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The integration between the traditional lean management and Industry 4.0, namely called Lean 4.0, is under the spotlight of both academia and practitioners. While we agree on the benefits Lean 4.0 may bring to companies performance, we still lack a deep understanding of the characteristics of this paradigm, such as its effective application space. Recalling traditional lean better suits repetitive companies, we are keen to understand whether the anew Lean 4.0 will enlarge its application space.

We performed an exploratory study, using a quantitative analysis based on Bayesian network approach to investigate whether Lean 4.0 results to be as effective in repetitive companies as in non-repetitive ones, in terms of operational performance.

While our findings confirm that Lean 4.0 will enhance companies' performance regardless their production strategies, companies adopting a repetitive strategy do benefit from a much higher improvement. Our findings provide an insight on the true applicability space of Lean 4.0, which seems to be the same as the traditional lean.

We contribute to the ongoing debate of Lean 4.0 providing initial empirical evidence on how to improve the operational performance in non-repetitive companies, seeing that Lean 4.0 might not be the best choice in its current format.

The purpose of this paper is to provide the description of an original framework for maintenance management plan development. The research aims to use in an integrated way different World Class Manufacturing (WCM)-based tools, in order to obtain a model which can be used for preventive maintenance in different industrial contexts.

In this research, a conceptual framework of preventive maintenance was described and then it was evaluated through a qualitative study in an Italian company. The company was chosen based on an initial interview with the operations team and the model area was selected. Then, the location was reorganized in order to obtain a green field which could sustain the implementation of the framework tools.

The case study was carried out in a small-medium manufacturing company which produces quick-release couplings and multiconnections, ranging from medium to ultra-high pressure. The defined framework has proved to be easy to implement in a company with a corrective maintenance plan, allowing the maintenance department to embrace the preventive maintenance culture. The maintenance model has been well received from the employees.

The framework allows a standardization of maintenance plans. Firstly, the standardization design itself allows finding previous wastes and consequent improvement areas. Then, it brings the improvement of a single machine which impacts all other machines in its family.

The added value of this study is the ability to integrate different WCM-based tools. Since the framework depicts a step-by-step process; it is also a starting point for companies that want to approach preventive maintenance for the first time.

This study aims to study the implementation of lean thinking at the strategic level of an Italian manufacturing company. Companies implementing continuous improvement (CI) projects in their production processes often take the monitoring phase for granted. This research deploys an A3 lean thinking project in the monitoring phase of strategic KPIs upon completion of several ongoing improvement projects.

The research methodology is action research aiming at disseminating the problems that the company is facing. The study relies on the lean action plan developed by Womack and Jones (2003): Planning for lean and Lean action. Lean planning consists of the following steps: find a change agent; get the knowledge; find a lever. Lean action uses the A3 lean approach.

The company reached high-performance improvements due to the proposed lean action plan.

This study contributes by presenting a lean action plan in the monitoring phase, highlighting the importance of the lean thinking-monitoring continuum in reducing time waste for faster diagnosis and using action research to analyze and instill reflective learning.

The research relies on the A3 methodology to showcase the benefits that a mature paradigm, often coined to production, still has unexplored potentials.

While Digitalisation is gaining momentum among practitioners and the scientific world, there is still a struggle to embark on the digitalisation journey successfully. The struggles are more significant for SMEs compared to large companies. Such transformation could face internal resistance, which evokes the need to put it into a socio-technical perspective such as lean. This paper investigates how SMEs could implement digital tools and technologies in their operations.

We relied on a multiple case study design in three SME manufacturing companies in Italy. Based on the experience of those companies, the struggles in the implementation and the lessons learned, we formulate an implementation model of digital tools driven by lean thinking.

Companies tend to implement first digital tools that help with real-time data collection and stress that introducing digital tools becomes challenging without reducing waste in production. The model stresses top management commitment, middle-line involvement and operator training to resist change. All these factors coincide with socio-technical lean bundles developed by seminal works. In addition, the study highlights that financial incentives are not necessarily the common barrier to digital tools implementation in SMEs but rather the cultural aspect.

Our paper enriches the extant body of knowledge by deriving knowledge around digitalisation implementation through lessons learned and corrective actions. It allows managers to benchmark and compare the current state of the implementation process with that of other companies and the one proposed to make corrective actions when necessary.

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.

The purpose of this study is to identify the critical readiness factors (CRFs) that mainly affect the implementation of Lean Six Sigma (LSS) in an organization and their interactions, and to develop a model that allows the management to assess the Lean Implementation Readiness Level.

The methodology is separated into two main parts: the literature review and the assessment model development. In the literature review, the main CRFs and their interactions for LSS implementation in Scopus Data Base were identified. The second part of the methodology is the model development. It was built on a stepwise framework that considers the relations among the CRFs and their importance. Moreover, it was used fuzzy-based linguistic variables given by the experts working in the company to consider the actual performance rating of each CRF. The model has been validated in the healthcare sector in nine hospitals.

From the model application, it is possible to note that the most frequent level among the nine hospitals interviewed is “Average Ready”. Also, the most extreme level of readiness occurred ones while the most extreme level of not readiness never occurred. Results show that in 78% of the cases, there would have been a high probability of implementation failure. Also, it was possible to identify for each hospital if the CRFs are good, if they are weak and need to change or if another factor needs to be improved before it and what this factor is.

This work proposes a new methodology that eliminates the negative aspects and limitations of the total interpretive structural modeling methodology and the fuzzy logic approach currently applied to evaluate the LSS readiness of a company. The present methodology lies in the fact that it provides a solution not only by defining the weak CRFs but also by giving an indication of priority as it identifies the weak antecedent factors that inhibit the preparedness of the depending factors.

Industry 4.0 and Lean Production are a successful match in terms of performance improvement. While we understand the combined potential, there is still poor understanding of how companies should embrace digital transformation to make it successful and sustainable, and the role that lean plays in it. In this paper, we investigate how manufacturing companies embark upon digital transformation and how being lean might affect it.

We conducted multiple case studies with 19 manufacturing companies. We identified two clusters of companies according to their Lean maturity, and we assessed digital transformation patterns by analyzing insights coming both from cases and from the literature. Integrating cross-case analysis results, we developed a framework that shows two different digital transformation patterns according to companies’ commitment to Lean.

Our findings first and foremost show the significant role of lean in driving digital transformation. We identify two patterns, namely Sustaining digital transformation pattern, characterized by the pervasive role of lean culture with small and horizontal digital changes, involvement of people and willingness to maintain continuous process improvement, and Disruptive digital transformation pattern, characterized by few and large digital steps that imply a disruptive and radical change in the company system.

Empirical evidence supports the relevance of the proposed model and its practical usefulness. It can be used to design digital transformation, prepare properly the introduction of Industry 4.0 through a lean approach, and plan the future desired state, identifying the Industry 4.0 technologies that should be implemented.

It is widely recognized that the relationship between Industry 4.0 and lean is significant and positive, yet little evidence was presented to back that. We aim at bringing this debate forward by providing initial empirical evidence of the significant role that lean has on digital transformation, showing how lean drives the digital transformation pattern of companies.

This paper aims at examining the impact that COVID-19 pandemic and its related work implications have on the relationship between lean implementation and service performance.

The author surveyed service organizations that have been implementing lean for at least two years and remotely maintained their activities during the COVID-19 outbreak. Multivariate data techniques were applied to analyze the dataset. This study was grounded on sociotechnical systems theory.

The findings indicate that organizations that have been implementing lean services more extensively are also more likely to benefit from the effects that the COVID-19 had on work environments, especially in the case of home office. Nevertheless, social distancing does not appear to mediate the effects of lean services on both quality and delivery performances.

Since the pandemic is a recent phenomenon with unprecedented effects, this research is an initial effort to determine the effect the pandemic has on lean implementation and services' performance, providing both theoretical and practical contributions to the field.

The purpose of this paper is to identify the lean production (LP) practices applied in healthcare supply chain and the existing barriers related to their implementation.

To achieve that, a scoping review was carried out in order to consolidate the main practices and barriers, and also to evidence research gaps and directions according to different theoretical lenses.

The findings show that there is a consensus on the potential of LP practices implementation in healthcare supply chain, but most studies still report such implementation restricted to specific unit or value stream within a hospital.

Healthcare organizations are under constant pressure to reduce costs and wastes, while improving services and patient safety. Further, its supply chain usually presents great opportunities for improvement, both in terms of cost reduction and quality of care increase. In this sense, the adaptation of LP practices and principles has been widely accepted in healthcare. However, studies show that most implementations fall far short from their goals because they are done in a fragmented way, and not from a system-wide perspective.

Maintenance is a key lever for reducing costs in manufacturing companies. Researchers have focused in the past on the importance of integrating maintenance and production planning to obtain total cost savings and have proposed a number of models. Nonetheless, all such contributions take a static perspective and do not consider the dynamics of markets, e.g. when some already committed preventive maintenance (PM) interventions are to be rescheduled. Therefore, the aim of this paper is to suggest a new decision‐making process to manage the rescheduling of PM interventions and to show – through the proposal of an ad hoc planning model – the economical savings from expressly considering rescheduling costs in re‐planning PM interventions.

In this paper the authors used six case studies to infer both the current process of production and maintenance integrated planning and the decision‐making process followed for rescheduling PM interventions. From the current practices the authors derived a new managerial framework for managing rescheduling events and they have built two mathematical models to compare the current and the proposed frameworks by using real data.

Considering rescheduling costs in the joint production and maintenance planning process leads to significant economical savings under all the scenarios tested. In particular, the use of the proposed model allows significant reductions in maintenance costs while keeping constant production costs. This makes the implementation of the authors' proposal in real companies easier.

This research originates from the wish of a real company to better investigate the issue of rescheduling interventions. To the best of the authors' knowledge this is the first study to face the problem of rescheduling PM interventions and to present a mathematical model to support managers in such a decision‐making process.