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Lean production has been proved to be a cost-effective and efficient means of production that reduces non-valve added activities. Industry 4.0 (I4.0) is a technology-driven platform that allows machines to interact with other systems through artificial intelligence, machine learning, industrial Internet of Things (IoT), etc. that improve the production system with flexibility, quality and customization throughout the whole value chain. New approaches to digitization of lean production have recently been emerged and they are transforming the industry and increasing productivity throughout the value chain. Through this article, an effort has been made to review the research published in this field.

This paper reviews the literature published in various journals, the databases Web of science (WoS), ScienceDirect, Scopus, Emerald etc. were referred with a focus on lean concepts and tools and I4.0 technologies; it has been noticed that the integration of the lean tools with I4.0 technologies is a very effective tool for the industry.

It has been found in the literature published earlier in various journals that lean manufacturing (LM) is commonly acknowledged and considered a best practice to improve the productivity. It is concerned with the tight integration of people into the industrial process through continuous improvement which leads to value addition throughout the whole value chain by eliminating non vale added activities. The findings show that organizations can improve their productivity and flexibility with speed and accuracy by integrating I4.0 technologies with LM, which is foremost need of any industry across the world.

This article accentuates the connections between the principles and tools developed under the umbrella of I4.0 and those developed by the LM techniques, with a specific emphasis on how some of the principles and tools of I4.0 improve the implementation of lean principles dependent on the competence levels of the technology. Very few articles have been published in this area, and this paper is an original piece of research covering a review of extant research published in various journals.

Industry 4.0 has put forward a smart perspective on managing supply chain networks and their operations. The current manufacturing system is primarily data-driven. Industries are deploying new emerging technologies in their operations to build a competitive edge in the business environment; however, the true potential of smart manufacturing has not yet been fully unveiled. This research aims to extensively analyse emerging technologies and their interconnection with smart manufacturing in developing smarter supply chains.

This research endeavours to establish a conceptual framework for a smart supply chain. A real case study on a smart factory is conducted to demonstrate the validity of this framework for building smarter supply chains. A comparative analysis is carried out between conventional and smart supply chains to ascertain the advantages of smart supply chains. In addition, a thorough investigation of the several factors needed to transition from smart to smarter supply chains is undertaken.

The integration of smart technology exemplifies the ability to improve the efficiency of supply chain operations. Research findings indicate that transitioning to a smart factory radically enhances productivity, quality assurance, data privacy and labour efficiency. The outcomes of this research will help academic and industrial sectors critically comprehend technological breakthroughs and their applications in smart supply chains.

This study highlights the implications of incorporating smart technologies into supply chain operations, specifically in smart purchasing, smart factory operations, smart warehousing and smart customer performance. A paradigm transition from conventional, smart to smarter supply chains offers a comprehensive perspective on the evolving dynamics in automation, optimisation and manufacturing technology domains, ultimately leading to the emergence of Industry 5.0.

Lean Six Sigma 4.0 has brought about a paradigm shift in customization, automation, value creation and digitalization to achieve excellence in human factors, operations and sustainable development. Despite its potential, LSS 4.0 is still in its nascent stage, with researchers striving to identify the key and relevant components of LSS in relation to Industry 4.0. The present study aims to address this knowledge gap through a literature review and subsequently provide a conceptual framework for LSS within the context of digital transformation.

In this study, the authors have conducted a thorough review of reputable articles published between 2011 and 2022, focusing on the integration of Lean Six Sigma (LSS) and Industry 4.0 (I4.0). By using appropriate keywords, the authors identified around 85 relevant articles. The main objective of this integrative literature review was to analyze and extract valuable knowledge from the existing literature on LSS and I4.0. Based on the authors’ findings, a conceptual framework was developed.

The review revealed the motivators, building blocks, tools and challenges of LSS 4.0. The conceptual framework delves into the key aspects of LSS 4.0, focusing on the dimensions of people, process and technology, as well as their subdimensions. These subdimensions serve as the building blocks for developing LSS 4.0 capabilities. The proposed framework visually represents the conceptualization and the relationships among its components.

Only a few conceptual approaches to LSS are developed that include the concepts, new roles and elements of I4.0. As a result, this research investigates the gap in current LSS models preceding I4.0 and develops a conceptual framework to provide a novel and comprehensive summary of the new concepts and components driving nascent and current LSS practices in the digital era.

This study offers practical guidance for implementing LSS in the context of I4.0, emphasizing digital transformation. The findings highlight motivators, building blocks, tools, challenges and spread of LSS 4.0 practices, and present a conceptual framework of LSS 4.0. These insights can help organizations enhance their LSS capabilities and achieve excellence in human factors, operations and sustainable development.

This study aims to make a significant contribution to the model-building efforts of researchers focusing on LSS 4.0. By offering practical guidance, the points discussed in this study help enhance the implementation efforts of practitioners and organizations in the context of I4.0, with a specific focus on digital transformation. The guidance provided takes into account the perspectives of people, processes and technology, providing valuable insights for successful integration.

Prior to managing a company’s processes in the presence of a combination of paradigms, there is a need to understand their underlying interaction. This paper systematically reviews the existing literature that discusses the interaction between lean production (LP) and the fourth industrial revolution (i.e. Industry 4.0). The study aims to understand how the interaction unfolds and whether it is synergistic.

The research relies on a systematic literature review of peer-reviewed articles from Scopus and Web of Science that discuss the interaction between the two paradigms. The final set of articles pertaining to the topic was analysed.

The article presents that the interaction between the two paradigms occurs through a representation of the pillars of the House of Lean (HoL) interacting with the nine technological pillars of Industry 4.0. There is a consensus on the synergistic nexus among the pillars and their positive impact on operational performance. We also demonstrate the weights of the interactions between the two paradigms and the areas of operations management where this interaction takes place through Sankey charts. Our research indicates that the largest synergistic interaction occurs between just-in-time and industrial Internet of Things (IIoT) and that companies should invest in IoT and cyber-physical systems as they have the greatest weight of interactions with the pillars of the HoL.

This research facilitates a deeper insight into the interaction between LP and Industry 4.0 by organising and discussing existing research on the subject matter. It serves as a starting point for future researchers to formulate hypotheses about the interaction among the various pillars of LP and Industry 4.0, apply these interactions and test them through empirical research.

It could serve as a guide for managers to understand with which interactions they should start the digitalisation process.

With the rise in discussions on the interaction between the two paradigms, there is still an opportunity to understand the specificity of this interaction. Compared to the initial seminal works on the subject, such as Buer et al. (2018b), which investigated the direction of interaction between the two paradigms, this research contributes to further investigating this specificity and gaining a better understanding of the relationship governing the interaction between LP and Industry 4.0 by delineating the interaction state among the pillars of the two paradigms and its relevant importance.

Nowadays, the agri-food industry is called to face several sustainability challenges that require the development of new sustainable models. The adoption of new technological assets from Industry 4.0 supports the companies during the implementation of sustainability practices. Several models design the operation management of the food supply chains (FSCs). Because none extant models resulted complete in technological and sustainability elements, this paper aims to propose an innovative and sustainable agri-food value chain model, contributing to extend understating of how supply chains can become more sustainable through the Industry 4.0 technologies.

Thanks to a well-structured and replicable systematic literature review and sequent content analysis, this work recognized and compared the extant FSC models, focusing on the interaction of five key elements: activities, flows, stakeholders, technologies and sustainability. The output of the comparison leading in the definition of the proposed model is discussed in a focus group of 10 experts and tested in a case study.

Fifteen extant models were recognized in literature and analysed to discover their features and to putt in light peculiarities and differences among them. This analysis provided useful insights to design and propose a new innovative and sustainable agri-food value chain model; an example for the olive oil business case is provided.

The adding value of the work is the proposed model which regards innovative elements such as recirculation flows, external stakeholders and Industry 4.0 technologies usage which allows enhancing the agri-FSCs operational efficiency and sustainability.

This study aims to analyze the effect of cognitive load and social value orientation on managers’ preferences when they face with two types of restructuring choices in financially distressed firms: the first belonging to the family of organizational restructuring (massive layoffs) and the second to the family of financial restructuring (debt increases).

The authors investigate experimentally the impact of managers’ cognitive load and social value orientation on the decision to restructure leveraged buyout (LBO) firms in financial distress by using either massive layoffs or debt increases.

By investigating the impact of managers’ cognitive load and social value orientation on the restructuring decision of an LBO firm in financial distress, the research reveals that, on average, cognitively loaded managers prefer massive layoffs over increased debt levels. The massive layoffs seemingly provide a relatively easier way to avoid conflict with influential, residual claimants. In contrast, social value–oriented managers actively avoid massive layoffs and prefer to increase debt.

These results imply that the performance mechanisms emphasized to improve agency relations, for example, in LBOs, have their own limitations during periods of financial distress. This study shows that one of these limits is related to cognitive distortions and personality traits.

In this research, the originality lies in understanding how managers’ internal factors affect their restructuring decision-making, in the case of LBO firms in financial distress.

This paper investigates the semantic association mechanisms of weak demand signals that facilitate innovative product development in terms of conceptual and temporal precedence, despite their inherent ambiguity and uncertainty.

To address this challenge, a domain ontology approach is proposed to construct a customer demand scenario-based framework that eliminates the blind spots in weak demand signal identification. The framework provides a basis for identifying such signals and introduces evaluation indices, such as depth, novelty and association, which are integrated to propose a three-dimensional weak signal recognition model based on domain ontology that outperforms existing research.

Empirical analysis is carried out based on customer comments of new energy vehicles on car platform such as “Auto Home” and “Bitauto”. Results demonstrate that in terms of recognition quantity, the three-dimensional weak demand signal recognition model, based on domain ontology, can accurately identify six demand weak signals. Conversely, the keyword analysis method exhibits a recognition quantity of four weak signals; in terms of recognition quality, the three-dimensional weak demand signal recognition model based on domain ontology can exclude non-demand signals such as “charging technology”, while keyword analysis methods cannot. Overall, the model proposed in this paper has higher sensitivity.

This paper proposes a novel method for identifying weak demand signals that considers the frequency of the signal's novelty, depth and relevance to the target demand. To verify its effectiveness, customer review data for new energy vehicles is used. The results provide a theoretical reference for formulating government policies and identifying weak demand signals for businesses.

The purpose of this study is to understand how and why consumers engage in market-shaping activities on behalf of firms.

This study uses a combination of archival, netnographic and interview methods to examine how consumers responded to the entry of Tesla into the U.S. automotive market.

Consumers are driven to engage in supportive institutional work by the culturally resonant ideologies embodied in Tesla’s strategic orientation. This work takes both discursive and practical forms and sees consumers adopting responsibilities typically associated with other actors, including activists and sales professionals.

In developing an account of an understudied phenomenon – consumers’ firm-supportive market shaping – this research extends theorization around institutional work and cultural branding.

Internet of Things (IoT) is a network, which provides the connection with various physical objects such as smart machines, smart home appliance and so on. The physical objects are allocated with a unique internet address, namely, Internet Protocol, which is used to perform the data broadcasting with the external objects using the internet. The sudden increment in the number of attacks generated by intruders, causes security-related problems in IoT devices while performing the communication. The main purpose of this paper is to develop an effective attack detection to enhance the robustness against the attackers in IoT.

In this research, the lasso regression algorithm is proposed along with ensemble classifier for identifying the IoT attacks. The lasso algorithm is used for the process of feature selection that modeled fewer parameters for the sparse models. The type of regression is analyzed for showing higher levels when certain parts of model selection is needed for parameter elimination. The lasso regression obtains the subset for predictors to lower the prediction error with respect to the quantitative response variable. The lasso does not impose a constraint for modeling the parameters caused the coefficients with some variables shrink as zero. The selected features are classified by using an ensemble classifier, that is important for linear and nonlinear types of data in the dataset, and the models are combined for handling these data types.

The lasso regression with ensemble classifier–based attack classification comprises distributed denial-of-service and Mirai botnet attacks which achieved an improved accuracy of 99.981% than the conventional deep neural network (DNN) methods.

Here, an efficient lasso regression algorithm is developed for extracting the features to perform the network anomaly detection using ensemble classifier.

Reverse logistics services are designed to move goods from their point of consumption to an endpoint to capture value or properly dispose of products and materials. Artificial intelligence (AI)-based reverse logistics will help Micro, Small, and medium Enterprises (MSMEs) adequately recycle and reuse the materials in the firms. This research aims to measure the adoption of AI-based reverse logistics to improve circular economy (CE) performance.

In this study, we proposed ten hypotheses using the theory of natural resource-based view and technology, organizational and environmental framework. Data are collected from 363 Indian MSMEs as they are the backbone of the Indian economy, and there is a need for digital transformation in MSMEs. A structural equation modeling approach is applied to analyze and test the hypothesis.

Nine of the ten proposed hypotheses were accepted, and one was rejected. The results revealed that the relative advantage (RA), trust (TR), top management support (TMS), environmental regulations, industry dynamism (ID), compatibility, technology readiness and government support (GS) positively relate to AI-based reverse logistics adoption. AI-based reverse logistics indicated a positive relationship with CE performance. For mediation analysis, the results revealed that RA, TR, TMS and technological readiness are complementary mediation. Still, GS, ID, organizational flexibility, environmental uncertainty and technical capability have no mediation.

The study contributed to the CE performance and AI-based reverse logistics literature. The study will help managers understand the importance of AI-based reverse logistics for improving the performance of the CE in MSMEs. This study will help firms reduce their carbon footprint and achieve sustainable development goals.

Few studies focused on CE performance, but none measured the adoption of AI-based reverse logistics to enhance MSMEs’ CE performance.