Abstract:Purpose
A reconfigurable manufacturing system (RMS) can provide manufacturing flexibility, meet changing market demands and deliver high performance, among other benefits. However, adoption and performance improvement are critical activities in it. The current study aims to identify the important factors influencing RMS adoption and validate a conceptual model as well as develop a structural model for the identified factors.
Design/methodology/approach
An extensive review of RMS articles was conducted to ide… Show more
Purpose
Agile new product development (ANPD) attracts researchers and practitioners by its ability to rapidly reconfigure products and related processes to meet the needs of emerging markets. To increase ANPD adoption, this study aims to identify ANPD enablers (ANPDEs) and create a structural framework that practitioners can use as a quick reference.
Design/methodology/approach
Initially, a comprehensive literature review is conducted to identify ANPDEs, and a structural framework is developed in consultation with an expert panel using a hybrid robust best–worst method interpretive structural modeling (ISM). During the ISM process, the interactions between the ANPDEs are investigated. The ISM result is used as input for fuzzy Matrice d’Impacts croises-multiplication appliqúean classment means cross-impact matrix multiplication applied to classification (MICMAC) analysis to investigate enablers that are both strong drivers and highly dependent.
Findings
The study’s findings show that four ANPDEs are in the low-intensity cluster and thus are excluded during the structural frame development. ISM output shows that “Strong commitment to NPD/top management support,” “Availability of resources,” “Supplier commitment/capability” and “Systematic project planning” are the important ANPDEs. Based on their driving and dependence power, the clusters formed during the fuzzy MICMAC approach show that 16 ANPDEs appear in the dependent zone, one ANPDE in the linkage zone and 14 ANPDEs in the driving zone.
Practical implications
This research has intense functional consequences for researchers and practitioners within the industry. Industry professionals require a conservative focus on the established ANPDEs during ANPD adoption. Management has to carefully prepare a course of action to avoid any flop during ANPD adoption.
Originality/value
The framework established is a one-of-a-kind study that provides an integrated impression of important ANPDEs. The authors hope that the suggested structural framework will serve as a blueprint for scholars working in the ANPD domain and will aid in its adoption.
Purpose
Agile new product development (ANPD) attracts researchers and practitioners by its ability to rapidly reconfigure products and related processes to meet the needs of emerging markets. To increase ANPD adoption, this study aims to identify ANPD enablers (ANPDEs) and create a structural framework that practitioners can use as a quick reference.
Design/methodology/approach
Initially, a comprehensive literature review is conducted to identify ANPDEs, and a structural framework is developed in consultation with an expert panel using a hybrid robust best–worst method interpretive structural modeling (ISM). During the ISM process, the interactions between the ANPDEs are investigated. The ISM result is used as input for fuzzy Matrice d’Impacts croises-multiplication appliqúean classment means cross-impact matrix multiplication applied to classification (MICMAC) analysis to investigate enablers that are both strong drivers and highly dependent.
Findings
The study’s findings show that four ANPDEs are in the low-intensity cluster and thus are excluded during the structural frame development. ISM output shows that “Strong commitment to NPD/top management support,” “Availability of resources,” “Supplier commitment/capability” and “Systematic project planning” are the important ANPDEs. Based on their driving and dependence power, the clusters formed during the fuzzy MICMAC approach show that 16 ANPDEs appear in the dependent zone, one ANPDE in the linkage zone and 14 ANPDEs in the driving zone.
Practical implications
This research has intense functional consequences for researchers and practitioners within the industry. Industry professionals require a conservative focus on the established ANPDEs during ANPD adoption. Management has to carefully prepare a course of action to avoid any flop during ANPD adoption.
Originality/value
The framework established is a one-of-a-kind study that provides an integrated impression of important ANPDEs. The authors hope that the suggested structural framework will serve as a blueprint for scholars working in the ANPD domain and will aid in its adoption.
Purpose: Global competition, individualized customer requirements, and volatile market conditions create an environment conducive to agile new product development (ANPD). This research seeks to identify the key factors that influence ANPD adoption along with the development of a conceptual framework for the identified factors. Design/methodology: Through a literature review, eight factors having 47 sub-factors pertinent to ANPD adoption and its performance improvement were identified. Considering all of these factors, the development of conceptual framework and research hypotheses was carried out. A structured questionnaire was used to collect 118 online responses from both domestic and foreign subject matter experts. The structural equation modeling (SEM) approach was used for validation of the conceptual framework along with the research hypotheses testing. Findings: This study supported six hypotheses: “Technology management competencies”, “Product development competencies”, “Organizational management competencies”, “Human resource competencies”, “Software management competencies”, and “Policy management competencies”. These supported hypotheses influence ANPD adoption significantly. However, the analysis did not support the two more positive factors, namely “Integrated system competencies” and “Supply chain competencies”, showcasing the necessity for a better understanding of them among the product development experts. Research limitations: As the proposed methodology relies on qualitative data, it is somewhat complex and time-consuming. While SEM can verify the linear relationship, a hybrid approach involving the SEM-MCDM technique can be employed to comprehend the impact of ANPD adoption and performance improvement. Practical implications: The findings of this study will assist product development experts, manufacturing executives, and managers in developing effective ANPD adoption policies. It will help in improving the new product development success rate and highlighting the causes of poor performance. Originality/value: This is a one-of-a-kind and highly beneficial structural modeling-based decision-making tool. This framework can be effective across multiple domains, and incidents of ANPD adoption failure can be mitigated.
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