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The objective of the study is to identify and analyse drivers of smart manufacturing using integrated grey-based approaches. The analysis facilitates industry practitioners in the identification of preference of drivers through which smart manufacturing can be implemented. These drivers are explored based on existing literature and expert opinion.

Modern manufacturing firms have been adopting smart manufacturing concepts to sustain in the global competitive landscape. Smart manufacturing incorporates integrated technologies with a flexible workforce to interlink the cyber and physical world. In order to facilitate the effective deployment of smart manufacturing, key drivers need to be analysed. This article presents a study in which 25 drivers of smart manufacturing and 8 criteria are analysed. Integrated grey Technique for Order Preference by Similarity to Ideal Solution (grey TOPSIS) is applied to rank the drivers. The derived ranking is validated using “Complex Proportional Assessment – Grey” (COPRAS-G) approach.

In total, 25 drivers with 8 criteria are being considered and an integrated grey TOPSIS approach is applied. The ranking order of drivers is obtained and further sensitivity analysis is also done.

In the present study, 25 drivers of smart manufacturing are analysed. In the future, additional drivers could be considered.

The study presented has been done with inputs from industry experts, and hence the inferences have practical relevance. Industry practitioners need to focus on these drivers in order to implement smart manufacturing in industry.

The analysis of drivers of smart manufacturing is the original contribution of the authors.

The benefits of artificial intelligence (AI) related technologies for manufacturing firms are well recognized, however, there is a lack of industrial AI (I-AI) maturity models to enable companies to understand where they are and plan where they should go. The purpose of this study is to propose a comprehensive maturity model in order to help manufacturing firms assess their performance in the I-AI journey, shed lights on future improvement, and eventually realize their smart manufacturing visions.

This study is based on (1) a systematic review of literature on assessing I-AI-related technologies to identify relevant measured indicators in the maturity model, and (2) semi-structured interviews with domain experts to determine maturity levels of the established model.

The I-AI maturity model developed in this study includes two main dimensions, namely “Industry” and “Artificial Intelligence”, together with 12 first-level indicators and 35 second-level indicators under these dimensions. The maturity levels are divided into five types: planning level, specification level, integration level, optimization level, and leading level.

The maturity model integrates indicators that can be used to assess AI-related technologies and extend the existing maturity models of smart manufacturing by adding specific technical and nontechnical capabilities of these technologies applied in the industrial context. The integration of the industry and artificial intelligence dimensions with the maturity levels shows a road map to improve the capability of applying AI-related technologies throughout the product lifecycle for achieving smart manufacturing.

The present study is aimed to determine the infoecology of scientific articles in the field of smart manufacturing (SM). The researchers designed a general framework for the investigation of infoecology.

The qualitative and quantitative data collection methods are applied to collect data from the Scopus and experts. The bibliometric technique, clustering and graph mining are applied to analysis data by Scopus data analysis tools, VOSviewer and Excel software.

It is concluded that researchers paid attention to “Flow Control”, “Embedded Systems”, “IoT”, “Big Data” and “Cyber-Physical System” more than other infocenose. Finally, a thematic model presented based on the infoecology of SM in Scopus for future studies. Also, as future work, designing a “research-related” metamodel for SM would be beneficial for the researchers, to highlight the main future research directions.

The results of the present study can be applied to the following issues: (1) To make decisions based on research and scientific evidence and conduct scientific research on real needs and issues in the field of SM, (2) Holding the workshops on infoecology to determine research priorities with the presence of experts in related industries, (3) Determining the most important areas of research in order to improve the index of applied research, (4) Assist in prioritizing research in the field of SM to select a set of research and technological activities and allocate resources effectively to these activities, (5) Helping to increase the relationship between research and technological activities with the economic and long-term goals of industry and society, (6) Helping to prioritize the issues of SM in research and technology in order to target the allocation of financial and human capital and solving the main challenges and take advantage of opportunities, (7) Helping to avoid fragmentation of work and providing educational infrastructure based on prioritized research needs and (8) Helping to hold start-ups and the activities of knowledge-based companies based on research priorities in the field of SM.

The analysis results demonstrated that the information ecosystem of SM studies dynamically developed over time. The continuous conduction flow of scientific studies in this field brought continuous changes into the infoecology of this field.

The implementation of Smart Manufacturing (SM) is deemed a key enabler in the enhancement of manufacturing competitiveness and performance. Nevertheless, SM's repercussion on consumer perceptions and the contextualization of SM's performance-enhancement effects remain undetermined and have yet to be clarified. This study analyzes the effect of SM on operational and customer performance. Moreover, this study explores how these relationships change depending on a firm's geography of production (i.e. national/local vs transnational operations) and the relational arrangement adopted (i.e. service-oriented vs transaction-oriented manufacturers).

This research surveys 351 Spanish manufacturing firms operating in an SM environment. The theoretical framework comprises a Multiple-Indicators Multiple-Causes (MIMIC) model and is tested using a Generalized Structural Equations Model.

The results obtained substantiate the positive effect of SM implementation on both of the performance measures analyzed (i.e. operational and customer focused). Moreover, the study reveals that while geography of production moderates the effect on a firm's operational performance, relational arrangement also does so in terms of customer performance.

This research clearly differentiates the benefits of SM depending on business context. In this regard, transnational production firms tend to gain in operational performance while service-oriented manufacturers gain in customer performance.

Smart manufacturing (SM) lies at the core of Industry 4.0. Uniform adoption of SM across business partners is crucial to exploit its value creation potential. However, firms' willingness to invest in SM is limited by insufficient or inconclusive evidence on its performance-related benefits. To close this gap, this paper develops and tests a model linking SM adoption to firms' financial performance. Improvements along the four dimensions of operational performance (i.e. cost quality, delivery and flexibility) mediate this relation.

This study follows an empirical research approach. In particular, survey data from 234 automotive component suppliers are analyzed via covariance-based structural equation modeling to explore the link between SM adoption and operational performance. Survey data are then matched with secondary data from balance sheets of 81 firms to investigate the impact of SM on financial performance via partial least square structural equation modeling.

Findings highlight that adoption of SM results in improvements in cost, quality, delivery performance, thus suggesting that SM is a mean to overcome performance trade-offs. Improvements in operational performance enabled by SM do not give rise to superior financial performance, thus implying that SM might support firms in maintaining the competitive position in the market, but could be insufficient to generate higher margin.

Results have implications for SM research and for manufacturing executives engaged in the adoption of SM, as they provide a detailed analysis of the impact of SM on operational performance and clarify the effect that SM adoption has on financial performance.

The purpose of this paper is to provide evidence on how smart manufacturing (SM) affects work organization at both micro-level – i.e. work design, described in terms of operator job breadth and autonomy, cognitive demand and social interaction – and at macro-level – i.e. organizational structure, described in terms of centralization of decision making and number of hierarchical levels in the plant.

The paper reports on a multiple-case study of 19 companies implementing SM.

Results present four main configurations differing in terms of technological complexity, and micro and macro work organization.

The paper contributes to the academic debate about the interplay between technology and work organization in the context of SM, specifically the authors find that the level of technology complexity relates to different characteristics of micro and macro work organization in the plant.

Findings offer valuable insights for practice, with implications for the design of operator jobs, skills and plant organizational structure, in light of the challenges generated by the implementation of SM technology. Guidelines on how policymakers can foster the implementation of SM technology to enhance social sustainability are proposed.

This study advances a novel focus in studying SM, i.e. work organization implications of this new manufacturing paradigm instead of its mere technological implications.

The purpose of the article is to report a study on evaluation of smart manufacturing (SM) performance using a grey theory-based approach.

In total, 30 criteria and 79 attributes for SM performance have been developed. A grey theory-based approach has been used for SM performance evaluation. The grey index has been calculated, and weaker areas have been derived. Performance level of SM has been evaluated using the Euclidean distance approach.

The SM performance index is found to be (3.036, 12.296). The ideal grey performance importance index (GPII) is obtained as (3.025, 4.875). The level of visibility and traceability, vertical integration, lead time and configuration data espionage and control ability are strong performing attributes. Integration abilities of services and manufacturing systems, ability of self-control, worker and raw material productivity, collaboration among buyers and suppliers and dynamic scheduling are identified as weaker areas, and suggestions for improvement have been derived. SM performance level has been identified as “Good.”

Additional performance measures could be included as a part of evaluation. Practitioners can overcome weaker areas in the early phase. Management achieves confidence and practitioners attain success in implementation of SM in industry through the developed SM performance indexing system.

Identification of SM performance measures and analysis of SM performance is the original contribution of the authors. The developed approach assists practitioners and managers to focus more on specific areas for performance improvement.

The purpose of this paper is to review existing smart manufacturing (SM) maturity models' dimensions and maturity levels to assess their applicability and drawbacks. There are many maturity models available but many of them have not been validated or do not provide a useful guide or tool for applications. This gap creates the need for a review of the existing maturity model's applicability.

Nineteen peer-reviewed maturity models related to “Digital Transformation,” “Industry 4.0” or “Smart Manufacturing” were selected based on a systematic literature review and five consulting firm models were selected based on the author's industry knowledge. The chosen models were analyzed to determine 10 categories of dimensions. Then they are assessed on a 1–5 scale for how applicable they are in the 10 categories of dimensions.

The five “consulting firm” models have a first-mover advantage, are more widely used in industry and are more applicable, but some require payment, and they lack published details and validation. The 19 “peer reviewed” models are not as widely used, lack awareness in the industry and are not as easy to apply because of no web tool for self-assessment, but they are improving. The categories defined to characterize the models and facilitate comparisons for users include “Information Technology (IT) and Cyber-Physical System (CPS) and Data,” “Strategy and Organization,” “Supply Chain and Logistics,” “Products and Services,” “Culture and Employees,” “Technology and Capabilities,” “Customer and Market,” “Cybersecurity and Risk,” “Leadership and Management” and “Governance and Compliance.” The analyzed maturity models were particularly weak in the areas of cybersecurity, leadership and governance.

Researchers and practitioners can use this review with consideration of their specific needs to determine if a maturity model is applicable or if a new model needs to be developed. The review can also aid in the development of maturity models through the discussion of each of the dimension categories.

Compared to existing reviews of SM maturity models, this research determines comprehensive dimension categories and focuses on applicability and drawbacks.

Smart Manufacturing (SM) lies at the core of Industry 4.0. Operations management research has investigated the determinants of SM advancement but there is still limited understanding of the linkages between SM and organizational factors and about whether both the technological and organizational subsystems for SM are guided by firms’ competitive priorities. To close these gaps, building on operations strategy theory, this paper aims to empirically test a model positing that competitive priorities drive SM advancement. The relation between competitive priorities and SM advancement is assumed to be mediated by organizational microfoundations.

Using data from a single respondent survey with 234 firms in the automotive component industry, structural equation modeling is adopted to test the model hypotheses. Relevant constructs are measured with reference to the lead plant for SM.

Findings highlight that SM advancement is driven by the need to simultaneously compete in terms of cost, quality and delivery, thus suggesting that manufacturers view SM as a mean to develop multiple manufacturing capabilities. Organizational microfoundations fully mediate the relation between competitive priorities and SM advancement.

Results have implications for SM research, as they provide an understanding of the strategic priorities of firms engaging in SM. Findings also bear relevance for manufacturing executives engaged in the SM transformation, as they provide quantitative evidence that shaping an adequate organizational environment is a prerequisite for SM advancement.

This infecological study mainly aimed to know the thematic and conceptual relationship in published papers in deep learning (DL) and smart manufacturing (SM).

The research methodology has specific research objectives based on the type and method of research, data analysis tools. In general, description methods are applied by Web of Science (WoS) analysis tools and Voyant tools as a web-based reading and analysis environment for digital texts. The Yewno tool is applied to draw a knowledge map to show the concept's interaction between DL and SM.

The knowledge map of DL and SM concepts shows that there are currently few concepts interacting with each other, while the rapid growth of technology and the needs of today's society have revealed the need to use more and more DL in SM. The results of this study can provide a coherent and well-mapped road map to the main policymakers of the field of research in DL and SM, through the study of coexistence and interactions of the thematic categories with other thematic areas. In this way, they can design more effective guidelines and strategies to solve the problems of researchers in conducting their studies and direct. The analysis results demonstrated that the information ecosystem of DL and SM studies dynamically developed over time. The continuous conduction flow of scientific studies in this field brought continuous changes into the infoecology of subjects and concepts in this area.

The paper investigated the thematic interaction of the scientific productions in DL and SM and discussed possible implications. We used of the variety tools and techniques to draw our own perspective. Also, we drew arguments from other research work to back up our findings.