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The purpose of this research is to establish a conceptual model to understand the impact of Total Productive Maintenance (TPM) and Industry 4.0 (I4.0) on the transition of a Circular Economy (CE). Also, the paper explores the combined impact of TPM, I4.0 and CE on the sustainability performance (SP) of manufacturing firms.

The conceptual model is proposed using the dynamic capability view (DCV) and empirically validated by partial least squares-structural equation modelling (PLS-SEM) using 304 responses from Indian manufacturing firms.

The results suggest that I4.0 positively impacts TPM, CE and SP, also showing TPM's positive impact on CE and SP. In addition, CE has a positive influence on the SP of manufacturing firms. Furthermore, CE partially mediates the relationship between I4.0 and SP with TPM and SP. The study also identifies TPM, I4.0 and CE as a new bundle of dynamic capabilities to deliver SP in manufacturing firms.

The present research adds to the knowledge and literature on DCV by identifying the importance of CE in the settings of I4.0 and TPM, especially in the context of sustainability. Also, the current study offers a new set of dynamic capabilities and provides some significant future recommendations for researchers and practitioners.

The study presents various barriers to adopt big data analytics (BDA) for sustainable manufacturing operations (SMOs) post-coronavirus disease (COVID-19) pandemics. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the graph theory matrix approach (GTMA) is applied.

The study presents various barriers to adopt BDA for the SMOs post-COVID-19 pandemic. In this study, 17 barriers are identified through extensive literature review and experts’ opinions for investing in BDA implementation. A questionnaire-based survey is conducted to collect responses from experts. The identified barriers are grouped into three categories with the help of factor analysis. These are organizational barriers, data management barriers and human barriers. For the quantification of barriers, the GTMA is applied.

The study identifies barriers to investment in BDA implementation. It categorizes the barriers based on factor analysis and computes the intensity for each category of a barrier for BDA investment for SMOs. It is observed that the organizational barriers have the highest intensity whereas the human barriers have the smallest intensity.

This study may help organizations to take strategic decisions for investing in BDA applications for achieving one of the sustainable development goals. Organizations should prioritize their efforts first to counter the barriers under the category of organizational barriers followed by barriers in data management and human barriers.

The novelty of this paper is that barriers to BDA investment for SMOs in the context of Indian manufacturing organizations have been analyzed. The findings of the study will assist the professionals and practitioners in formulating policies based on the actual nature and intensity of the barriers.

The monograph analyses (a) the potential impact of information technology (IT) on organisational issues that directly concern the personnel function; (b) the nature of personnel’s involvement in the decision making and activities surrounding the choice and implementation of advanced technologies, and (c) their own use of IT in developing and carrying out their own range of specialist activities. The monograph attempts to explain why personnel’s involvement is often late, peripheral and reactive. Finally, an analysis is made of whether personnel specialists – or the Human Resource Management function more generally – will play a more proactive role in relation to such technologies in the future.

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.

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.

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.

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

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.

The purpose of this study is to investigate the perceived effects between soft management practices, employee behaviours and the implementation of digital technologies in manufacturing plants, as well as how these relate to the emergence of digital waste.

This paper uses case-based research. Data was collected in two large manufacturing companies based in Norway and Sweden through semi-structured interviews with two management representatives and four shop-floor employees. The data was used to evaluate 29 variables describing lean- and total quality management (TQM)-associated employee behaviours and soft management practices, in light of digital transformation.

The results suggest that several variables were positively influenced by the digital transformation process. These were top management leadership, middle management involvement, employee education, corporate social responsibility focus, innovation, knowledge sharing, work-family balance, psychological capital, job satisfaction and career commitment. Training employees, creativity, discretionary effort, turnover intention and proactivity appear to be negatively influenced by digital transformation The findings also indicate that several soft management practices and employee behaviours were not only influenced by manufacturing digitalization but also themselves influenced the process. The potential for digital waste creation was also detected in several variables, including reward and recognition and training employees.

Managers, practitioners and academics may learn about the importance of certain managerial practices and employees’ behavioural needs during the digital transformation process. The findings may help in prioritizing TQM and soft lean management practices and certain employee behaviours during the digital transformation and in creating awareness of digital waste.

This study builds on several existing studies discussing the impact of digital transformation on soft management practices and employee behaviours. It provides insights from a lean and TQM angle and offers a means of prioritizing certain practices and behaviours during a digital transformation. This study also highlights the significance of digital waste.

The role of industry 4.0 (I4.0) technologies for organizations to achieve a competitive advantage and mitigate disruptive emergency situations are well exhibited in literature. However, more light needs to be thrown into implementing I4.0 technologies to digitally transform organizations. This paper introduces a novel framework for formulating manufacturing strategy 4.0 (MS 4.0) that guides organizations to implement I4.0 successfully.

The experts working in I4.0 and technology management domains were interviewed to determine the definition, role and process for formulating MS 4.0. Text mining using VOSViewer© is performed on the experts' opinions to determine the key terms from the opinions through keyword analysis. The identified key terms are mapped together using the existing traditional manufacturing strategy formulation framework to develop the MS 4.0 framework. Finally, the proposed MS 4.0 framework is validated through a triangulation approach.

This study captured the role, definition and process to formulate MS 4.0 and proposed a framework to help practitioners implement I4.0 at manufacturing organizations to achieve competitiveness during normal and emergency situations.

The proposed MS 4.0 framework can assist industry practitioners in formulating the strategy for implementing the I4.0 technology/gies to digitally transform their manufacturing firm to retain the maximum manufacturing output and become market competent in normal and emergency situations.

This study is the first of its kind in the body of knowledge to formulate a digital transformation strategy, i.e. MS 4.0, to implement I4.0 technologies through a manufacturing strategic lens.

This paper aims to investigate the characteristics and dynamics of the organizational changes needed to facilitate the management of an Industry 4.0 transformation in manufacturing SMEs and propose an approach to manage them.

This research focuses on a single manufacturing SME in North America, and data were collected using a research intervention method. Data were collected through observation and intervention within the SME over 27 months.

The research has shown that organizational changes are required in manufacturing SMEs to better manage their Industry 4.0 transformation projects.

Using the case study method limits the generalization of the results. The organizational changes observed, and their characteristics might be specific to the studied manufacturing. Although results could vary in different contexts, many manufacturing SMEs have similar characteristics to those observed in this study.

This research provides preliminary evidence of an iterative organizational change management approach that manufacturing SMEs must adopt to facilitate the management of their digital transformation.

This research provides a better understanding of how a manufacturing SME can improve its capabilities to manage its digital transformation by introducing iterative organizational changes. From these results, a link to the organizational learning literature can be drawn and developed upon.

This research aims to identify, examine and evaluate barriers to the adoption of digital servitization in manufacturing firms in the case of the Sri Lankan manufacturing sector and analyze the inter-relationships among digital servitization barriers.

Based on the comprehensive literature review, 13 barriers were identified. The grey decision-making trial and evaluation laboratory (grey-DEMATEL) approach was used to uncover and analyze the relationships among barriers in terms of their overall influence and dependencies.

A prominent barrier to the success of adopting digital servitization in the Sri Lankan manufacturing sector is the lack of digital strategy in developing activities related to the design of digital service packages, organizational structures and processes. Supply chain integration is the most influential factor, which plays an important role in developing a competitive advantage by encouraging innovation process capabilities in servitized companies.

Industry practitioners can develop guidelines for adopting digital servitization practices based on the importance and interdependencies of different barriers and thereby prioritize projects within a program of digital servitization adoption in their organizations.

Research studies on barriers to digital servitization are limited to exploratory nature and have adopted mainly the qualitative approach, such as in-depth interviews. No empirical study has investigated the inter-relationships among digital servitization adoption barriers in the manufacturing sector. This study provides a holistic view of different barriers to the adoption of digital servitization in the manufacturing sector as a basis for developing comprehensive digital servitization strategies to manage and leverage complexity in digital transformation.

The study proposes to determine the impending challenges to lean integration with Industry 4.0 (I4.0) in manufacturing that aims at achieving desired operational performance. Integrating lean and Industry 4.0 as the two industrial approaches is synergetic in providing operational benefits such as increasing flexibility, improving productivity, reducing cost, reducing delivery time, improving quality and value stream mapping (VSM). There is an urgent need to understand the integrated potential of OPEX strategies like lean manufacturing and also to determine the challenges for manufacturing SMEs and further suggest a strategic roadmap for the future.

The current work has used a combined approach on interpretative structural modeling (ISM) and fuzzy Matrice d'impacts croisés multiplication appliquée á un classment (MICMAC) approach to structure the multiple level analysis for the implementation challenges to integrate OPEX strategies with Industry 4.0.

The research has found that the indulgence of various implementation issues like lack of standardization, lack of vision and lack of trained support, all are the major challenges that inhibit the integration of OPEX strategies with I4.0 technologies in manufacturing.

The research has investigated the internal factors acting as a roadblock to lean and Industry 4.0 adoption. Further studies may consider external factors to lean and Industry 4.0 implementation. Also, further research may consider other operational excellence approaches and extend further to relevant sectors.

This study provides the analysis of barriers that is useful for the managers to take strategic actions for implementing OPEX strategies with I4.0 in smart manufacturing.

The research determines the adoption challenges towards the integrated framework. This is the first study to explore challenges in integrating OPEX strategies with I4.0 technologies in manufacturing SMEs.

Scholars have widely asserted that a firm’s manufacturing technologies must be aligned with its competitive strategy. This study tests the existence of such a strategy‐technology “fit”, determines whether good fit results in better performance, and examines the nature of fit in light of computer controlled or “advanced manufacturing technologies”. For a sample of 399 metal machining firms, a strategy‐technology alignment was found to exist and relate to higher financial performance. Advanced manufacturing technologies were found to both reinforce and alter conventional thinking about the flexibility‐efficiency tradeoff. Specific technologies were found to be uniquely bundled or combined to support specific competitive requirements.