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Article
Publication date: 19 September 2020

Juite Wang and Chih-Chi Hsu

Smart manufacturing can lead to disruptive changes in production technologies and business models in the manufacturing industry. This paper aims to identify technological…

Abstract

Purpose

Smart manufacturing can lead to disruptive changes in production technologies and business models in the manufacturing industry. This paper aims to identify technological topics in smart manufacturing by using patent data, investigating technological trends and exploring potential opportunities.

Design/methodology/approach

The latent Dirichlet allocation (LDA) topic modeling technique was used to extract latent technological topics, and the generalized linear mixed model (GLMM) was used to analyze the relative emergence levels of the topics. Topic value and topic competitive analyses were developed to evaluate each topic's potential value and identify technological positions of competing firms, respectively.

Findings

A total of 14 topics were extracted from the collected patent data and several fast growth and high-value topics were identified, such as smart connection, cyber-physical systems (CPSs), manufacturing data analytics and powder bed fusion additive manufacturing. Several leading firms apply broad R&D emphasis across a variety of technological topics, while others focus on a few technological topics.

Practical implications

The developed methodology can help firms identify important technological topics in smart manufacturing for making their R&D investment decisions. Firms can select appropriate technology strategies depending on the topic's emergence position in the topic strategy matrix.

Originality/value

Previous research studies have not analyzed the maturity levels of technological topics. The topic-based patent analytics approach can complement previous studies. In addition, this study provides a multi-valuation framework for exploring technological opportunities, thus providing valuable information that supports a more robust understanding of the technology landscape of smart manufacturing.

Details

Journal of Manufacturing Technology Management, vol. 32 no. 1
Type: Research Article
ISSN: 1741-038X

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Article
Publication date: 13 November 2019

Diamantino Torres, Carina Pimentel and Susana Duarte

The purpose of this study intends to make a characterization of a shop floor management (SFM) system in the context of smart manufacturing, through smart technologies and…

Abstract

Purpose

The purpose of this study intends to make a characterization of a shop floor management (SFM) system in the context of smart manufacturing, through smart technologies and digital shop floor (DSF) features.

Design/methodology/approach

To attain the paper objective, a mixed method methodology was used. In the first stage, a theoretical background was carried out, to provide a comprehensive understanding on SFM system in a smart manufacturing perspective. Next, a case study within a survey was developed. The case study was introduced to characterize a SFM system, while the survey was made to understand the level of influence of smart manufacturing technologies and of DSF features on SFM. In total, 17 experts responded to the survey.

Findings

Data analytics is the smart manufacturing technology that influences more the SFM system and its components and the cyber security technology does not influence it at all. The problem solving (PS) is the SFM component more influenced by the smart manufacturing technologies. Also, the use of real-time digital visualization tools is considered the most influential DSF feature for the SFM components and the data security protocols is the least influential one. The four SFM components more influenced by the DSF features are key performance indicator tracking, PS, work standardization and continuous improvement.

Research limitations/implications

The study was applied in one multinational company from the automotive sector.

Originality/value

To the best of the authors’ knowledge, this work is one of the first to try to characterize the SFM system on smart manufacturing considering smart technologies and DSF features.

Details

International Journal of Lean Six Sigma, vol. 11 no. 5
Type: Research Article
ISSN: 2040-4166

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Article
Publication date: 9 November 2018

Rafif Al-Sayed and Jianhua Yang

The purpose of this paper is to examine empirically China’s determined thrust to attain a high level of technological innovation and the factors affecting moving towards a…

Abstract

Purpose

The purpose of this paper is to examine empirically China’s determined thrust to attain a high level of technological innovation and the factors affecting moving towards a smart and sophisticated manufacturing ecosystem in conjunction with the Belt and Road Initiative (OBOR).

Design/methodology/approach

This research provides empirical determination of the factors affecting moving towards smart manufacturing ecosystems in China. The method is based on combining two approaches: semi-structured interview and questionnaire-based with academics, experts and managers in various Chinese industrial sectors. The results are based on the multivariate analysis of the collected data. A case study of the current manufacturing ecosystem was also analyzed, in order to understand the present state as well as the potential for China’s competitive edge in the developed OBOR countries.

Findings

The results illustrate the importance of the infrastructure dimension comprising variables related to ecosystems, industrial clusters and Internet of Things IoT and other advanced technologies. A case study of the city of Shenzhen’s transformation into a smart cluster for innovative manufacturing points out how China’s OBOR initiative for regional collaboration will further transform the regional smart clusters into an ultra-large innovation based smart ecosystem.

Originality/value

This research is the first to study China’ policies towards playing a prominent role in the Fourth Industrial Revolution 4IR in the context of the OBOR initiative, through empirically defining the factors affecting moving towards a knowledge-intensive smart manufacturing ecosystem where the added value is mostly innovation based.

Details

Journal of Science and Technology Policy Management, vol. 11 no. 3
Type: Research Article
ISSN: 2053-4620

Keywords

Open Access
Article
Publication date: 30 April 2021

Camilla Lundgren, Jon Bokrantz and Anders Skoogh

Technological advancements are reshaping the manufacturing industry toward digitalized manufacturing. Despite the importance of top-class maintenance in such systems, many…

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1391

Abstract

Purpose

Technological advancements are reshaping the manufacturing industry toward digitalized manufacturing. Despite the importance of top-class maintenance in such systems, many industrial companies lack a clear strategy for maintenance in digitalized manufacturing. The purpose of this paper is to facilitate the implementation of maintenance in digitalized manufacturing by proposing a strategy development process for the Smart Maintenance concept.

Design/methodology/approach

This study is designed as a multiple-case study, where the strategy development in three industrial cases is analyzed. Several methods were used to collect data on the case companies' development of smart maintenance strategies. The data were analyzed with an inductive approach.

Findings

A process of strategy development for smart maintenance is proposed, including six steps: benchmarking, setting clear goals, setting strategic priority, planning key activities, elevating implementation and follow-up.

Practical implications

The proposed process provides industry practitioners with a step-by-step guide for the development of a clear smart maintenance strategy, based on the current state of their maintenance organization. This creates employee engagement and is a new way of developing maintenance strategies.

Originality/value

Maintenance strategies are traditionally regarded as a selection of corrective/reactive and preventive maintenance actions using a top-down approach. By contrast, the proposed process is starting from the current state of the maintenance organization and allows a mixture of top-down and bottom-up approaches, supporting organizational development. This is a rare perspective of maintenance strategies and will make maintenance organizations ready for the demands of digitalized manufacturing.

Details

Journal of Manufacturing Technology Management, vol. 32 no. 9
Type: Research Article
ISSN: 1741-038X

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Article
Publication date: 19 November 2021

Zhiting Song and Jianhua Zhu

Smart manufacturing is the prime gripper for the transformation and upgrading of the manufacturing industry. Smart manufacturing systems (SMSs) largely determine how smart

Abstract

Purpose

Smart manufacturing is the prime gripper for the transformation and upgrading of the manufacturing industry. Smart manufacturing systems (SMSs) largely determine how smart manufacturing evolves in technical and organizational dimensions and how it realizes values in products, production or services. SMSs are growing rapidly and receiving tons of attention from academic research and industrial practice. However, the development of SMSs is still in its fancy, and many issues wait to be identified and solved, such as single point failures, low transparency and ineffective resource sharing. Blockchain, an emerging technology deriving from Bitcoin, is competent to aid SMSs to conquer troubles due to its decentralization, traceability, trackability, disintermediation, auditability and etc. The purpose of this paper is to investigate the blockchain applications in SMSs, seek out the challenges faced by blockchain-enabled SMSs (BSMSs) and provide referable research directions and ideas.

Design/methodology/approach

A comprehensive literature review as a survey is conducted in this paper. The survey starts by introducing blockchain concepts, followed by the descriptions of a literature review method and the blockchain applications throughout the product life cycle in SMSs. Then, the key issues and challenges confronting BSMSs are discussed and some possible research directions are also proposed. It finally presents qualitative and quantitative descriptions of BSMSs, along with some conclusions and implications.

Findings

The findings of this paper present a deep understanding about the current status and challenges of blockchain adoption in SMSs. Furthermore, this paper provides a brand new thinking for future research.

Originality/value

This paper minutely analyzes the impacts that blockchain exerts on SMSs in view of the product life cycle, and proposes using the complexity science thinking to deal with BSMSs qualitatively and quantitatively, including tackling the current major problems BSMSs face. This research can serve as a foundation for future theoretical studies and enterprise practice.

Details

Chinese Management Studies, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1750-614X

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Article
Publication date: 26 March 2021

Anilkumar Malaga and S. Vinodh

The objective of the study is to identify and analyse drivers of smart manufacturing using integrated grey-based approaches. The analysis facilitates industry…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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

Practical implications

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.

Originality/value

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

Open Access
Article
Publication date: 4 July 2020

Camilla Lundgren, Jon Bokrantz and Anders Skoogh

The purpose of this study is to ensure productive, robust and sustainable production systems and realise digitalised manufacturing trough implementation of Smart

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2037

Abstract

Purpose

The purpose of this study is to ensure productive, robust and sustainable production systems and realise digitalised manufacturing trough implementation of Smart Maintenance – “an organizational design for managing maintenance of manufacturing plants in environments with pervasive digital technologies”. This paper aims to support industry practitioners in selecting performance indicators (PIs) to measure the effects of Smart Maintenance, and thus facilitate its implementation.

Design/methodology/approach

Intercoder reliability and negotiated agreement were used to analyse 170 maintenance PIs. The PIs were structurally categorised according to the anticipated effects of Smart Maintenance.

Findings

Companies need to revise their set of PIs when changing manufacturing and/or maintenance strategy (e.g. reshape the maintenance organisation towards Smart Maintenance). This paper suggests 13 categories of PIs to facilitate the selection of PIs for Smart Maintenance. The categories are based on 170 PIs, which were analysed according to the anticipated effects of Smart Maintenance.

Practical implications

The 13 suggested categories bring clarity to the measuring potential of the PIs and their relation to the Smart Maintenance concept. Thereby, this paper serves as a guide for industry practitioners to select PIs for measuring the effects of Smart Maintenance.

Originality/value

This is the first study evaluating how maintenance PIs measure the anticipated effects of maintenance in digitalised manufacturing. The methods intercoder reliability and negotiated agreement were used to ensure the trustworthiness of the categorisation of PIs. Such methods are rare in maintenance research.

Details

International Journal of Productivity and Performance Management, vol. 70 no. 6
Type: Research Article
ISSN: 1741-0401

Keywords

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Article
Publication date: 4 July 2020

Rohit Sharma, Charbel José Chiappetta Jabbour and Ana Beatriz Lopes de Sousa Jabbour

The emergence the fourth industrial revolution, known as well as industry 4.0, and its applications in the manufacturing sector ushered a new era for the business…

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1477

Abstract

Purpose

The emergence the fourth industrial revolution, known as well as industry 4.0, and its applications in the manufacturing sector ushered a new era for the business entities. It not only promises enhancement in operational efficiency but also magnify sustainable operations practices. This current paper provides a thorough bibliometric and network analysis of more than 600 articles highlighting the benefits in favor of the sustainability dimension in the industry 4.0 paradigm.

Design/methodology/approach

The analysis begins by identifying over 1,000 published articles in Scopus, which were then refined to works of proven influence and those authored by influential researchers. Using rigorous bibliometric software, established and emergent research clusters were identified for intellectual network analysis, identification of key research topics, interrelations and collaboration patterns.

Findings

This bibliometric analysis of the field helps graphically to illustrate the publications evolution over time and identify areas of current research interests and potential directions for future research. The findings provide a robust roadmap for mapping the research territory in the field of industry 4.0 and sustainability.

Originality/value

As the literature on sustainability and industry 4.0 expands, reviews capable of systematizing the main trends and topics of this research field are relevant.

Details

Journal of Enterprise Information Management, vol. 34 no. 1
Type: Research Article
ISSN: 1741-0398

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Article
Publication date: 22 June 2021

Wenting Chen, Caihua Liu, Fei Xing, Guochao Peng and Xi Yang

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…

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210

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Journal of Enterprise Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0398

Keywords

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Article
Publication date: 20 December 2019

Katarzyna Nosalska, Zbigniew Michał Piątek, Grzegorz Mazurek and Robert Rządca

The purpose of this paper is to introduce coherent Industry 4.0 definition via a rigorous analysis framework, and provide a holistic view of technological, organizational…

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1456

Abstract

Purpose

The purpose of this paper is to introduce coherent Industry 4.0 definition via a rigorous analysis framework, and provide a holistic view of technological, organizational and other key aspects (variables) of Industry 4.0 along with the identification of interdependencies that co-occur between them.

Design/methodology/approach

The study conducts a systematic literature review using Preferred Reporting Items for Systematic Review and Meta-Analysis methodology, and includes 675 papers analyzed both quantitatively and qualitatively. The former utilizes TIBCO Statistica. Furthermore, to define Industry 4.0, the authors reviewed 52 publications.

Findings

Industry 4.0 is a multidimensional system of value creation that includes 42 groups of terms in management, organizational and business-related variables, 30 technological and manufacturing-related variables – classified into seven categories – and several interdependencies that co-occur between them.

Practical implications

The analyses’ outcomes are of high importance both for academia and industry practitioners, as the findings elucidate the meaning of Industry 4.0 and may be used as the basis of future research in management, production management, industrial organizations and other Industry 4.0-related disciplines. Regarding industrial companies, the publication serves as a compendium, and should support industrial businesses in the transition from traditional manufacturing into the Industry 4.0 era.

Originality/value

This work’s novelty and value is threefold: first, the paper introduces an Industry 4.0 definition framework based on the most popular publications in the field. Second, the paper identifies and presents Industry 4.0’s common technologies and organizational variables via a systematic and current literature review. Finally, the paper extends the ongoing discourse on Industry 4.0. For the first time in this discipline, interdependences between identified Industry 4.0 variables are presented and discussed.

Details

Journal of Manufacturing Technology Management, vol. 31 no. 5
Type: Research Article
ISSN: 1741-038X

Keywords

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