Industry 4.0 and Lean Six Sigma integration in manufacturing: A literature review, an integrated framework and proposed research perspectives

Abstract This article explores the literature on lean management (LM), Six Sigma (SS), Industry 4.0 (I4.0) and their relationship. A systematic literature review (SLR) combined with bibliometric analysis was conducted to identify, select and evaluate articles and was supported by content analysis to classify papers into group-discussed clusters. A total of 134 articles were retrieved from relevant databases and publisher engines between 2011 and June 2022. The analysis of these articles enabled us to identify the impact of Industry 4.0 technologies on Lean SS; the relationship between LM, SS and Industry 4.0 and the implications of their combination on operational excellence. The results show that while a majority of researchers consider Industry 4.0 to be a driver of LSS and a prerequisite for helping companies access the data and analytics needed, others find them to be complementary and synergistic. Similarly, various authors support the idea that LSS could be a facilitator of Industry 4.0. This study provides an overview of the main research streams in this field and its shortcomings and presents an LSS4.0 framework integrating Lean SS and Industry 4 which will be of great value to academics and practitioners working in this area.


Introduction
Manufacturing companies are facing and continue to undergo various challenges such as the evolution of customer requirements, e.g., shorter lead times, higher product quality and customized products and services, among others, increased competition, market share, financial crisis and economic decline (Antony et al. 2022;Cherrafi et al. 2016;Lameijer, Pereira, and Antony 2021;Psomas and Antony 2019). Competitiveness is the main concern of organizations, which are continually looking for ways to reduce complexity and waste and increase value and revenues. Since the rise of Industry 4.0(I4.0) and related technologies, additional pressure and challenges have been added to manufacturing companies on how to digitally transform operations management structure to compete in a highly digitized business environment (Ghobakhloo 2020a). I4.0 is expected to have a positive impact on manufacturing processes and operational performance (Ali and Xie 2021;Calış Duman and Akdemir 2021) wich have led companies to rethink their operational processes and manufacturing approaches to accommodate advanced I4.0 technologies and meet customer expectations seeking for smart products and services. Given a series of enabling technologies offered by the new I4.0 paradigm (Culot et al. 2020;Schwab n.d.), operations management is currently exposed to a significant "shift" of many traditional approaches, namely Lean Six Sigma (LSS) (Arcidiacono and Pieroni 2018). Manufacturing companies need to redesign the way they manage processes and adapt them to integrate information and physical data into an intelligent workflow. Today, continuous improvement and digitization are not merely good practices or buzzwords, but rather business necessities. The combination of LSS and I4.0 is an effective way to address the stated challenges (Jayaram 2016). The philosophy of LSS is to design an efficient production system that generates less waste and delivers high quality products with optimal use of resources (Chiarini 2020;Pepper and Spedding 2010).
Similarly, I4.0 enables the transformation of manufacturing tools into smart and efficient ones (Cresnar et al. 2020), to boost operational performance and customer satisfaction. Both LSS and I4.0 paradigms share a common goal, which is improving business performance (Antony et al. 2022;Lameijer, Pereira, and Antony 2021). As stand-alone approaches, LSS and I4.0 are good and effective drivers for business performance and process improvement. When combined, they have the potential to be an exceptionally powerful tool. Aligning I4.0 technologies with Lean and Six Sigma (SS) tools will provide enormous potential for improvement and help companies achieve better performance (Anass et al. 2021;Sodhi 2020;Park et al. 2020;Tissir et al. 2022).The integration of LSS and I4.0 is gathering the interest of both researchers and practitioners. Many authors have been involved in the investigation and advancement of this field (Alexander, Antony, and Cudney 2022;Anass et al. 2021;Antony et al. 2022;Anvari, Edwards, and Yuniarto 2021;Arcidiacono and Pieroni 2018;Belhadi et al. 2020;Bittencourt, Alves, and Leão 2021;Sony 2020;Tissir et al. 2022;Tortorella, Giglio, and van Dun 2019b;Yadav, Shankar, and Singh 2020).While there is a great scientific interest in the current research topic, as evidenced by scientific conferences and a large number of publications to date, there are a limited number of articles that focus on LSS and I4.0. A limited number of articles have attempted to assess the state of research on the integration of LSS and I 4.0 (Antony et al. 2022;Anvari, Edwards, and Yuniarto 2021;Arcidiacono and Pieroni 2018;Bittencourt, Alves, and Leão 2021;Duarte, Cabrita, and Cruz-Machado 2020;Tissir et al. 2022).The majority of studies have addressed lean and I4.0 integration (Al-Futaih and Demirkol 2020; Antony et al. 2022;Buer et al. 2021;Duarte, Cabrita, and Cruz-Machado 2020;Mahdavisharif, Cagliano, and Rafele 2022;Prinz, Kreggenfeld, and Kuhlenk€ otter 2018;Rossini et al. 2019;Sanders, Elangeswaran, and Wulfsberg 2016) studied the benefits, drivers, CSFs and challenges of LSS and I 4.0 integration, theoretically using the literature review. Authors found that most studies focus on Lean and I4.0 integration and that there is a lack of literature addressing the challenges and CSFs related to the integration of LSS and I4.0. These results need to be proven empirically. Yet, there is no comprehensive study in which drivers, barriers and CSFs for a potential integrated model are explored empirically. Existing knowledge about the potential synergies between the two concepts is still in its infancy. The literature debates the role of Industry 4.0, on whether it is an enabler/ driver in the implementation of LSS or the reverse. The results of this review show that researchers agree on three views regarding the relationship between LSS and I4.0: some authors argue that I4.0 can drive continuous improvement and is, therefore, a prerequisite for LSS, others argue that they are complementary, and a few believe that LSS can facilitate the implementation of I4.0. Industry 4.0 is presented as a driver and enabler of LSS implementation. The authors can emphasize that technologies such as cloud computing, Industrial Internet of Things, BDA, CPS and machine-to-machine communication will enable organizations to have the ability to better manage LSS projects in time and data accessibility (Pasi et al. 2020). An organization that has Industry 4.0 technologies as dynamic capabilities will be able to smoothly move its processes and operations toward LSS and operational excellence.
To fill this gap, the main purpose of this article is to provide a state of the art of literature regarding the integration of the two concepts LSS and I4.0 (LSS4.0) using a Systematic Literature Review. Accordingly, the research questions that arise are as follows: RQ1: What is the current state of research on the linkage between I4.0 and LSS?
RQ2: How can I4.0 and LSS be integrated to achieve better operational performance?
This article is structured as follows: Section 2 presents conceptual terminology that guided the research. Section 3 describes the research methodology. Descriptive analysis is presented in Section 4 while Section 5 describes the bibliometric analysis. A qualitative content analysis to illustrate the research streams is presented in Section 6, whereas in Section 7, the conceptual framework is developed and a discussion of theoretical elements of our integrated model is provided. Also, the research gaps and future research directions are proposed in Section 8. Finally, the conclusion and the research limitations are presented.

Theoretical background
Given the extensive literature on I4.0 and LSS and the various definitions, this section aims to present the conceptual terminology used in the remaining work.

Lean management
Lean is an organizational philosophy and approach to business efficiency developed by the Japanese company Toyota, designed to reduce waste and nonvalue added activities in manufacturing. Lean manufacturing uses a set of tools and philosophies that impacts positively quality and productivity and reduces manufacturing costs (Sanders, Elangeswaran, and Wulfsberg 2016) including value stream mapping (VSM), Just in time(JIT), Kanban, Jiduka, among others. LM was widely applied by both larger companies and small and medium-sized businesses and has led to improved business performance such as reducing waste and costs (Cherrafi et al. 2016;Garza-Reyes 2015;Leong et al. 2019), improving customer satisfaction and increasing process efficiency (Bhattacharya, Nand, and Castka 2019;Garza-Reyes 2015). Although lean has proven its ability and support for process optimization and operational performance by eliminating waste and engaging people in daily process improvement, it does not take into account the analysis of process variability and the causes of defects covered by the SS methodology (Lai et al. 2020). Defects require additional work to be addressed, which results in lost time and losses. Lean is a state of mind rather than a methodology that requires the involvement of people, changes in attitude and process improvement wich the need to be integrated with SS for better process efficiency and business performance. Six-Sigma, therefore, aims to identify defects, determine their cause and eliminate them.

Six Sigma
SS is a powerful concept used to achieve continuous improvement, and identify and eliminate the causes of error in processes. Using statistical and nonstatistical tools and techniques, the method addresses process variability and deviations. With SS, manufacturers can achieve greater customer satisfaction while simultaneously maximizing economic gains. After its success in manufacturing companies where it was first introduced, SS has been extended to several sectors, e.g., healthcare, public service, construction and education (Antony and Sony 2020;Hseng-Long Yeh 2011;Jim enez et al. 2020;Pardamean Gultom and Wibisono 2019).SS is well known as a problem-solving approach using qualitative and analytical tools to develop core processes based on the DMAIC or DMADV methodologies. DMAIC stands for Define, Measure, Analyze, Improve and Control while DMADV is the acronym of Define, Measure, Analyze, Design and Verify and is used when companies need to develop a new product or process. While lean thinking brings innovation and business change, SS does not drive innovation within companies. SS can generate higher results when combined with LM.

Lean Six Sigma
The union of the two very powerful approaches to continuous improvement namely Lean and SS gave birth to an integrated approach called LSS (Cherrafi et al. 2016). As an integrated methodology, LSS includes the speedy capability of Lean through process flow and the robustness of SS through a disciplined and systematic approach to problem-solving (Antony et al. 2018). Lean and SS methodologies are being used and examined as a whole (Shah, Chandrasekaran, and Linderman 2008).
The LSS approach can solve complex industrial problems that generate financial and operational improvements (Alexander et al. 2021). Manufacturers are applying the LSS methodology to achieve better performance and reduce losses and nonvalue added activities (Panayiotou et al. 2021).

Industry 4.0
The term I4.0 refers to the fourth industrial revolution, which represents a technological alongside an economic, sociological and strategic revolution (Arcidiacono and Pieroni 2018). The advanced technologies of I4.0, enable the collection, storage, analysis and exchange of massive data between man and machine in a fast and efficient way (Angreani, Vijaya, and Wicaksono 2020;Radziwill 2018).I4.0 enables the design of smart products and services with features such as more insight into customer requirements, better connectivity with customers, and real-time monitoring for better performance (Koh, Orzes, and Jia 2019;Tay et al. 2018). The term "I4.0" was first coined in 2011 at the Hannover Fair, with the digitalization of the manufacturing industry as the main goal. Since that time, I4.0 has become a sought-after topic among experts and academics around the world due to its novelty and has given rise to numerous conferences on the topic. Several recent studies have been involved in the promotion and advancement of knowledge on the subject, resulting in interesting papers (Berm udez and Ju arez 2017; Bittencourt, Alves, and Leão 2019; Buer, Fragapane, and Strandhagen 2018a; Dogan and Gurcan 2018; Karadayi-Usta 2020; Kolberg and Z€ uhlke 2015;Powell et al. 2018;Raji and Rossi 2019;Rossini et al. 2019;Sanders et al. 2017aSanders et al. , 2017bShrouf, Ordieres, and Miragliotta 2014).I4.0 has been explored in the literature from different perspectives: definitions, technologies, a roadmap for implementation, performance impacts, potential barriers, drivers and key success factors for practical implementation and success stories (Angreani, Vijaya, and Wicaksono 2020;Chettri and Bera 2020;Culot et al. 2020;Gallab et al. 2021;Haddud et al. 2017Haddud et al. , 2017Kamble, Gunasekaran, and Sharma 2018;Karadayi-Usta 2020;Lee, Bagheri, and Kao 2015;Machado et al. 2019;Raj et al. 2020;Schumacher, Erol, and Sihn 2016;Sony and Naik 2020;Tay et al. 2018) presented an assessment of the benefits and challenges of adopting IoT. Machado et al. (2019) defined a model to measure manufacturing companies' readiness for digitalization. Sony and Naik (2020) have focused on the study of CSFs of I4.0 using a critical literature review and found 10 factors impacting the successful implementation of I4.0. The authors highlighted the need for specialized talent and a workforce to manage I4.0 projects. Studies conducted by Antony et al. (2022) confirmed that I4.0 technologies can help improve the performance of companies that are already working with the LSS methodology. This manifests the motivation and benefits of this integration.
In the recent literature, the terms "digitization," "digitalization" and "digital transformation" are closely related to I4.0 and are often used by authors to talk about the fourth industrial revolution . In our study, we build on this interpretation of I4.0, which means the integration of I4.0 enabling technologies into manufacturing processes.

Research methodology
The purpose of this study is to assess current research on the relationship between Lean, SS and I4.0 and to analyze the most relevant articles to identify gaps, concerns and potential insights for future research. A systematic review of the literature (SLR) was performed following the guidelines developed by Tranfield, Denyer, and Smart (2003) as described in Figure 1. The main reason for adopting the Tranfield model and an SLR is to adopt a comprehensive, scientific, methodical and reproducible design process that allows for a rigorous and efficient synthesis of existing information (Denyer and Tranfield 2009;Tranfield, Denyer, and Smart 2003). A SLR serves as an approach to conducting a comprehensive review of previous and current studies on a research topic (Vinodh et al. 2020).

Research questions
Given the objectives of the study, the two research questions as depicted in the introduction are as follow: RQ1: What is the current state of research on the linkage between I4.0 and LSS? RQ2: How can I4.0 and LSS be integrated to achieve better operational performance?

Scope of the study
At this stage, we define the keywords, research time, the inclusion and exclusion criteria and the research databases. The definition of keywords and terms was carried out following an iterative process. Terms and synonyms associated with "Lean," "SS" and "I4.0" were inventoried in literature and based on a discussion with senior researchers in the field. Due to the complexity of finding a precise definition and synonyms of the term I4.0, we have made a considerable effort to search and filter publications related to our research topic by examining their titles, abstracts and full text. In most cases, this task can be accomplished by focusing on the most relevant and influential peer-reviewed journals and conferences in the research area. Since the advent of the term I4.0 in 2011, there has been interest from governments, industries and researchers around the world (Yin, Stecke, and Li 2018). Such strategies have been developed by the governments of the world's leading industrial countries, mainly Future Factories by the European Union, Internet þ launched by China, Industrial Internet Consortium created by the United States, Industrie 2025 developed by Switzerland and e-Factory designed by Japan (Mrugalska and Wyrwicka 2017;Uriarte, Ng, and Moris 2020).
To define a set of synonyms for "I4.0," we studied the highest ranked literature reviews on Scopus and the Web of Sciences addressing I4.0 and we included the above names of strategies related to I4.0. To enrich the keyword list, a panel of academics and practitioner experts in the field was approached to support us in refining and validating the inventory of keywords. The keywords considered are summarized in Table 1. Searching online databases is now the leading practice to identify the most relevant articles. To cover a wide range of academic publications, the literature was identified using the following electronic databases and publication engines: Scopus, Elsevier, Emerald, Taylor & Francis, Springer, IEEE and Google Scholar. Table 2 describes the inclusion and exclusion selected criteria.

Papers selection and evaluation
The selection and evaluation process was carried out in three phases: (1) elimination of duplicates, (2) evaluation of the relevance and finally (3) evaluation of the availability of the articles in full text. A number of 786 papers were extracted from databases. By eliminating 352 duplicated papers, the remaining papers were assessed for eligibility. The first eligibility filter is about the relevance of papers. To ensure that the selected articles were relevant to our study, an abstract review was performed by the authors. The assessment of the relevance of the articles to the subject matter resulted in the elimination of 292 articles that were considered offtopic. The second eligibility filter was to assess the accessibility of the articles. Only articles that were accessible in full text were retained. This process resulted in 142 articles being selected for further reading and evaluation. Nine articles were excluded because of the unavailability of the full text. Finally, 133 articles were selected for analysis. A databank was generated in Excel to codify and classify the selected materials and group them by theory, method, objective, outcomes and the main discussion areas. The detailed research methodology is shown in Figure 2.

Descriptive analysis
The descriptive analysis focuses on the following five parameters: Publication Year ( Figure 3): The distribution of publications by year, to identify the trend in the number of studies on the research theme.
Geography Distribution ( Figure 4): Considering the affiliation of the first author, we aim to identify the country's most active on the research theme.
Publications breakdown ( Figure 5) and Distribution across journals (Table 3): Publications breakdown informs on the proportion of publications by journal, conference and chapter while the distribution of publications by journal aims to identify the journals most involved in the research theme.
Research Types ( Figure 6): The purpose is to gain insight into the research type used in the reviewed articles that discuss the combination of LSS and I4.0.
Enabling I4.0 technologies for Lean and SS ( Figure  7): We aim to identify the different technologies discussed in the field of I4.0 and LSS.  Distribution of empirical studies across industry sectors ( Figure 8): We seek to identify and define the industrial sectors most affected by this integration.

Year of publication
The articles published in the last five years follow a progressive tendency, with 75% of publications appearing between 2020 and 2022 indicating that the topic of lean, SS and I4.0 has gained interest and popularity within the research community since 2020 (Figure 3). Through a depth analysis of the statistics related to the number of publications in 2020 (57 papers) which is graphically highest, we notice that only 28% of the publications this year are related to the main keywords "LSS" and "I 4.0" while the majority of publications focus on the combination of lean manufacturing and I4.0.    Figure 4 shows the most active countries in the research field. Moreover, Taylor and Francis is the leading publisher in this field (30%), represented by two journals IJPR and PPC. Presumably, research on the integration of LSS and I4.0 has appeared in a range of highly ranked journals.       (Makris et al. 2019). BDA offers the possibility to save, exploit and integrate practical solutions to current business problems in a timely manner. Big data techniques, that is, video mining, machine learning and text mining support the identification of problem causes for better decision-making by providing in-depth information about the process (Dogan and Gurcan 2018).

Distribution by sources
Distribution of studies across manufacturing industry sectors Figure 8 shows the distribution of papers by manufacturing sector. This distribution suggests that the evaluated papers cover several different sectors. There is a predominance of automotive manufacturing industries for both LSS and I4.0 studies. The majority of empirical studies have examined manufacturing companies in automotive (38%), followed by metal industries (25%), food (15%) and textile (12%)while the chemical, heavy and electronics industries have attracted less attention from researchers (10%) and classed under others. The results reveal that 40% of papers were conducted in the manufacturing environment with no specification of the sector are placed in multisectors.

Bibliometrics analysis
The bibliometric analysis serves as a tool to create, visualize and analyze maps based on network data (Laengle et al. 2020). We conducted a bibliometric analysis using VOS software. Three co-occurrence networks have been evolved to identify the relationship between the concepts discussed: the coauthor network, abstract co-occurrence terms and keyword clusters.

Coauthorship analysis
In terms of coauthorship analysis, we have set three as the minimum of papers published by authors, 27 have been found to meet the criteria, but they are not connected to each other. The largest connected group has five authors, as shown in Figure 9. We conclude that there is a poor connection and collaboration between author clusters, which explains the novelty and scarcity of the topic. This may result in a lack of productivity and research intensity in this area and can be explained by the avoidance or inability of authors working in combined disciplines due to the scarcity of the topic. Hence, a collaboration between authors is greatly recommended.
Abstract occurrence density visualization Figure 10 shows the abstract occurrence density visualization represented by three clusters. Ten was set as the minimum number of occurrences of a word, hence, 15 of the 1287 terms match this criterion and eleven most relevant words were selected. The red cluster is the most prominent and represents the integration between lean and I4.0 while the green cluster related to LSS and the blue cluster representing I4.0 are discussed separately.

Keywords' occurrence
The main purpose of the keyword occurrence analysis is to assess the most used terms and their interactions. By setting the minimum number of occurrences for the keywords to three, we noticed that out of 100 keywords, 18 reached the criteria. However, 11 of the most relevant keywords were selected ( Figure 11). The most frequently used word was "I4.0," followed by "lean manufacturing" and "LSS." I4.0 was linked to almost all other keywords, especially "lean." Indeed, the I4.0 tools par excellence are IoT and Big data. That is to say, numerous articles have addressed the link between lean, SS and I4.0, indicating the relevance of this integration.

Content analysis
A content analysis's main purpose is to identify, organize and categorize ideas about a particular topic. As such, an inductive content analysis was conducted, where data was extracted and coded into an Excel spreadsheet, including the title, research objective, concepts discussed and I4.0 technologies discussed, among others. Next, we clustered the articles according to common themes. As a result, three main research foci emerged: (1) the relationship between Lean Six Sigma and I4.0; (2) the effects of combining I4.0 and LSS; and (3) performance (outcomes). The researchers have been focused on analyzing the relationship between LSS and I4.0 and the performance gathered through descriptive analysis and empirical studies, while integration model and implementation issues were neglected.

Industry 4.0 and LSS correlation
The majority of publications have discussed the correlation and synergies between LSS and I4.0. An  analysis of the relationship between LSS and I4.0 is necessary before an implementation framework can be proposed (Antony et al. 2022). The detailed correlations that emerged from the literature are explained in Subsection 7.3 and summarized in Figure 13.

I4.0 impacts on LSS concept
One of the objectives of our study is to investigate how Industry 4.0 (I4.0) technologies can enhance LSS implementation. This section illustrates the impact of I4.0 technologies on the LSS subfields using the DMAIC methodology. Based on the authors' insights, we evaluate and report in Table 4 whether the technology has a moderate (þ), strong (þþ) or no (0) impact on each DMAIC step and the corresponding activities. Some technologies have a cross-cutting impact on the DMAIC process, others affect only one step. The authors can highlight the evolving nature of literature on this topic. Most of the potential effects studied have been found to improve specific phases or sub-phases of LSS, which will ultimately lead to improved design and performance of LM/SS. For example, in their literature review study (Ahmed, Page, and Olsen 2020), the authors indicated that simulation techniques impact positively and directly all DMAIC stages, mainly the analysis, improvement and control phases, due to their ability to investigate and capture potential problems and improvement.

Performance (outcomes)
Another cluster we identified was the LM, SS and I4.0 combination outcomes. We can highlight that researchers have studied the impact of this combination on firm performance in general and on the value chain and operational excellence in particular.
Previous studies (Acosta-Vargas et al. 2020; Buer et al. 2021;Kolberg and Z€ uhlke 2015;Prinz, Kreggenfeld, and Kuhlenk€ otter 2018;Yadav, Shankar, and Singh 2020) have suggested that the combination of Lean and I4.0 positively supported organizational performance and lead to improvements. Sodhi (2020) stated that by using IoT techniques with LSS methodology, the company can achieve higher performance by taking effective decisions and producing high-quality products. Prinz, Kreggenfeld, and Kuhlenk€ otter (2018) have predicted that productivity can be increased by Lean and I 4.0 implementations. This means that the integration of LSS and I4.0 promises a smarter, more efficient future for manufacturing processes. Due to the paucity of research and empirical studies on the LSS and I4.0 integration benefits, the increase in productivity and process efficiency can only be roughly estimated. McKinsey estimates that switching to automated production 4.0. Can boost productivity by 45-55%. Referring to these authors (Buer et al. 2021;Kolberg and Z€ uhlke 2015) I4.0 is expected to drive companies' operational performance by improving productivity and process efficiency, increasing profits, flexibility and competitiveness. The literature shows that the combination has a positive effect on improving performance indicators which should be confirmed empirically.
Based on the content analysis and the results of the previous section, we developed an integrated model Section 7. An emergent framework to integrate LSS and I4.0 In light of the lack of a structured and comprehensive model for lean, SS and I4.0 integration, we propose a framework for the implementation of these three concepts, based on the combination of theoretical elements resulting from the literature review. The framework is illustrated in Figure 12 and follows a classic and iterative development process approach, from initial inputs and requirements to the final outcomes and benefits, where the traditional LSS-DMAIC process is translated into smart LSS called in this study LSS4.0 model. The framework outlines the drivers, barriers, synergies, challenges and critical success factors that are the primary component of the integrated model LSS4.0. A good understanding of these factors helps to define a managerial response on how best to implement LSS4.0. The proposed framework is part of a reflection and conception of the digital transformation of the LSS concept as a quality improvement tool, which tends to go beyond a technological perception in favor of a strategic vision of an intelligent and digital LSS. The objective of the framework is to support companies in their journey of development and transformation into digital LSS. The proposed model (Figure 12) is structured by coupling the three building blocks: lean and SS concepts, I4.0 enabling technologies and digitalization. I4.0 means the digitalization of industry. Hence, in our model, I4.0 is represented by digital technologies 4.0 and digitalization detailed in Digital strategy, Digital maturity and Digital transformation and resumed in 3D.
Our model starts with antecedents representing the enablers, i.e., the factors that make this integration possible. An analysis of the organization's antecedents is necessary. The questions that arise at this stage are: How are organizations prepared for the digitalization of LSS and what is the vision and strategy for moving toward digitalization? In other words, the company should identify its weaknesses and strengths related to the four dimensions of organization, people, process and technology by assessing their maturity level and clearly defining its objectives and expected results. It is necessary to assess the skills and competencies of the existing workforce. As stated by (Machado et al. 2019), digital awareness, skills and organization are the first steps for any digitalization initiative. The successful deployment of every continuous improvement initiative depends heavily on the people which represent the most strategic asset of any company (Buer, Fragapane, and Strandhagen 2018a;Ciano et al. 2019).
Conversely, we find drivers, barriers, CSFs and the relationship between LSS and I4.0 and their synergies on the top of our model representing the theoretical basis for such integration. Having knowledge of these factors and how the LM, SS ad I4.0 may impact or complement each other is crucial. Then, we found that the core of this model includes LM, SS, I4.0 technologies and the digitization process to explain how this integration will address the tradeoffs between these components to improve operational performance, The use of digital technologies and the resulting innovation can address many of the traditional challenges of LSS and provide benefits. Companies must   digital maturity and defining the future action plan by clearly integrating the objectives to achieve (Kane et al. n.d.). Determining the level of digital maturity is critical to defining the appropriate digital strategy and the most appropriate and prioritized digital technologies. Being a smart manufacturer or having smart operations management does not imply deploying all I4.0 technologies. Referring to the literature, every digitalization project starts by defining an I4.0 strategy and objectives to which the smart and digital transformation will lead. Companies need to adapt their strategies in the current digital revolution to remain competitive (Helfat and Raubtischek 2018;Tallon et al. 2019). Since each manufacturing company has its own process and operations management, it will have a digital strategy and goals specific to each scenario. Hence, organizations must define their digital strategy according to their business model and need to place digital at the heart of their business strategy.
To overcome the human resources resistance, a change management strategy must be defined, in order to allow a seamless shift to a digital management system (Fern andez-Caram es 2019). The objective of I4.0 is to digitalize the industry which concerns suppliers, corporate, operations, products and customers. Digital transformation means the integration of emerging digital technologies to solve complex problems and increase performance. (Butt 2020). Digital transformation is a complex time and cost challenge. It is seen as a more general term that encompasses changes to business models, operations, processes and skills to take full advantage of the deployment of new technologies (Machado et al. 2019). Finally, we find the performance at the edge of the model, representing the result of the integration of the three concepts (Lean, Six Sigma and Industry 4.0). The outcomes involve performance and capabilities improvement to achieve represented by KPIs. Considering the following drivers, barriers, CSFs, synergies and benefits discussed below, a detailed comprehensive theoretical element of the LSS4.0 model is proposed in Figure 13.

Drivers and barriers
Drivers are the factors and reasons that motivate companies to embark on a project, while barriers are the factors that can impede successful implementation. Given that our research topic is an emerging research area, there is a lack of literature addressing motivations for the integration of LSS and I4.0, also empirical evidence is missing. The most quoted drivers behind LSS adoption are improving efficiency and performance of the manufacturing process (Cherrafi et al. 2016), cost reduction and profitability (Ghobakhloo 2020b) and market image (Stentoft et al. 2021). The discussed drivers are summarized in Table  6. Conversely, the barriers that may hinder the LSS4.0 implementation are financial constraints, poor management support, low awareness, resistant behaviors and lack of skills, which are also the main barriers to I4.0 implementation (Butt 2020;Sony et al. 2021;Khan and Turowski 2016) presents some I4.0 adoption barriers that include lack of expertise, lack of quantified financial benefits and lack of skilled labor. The factors that emerged from the literature were regrouped into five family factors: managerial, environmental, people, financial and technological and listed in Table 14.

CSFs
It is worth noting that the barriers to the LSS concept have been widely discussed in the literature. However, Industry 4.0, which was only mainstreamed in 2011 following an initiative launched by a group of business and industry, academia and government leaders in Germany, is still recent. The main objective of the I4.0 initiative was to promote German manufacturing companies and improve their competitiveness and business performance. Nevertheless, I4.0 faces many obstacles, including cybersecurity management, appropriate skills and high investment costs. Thus, studies on its barriers remain limited, especially those where I4.0 is combined with LSS. (Sony et al. 2021) have empirically investigated the CSFs of implementing I4.0 in both manufacturing and services.  studied the critical factors and subfactors for I4.0 adoption in manufacturing industries and observed that nontechnical factors including "organization, people, culture, skills" and "strategy, leadership" are the most prioritized, whereas technical aspects of technology, digital factory, operations, processes, applications are less prominent among the authors.

Benefits
As evidenced in the literature, both LSS and I4.0 have a positive impact on business performance and, when combined, they should lead to greater operational excellence. Mrugalska and Wyrwicka (2017) stated that lean manufacturing integrated with I4.0 can help achieve great flexibility of production systems and processes, realizing complex products and supply chains. (Kiel et al. 2017) have identified various benefits of I4.0 mainly, productivity and efficiency increase, expanded knowledge sharing and collaborative labor, agile and flexible process, better regulations conformity, better customer satisfaction, cost savings and increased business profits.

Synergies between LSS and I4.0
In terms of the link between LSS and I4.0, the authors point out in this section the synergies discussed by researchers. Several studies state that the two concepts are synergic and influence each other. Table 5 summarizes the main findings in the literature on the correlation between LM, SS and I4.0.The findings are categorized into three relationship perspectives: (1) Lean-SS is a prerequisite for Industry 4.0. Buer, Strandhagen, and Chan (2018b) explain that companies with a relatively advanced Lean maturity level are more likely to implement I4.0 in emerging economies. Rossini et al. (2019) carried out a survey of 108 European manufacturers that have already adopted lean philosophy. Their conclusions align strongly with (Buer, Strandhagen, and Chan 2018b) and imply that manufacturers aiming to integrate Industry 4.0 need to simultaneously implement lean manufacturing to drive process improvements. The same findings were stated by Tortorella, da Silva, and Vargas (2018) as a    In an empirical study (Kamble, Gunasekaran, and Dhone 2020;Wagner et al., 2017) investigated the impact of I4.0 on LM based on a survey of 115 Indian manufacturing firms and found that I4.0 positively and directly impacts LM. (Tortorella et al. 2021) Investigate the moderating effect of I4.0 technologies on lean supply chain practices and performance improvement through a survey of 147 Brazilian manufacturing companies. The results confirm that I4.0 has a positive impact on lean and improves performance. Industry 4.0 technologies have changed how organizations operate and react face to operational gaps. Sensors used in the IoT, which collect data at all levels of the manufacturing chain, are an important driver of innovation. This data helps to improve the analysis level in DMAIC approach (Arcidiacono and Pieroni 2018). We synthesized drivers, barriers, CSFs and benefits found in the literature in Table 6.

Research gaps and future research directions
The literature review provided us with in-depth knowledge about the research work related to the LSS4.0 concept. Few studies introduced LSS with I4.0, the research work is more focused on the lean combined with I4.0 rather than the potential integration of LSS and I4.0. The academic community's interest in the Lean 4.0 topic, revealed by the results of this study, is in line with the results of the SLR study conducted by Tissir et al. (2022). We recommend more studies to empirically validate the existing findings. The reasons for the industry's delay in its digital journey include the lack of a roadmap that provides guidance for this transformation, the lack of awareness of digital capabilities and the lack of required skills among employees and stakeholders. Based on the results, we identify gaps ( Figure 14) in the literature. We listed the future research paths for LSS4.0 ( Figure 15). We suggest that future studies explore empirically the drivers and the challenges of LSS4.0. We highly recommend the study of this integration model for SMEs. The proposed framework can be used in subsequent studies to conduct empirical studies to develop and validate the integration model of LSS and I4.0. Structural equation modeling can be performed to analyze the effect of I4.0 on LSS and Operational excellence.

Implications for practitioners and researchers
The findings of the SLR study presented in the proposed framework will guide manufacturing companies in their journey toward operational excellence. The study identifies the relationships between I4.0 technologies and LSS and the key I4.0 technologies discussed in the literature to achieve integration leading to improved operational performance. Understanding the potential of digital technologies such as the IoT, cloud, big data, 3D printing and simulation, among others, will assist managers in driving smart and digital continuous improvement trends in their production systems.
This article provides five main implications for both theory and practice.
It is a good background about LSS4.0 The literature review provides a comprehensive overview of the topic It describes the drivers, motivations, barriers, CSFs and impact of the novel technologies on LSS It can be used as a baseline for future research studies.  A conceptual framework for LSS4.0 implementation is proposed that can serve as a roadmap for future work.
The insights gained from this study will inform future research programs on the integration of LSS4.0 with other management strategies such as Green manufacturing, Resilience and Agility. We identified five emerging LSS4.0 trends (Figure 16).

Conclusions
The purpose of this study was to explore the relationship between Lean Manufacturing, SS and I4.0 and investigate the current state of research by conducting a SLR. We identified 139 articles published between 2011 and May 2022 that were related to our research field. Several researchers in this area have examined quality management with emerging I4.0 technologies from a holistic perspective. However, literature focused on combining LSS with I4.0 technology components is scarce. Therefore, this study explores this area with a focus on LSS at the source. To the best of our knowledge, there is one systematic review article presenting a comprehensive review and classification of the literature, focusing specifically on the topic of LSS4.0. Rigorous bibliometric approaches revealed new insights that have not been fully evaluated elsewhere. Results show that LSS and I4.0 are mutually synergistic and compatible. The literature has mapped the links between LSS and I4.0 from three different perspectives: "LSS as the basis for I4.0," "I4.0 as an enabler of LSS" and "I4.0 and lean complement each other." Further empirical studies that include case studies and surveys must be conducted to confirm and validate the findings. This review identified the literature trends and gaps to define the theoretical elements of an integration model. We proposed a structured and integrated conceptual model for the combination of the two paradigms LSS and I4.0 in the context of manufacturing companies. The model will be applicable, independently of the industry, the area or the size of the business. We proposed a clear and coherent conceptual framework, which provides a structural synthesis of the literature findings and describes the relationships among the key concepts explored in this study and is supported by the results of the review. The framework will help managers to align I4.0's advanced technologies with the existing LSS data-driven methodology and guide future researchers to know emerging themes and existing collaborative opportunities in this research area. The limitation of this article is the subjectivity of the article selection. Also, we have limited our review to the manufacturing area. Publications on LSS and I4.0 are scarce and limited, as the research topic is an emerging area and still in its infancy. Furthermore, as Industry 4.0 was launched in Germany, there may have been relevant publications in the German language that we missed since we only consider articles published in English.

Disclosure statement
No potential conflict of interest was reported by the authors.

About the Authors
Dounia Skalli is a research scholar at FST-SETTAT, Hassan First University, Morocco and an Industrial engineer from ENSA Safi, Cadi Ayyad University, Morocco. She has more than 8 years of experience as a QSE Manager and lead auditor in the Oil and Gaz industry, she Anass Cherrafi is an Associate Professor at EST-Safi, Cadi Ayyad University, Morocco. Holding a Ph.D. in Industrial Engineering, he has nine years of industry and teaching experience. He has published a number of articles in leading international journals and conference proceedings, and has been a Guest Editor for special issues of various international journals. His research interests include Industry 4.0, green manufacturing, Lean Six Sigma, integrated management systems and supply chain management.
Jose Arturo Garza-Reyes is a professor of Operations Management and Head of the Center for Supply Chain Improvement at the University of Derby, UK. He is actively involved in industrial projects where he combines his knowledge, expertise and industrial experience in operations management to help organizations achieve excellence in their internal functions and supply chains. He has also led and managed international research projects funded by the British Academy, British Council, European Commission and Mexico's National Council of Science and Technology (CONACYT). As a leading academic, he has published over 200 articles in leading scientific journals, international conferences and seven books. Prof Alireza Shokri is an Associate Professor (Reader) in Operations and Supply Chain Management and the subject group leader of "Operations and Supply Chain Management" in the Newcastle Business School at Northumbria University in the UK. Prior to the current position, Alireza was the programme leader for the BA (Hon) International Business Management Programme. Before joining Newcastle Business School in 2011, Alireza had different practitioner roles including supply chain manager, quality manager, and ISO9001 internal consultant in food sector. Alireza completed his PhD in Lean Six Sigma application within food distribution SMEs in 2011 and since then he is working as an academic. Alireza is currently leading the "Global Operations and Supply Chain Competitiveness" Research Interest Group with academic members across the globe. Alireza is also member of editorial board for the "International Journal of Lean Six Sigma". As the principal investigator, Alireza currently led different projects funded by the British Academy and Innovate UK. He is also a co-investigator of a multi-disciplinary global EU-funded (1m Euros) project as part of "Horizon 2020" called "GETM3 Project". Alireza's research focuses on Lean Six Sigma and its application in service and manufacturing, Lean Six Sigma integration with Supply Chain Management, Industry 4.0, HR and Sustainability, Supply Chain Quality Management and process improvement and Lean Management. His research has been published in international leading/excellence level journals in his research including "International Journal of Operations and Production Management (ABS 4 Ã ). Alireza is the member of Chartered Quality Institute (CQI) as the practitioner. Alongside that, Alireza is the Fellow of Higher Education Academy (FHEA). Alireza is also a certified Lean Six Sigma Green belt with professional capability of leading on Lean Six Sigma projects. Alireza is keen on working with local, national and international businesses and he is the strong believer on "Research in Practice".