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The purpose of this paper is to replicate the taxonomic study of Miller and Roth and to validate its applicability in a small newly industrialised country.

An empirical survey of manufacturing companies in Ireland was conducted using a research instrument that replicated the questions asked in the original 1994 study. Data was collected using mail questionnaires posted to 874 manufacturers. A total of 199 respondents were used for this study. The study tested two central hypotheses using advanced statistical data analysis techniques, such as canonical discriminant analysis.

The results of the research identified three clustered strategic groups (Best Value, Budget and Multi Focus) which were different from those of Miller and Roth: caretakers, marketeers and innovators. The study supported the dynamic nature of manufacturing strategy, and shows how new manufacturing strategies evolve over time and differ between regions.

Further research should replicate this study in other small newly industrialised countries. Given the recently changed nature of the economy within Ireland, a longitudinal study would provide useful insights into the dynamics of manufacturing capabilities.

The study provides important insights into manufacturing strategy within a small newly industrialised country. The results suggest that manufacturing strategies appear to change depending on the country of plant location. The manufacturing strategies may be attributed to the unique business environment, challenges and constraints of the country.

There is a scarcity of taxonomy studies in general, and there is a dearth of such studies in small newly industrialised countries such as Ireland. Through identifying a new taxonomy the study contributes to the existing literature on manufacturing strategy, and challenges the global applicability of taxonomies developed in the USA.

The purpose of this paper is to identify a classification scheme which represents the variation in business and supply chain performance of supply chains in Indian manufacturing industry. Classification is done by presenting an empirical taxonomy of clusters representing supply chains in Indian manufacturing industry based on variation in supply chain excellence index (SCEI) and business performance index (BPI).

The clustering of supply chains in Indian manufacturing industry is done by considering BPI and SCEI as clustering variables, which were found by using survey responses and results of a prior empirical study which was carried out in Indian manufacturing industry. The cluster analysis is performed by using Ward’s agglomerative hierarchical clustering followed by using K-means clustering algorithm to establish final set of clusters.

It was found that supply chains in Indian manufacturing industries can be clustered in four major clusters which are named as strategic, celebrity, capable and undeveloped cluster. The characteristics of these clusters reveal some major characteristics of supply chains in Indian manufacturing industry.

The research work presented in this paper takes a novel way to introduce the clusters of supply chains in Indian manufacturing industry. The researchers who are seeking patterns in large data sets of manufacturing companies of Indian industry will be benefitted by using the proposed clusters. While practitioners who are seeking to move their supply chain one step ahead will also reap the benefits of the paper by seeking the characteristics of particular cluster.

This study is an exploratory study aiming to explore whether different groups of manufacturing firms with similar business characteristics and enterprise resource planning (ERP) implementation approaches would experience different business outcomes from ERP implementation. The paper aims to discuss these issues.

Cluster analysis with data collected from 256 Korean manufacturing firms was employed to identify groups of manufacturing companies having similar business characteristics and adopting similar ERP implementation approaches. Then, the differences in business outcomes from implementing ERP systems among these groups of companies were examined.

Company size and production approaches are useful variables for grouping manufacturing firms into clusters of companies with similar characteristics. Additionally, large manufacturing firms with make-to-order production approach have significantly higher perceived benefits from implementing ERP systems regarding external coordination and competitive impact than other firms do.

This study was conducted in only one industry of one country and used the data collected by self-reporting instrument. Thus, further studies conducted in other industries and/or other countries and using more objective measures would allow more generalizability of the findings of this study. It would also be interesting to investigate the effects of the logistics practices adopted by small manufacturing firms even though these practices may be more suitable for large manufacturing firms.

This study contributes to the literatures on benefits obtained from implementing ERP systems as none of the previous studies has focused on the relationship among business characteristics, ERP implementation approaches, and business outcomes from ERP implementation.

The application of digital twins to optimise operations and supply chain management functions is a bourgeoning practice. Scholars have attempted to keep pace with this development initiating a fast-evolving research agenda. The purpose of this paper is to take stock of the emerging research stream identifying trends and capture the value potential of digital twins to the field of operations and supply chain management.

In this work we employ a bibliometric literature review supported by bibliographic coupling and keyword co-occurrence network analysis to examine current trends in the research field regarding the value-added potential of digital twin in operations and supply chain management.

The main findings of this work are the identification of four value clusters and one enabler cluster. Value clusters are comprised of articles that describe how the application of digital twin can enhance supply chain activities at the level of business processes as well as the level of supply chain capabilities. Value clusters of production flow management and product development operate at the business processes level and are maturing communities. The supply chain resilience and risk management value cluster operates at the capability level, it is just emerging, and is positioned at the periphery of the main network.

This is the first study that attempts to conceptualise digital twin as a dynamic capability and employs bibliometric and network analysis on the research stream of digital twin in operations and supply chain management to capture evolutionary trends, literature communities and value-creation dynamics in a digital-twin-enabled supply chain.

Additive manufacturing (AM) allows companies to create additional value in the processes of new product development and order fulfillment. One of the challenges for engineers is to identify suitable parts and applications for additive manufacturing. The purpose of this paper is to investigate the relation between value creation and the design process. The implications of this relation provide an orientation on the methods for identifying parts and applications for additive manufacturing.

Mapping the value clusters of AM on design strategies allows determining the expected degree of change in design. A classification into major and minor design changes is introduced to describe the predictability of the impact of AM on past performance and business model. The ability to predict the future properties of an AM part determines the suitability of identification and selection methods from literature. The mapping is validated by an identification process that creates a shortlist of potential AM parts based on the strategic decision for a value cluster. Shortlisted parts are then evaluated based on the criteria technology readiness, required post-processing, customer benefit and manufacturer benefit.

The mapping of value clusters on expected design changes determines the type of selection process. For minor design changes, automated part identification serves as a powerful tool while major design changes require the judgment of skilled engineers.

The mapping of value clusters to design strategies and degree of change in design is based on empirical observations and conclusions. The mapping has been validated in an industrial context in different identification and selection processes. Nevertheless the versatility of AM and industrial environments impede a universal validity of high-level concepts.

This value-driven process of identification and selection was applied in technology transfer projects and proved to be useful for AM novices and experts. The mapping supports the identification and selection process, as well as the general product development process by providing an indication of the design effort for implementing AM.

The novel mapping links the economic domain of value creation to the engineering domain of design strategies to provide guidance in the selection of economically and technically suitable parts for additive manufacturing.

This paper discloses the risk management response strategies and the perceived effectiveness of the strategies employed by companies operating within manufacturing clusters in Puerto Rico from 2016 until 2020, the second year of the coronavirus disease 2019 (COVID-19) pandemic.

The research design consists of questionnaire-based survey responses from companies belonging to manufacturing clusters, followed by semi-structured interviews and secondary sources of information.

The results reveal the risk responses used to manage specific risk types. Albeit respondents' dependency on an assortment of company-centric and cluster-bound risk response strategies, the perception is that the former is more effective when adequate local sources are available and the latter when the cluster has strong interconnectedness among the cluster's members.

Furthermore, there is a generalized belief that long-term cluster-bound strategies are required to complement individual companies' overall risk management strategies.

This paper demonstrated that due to the volatility, uncertainty, complexity and ambiguity (VUCA) nature of the Caribbean region, mixed risk management might result in better and more favorable long-term performance.

The purpose of this paper is to present an empirical study which examines the relationships among design-driven innovation, quality and customer value in manufacturing companies.

A research project was carried out in 193 Italian manufacturing companies, using the questionnaire method. An exploratory research study was conducted with hierarchical cluster analysis and regression analysis.

The analysis shows the existence of four clusters of manufacturing companies which differ by firm size, expenditure in innovation and type of innovation. Furthermore, the elements of a quality product and a design product have a significant impact on customer value, and the importance of these elements changes within the different cluster.

The small size of the sample and the geographic origin of companies imply limited generalizability and further research on the topic is recommended.

The study suggests that companies should simultaneously pursue quality and innovation to increase customer value. To achieve high levels of innovation, and thus increase their quality standards, manufacturing companies should consider the importance of the elements related to design which have impact on customer value.

Focusing on the relationship between design-driven innovation and quality which has not yet been investigated, the present study reveals many common elements of product design and quality product and their positive influence on the perception of customer value.

Additive manufacturing has achieved rapid development in recent years. The purpose of this paper is to visualize the intellectual landscapes of additive manufacturing and identify the hotspots and emerging trends of additive manufacturing, which can provide references for scholars, enterprises and governments to promote the development of theory and practice in the additive manufacturing field.

Science mapping is a fast-growing interdisciplinary field originated in information science and technology. Based on this methodology, guided by a computational approach, the paper visualizes the co-occurring keywords network and co-citation references network by CiteSpaceIII software to explore the hotspots and emerging trends of additive manufacturing by the following five indicators: highly cited keywords, burst keywords, clusters, landmark references and burst references.

“Additive manufacturing,” “3D printing,” “3D powder printing,” “consolidation phenomena,” “microstructure,” “rapid prototyping,” etc., are the main hotspots of additive manufacturing. The trends of additive manufacturing generally consist of three stages: the fundamental concepts stage from 1995 to 2000 (“rapid prototyping,” “additive manufacturing,” etc.), the approaches and techniques applications stage from 2001 to 2010 (“stereolithography,” “scaffold,” etc.), and the emerging trends stage from 2011 to the present (“stem cell”, “selective laser,” “ti-6al-4v,” etc.). The research is most abundant in 2010 and 2012. The medical field is an important hotspot of additive manufacturing. Additive manufacturing has been researched in interdiscipline.

The paper maps the perspective of additive manufacturing and explore the hotspots and emerging trends of additive manufacturing.

Without reliance on results obtained from applying a cell formation method, this paper aims to describe a simple quantitative approach to testing whether an underlying pattern of relationships exists between machines of a given system, such that the machines may be rearranged into manufacturing cells. It also aims to support the approach by an index for measuring the clustering tendency.

The eigenvalues of the similarity coefficient matrix and Kaiser's rule are used to: detect the number of clusters existing in the part‐machine matrix, and derive an index for predicting the goodness of the best possible obtainable cell formation.

The results of applying the proposed approach and the clustering tendency index to problems of different sizes taken from the literature have proven that both the approach and the clustering tendency index are powerful in performing the feasibility assessment and in predicting the right number of manufacturing cell to be formed.

This study is of considerable value to practitioners because it provides them with a powerful yet very easy to apply approach for assessing the feasibility of adopting cellular manufacturing in early stages of design. Another characteristic of this approach is the possibility of using it as a decision support tool for practitioners who opt to use a cell formation method which requires specifying the number of cells in advance. Moreover, the approach does not require any special software package, since it can be easily performed using several available software packages such as MATLAB and Mathematica.

A methodology for evaluating the adaptability of a system to cellular manufacturing has been proposed in a previous study. However, the methodology used is complex and uses a certain degree of subjectivity. In contrast, the proposed approach is simple and completely quantitative. Furthermore, a new index for measuring the clustering tendency is presented.

The paper attempts to analyze the network structure of value activity in manufacturing clusters, propose the model of value creation of cluster's value activity network, and explore the inner mechanism and optimization strategies of value creation in manufacturing clusters from the perspective of cluster's value activity network.

This paper applies a genetic algorithm to optimally search in the target space, and repeatedly exerts genetic operation (select, cross, variation) on the population to explore the optimal configuration strategy between value creation activity and resource utilization. It also analyzes the relation between object function of value creation and relative parameters.

The total value created by value activity network was impacted by the degree of effective configuration between all kinds of resources and value activities; the total value created by value activity network is positively related to activity units' elasticity coefficient of value creation of human resource, material resources and relations resource, and is negatively correlated to cost coefficient of human resource, material resources and relations resource; when the cooperative relations between activity units create positive relationship profit, the total value created by value activity network increases with the increase of cooperative relations between activity units.

Enterprises in clusters should reasonably configure and incorporate the resource among value activities through adding, deleting or reconfiguring activities, which makes the value activities network create maximum value; enterprises can transform the type of activity units to increase elasticity coefficient of value creation of human resources, such as transforming production activities into the high value‐added activities; enterprises can optimally incorporate the technical, material resources and human resources among activities to increase value creation elastic coefficient of material resources; enterprises can decrease cost coefficient by maintaining the stability of long‐term cooperation with the suppliers and strengthening the cultivation of talents; enterprises can increase profits from relation resource or reduce cost coefficient of relationship by updating activities, building trust mechanism and communication mechanisms and establishing long‐term cooperation relationship to improve value creation activities.

This paper proposes the model of value creation from the perspective of cluster's value activity network, and applies a genetic algorithm to explore the optimal configuration strategies between value creation activity and resource utilization.