selection. Decision-makers can apply the proposed method that in-depth knowledge of environmental impact assessment is not essential, and the qualitative input required is minimal compared to a comprehensive LCA study.

A formal consideration of user tactics during product evaluation in early-stage product development

A formal consideration of user tactics during product evaluation in early-stage product development

A formal consideration of user tactics during product evaluation in early-stage product development

A formal consideration of user tactics during product evaluation in early-stage product development

Higher or Lower? - The Resolution of Analytical Pipelines for the Evaluation of Lipid Nanoparticle Critical Quality Attributes

Dynamic light scattering (DLS) is a routinely used analytical technique to measure the size distribution of a colloidal sample. Nanoparticle tracking analysis (NTA) has more recently emerged as an orthogonal technique to determine particle size and estimated concentration, although widespread use of NTA for lipid nanoparticle (LNP) analysis is not reported. Here, we use DLS and NTA to screen the stability of oligonucleotide drug loaded lipid nanoparticles (oligo-LNPs) at ultra low storage temperatures (-80 °C) in the presence and absence of a cryoprotectant (20 % sucrose, w/v) as a case study to highlight differences in technique resolution. NTA was able to detect additional LNP subpopulations samples for all measured samples, whereas DLS was unable to detect these subpopulations. Our study conveys that the use of orthogonal sizing techniques can support early stage product development, where a more in-depth analysis of formulation and process parameters on LNP stability would support comprehensive understanding of variable defining product stability.

A Colourful Way to Unravel the Important Fluidization-Related Granule Size Effect on Semi-Continuous Drying.

Continuous twin screw wet granulation (TSWG) systems are possible pathways for oral solid dosage manufacturing in the pharmaceutical industry. TSWG requires a drying step after granulation before the tableting process. Typically, semi-continuous fluidized bed dryers (FBDs) are used for this purpose. At the same time, the pharmaceutical sector is interested in mathematical prediction models to save resources during the early drug product development (DPD) stage or to control manufacturing. Several authors have already developed prediction models for semi-continuous drying processes. However, these model structures reported systematic prediction offsets, which could be related to the incomplete implementation of fluidization and granule segregation phenomena. This study evaluates the complex fluidization behavior of wet granules in industrially relevant semi-continuous FBDs. A transparent perspex version of the dryer was used for the analysis of bed height, pressure drop, porosity, segregation, and spatial heating patterns at varying process settings. The investigated behaviors of the fluidizing bed will be helpful to derive phenomenological (sub)models for the detailed description of segregation in the semi-continuous fluidized bed system. In this study, it was found that semi-continuous FBDs are characterized by a change in fluidization regime from plug flow to a bubbling bed at the moment that the granule bed slumps. Secondly, the presence of size-based vertical segregation phenomena as well as spatial temperature differences were proven. The experimental results suggest that larger granules are dried under more intense drying conditions than smaller granules.

Big data and machine learning in cost estimation: An automotive case study

This paper presents a case study on the applicability of machine learning and big data technology for product cost estimation, using data on the material cost of passenger cars. The study provides contributions to six research aspects. First, we show which machine learning algorithms are appropriate when dealing with product cost estimation for highly complex products with more than 2000 parts and hundreds of cost drivers. Second, our case study provides a novel approach to increasing the predictive accuracy of cost estimates for subsequent product generations. Third, we show that the accuracy is up to 3.5 times higher when using big data compared to an intermediate size of data. Fourth, machine learning can outperform cost estimates from cost experts during the early stage of new product development, even when dealing with highly complex products. Then, we evaluate the use cases, issues, and benefits of machine learning and big data from the perspective of cost experts. Specifically, the case study shows that machine learning can reliably select the most important cost drivers (fifth aspect) and calculate the average cost of cost drivers over thousands of product configurations (sixth aspect). However, cost experts must be knowledgeable about the product and remain careful when interpreting machine learning outcomes, as they can yield misleading outcomes in some exceptional cases. In conclusion, machine learning and big data empirically proved to be able to generate additional value in many aspects for managing costs during the early phase of new product development.

Hitting the bullseye: Accurately measuring willingness to pay for innovations with open and closed direct questions

Knowledge of a customer’s willingness to pay (WTP) at early stages of product development is key to the success of innovations. However, since innovative products do not exist yet, only the hypothetical WTP can be surveyed, inducing a measurement bias. Unfortunately, little is known about the factors that induce this bias and how it differs depending on the method utilized in measuring WTP. We address this gap by focusing on direct methods to survey hypothetical WTP. Based on anchoring theory and the corresponding psychological mechanisms for open questions as well as closed questions, we conducted two experiments, each comprised of a survey and a field study. The experiments differ regarding the product category and the product’s degree of innovativeness. Our results show that open questions are less accurate in estimating real WTP than closed questions. Further, our research offers insights into moderating factors that influence the efficacy of open and closed questions. For example, for customers with a very high product category knowledge, open questions are applicable, while closed questions result in higher accuracy when accounting for the customers’ cognitive abilities.

Systems Engineering in Technology Development

ABSTRACTTechnology development is the crucial first step in designing new products and systems. It is a unique phase of product development in that it incorporates both scientific exploration and reduction to an engineered result. Too often, systems thinking and systems engineering principles aren't applied at this stage, leading to technologies that solve the wrong problems, inability to progress to higher maturity levels, and unworkable implementation architectures. In practice, this means higher development costs, extended timelines, and failed technology development projects. This article presents a framework for and provides guidance on systems engineering activities that add value and improve outcomes if applied during early stages of product development.

Analysis of conditions for selecting the number of teeth of planetary gearboxes

The issues of selecting the number of teeth for 2k-h type A mechanisms based on the study of the known conditions for their assembly used in the development of planetary gearboxes of various shipboard both deck and main power ship installations are highlighted in the paper. Considering the design practice, when selecting the number of teeth, it is important to maintain a given gear ratio, ensure the alignment of the mechanism gears, withstand the condition of the neighborhood of satellites, ensure the assembly condition and the absence of interference of internal gearing. These conditions are expressed in terms of the number of teeth of gears and impose restrictions on the parameters of 2k-h mechanisms. Checking the interference condition of internal gearing at the stage of selecting the number of teeth in existing methods is usually not done, but it is performed at the stage of checking the quality of gearing according to geometric conditions. With this approach, the selection of tooth numbers is considered as a problem solved by the method of successive approximations: selection of tooth numbers — calculation of geometric dimensions — checking the quality of gearing — selection of a new combination of tooth numbers, etc. The condition of interference of internal gearing obtained in the work is represented by a closed formula, which eliminates the use of the iteration method, which requires a large amount of computational work and allows it to be tested at an early stage of product development. The neighborhood condition is expressed by an equation in which the design parameter is associated with the maximum possible number of satellites and a guaranteed gap between their initial circles. It makes it possible to theoretically explain well-known constructions that do not agree with the theoretical views existing at that time. The alignment condition allows for both an even and an odd difference in the number of teeth of the central wheels. Thus, the results obtained make it possible to solve the problems of creating a mechanism with the maximum possible number of satellites for reducing the size of a planetary mechanism as a unit completing a marine gearbox of any complexity, to expand the set of possible combinations of the teeth numbers that provide a given design and require less computational work.

Online data-driven concurrent product-process-supply chain design in the early stage of new product development

The ability to accelerate the discovery of product innovation ideas is critical for manufacturers with short life cycles, such as smartphones. Companies with short product lifespans require innovative strategies to improve their products based on customer needs. Three-dimensional concurrent engineering (3DCE) has the potential to speed up the process of developing new products. On the other side, the availability of online data, such as social media and intellectual property, has the potential to identify and prioritize product, technology, and supplier opportunities in a cost-effective, quick, and real-time manner. This study proposes an online data-driven concurrent product, process, and supply chain design (3DCE) for linking customer requirements to potential technology and supplier opportunities in early-stage new product development. Three case studies on the leading smartphone industry demonstrate the proposed method's feasibility and effectiveness. To identify customer requirements, opinions from social media data are collected. Product opportunities are developed using latent Dirichlet allocation to identify product topics and sentiment analysis to assess satisfaction levels. Then, a novel approach by association rule mining is developed to mine specific customer requirements for each promising topic. In the 3DCE approach, the potential technology and supplier opportunities are tailored to specific customer requirements based on intellectual property mining. Finally, this study discovers improvement ideas and potential alternative solutions for product development with an unsupervised approach. The proposed method has also been validated and aligns with successful innovation products by Apple, Samsung, and Huawei.

MANAGEMENT PRACTICES FOR SUSTAINABLE PRODUCT DEVELOPMENT: INSIGHTS FROM A SYSTEMATIC LITERATURE REVIEW

AbstractSustainable Product Development (SPD) has been gaining increased attention in academia, industry, and policy. Over the past three decades, significant progress has been observed in incorporation of environmental issues into the product development process, through the so-called ecodesign management practices. Nevertheless, systematisation of the SPD practices, which simultaneously consider the environmental, social and economic dimensions of sustainability, is still missing. To address this gap, this research aims to identify the existing SPD management practices in the academic literature, with special focus on how sustainability dimensions are currently being considered, their coverage in relation to key knowledge areas for product development and their applicability across the SPD phases. Through a systematic literature review, 362 practices were identified and further classified according to a classification criteria. While environmental considerations are still the most prominent ones, the research highlights the importance of the early stages of product development for SPD, as well as the key knowledge areas which are currently being covered by the practices, such as sustainability evaluation and sustainability improvement.

Predictive Potential of Acido-Basic Properties, Solubility and Food on Bioequivalence Study Outcome: Analysis of 128 Studies.

Risk assessment related to bioequivalence study outcome is critical for effective planning from the early stage of drug product development. The objective of this research was to evaluate the associations between solubility and acido-basic parameters of an active pharmaceutical ingredient (API), study conditions and bioequivalence outcome. We retrospectively analyzed 128 bioequivalence studies of immediate-release products with 26 different APIs. Bioequivalence study conditions and acido-basic/solubility characteristics of APIs were collected and their predictive potential on the study outcome was assessed using a set of univariate statistical analyses. There was no difference in bioequivalence rate between fasting and fed conditions. The highest proportion of non-bioequivalent studies was for weak acids (10/19 cases, 53%) and neutral APIs (23/95 cases, 24%). Lower non-bioequivalence occurrence was observed for weak bases (1/15 cases, 7%) and amphoteric APIs (0/16 cases, 0%). The median dose numbers at pH1.2 and pH3 were higher and the most basic acid dissociation constant (pKa) was lower in the non-bioequivalent group of studies. Additionally, APIs with low calculated effective permeability (cPeff) or low calculated lipophilicity (clogP) had lower non-bioequivalence occurrence. Results of the subgroup analysis of studies under fasting conditions were similar as for the whole dataset. Our results indicate that acido-basic properties of API should be considered in bioequivalence risk assessment and reveal which physico-chemical parameters are most relevant for the development of bioequivalence risk assessment tools for immediate-release products.

Exploiting the generative design potential to select the best conceptual design of an aerospace component to be produced by additive manufacturing

Since the advent of Industry 4.0, the manufacturing sector has had to face new challenges, which require the development of new skills and innovative tools. This scenario includes innovative production processes such as additive manufacturing (AM), a technology capable of producing a component layer-by-layer directly from the 3D model without needing specific tools during the building phase. Generative design (GD) may represent an opportunity to maximise the potential of AM techniques. GD is based on parametric computer-aided design (CAD) tools capable of generating multiple optimised outputs, among which the designer could select the most promising solution. This paper presents a general methodology for evaluating the GD outputs in the conceptual phase of design to select the best possible solution through a series of criteria at several levels. The evaluation method is deployed in an aerospace field case study. The procedure demonstrates the benefits of synergising GD with AM in the early stages of product development. This indicates that the developed methodology could reduce the number of iterations during the design process, and the result is a decrease in the overall time spent on the project, avoiding problems during the final stages of the design.

Usefulness of the Beagle Model in the Evaluation of Paracetamol and Ibuprofen Exposure after Oral Administration to Pediatric Populations: An Exploratory Study.

The present study aimed to explore the usefulness of beagle dogs in combination with physiologically based pharmacokinetic (PBPK) modeling in the evaluation of drug exposure after oral administration to pediatric populations at an early stage of pharmaceutical product development. An exploratory, single-dose, crossover bioavailability study in six beagles was performed. A paracetamol suspension and an ibuprofen suspension were coadministered in the fasted-state conditions, under reference-meal fed-state conditions, and under infant-formula fed-state conditions. PBPK models developed with GastroPlus v9.7 were used to inform the extrapolation of beagle data to human infants and children. Beagle-based simulation outcomes were compared with published human-adult-based simulations. For paracetamol, fasted-state conditions and reference-meal fed-state conditions in beagles appeared to provide adequate information for the applied scaling approach. Fasted-state and/or reference-meal fed-state conditions in beagles appeared suitable to simulate the performance of ibuprofen suspension in pediatric populations. Contrary to human-adult-based translations, extrapolations based on beagle data collected under infant-formula fed-state conditions appeared less useful for informing simulations of plasma levels in pediatric populations. Beagle data collected under fasted and/or reference-meal fed-state conditions appeared to be useful in the investigation of pediatric product performance of the two investigated highly permeable and highly soluble drugs in the upper small intestine. The suitability of the beagle as a preclinical model to understand pediatric drug product performance under different dosing conditions deserves further evaluation with a broader spectrum of drugs and drug products and comparisons with pediatric in vivo data.

A Machine Learning Approach for Automated Cost Estimation of Plastic Injection Molding Parts

Market competition leads to shorter cycle times for new or updated products. Therefore, flexibility in reacting to market changes, product development, and all related processes must be accelerated. In this regard, accurate cost estimation in the early stages of product development is critical for assessing the economic viability of a product. However, cost estimation requires data and expertise from several departments. Machine learning approaches could improve the accuracy and reduce the time needed for cost estimation. To investigate the eligibility of machine learning based cost estimation, a case study was conducted on an industrial company that produces plastic molding parts as key components of its products. The study involved training various supervised machine learning algorithms on a dataset of plastic injection molding parts using three different cost calculation methods. The three methods differed in the extent to which they considered the different process steps involved in the production of the parts. Different tree-based machine learning regression models and neural network models were trained to identify the most suitable approach for cost estimation in the given context. The results showed that tree-based machine learning algorithms outperformed neural networks and that individually predicting manufacturing parameters for cost calculation of each manufacturing process step leads to the most accurate cost estimation. This paper demonstrates how machine learning can support cost estimation in the early stages of the product lifecycle, reducing development times and improving cost estimation accuracy.