Stages Of New Product Development Research Articles

Two forms of customer involvement and new product development performance in the digital context: The moderating role of new product development stage

Customer involvement plays a critical role in firms' new product development (NPD), especially in the digital context. Prior research has distinguished between two forms of customer involvement: customers as an information source (CIS) and customers as co-developers (CIC). We build on this stream of literature and investigate the impact of CIS and CIC on firms’ NPD performance in the digital context by considering both the benefits and costs of these two forms of customer involvement. Furthermore, we explore the moderating role of NPD stage. Based on survey data collected from 263 informants, we show that whereas CIS has a positive effect on NPD performance, CIC has an inverted U-shaped relationship with NPD performance. More interestingly, the positive relationship between CIS and NPD performance is more pronounced in the ideation stage than in the product development stage, and the inverted U-shaped relationship between CIC and NPD performance is steeper in the product development stage than that in the ideation stage. In our additional analyses, we further divide the NPD stage into four stages and found similar results as our main analyses. Our study enriches the customer involvement literature and offers practical implications regarding whether, how, and when to involve customers in the NPD process.

Towards a method for human-centred analysis of external variety

AbstractAddressing VUCA challenges in product development, the Human-Centred Analysis of External Variety (HAV) method is introduced. Focusing on the early stages of new product development, HAV provides a structured approach, reducing uncertainty through quick market analysis, customer engagement, and goal setting. HAV is illustrated using a cargo bike product family case study and emphasizes its role in aligning human-centred methods with project goals. The first steps of the HAV method are presented, but also the need for further action towards a validated and elaborated method is discussed.

Identification of potential markers, key aroma compounds, and hazardous compounds in different types of smoked meat products

SummaryThis study aimed to characterise the volatile aroma and hazardous compounds presented in smoked meat from various regions and types. The analysis was conducted using chromatographic/mass spectrometric techniques. In six types of smoked meat, 15 of the 40 key aroma compounds were found to be co‐occurring. The highest contributor among them was 2‐methoxy‐phenol. Compared to other smoked meat, Norharman was more abundant in smoked pork with a range of 4.97~81.04 ng g−1, and Nε‐carboxymethyl lysine (CML) and Nε‐carboxyethyl lysine (CEL) were higher in smoked pork and chicken with a range of 9.32~138.91 μg g−1 and 6.35~141.74 μg g−1, respectively. Benzo[a]pyrene (BaP) content did not exceed 5 μg kg−1 in any of the samples. Concerning aroma compounds, phenolics were higher in smoked pork and mutton, aldehydes in smoked duck and goose and nitrogenous compounds mainly in smoked chicken. Additionally, 12 key potential markers were identified for distinguishing between different regions and types of smoked meat. In comparison, southwest smoked meat showed higher levels of key phenolics such as 2‐methoxy‐phenol. This result contributed to a comprehensive understanding of smoked meat while being valuable in both retaining key aroma and reducing hazardous compounds in later stages of new product development.

Evolution of motivation in co‐creation: Recruit, retain and complete in a project on new food product co‐creation

The study explores the motivation of consumers to join and participate in new product co‐creation, distinguishing between recruitment before, retainment during and finalizing of a co‐creation project. The research is based on follow‐up interviews with participants in a project covering the initial stages of new food product development (i.e., idea generation, idea screening and concept development) involving older consumers and carried out in a physical environment. Different motivation factors were deemed important by consumers before, during and at the closure of the project. Consumers joined the project motivated by curiosity and desire to meet new people. While performing the creative tasks, they continued the engagement due to the positive anticipation and enjoyment of new activities, as well as positive feedback loops. At the closure of the project, the usefulness of outcomes and the received recognition became important and strengthened the perceived creative self‐efficacy of participants. The findings offer useful recommendations for organizers of co‐creation, enabling the recruitment and retainment of consumers who could successfully deliver creative outputs and complete the project. The study offers novel insights relevant to participatory innovations looking at the temporal dimension of motivation to co‐create and highlights the dynamic changes in salience of the motivation factors.

Insights into environmental sustainability implementation during the design stage of New Product Development: An industry perspective

Design is an important stage within New Product Development (NPD) for sustainable development, with increased research on the roles of designers and other stakeholders. However, previous studies have been predominately theoretical, which has provided little insight into the current status of industry when aiming to produce sustainable products. This study investigates these themes further from the perspective of practicing designers to determine how their role supports sustainable NPD. This has enabled the development of an illustrative process model, highlighting the thought and decision-making process of practicing designers when implementing key factors of environmental sustainability. Future research directions have also been outlined.

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.

Navigating new product development: Uncovering factors and overcoming challenges for success

New product development (NPD) frequently encounters failures, whether during the development phase or in the subsequent commercialization stage. These failures can often be attributed to root causes originating in the early stages of NPD, known as the front end. This study aims to investigate the factors that contribute to NPD success and the challenges faced by firms throughout the development process. To achieve this objective, an in-depth qualitative approach utilizing interviews was employed to explore the internal and external factors influencing NPD success. Additionally, the study aims to identify specific challenges encountered by firms during the NPD process. The identification of these factors and challenges are crucial for companies as it can lead to long-term cost savings.The findings of this study have broad implications for firms regardless of their product plans. By adopting the derived approach, companies can effectively navigate the product development process and gain insights into the potential challenges associated with introducing a new product into an existing market. As customer preferences evolve with technological advancements, the barriers and challenges faced by new products entering established markets also increase. Therefore, firms that incorporate the results of this study into their product development practices across various industries can avoid pitfalls and achieve greater efficiency in terms of time and costs. This research sheds light on critical areas that contribute to successful product development and provides valuable guidance for firms striving to excel in their NPD endeavors.

Conceptual design decision-making considering multigranularity heterogeneous evaluation semantics with uncertain beliefs

Conceptual design decision-making, as an important stage of new product development, is used to select the most appropriate design by decision-makers (DMs) carrying out semantic judgments of conceptual schemes (CSs). However, evaluation semantics involve uncertainties such as subjectivity, randomness, and heterogeneity, and how to capture and handle these uncertainties is one of the challenging topics. Previous studies have focused on the introduction of heterogeneous semantics with different expressions for CS ranking while neglecting the negative impact of multigranularity semantics as well as fuzzy cognition on CS decision results. Furthermore, the randomness embedded in the evaluation semantics has not yet been addressed in heterogeneous semantics integration. To fill these gaps, a conceptual design decision-making approach considering multigranularity heterogeneous evaluation semantics with uncertain beliefs is proposed. First, multigranularity semantics with uncertain beliefs are assigned by DMs, and an initial decision matrix for CSs with a mixed distribution of crisp values, interval values, and monotonic values is constructed. Second, two transformation rules for the normal cloud model and the exponential cloud model are constructed to transform the multigranularity heterogeneous semantics into cloud models, and the cloud model decision matrix is built. Third, by generating random numbers based on the distribution of cloud models to construct the dominance matrix, the Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) model is used to select the optimal CS. A case study of a tree climbing and trimming (TCT) machine is used to validate the effectiveness of the proposed model, and the sensitivity analysis and comparison results confirm the practicality of the concept selection process considering the multigranularity heterogeneous evaluation semantics.

Engaging with ‘Engineer for Supply Chain’ (EfSC): insights from two engineer-to-order manufacturers

The practice of ‘Design for Supply Chain’ (DfSC) aims at integrating strategic sourcing into manufacturers’ new product development (NPD) processes. The literature on this topic, however, mainly focuses on contexts involving high-volume, standardised products, while the engineer-to-order (ETO) context has received only limited attention. As argued in this paper, this constitutes a gap in the literature since the findings from high-volume, standardised contexts may not be directly applicable to the ETO context. To support this claim, a case study approach is used to explore DfSC in two ETO manufacturers. This paper terms this practice ‘Engineer for Supply Chain’ (EfSC) and identifies four dimensions that it comprises: (1) consideration of strategic sourcing in NPD, (2) representation of the sourcing function in NPD, (3) collaboration between the R&D and sourcing functions, and (4) adoption of methods for considering strategic sourcing in NPD. Although these dimensions partly overlap with the literature on DfSC, the characteristics of EfSC differ—most notably by requiring the consideration of strategic sourcing before the product design stage of NPD, as well as procedures that encourage this consideration. Finally, the study identifies relationships among the dimensions and develops a holistic four-step process for engaging with EfSC.

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.

The effects of the fuzzy front end and cross-functional interfaces in the NPD process

PurposeAs the start of a new product development (NPD) process, the front fuzzy end (FFE) is believed to determine new product performance to a large extent. However, its effects on new product performance, particularly in terms of quality and cost, lack empirical evidence in the extant literature. Moreover, the joint performance effects of the FFE and cross-functional interfaces in later NPD stages (i.e. product development and product launch) are largely overlooked and deserve further investigation. Therefore, this study aims to explore the direct effects of the FFE and later stages’ joint moderating effects on new product performance (i.e. quality and cost) from a holistic process view.Design/methodology/approachA conceptual model is proposed to hypothesize the FFE–new product performance relationships and the joint performance effects of cross-functional interface management. A sample of 196 firms from an international survey is used and hierarchical linear regression is employed to test the proposed hypotheses.FindingsThis study finds that FFE implementation contributes to both new product quality and cost performance. Moreover, interface management in multiple NPD stages has synergistic performance effects. Specifically, the FFE, customer involvement in product development and manufacturing flexibility in product launch jointly improve new product quality performance, while the FFE, supplier involvement in product development and manufacturing flexibility in product launch jointly improve new product cost performance.Originality/valueThis study extends the NPD literature by deepening the understanding of the key roles of the FFE on new product performance and evidencing the synergistic effects of cross-functional interfaces in multiple NPD stages. Further, this study also highlights the differential joint moderating effects of interface management in later NPD stages on new product quality and cost performance. This study also offers insightful implications to NPD managers.

Supplier involvement and supplier performance in new product development: Moderating effects of supplier salesperson behaviors

Supplier involvement (SI) for new product development (NPD) entails multiple relationships between buyers and their suppliers, demanding appropriate coordination mechanisms for successful NPD collaboration. This study proposes that supplier salesperson behaviors act as coordination mechanisms in the SI setting for NPD information regulating and process regulating behaviors. Drawing on information processing theory and using survey data matched with archival data, this study unravels the role of supplier salesperson behaviors in the relationship between the timing of SI and supplier performance, which is the precursor to the buyer’s NPD performance. Our results indicate that although information regulating behaviors positively moderate the timing of SI–supplier performance linkage, process regulating behaviors weaken this linkage across multiple NPD stages. This study contributes to the literature by highlighting a new salesperson’s role in the SI setting (i.e., salesperson as coordinator), which has been previously overlooked.

An integrated design concept evaluation method based on best–worst entropy and generalized TODIM considering multiple factors of uncertainty

Design concept evaluation (DCE) plays a vital role in the early design stage of new product development because it directly affects the direction of subsequent design and manufacturing activities. However, the previous DCE methods have some limitations, such as (i) they usually only consider one or two factors of uncertainty, while multiple factors of uncertainty are always involved in the group decision-making problems; (ii) the criteria weight calculations usually only consider the subjective expert experience or the objective statistical analysis, which is not complete; and (iii) the psychological behaviors of experts are ignored when considering gains or losses, all of which affect their precision. Hence, this study develops an integrated DCE model to address the above limitations. First, a new uncertain information manipulation method, namely interval-valued intuitionistic fuzzy rough clouds (IVIFRCs), is developed to handle the multiple factors of uncertainty in the group decision-making problems by integrating the interval-valued intuitionistic fuzzy sets (IVIFS)-based trapezium cloud model and the interval rough number (IRN) theory. Then, the arithmetic operations, distance measure, and aggregation operators for IVIFRCs are introduced and discussed. Furthermore, the best–worst and entropy methods are integrated as the best–worst entropy (BWE) to identify the hybrid criteria weights with IVIFRC information. Finally, the generalized TODIM (TOmada de Decisão Iterativa Multicritério) is applied to handle experts’ psychological behaviors as well as rank design concepts. Additionally, a case study, sensitivity analysis, and several comparisons are conducted. Results demonstrate that the developed model is superior to the existing DCE approaches.

A Complexity-based Framework for Social Product Development

The development of new products using social technologies among defined crowds of individuals or groups, commonly known as Social Product Development (SPD), has become a growing trend in engineering design and manufacturing literature. This paper proposes an approach to studying the actors involved in SPD from a complexity science point of view. Specifically, we suggest that organizational efforts towards SPD must be concentrated on human and technological elements to achieve success in New Product Development (NPD). The paper explores the differences and similarities between the creation of new products and SPD and proposes a differentiated approach, based on complexity science for the different stages of NPD and SPD. Our findings show that SPD requires the integration of the complexity approach due to the combination of the knowledge, skills, and abilities of those involved during the development of new products. In addition, it is recommended that engineering design managers consider the importance of leadership styles, work structure, and organizational culture to achieve the expected outcomes of SPD.

An approach for radical innovative design based on cross-domain technology mining in patents

The key to radical innovation lies in technological breakthroughs, and the key to technical breakthroughs lies in the application of cross-domain knowledge. However, in the conceptual design stage of new product development, the designer’s cognition and knowledge are limited and the retrieval and evaluation of cross-domain technologies is often subjective, making it difficult to generate radical concepts. To address the above issues, this paper proposes a method for cross-domain technology recommendation based on patent analysis. In this method, feature words in various domains of patents are used to generalise the abstract functions of product core subsystems, where designers can obtain more comprehensive cross-domain technologies. This is conducive to breaking the design fixation of designers. Subject–Action–Object structural and functional similarity are combined to quickly extract cross-domain technologies in patents. Then multiple technical indicators are used to objectively evaluate and rank cross-domain technologies. This approach helps designers objectively select cross-domain technologies to change the way next-generation products work, enabling early planning of production conditions and a head start in the market. The method has been verified in a new product development project on peeling equipment, a new product that meets radical indicators is finally obtained.

The Effects of Integrative Strategies Along the Supply Chain on NPD Success

This study analyses the effects of different integrative strategies related to supply chain (SC) actors (suppliers, customers and internal manufacturing) in the early stages of new product development (NPD) on NPD success. Relying on various theories and related empirical evidence, we state three hypotheses and empirically test them using multi-country and multi-industry data from 309 manufacturing plants. The results show that different strategies of involvement explain different NPD success. We observed that integrative practices involving three distant actors have a positive impact on all NPD success indicators. We also observed that NPD success related to specific purposes, for example, market or internal processes, can be obtained with simpler combinations of collaboration. This shows the significance of choosing a differentiated portfolio of SC partners based on cognitive distance to achieve success in different NPD performance measures. Our results confirm that the types of partners are determinants in achieving different NPD success and that there are different alternatives to achieve success. These results have implications for managers to optimise the allocation of limited resources when cooperating with different agents in NPD projects.

An MAGDM method for design concept evaluation based on incomplete information.

Design concept evaluation is a huge challenge in the R&D stage of new product development. The information in the assessments often depends on the decision-makers' individual preferences. However, sometimes the decision-makers cannot give precise and complete information because it is very difficult for them to be familiar with all the criteria. In this situation, an incomplete information decision-making matrix is established. In this paper, decision-making methods based on incomplete information are compared in the literature review. Incomplete information determination method using trust mechanism is proved as a proper way to solve this problem, and the missing information are computed based on the alternatives However, in design concept evaluation, experts commonly provide their preferences using linguistic words according to the different attributes. Hence, we propose a three-step Multiple Attributes Group Decision-making (MAGDM) method where the missing value are determined by attributes. In step one, a data repairing method is proposed based on trust theory. After that, in step two, a comprehensive weight determination method combining AHP and entropy is proposed to obtain the weight of index attributes. Finally, the Rough-TOPSIS method is applied in the design scheme ranking step. In the case study, the proposed method is implemented in a tourism product design process to show its effectiveness.

A quantitative simulation-based conceptual design evaluation approach integrating bond graph and rough VIKOR under uncertainty

Conceptual design evaluation is a vital stage of new product development, which determines the sustainability, safety, quality of the final design, as well as its impact on the production cycle cost and manufacturability. Notwithstanding, pertinent evaluation methods advocate quantifying the subjective assessments of conceptual schemes (CSs) by decision makers (DMs) based on qualitative evaluation objectives, but the lack of physical performance verification of CSs with respect to quantitative objectives, leading to inconsistency between the optimal CS and the actual design requirements. To fill the issue, a quantitative simulation-based CS evaluation approach integrating bond graph and rough VIKOR is proposed. First, the bond elements that satisfy the physical effects corresponding to the realization of the function is obtained by function-effect mapping, and the functional structure is transformed into a bond graph model. Second, the AMESim components that satisfy the mathematical model of the above bond graph model are sought, the simulation model without detailed structural parameters is constructed to realize the simulation analysis of the scheme performance. Third, the rough VIKOR model is used to integrate the simulation data and the subjective assessments for the optimal ranking of CSs under uncertainty. Besides, rough number is introduced to aggregate the evaluation information of multi-DMs to improve the objectivity of decision process. Eventually, a cutting variable-speed device case study is further employed to verify the proposed simulation approach, and comparison results show that the proposed model can provide design decision support for value product development and reduce the waste of design resources through early design verification.

Numerical Prediction of In-Plane Vertical Dynamics(IPVD) Performance on a Quarter Car at the Pre-CAD Preliminary Stages of Product Development

<div class="section abstract"><div class="htmlview paragraph">In the present time, almost every motor vehicle company plans to launch a new vehicle or two on a per annum basis. With the time crunch and cost reduction in mind the R&D(Research and Development) process for every vehicle needs to be wrapped up in a hurry. Due to this, testing time for every vehicle is shortened drastically. Yet, to have safe, sound vehicle in the market, to maintain the company's rapport in the market and among the competition, the testing of new product in comparison with the benchmark vehicle is given its due importance always. Traditional vehicle performance takes use of a series of physical prototype evaluations by skilled drivers, who analyze the vehicle performance in subjective and objective terms either in lab or in road in comparison with benchmark. The problem with this approach is the fact that it is completely dependent upon physical component prototypes, whose costs and construction lead times cannot be afforded in the current tight development cycles. All these lead to innovative and research oriented development in the virtual testing environment.</div><div class="htmlview paragraph">This paper presents an approach based on ranking score derived from simulation tools technical data that has been successfully applied at Tata Consultancy Services Ltd, which not only defines optimized solution for the achieving the target but also to evaluate the benchmark performance which aids in preliminary stages of new product development. This work focuses on the methodology development on estimation of In-Plane Vertical Dynamics(IPVD) performance characteristics on a quarter car model through numerical simulation which is very much necessary for predicting the ride comfort and road holding capabilities. A 2 D.O.F(degrees of freedom) model that represents a quarter car was used for this purpose. The model is constructed with parameterization, customized for this application in order to reduce the time required to build different configuration models and to get the optimum design choice for IPVD performance. The model that has been built has 2 D.O.F and excited with road displacements either in terms of frequency inputs or actual road profiles. Detailed procedure for evaluating the ride comfort, road holding, and its quantifying metrics has been explained and relatively compared with benchmark vehicle having different vehicle configuration.</div><div class="htmlview paragraph">The use of this numerical technique and ranking scores to evaluate the IPVD characteristics addressed challenges due to the non-availability of benchmark test data in the initial phase of new vehicle development where the rational decisions are taken and most of project cost has been decided.</div></div>

A cerebellar operant conditioning-inspired constraint satisfaction approach for product design concept generation

Conceptual design is a pivotal stage of new product development. The function-behaviour-structure framework is adopted in this stage to help designers search design space and generate conceptual solutions iteratively. Computer-aided methods developed within this framework will yield significant insight into facilitating the cognitive activities of designers. In order to solve the mapping process from behaviours to structures which is a typical constraint satisfaction problem, a cerebellar operant conditioning-inspired constraint satisfaction approach is proposed in this paper. The design constraints-driven operant conditioning and its regulation mechanism by the cerebellum are analysed for the first time. Proposition logic is applied to transfer the constraint satisfaction problem into a propositional satisfiability problem while an undirected graph is utilised to model design space. Inspired by the modularised cerebellar structure, a modularised constraint satisfaction neural network is constructed to determine the satisfiability of design problems. Conceptual solutions can be generated by clustering the embedding of nodes in this network. The proposed approach imitates the design constraint-driven operant conditioning to narrow down design space without assigning specific values to design components. It reduces design iterations and avoids combinatorial explosions during conceptual design.