Effect of I4.0 Technologies on Lean Manufacturing Practices

Javier Salvador; Maria Jose Oltra-Mestre

doi:10.5772/intechopen.1013227

Abstract

This chapter provides a literature review of the interaction between Lean Manufacturing (LM) practices and Industry 4.0 (I4.0) technologies, with a doctrinal and operational approach. The objective is to analyse the effect of applying I4.0 technologies on the different operational constructs of LM and, more specifically, to discern whether this effect or impact broadens and enhances the properties or attributes of these constructs. Unlike fragmented approaches, this study provides a systematic and detailed mapping of synergies, tensions, and limitations across each Lean construct. Based on more than two decades of research in LM and recent literature on I4.0, the findings suggest that I4.0 does not replace Lean but rather acts as a moderator that amplifies its potential, hinting at how this interplay may foster improvements in cost, quality, flexibility, delivery speed, and reliability. This review contributes to clarifying the Lean 4.0 paradigm, an evolution of both domains as they converge, generating a greater impact than they would separately.

Keywords

Lean Manufacturing
I4.0 technologies
Lean 4.0
operational constructs
systematic literature review

Author Information

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Javier Salvador *
- Department of Business Administration and Marketing, Universitat Jaume I de Castelló, Castellón, Spain
Maria Jose Oltra-Mestre
- Department of Business Administration and Marketing, Universitat Jaume I de Castelló, Castellón, Spain

*Address all correspondence to: jasalvad@uji.es

1. Introduction

Lean Manufacturing (LM) is a universal philosophy that can be practised and implemented to a certain extent in any industry [1]. Its main objective is to improve operational efficiency by eliminating waste [2] and reducing costs by decreasing non-value-added activities [1].

The term ‘Industry 4.0’ (I4.0) was coined in 2011 by a German federal government initiative involving universities and private companies. It was a strategic programme to develop advanced production systems with the aim of increasing the productivity and efficiency of the national industry [3]. This concept represents a new stage in the manufacturing system industry through the integration of a set of emerging and converging technologies that add value to the entire product life cycle [4].

Both I4.0 and Lean Management are industrial paradigms that serve as the basis for the design and development of production operations. Industry 4.0 focuses on the application of new technologies to solve the challenges faced by production systems, while Lean Management revolves around people, processes, and a culture of continuous improvement. Not only can both concepts coexist, but they also complement and enable each other, resulting in an integrated paradigm called Lean 4.0, a model resulting from the union of the two concepts that companies can use to improve operational performance and supply chains. Lean 4.0 is the next generation of LM and focuses on bringing Lean methodology to the digital transformation of Industry 4.0.

In general, previous studies [5, 6, 7, 8] agree that the integration of LM and I4.0 generates a positive synergistic effect, as it allows for superior results in terms of operational performance compared to the isolated application of each domain [9, 10]. The main objective of our study is to complement previous reviews on the influence of I4.0 on the effect of Lean practices, paying special attention to the justification and the way it impacts.

2. LM principles and practices

Lean Manufacturing is based on principles that are the strategic components referring to the ideals of the system [11], while practices are the components that operationalise these principles. There are five suggested principles of LM [12]: Define Value, Map Value Stream, Create FLOW, Establish PULL, and Pursuit Perfection (Figure 1).

Figure 1.
Lean Manufacturing principles.

The principles of LM are implemented through various practices collectively referred to as Lean practices [13]. Shah and Ward [14] expanded on their 2003 study and proposed a model comprising 48 practices to represent the operational space of LM; these practices are grouped into 10 operational constructs (bundles) that characterise the dimensions of an agile system.

In turn, these bundles are grouped into three underlying constructs: supplier-related, customer-related, and internally related [14].

Each underlying operational grouping has a specific scope of application within the Lean environment, defined by the three dimensions mentioned above (supplier-related, customer-related, and internally related), as shown in Figure 2.

Supplier-related bundle

Supplier feedback: Provides regular feedback to suppliers on their performance through control audits (company–supplier), actively shares demand forecasts, and involves them in the design and development of new products, etc.
JIT delivery: Ensures that suppliers deliver the correct quantity at the right time and to the right place.
Developing suppliers: Develops relationships with suppliers so that they become more involved in the parent company’s production process; for example, by promoting the improvement of supplier capabilities, training, technical assistance, quality certification, standardisation, technology transfer, advising and assisting in the implementation of LM techniques, joint development of new products, etc.

Customer-related bundle

Involved customers: Focusses on the company’s customers, involves them in product design, maintains feedback on demand, etc.

Internally related bundle

PULL: The objective of PULL practices is to develop manufacturing so that only the required units are produced on time [15]. This includes the use of Kanban cards as signals to activate production.
FLOW: Establishes mechanisms that enable and facilitate the continuous flow of products. Flow practices encompass improvements such as defining product families to balance workstation cycle times in order to improve inventory levels and manufacturing times [16]. Therefore, their implementation is beneficial to a company’s operational performance [17].
SETUP: Reduces process downtime between product changes. As customer needs diversify, product variety also increases. Reducing batch sizes can lead to frequent changeover times and, therefore, process downtime, which can become an obstacle to process performance [16].
Total productive maintenance (TPM): Performs autonomous, preventive, and corrective maintenance by operators to reduce breakdowns and unplanned equipment downtime, thus achieving a high level of equipment availability.
Statistical process control (SPC): Online quality control and the use of control charts to ensure that each process delivers defect-free units to the next process.
Involved employees: Encourages the role of employees in problem-solving, their multifunctional nature, their versatility, the creation of improvement teams, job rotation, participatory decision-making, etc.

3. Classification of Industry 4.0 technologies

The I4.0 domain requires an analytical framework that allows technologies to be differentiated and classified according to their degree of impact and function in production processes. From a doctrinal point of view, the specialised literature has vehemently stated that not all technologies associated with the I4.0 paradigm have the same instrumental, applicative, operational, or strategic character within production systems. This classification effort attempts to channel the diversity of technologies into groups or strata according to their level of integration, interaction, use, or other systemic or interpretative judgements of value.

Industry 4.0 technologies can be classified [18] into two groups according to their main aim: front-end technologies and basic technologies (Table 1). Front-end technologies are those whose objective is related to operational needs and, therefore, have an end-use purpose for the value chain of companies. In other words, they consider the transformation of manufacturing activities based on emerging technologies. For their part, basic technologies provide connectivity and intelligence for front-end technologies. This last layer is what enables the concept of I4.0, differentiating this concept from previous industrial stages. This is because core technologies allow front-end technologies to be connected in a fully integrated manufacturing system [19, 20, 21].

Basic technologies	Internet of Things (IoT)
	Cloud
	Big Data
	Analytics (IA)
Front-end technologies	Smart Factory	Vertical integration
		Virtualisation
		Automatisation
		Traceability
		Flexibility
		Energy management
	Smart Product
	Smart Supply Chain
	Smart Work

Table 1.

Industry 4.0 technologies classification [18].

4. Research methodology

This section describes the methodology adopted for conducting the literature review. Following the guidelines of Xiao and Watson [22], for a review to be successful, it must consist of three phases: planning, execution, and reporting.

4.1 Planning

This phase consists of two activities:

Formulate the problem, such as the question that the research will address. In our case, this is determining the effect or impact that I4.0 technologies have on LM practices.

Review the protocol in order to reduce bias during the analysis of the selected data [22].

4.2 Execution

This phase consists of five activities or steps:

Search literature, which was carried out by entering search strings into scientific search engines, mainly Google Scholar (scholar.google.com) and Scopus (scopus.com), to find relevant contributions on the topic analysed. The search strings entered in the search engines include the keywords mentioned at the beginning of this chapter (LM, I4.0 Technologies, Lean 4.0, Operational Constructs).

Screen for inclusion of the papers. Once the initial list of studies to be considered was generated, a review was conducted based on the abstract of each article in order to select studies according to the inclusion and exclusion criteria [23].

Assess quality. The authors have assessed the quality of the articles in parallel [22]. Any discrepancies have been resolved through discussion among the authors or consultation with external agents. This decision is in line with the desire to contribute as many articles as possible, given the interest generated by this study.

As a result of the steps described above, a total of 84 articles were selected that are representative of the concepts discussed in this chapter. Of these, 64 are journal papers (76.2%), 11 are indexed international conference papers (13.1%), and 9 are book chapters (10.7%). These works have been classified by year of publication, as shown in the following graph (Figure 3) and by editorial (Figure 4), reflecting that the majority of them have been published in the last 10 years.

Figure 3.
Yearly distribution of the considered papers.

This information is entirely consistent, since, as stated in Point 2, the concept of I4.0 was coined in 2011 and, consequently, most of the research that has analysed the interaction between the two domains has been carried out in subsequent years (mainly since 2014), demonstrating the growing interest in this paradigm.

Figure 4.
Selected papers published by the editorial.

Extract data from selected papers. As recommended by the study by Gomersall et al. [23], the papers have been coded to identify study characteristics and variables that could be used to explain emerging themes or differences in the studies.

Analyse and synthesise is the preliminary step prior to presenting the results obtained [22]. The authors have analysed and discussed how to translate the quantitative results found into results that can be presented and communicated, while maintaining the essential objective of responding to the problem initially posed.

4.3 Summary of the research process

The next flow diagram (Figure 5) schematically represents the research methodology process for a better understanding of the mentioned steps.

Figure 5.
Flow diagram of research method.

4.4 Report findings

The outcomes of this literature review are grouped for each operational construct (bundles) of LM (Figure 2), framing, in turn, this effect within the classification framework of I4.0 technologies proposed in Table 1.

The following section details the most relevant findings, resulting from the research methodology implemented.

5. Effect of I4.0 technologies on LM bundles

The convergence between LM and I4.0 is arousing considerable interest in the current research field, as both paradigms, although of different origins, pursue a common goal: the creation of value.

In order to simplify the basic concept of the research, the following scheme (Figure 6) reflects the direct impact of I4.0 technologies on LM bundles.

Figure 6.
Impact of I4.0 technologies on LM bundles.

Below, the most relevant findings derived from the research methodology are developed individually and in detail, regarding the mentioned impact of I4.0 technologies on the operational constructs of LM.

5.1 Supplier feedback and I4.0

Supplier feedback practices improve supply chain coordination and reliability, reducing costs and improving delivery times in terms of both speed and reliability [14].

Internet of Things technologies provide real-time information on production status, available inventory quantities, and the location of sub-assemblies, both for suppliers and manufacturers, on various platforms such as smartphones, tablets, and computers. This improves communication, which is the basis of Lean supplier feedback practices [24].

Real-time monitoring of delivery status, as a result of the application of IoT, provides greater traceability of the item to be delivered [25] and, therefore, promotes end-to-end (customer–supplier) supply chain visibility.

This study [26] reports that AI (artificial intelligence) predictive models are used to forecast demand, enabling suppliers to adjust their production more quickly and accurately. AI also improves shared visibility with suppliers, optimises routes, monitors deliveries, and strengthens trust [27].

Smart Factory technologies, such as 3D printing [28], enable rapid prototyping and customisation, reducing development times and encouraging early collaboration with suppliers. This accelerates the design, prototyping, and testing phases.

The application of cyber–physical systems (CPS) enables the physical–digital integration of processes, allowing automatic synchronisation with suppliers and promoting feedback with them.

Smart Supply Chain technologies, such as Customer Relationship Management (CRM) applications or software, enable the management of interaction data, the sharing of performance metrics, the issuance of alerts, and the visibility of orders and deadlines [29]. This aligns directly with the objective of feedback, as CRM improves structured, two-way communication with the supplier.

5.2 JIT delivery and I4.0

The development and application of JIT delivery practices increase the security of timely delivery of materials to the point of use, stabilising the flow of materials (lower costs) and providing higher levels of service (reliability and speed of delivery) [1, 30, 31].

IoT sensors, radio frequency identification (RFID), and CPS enable materials to be tracked from the supplier to the point of use, reducing uncertainty and enabling more accurate JIT deliveries [25, 32].

Driouach et al. [28] show how 3D printing or additive manufacturing (AM) can facilitate the achievement of LM practice objectives. Specifically, they state that AM allows the production of parts on demand, eliminating the need to maintain large inventories of MMPs or intermediates and reducing the risk of parts obsolescence (JIT delivery).

The application of CPS improves upon classic JIT delivery through automatic order processing and stock reduction throughout the entire logistics chain of supply, production, and customer [33].

On the other hand, Xu and Chen [34] applied an IoT-based JIT manufacturing framework (JIT delivery) capable of reacting and adapting dynamically to current manufacturing progress and customer orders in the manufacturing process. The framework maximised production by adjusting manufacturing scheduling with limited resources and easy implementation.

AI-based systems automatically adjust production levels based on actual demand (PULL) and improve predictive planning and inventory control, optimising supplier deliveries (JIT delivery) [27].

5.3 Developing suppliers and I4.0

Developing suppliers' practices increases the likelihood of meeting the quality, flexibility, and efficiency requirements set by the customer, with a positive impact on operational performance [14].

IoT-driven CPS architectures [35] enable the co-creation and co-development of applications and services, improving interoperability and coordination between actors in the industrial network, including suppliers and customers.

Collaborative technologies or tools (Smart Work) for supplier training improve communication and knowledge management [18].

Additive manufacturing (Smart Factory) enables rapid prototype development with the supplier [28] and, therefore, testing; thus, in the researcher’s opinion, it streamlines the supplier’s internal manufacturing processes and reduces significant investment risks by anticipating operational and/or technical trials.

Virtualisation and Digital Twins [20] enable process improvement scenarios to be simulated prior to physical implementation, reducing technical and financial risks for the supplier.

5.4 Involved customers and I4.0

Practices associated with developing customer relationships promote active customer participation in the supplier’s product design, development, and manufacturing process. This translates into greater adaptation and flexibility, reducing errors and costs, and optimising all aspects of the operational flow [14].

IoT-driven CPS architectures enable the co-creation and co-development of applications and services, improving interoperability and coordination between actors in the industrial network, including involved customers [35].

The analysis of large volumes of data (Big Data) allows for an understanding of customer behaviour patterns and the anticipation of demand [36], improving the customer–supplier relationship.

Analytical systems (AI) based on large volumes of data (Big Data) facilitate customer-oriented decision-making [37], positively engaging the customer.

Digital transformation (based on cloud platforms) strengthens customer interaction and product co-creation [38].

Collaborative tools or platforms are used to integrate customers into the product innovation process [39].

5.5 PULL and I4.0

PULL techniques allow production to be adjusted according to actual demand and reduce response times by executing production orders based on actual consumption, thereby improving flexibility. They also create a flow where work is only carried out if there is demand for it (demand synchronisation), eliminating waste such as overproduction and transport, and, therefore, reducing costs [15].

The PULL operational construct is enhanced by I4.0 techniques (IoT, CPS, Smart Factory, and Virtualisation), reducing the likelihood of human error and the number of idle movements [40]. Furthermore, semi-finished or intermediate products are sent to workstations according to the specific needs of the next workstation.

On the other hand, the manoeuvring of automated guided vehicles (AGVs) is enhanced by the incorporation of smart products, which redirect the vehicle in the event of an unexpected obstacle. This self-organisation contributes to the development of reliable logistics networks for the manufacturing industry [41], ensuring that materials arrive on time at intermediate manufacturing stations without delays or distortion. This contributes to the reliability of the PULL system.

I4.0 simulation techniques (Virtualisation) enhance Lean techniques, such as Kanban, by identifying optimal Kanban parameters, such as batch size, stock level, and delivery frequency [42].

The use of intelligent machines (within the Smart Factory environment) allows information on Kanban cards (e-Kanban) to be read in real time, reducing response times and, therefore, downtime [43, 44].

e-Kanban enables the immediate detection of missing or empty containers, triggering their automatic replenishment. Physical Kanban systems are often undermined by cards being lost during their loops between workstations or facilities, leading to errors in production control or scheduling and, therefore, reduced operational performance [45, 46].

Radio frequency identification enables the identification and location of items, which can minimise the time spent searching for them [47]. This real-time location means that processes are only activated when the item is ready, driving upstream flow.

From a manufacturing process perspective, the implementation of technologies such as remote production control, process sensorisation, and production monitoring can enable the rapid identification of potential problems. In this way, the incorporation of I4.0 technologies will have a positive impact on the pace of [48].

The application of ICT in manufacturing processes thus contributes to a faster problem-solving time frame, allowing for a shift from reactive to preventive actions [49, 50].

AI-based systems automatically adjust production levels based on actual demand (PULL) and improve predictive planning and inventory control [27].

A key advantage of 3D printing, in terms of efficient small batch manufacturing, is the ability to manufacture a product on demand, eliminating the need for storage and thus complying with the concept of PULL systems and zero-stock efficient manufacturing [28].

5.6 FLOW and I4.0

Continuous flow eliminates the cost of storing and moving intermediate inventories, as well as reducing waste associated with overproduction, transport, and waiting [15]. It also speeds up delivery times by eliminating bottlenecks and making times more predictable, allowing for more accurate planning [14].

Industry 4.0 technologies, such as sensorisation and automation, can increase the connectivity of processes, products, and interactions, which can enable more efficient manufacturing process flows [51, 52, 53].

AI systems enable real-time monitoring of the process and dynamic adjustment of parameters to maintain a stable flow [27].

Improved interconnection and communication between cells and workstations can facilitate flexible, fast, and high-quality material flow [21, 54], enabling the feasibility of continuous flow implementation.

IoT-assisted sensorisation improves error and bottleneck detection without the need for human intervention [40]. All of this helps to reduce downtime, minimise process disruptions and interruptions, and streamline process flow.

Flow practices focus on continuous improvement through low-tech solutions [12], such as flowcharts and dashboards. However, I4.0 technologies do not replace these tools but rather increase their efficiency by providing them with real-time information and capacity [55].

Simulation of the manufacturing process through Digital Twins allows for the prediction, evaluation, and mitigation of manufacturing bottlenecks [56], thereby promoting performance and continuous flow.

Robotics and automation provide a more stable process flow [57], and, according to the researcher’s deduction, this greater flow stability comes (in part) from fewer interruptions or stoppages associated with human error, ergonomics, or fatigue.

3D printing [28] allows for the production of single or customised batches without significant penalties in terms of cost or time, eliminating waiting times between operations and positively affecting flow practices.

Self-organisation, via AGVs supported by Smart Factory technologies, develops reliable internal logistics networks [41].

5.7 SETUP and I4.0

Full adoption of low-configuration practices improves flexibility and agility in production delivery, as shorter setup times can lead to reductions in batch sizes [58]. Inventory levels are also likely to be reduced, which directly affects the organisation’s cash flow [59].

Industry 4.0 technologies can enhance the impact of low-configuration practices on operational performance. Companies that adopt 3D printing and integrated manufacturing engineering and product development systems can achieve shorter changeover times due to reduced complexity through strict modularisation [60].

3D printing is associated with automated processes, so it requires less operator attention, allowing that time to be devoted to other tasks such as continuous improvement (as the machine is not operating continuously), reduction of human error, and fewer tool changes or adjustment operations [28].

Modularisation, as a design principle of reconfigurable manufacturing systems (RMS), facilitates capacity adjustments in situations such as seasonal fluctuations, contributing to more flexible fabrication. Manufacturing processes can be converted into individual processes through modularity [57]. However, these may be closely interconnected, offering interchangeability. The simultaneous implementation of such technologies with low-configuration practices could improve the flexibility and productivity of manufacturing processes [49, 55].

Plug and Produce systems are equipped with automatic regulation and optimisation behaviours, allowing companies to adapt machines to particular products and produce small batches [61, 62]. Similarly, process and/or product control sensors allow process problems to be identified more quickly, anticipating solutions to issues. Therefore, these technologies not only mitigate the need for machine adjustments after configuration [60], but also increase the probability of producing correct products the first time [63].

CPS and Information and Communication Technologies (ICT) interfaces could expand SMED concepts from a single work unit to entire manufacturing areas, leading to more efficient product/service developments [64].

Moreover, one of the main reasons for the adoption of I4.0 by small businesses is increased flexibility through cloud computing [65]. Flexibility in manufacturing and product/service development, enhanced by higher levels of automation and adaptability, reinforces the benefits of implementing low-setup practices.

Industry 4.0 techniques for data transfer and vertical integration serve as enablers of SETUP practices in order to achieve a ‘batch size of one’ [66].

The application of RFID identification signals the right moments for machine changes [67].

Virtual simulation, enabled by Digital Twins [20], allows scenarios of change to be represented before their physical execution. In other words, with the help of advanced simulation tools and Digital Twins, manufacturers can test their assumptions in the virtual world before implementing or testing them in the physical world.

AI systems can predict tool wear, plan changes, and adjust settings automatically, reducing setup times [27].

5.8 TPM and I4.0

TPM practices increase equipment availability and reliability, which translates into more efficient operations, quality products, lower costs, agile change capacity (flexibility), and on-time deliveries [30].

The use of real-time data and sensorisation facilitates the identification of the underlying causes of failure or breakdown [68] and therefore supports TPM practices.

Big Data analysis allows time series of failures to be analysed and, consequently, these series to be correlated, creating possible and future patterns or scenarios. This promotes predictive and preventive maintenance [37, 69].

In addition, CPS, equipped with appropriate sensors, can detect malfunctions and initiate fault repair operations in other CPS without the need for human intervention [40], improving TPM and reducing downtime or stoppages.

Augmented Reality (AR) techniques (Smart Work) guide the operator in inspections and autonomous maintenance [70] by displaying step-by-step visual instructions.

3D printing of spare parts and tools facilitates the availability of these components, reducing downtime and inactivity [28].

The time between the occurrence of an error and the notification of that error is reduced using AR. The Andon method, which is one of the key elements of Jidoka, is implemented by displaying signal lights on a smartphone in real time, associated with an operator’s digital device [71].

Certain studies [72] cite that predictive maintenance technologies (TPM), which use industrial IoT, such as condition-based maintenance (CBM), can increase net productivity by 25% and reduce downtime by up to 75%.

5.9 SPC and I4.0

SPC techniques expand the monitoring and control capabilities of the manufacturing process by detecting deviations in operating variables and ensuring quality standards [14].

The availability of real-time data (IoT) on production processes facilitates statistical calculations and refines the information provided by control charts [68].

Sensorisation (Smart Factory) enhances the capture of critical data in real time [73], which provides a broader spectrum of information, allowing immediate detection of deviations and providing dynamic control charts.

Analytical processing (AI) of a significant volume (Big Data) of critical data generates historical series for predictive analysis and the early detection of anomalous patterns [37].

Virtual simulation, by Digital Twins, allows the impact of parameter changes to be evaluated before implementation [20], reducing the risk of process instability, previewing failures, and validating SPC schemes.

The application of artificial vision, combined with other basic technologies, improves the early [74] and accurate detection of defects, measurement data (integrated metrology), and other parameters, refining the information in control charts.

5.10 Involved employees and I4.0

This operational construct encourages employee participation, which translates into greater motivation in their performance and commitment to objectives [14].

Smart Factory technologies, such as Virtual Reality, facilitate immersive employee training, guiding them through complex tasks and real-time problem-solving [75].

Industrial automation and robotics relieve employees of physically demanding tasks [76], leaving them free for analysis, control, and process improvement activities.

Analytical techniques (AI) and data volumes (Big Data) provide operators with information (dashboards, predictive quality or maintenance analyses, etc.) for rapid decision-making based on objective and real data [73].

The implementation of advanced technologies may require new skills and generate new development opportunities for employees, which can positively impact their motivation and commitment [77].

Internet of Things technologies and collaborative tools (Smart Work) influence working conditions by monitoring health and circumstances in the workplace [78, 79], creating suitable working conditions and, consequently, increasing employee motivation and involvement.

5.11 Summary of direct impact

Table 2 summarises why I4.0 technologies impact and positively enhance the effect of LM practices.

Benefited bundles	Shocking I4.0 technologies	Why do they impact and enhance?
1. Supplier feedback	IoT	Provides real-time processing of supplier data, improving communication [24].Real-time monitoring of delivery status improves traceability [25] of items to be delivered.AI optimises logistics and strengthens supplier relationships by forecasting demand and enabling production adjustments [26].The application of CPS enables the physical–digital integration of processes, allowing automatic synchronisation with connected suppliers [35].3D printing reduces development times [28] and encourages early collaboration with suppliers, accelerating the design, prototyping, and testing phase.Smart Supply Chain applications (such as CRM) enable interaction data management, performance metric sharing, alert issuance, and order and deadline visibility [29].
	Cloud
	Big Data
	Analytics (IA)
	Smart Factory (CPS, 3D printing)
	Smart Supply Chain (CRM)
2. JIT delivery	IoT	IoT, RFID, and CPS improve traceability, reliability, and synchronisation of supplier deliveries [25, 32].AI systems improve predictive planning and inventory control, optimising deliveries [27].IoT-based manufacturing framework enables dynamic production adjustment and, therefore, a Lean delivery [34].CPS brings improvements through automatic order processing and stock reduction [33].
	Analytics (IA)
	Smart Factory (RFID, CPS)
3. Developing suppliers	IoT	Integration of CPS processes that enable co-creation and supplier–customer development [35].Collaborative tools for supplier training, improving communication, and knowledge management [18].AM enables rapid prototype development with the supplier, accelerating the testing phase [28]; anticipating operational and/or technical trials and reducing major investment risks.Virtualisation, by Digital Twins [20], allows process improvement scenarios to be simulated before physical implementation, reducing technical and financial risks for the supplier.
	Cloud
	Big Data
	Analytics (IA)
	Smart Factory (CPS)
	Smart Work (collaborative tools)
4. Involved customers	IoT	IoT-led CPS architectures enable the co-creation and co-development of applications and services [27].Digital transformation (based on cloud platforms) strengthens customer interaction and product co-creation [38].Big Data analysis enables understanding of customer behaviour patterns and anticipation of demand [36], improving the customer–supplier relationship.Analytical systems (AI) based on Big Data support customer-oriented decision-making [37], positively engaging customers.Collaborative tools or platforms are used to integrate customers into the product innovation process [39].
	Cloud
	Big Data
	Analytics (IA)
	Smart Factory (CPS)
	Smart Work (collaborative tools)
5. PULL	IoT	PULL construct is enhanced by I4.0 techniques (IoT, CPS, Smart Factory), reducing the likelihood of human error and the number of idle movements [40]. In addition, the intermediate product arrives at the workstations according to specific needs.The self-organisation of internal logistics through the incorporation of smart products into guided vehicles (AGVs) promotes the development of reliable logistics networks [41], thereby ensuring that materials arrive on time at intermediate manufacturing stations without delays or distortion.I4.0 simulation techniques (Virtualisation) enhance Lean techniques, such as Kanban, by identifying optimal Kanban parameters, such as batch size, stock level, and delivery frequency [42].The use of intelligent machines (within the Smart Factory environment) allows information from Kanban cards (e-Kanban) to be read in real time, reducing response times and, therefore, downtime [43, 44].e-Kanban enables the immediate detection of missing or empty containers, triggering their automatic replenishment [45, 46].RFID enables the identification and location of items, which can minimise the time spent searching for them [47].Remote production control, process sensorisation, and production monitoring can enable rapid identification of potential problems that could disrupt the original production schedule and negatively impact production rates. [48]. In this way, the incorporation of these technologies in Industry 4.0 will have a positive impact on the mentioned production rate.The application of ICT in manufacturing processes contributes to faster problem-solving, allowing for a shift from reactive to preventive actions that stabilises material availability at workstations aligned with actual demand [49, 50].AI-based systems automatically adjust production levels based on actual demand [27].3D printing enables the production of parts on demand, eliminating the need to maintain intermediate inventories and ensuring that downstream workstations trigger supply only when required [28].
	Cloud
	Big Data
	Analytics (IA)
	Smart Factory (3D printing, CPS, e-Kanban, Virtualisation)
6. FLOW	IoT	Process and product connectivity (Smart Factory and Product) enable more efficient manufacturing processes [51, 52, 53].AI systems enable real-time process monitoring and dynamic parameter adjustment to maintain a stable flow [27].Improved interconnection and communication between cells and workstations can facilitate flexible, fast, and high-quality material flow [21, 54], thus enabling the feasibility of continuous flow implementation.IoT-assisted sensorisation improves error detection without the need for human intervention [40] and bottlenecks. All of this helps to reduce downtime, minimise process disruptions and interruptions, and streamline process flow.Simulation of the manufacturing process through Digital Twins allows for the prediction, evaluation, and mitigation of manufacturing bottlenecks [56], thereby promoting performance and continuous flow.Robotics and process automation provide a more stable flow [57], resulting from fewer interruptions or stoppages associated with human error, ergonomics, or fatigue.3D printing [28] allows for the production of single or customised batches without significant penalties in terms of cost or time, eliminating waiting times between operations and positively affecting flow practices.Self-organisation, via AGVs supported by Smart Factory technologies, develops reliable internal logistics networks [41].
	Analytics (IA)
	Smart Factory (Virtualisation, Digital Twins, 3D printing, CPS, AGV, Automation, and Robotics)
	Smart Product (Sensorisation)
7. SETUP	IoT	3D printing and integrated manufacturing engineering and product development systems can achieve shorter changeover times due to reduced complexity through strict modularisation [60].IoT-based sensorisation allows process problems or failures to be identified more quickly, anticipating changeover issues and increasing the likelihood of first-time-right products [63].The simultaneous implementation of RMS with low-configuration practices could improve the flexibility and productivity of manufacturing processes [49, 55].Plug and Produce systems are equipped with automatic regulation and optimisation behaviours, allowing companies to adapt machines to particular products and produce small batches [61, 62].CPS and ICT interfaces could expand SMED concepts from a single work unit to entire manufacturing areas, leading to more efficient product/service developments [64].Industry 4.0 techniques for data transfer and vertical integration serve as enablers of SETUP practices to achieve a ‘batch size of one’ [66].RFID indicates opportune moments for machine changes [67].Virtualisation, by Digital Twins [20], allows scenarios to be simulated before they are physically executed.AI systems can predict tool wear, plan changes, and adjust settings automatically, reducing setup times [27].Increased flexibility through cloud computing likely reinforces the benefits of implementing low-setup practices [65].3D printing is associated with automated processes, reduced human error, and fewer tool changes or adjustments [28].
	Cloud
	Analytics (IA)
	Smart Factory (Virtualisation, Digital Twins, RFID, 3D printing, RMS)
8. TPM	IoT	The use of real-time data (IoT) and sensorisation facilitates the identification of the underlying causes of failure or breakdown [68], and therefore aids TPM practices.Data analysis (Big Data) allows time series [37, 69] of failures to be analysed and, consequently, these series to be correlated, creating possible and future patterns or scenarios. This facilitates predictive and preventive maintenance.CPS, equipped with appropriate sensors, can detect breakdowns and initiate fault repair operations in other CPS without the need for human intervention [40], improving TPM and reducing downtime or stoppages.Rapid 3D printing of spare parts and tools reduces downtime (TPM) and inactivity [28].The time between the occurrence of an error and the notification of that error is reduced by the use of AR, which displays signal lights (Andon method) on a smartphone in near real-time on an operator’s digital device [71].AR allows the operator to be guided through inspections and autonomous maintenance [70], displaying step-by-step visual instructions.Virtualisation or Digital Twins allow [20] the degradation status of equipment and maintenance scenarios to be simulated without stopping the production process, making them key to preventive maintenance.CBM improves results [72]; it is the result of combining core technologies (IoT and Big Data).
	Big Data
	Smart Factory (Virtualisation, CPS, 3D printing)
	Smart Work (AR)
9. SPC	IoT	The availability of real-time process data (IoT, Cloud) facilitates statistical calculations and improves the information provided by control charts [68].The application of artificial vision, combined with other basic technologies, improves the early [74] and accurate detection of defects and other parameters, refining the information in control charts.Virtualisation, through Digital Twins, allows the impact of parameter changes to be evaluated before implementation [20], reducing the risk of process instability, previewing failures, and validating SPC schemes.Analytical processing (AI) of a significant volume (Big Data) of critical data [37] generates historical series for predictive analysis and early detection of anomalous patterns.Sensorisation (Smart Factory) enhances the capture of critical data in real time [73], which provides a broader spectrum of information, generating dynamic control charts.
	Cloud
	Big Data
	Analytics (IA)
	Smart Factory (Virtualisation, Artificial Vision)
10. Involved employees	IoT (Collaborative e-Learning and Health Platforms)	AR facilitates immersive employee training, guiding them through complex tasks and real-time problem-solving.Industrial automation and robotics relieve employees of physically demanding tasks [76].Analytical techniques (AI) and data volumes (Big Data) provide operators with information (dashboards, predictive quality or maintenance analyses, etc.) for rapid decision-making based on objective and real data [73].The implementation of advanced technologies may require new skills and generate new development opportunities for employees, which can positively impact their motivation and commitment [77].Monitoring the health and circumstances of the working environment, raising awareness of change management, and creating suitable working conditions [78, 79], while increasing employee motivation and involvement.
	Smart Factory (Automatisation and Robotic)
	Smart Work (AR)

Table 2.

Explanatory rationale for why I4.0 technologies positively moderate practices of LM.

5.12 Deductions about the I4.0 technologies’ moderating effect

Referring to the results of the previous summary table (Table 2), the researchers can infer deductions about the moderating effect of these I4.0 technologies on the bundles of LM in the operational performances.

This assertion is supported by several recent studies [5, 80, 81, 82] that mention that the emergence of I4.0technologies enables LP to achieve higher manufacturing performance, and furthermore, other studies [83, 84, 85] show that the moderating effect of I4.0 technologies adoption can improve the results of LP implementation, leading to greater performance levels.

In order to delimit the operational performances, the authors will focus on the study by Slack et al. [86] to determine the mentioned performances, such as:

Cost: cost incurred in the transformation process.
Quality: consistent compliance with customers' expectations.
Speed delivery: time elapsed between request and receipt time.
Dependability: ability to do things on time according to agreement.
Flexibility: the ability of an operation to respond effectively to change.
- Of volume.
- Of product.
- Of delivery.
- Of variety or diversification.

The next flow diagram (Figure 7) reflects the interaction between the three concepts and schematises the moderating effect of I4.0 technologies.

Figure 7.
Moderating effect diagram of I4.0 technologies.

The inferred assumptions or deductions about the moderating effect are classified, organised, and summarised in Table 3.

LM bundles	Moderating I4.0 technologies	Moderation criteria
1. Supplier feedback	IoT	Cost: IoT and cloud computing reduce administrative and tracking costs through real-time traceability [24]. AI optimises inventories and transportation, minimising cost overruns [26]. Early prototyping with 3D printing reduces rework and waste [28].Quality: Physical–digital integration via CPS reduces human errors and ensures compliance in supply data [35]. CRM facilitates systematic monitoring of supplier quality [29], and early prototyping with 3D printing reduces errors and rework [28].Flexibility (product): AM (3D printing) allows rapid modification of prototypes and components without affecting flow [28].Speed: IoT monitoring and AI-powered demand prediction shorten replenishment times [24, 26]. Real-time monitoring of delivery status improves traceability [25] of items to be delivered.Dependability: CPS synchronisation and CRM transparency reduce delays and quantity errors [29, 35].
	Cloud
	Big Data
	Analytics (IA)
	Smart Factory (CPS, 3D Printing)
	Smart Supply Chain (CRM)
2. JIT delivery	IoT	Cost: RFID tracking and IoT visibility reduce losses due to excess or shortage of inventory [25, 32].Quality: CPS coordination ensures that materials arrive in the correct condition, avoiding errors at the point of use [33].Flexibility (volume): IoT and AI facilitate rescheduling deliveries and adjusting volumes in response to changes in demand [27, 34].Speed: Real-time monitoring and predictive algorithms streamline procurement and transportation [27].Dependability: CPS and RFID ensure greater accuracy in delivery times and quantities [32, 33].
	Analytics (IA)
	Smart Factory (RFID, CPS)
3. Developing suppliers	IoT	Cost: Simulation using Digital Twins allows for anticipating problems and avoiding failed investments [20].Quality: Collaborative tools and CPS synchronisation facilitate requirements transfer and quality control in the supply chain [18, 20, 35].Flexibility (process): 3D printing and Virtualisation allow for rapid modification of processes and prototypes, reducing adaptation times [20, 28].Speed: Simultaneous development and virtual testing accelerate design and validation cycles [20, 28].Dependability: Continuous training and real-time data sharing strengthen the supplier’s ability to meet deadlines [18, 35].
	Cloud
	Big Data
	Analytics (IA)
	Smart Factory (CPS)
	Smart Work (collaborative tools)
4. Involved customers	IoT	Cost: AI and Big Data optimise the forecasting of needs, avoiding cost overruns due to erroneous developments [36, 37].Quality: Continuous feedback through collaborative platforms improves product alignment with customer requirements [36, 38, 39].Flexibility (product): Virtual co-creation and predictive analytics facilitate rapid adjustments to specifications or features [27, 36].Speed: Direct interaction and anticipation of demand shorten the time from design to launch [36, 37].Dependability: Data integration and CPS communication make it possible to meet agreed-upon commitments in customised projects [27, 39].
	Cloud
	Big Data
	Analytics (IA)
	Smart Factory (CPS)
	Smart Work (collaborative tools)
5. PULL	IoT	Cost: Inventory reduction and automatic replenishment minimise storage and handling costs [27, 28, 40, 42, 47].Quality: IoT traceability and real-time control reduce errors at intermediate stations [48].Flexibility (volume and variety): AI adapts production to the current pace and allows for rapid assortment changes [28, 42].Speed: e-Kanban and RFID streamline the flow of materials, reducing downtime [43, 44, 47].Dependability: Autonomous logistics networks (AGVs) and CPS synchronisation reduce delays or shortages [40, 41].
	Cloud
	Big Data
	Analytics (IA)
	Smart Factory (3D printing, CPS, e-Kanban, Virtualisation)
6. FLOW	IoT	Costs: Bottleneck prediction and robotics reduce losses due to downtime or rework [56, 57].Quality: Real-time sensing and automation improve process consistency [40, 54].Flexibility (product and volume): Dynamic adjustments using AI and 3D printing allow for varying production rates or sequences without compromising efficiency [21, 27, 28, 54].Speed: Eliminating bottlenecks and automating internal logistics shorten the turnaround time between operations [21, 40, 54, 56].Dependability: The stability provided by AGVs and robotics ensures constant and predictable flows [21, 41, 54, 56, 57].
	Analytics (IA)
	Smart Factory (Virtualisation, Digital Twins, 3D printing, CPS, AGV, Automation, and Robotics)
	Smart Product (Sensorisation)
7. SETUP	IoT	Cost: IoT-based sensorisation and wear prediction prevent failed adjustments and reduce material losses [27, 63].Quality: The physical–digital integration via CPS and the use of Digital Twins reduce human errors [20, 64].Flexibility (product and volume): Modularisation (RMS) and Plug and Produce systems allow for changing references or sequences without time penalties [20, 49, 55, 61, 62, 64].Speed: 3D printing shortens changeover times [28, 60] and RFID identifies optimal moments for machine changes [67].Dependability: Standardisation of adjustments and continuous monitoring ensure consistent, error-free changeovers. [55]
	Cloud
	Analytics (IA)
	Smart Factory (Virtualisation, Digital Twins, RFID, 3D Printing, RMS)
8. TPM	IoT	Cost: Failure prediction and rapid part manufacturing reduce costly downtime and shutdowns [37, 68, 69]Quality: AR and CPS-guided maintenance ensures accurate repairs, reducing recurrences [40, 70, 71].Flexibility (volume): CBM adjusts the frequency of interventions based on usage, optimising availability [72].Speed: Rapid component replacement with 3D printing reduces the time required to resume production [28].Dependability: Digital Twins and predictive analytics stabilise equipment, ensuring availability and meeting deadlines [20, 71, 72].
	Big Data
	Smart Factory (Virtualisation, CPS, 3D Printing)
	Smart Work (AR)
9. SPC	IoT	Cost: Early detection reduces rework and losses caused by defective batches [37, 74].Quality: Dynamic graphics and computer vision enhance control over tolerances and defects [20, 68, 73].Flexibility (volume and variety): Preliminary simulation facilitates specification changes without instability [20, 37].Speed: Predictive analytics prevent unexpected downtime due to quality deviations [37].Dependability: Continuous monitoring maintains stable processes, ensuring consistent deliveries [68, 73].
	Cloud
	Big Data
	Analytics (IA)
	Smart Factory (Virtualisation, Artificial Vision)
10. Involved employees	IoT (Collaborative e-Learning and Health Platforms)	Cost: Automation reduces unproductive hours and decreases expenses caused by injuries or fatigue [76].Quality: AR training ensures accurate execution and reduces human errors [75, 76].Flexibility (general): Operators acquire skills to adapt to different tasks or environments [73, 77].Speed: The availability of predictive information and the reduction of breaks due to physical overload accelerate order completion [76].Dependability: Motivation and continuous learning strengthen commitment and reduce absences and variability in performance [77, 79].
	Smart Factory (Automatisation and Robotic)
	Smart Work (AR)

Table 3.

Moderating effect of I4.0 technologies on LM bundles in the operational performances.

6. Conclusions

Previous studies on the effect of the integration of LM and I4.0 showed that, from a strategic perspective, LM is a prerequisite for digital transformation, as the standardisation of processes and the elimination of waste create fertile ground for the successful implementation of I4.0 technologies [71]. I4.0 may enhance Lean at the strategic level by optimising processes, while I4.0 improves Lean practices at a higher operational level [81], facilitating data flow and providing advanced tools for workers. Analysis of the results reports that I4.0 technologies have a positive impact on the effect of LM practices, that is, they amplify the positive effect of their application. In our study, justifications for the effects are highlighted.

Constructs associated with supply chain management (supplier related) benefit directly from the application of technologies such as IoT, Big Data, Cloud, CPS, and Analytics (IA) by enabling real-time monitoring, demand forecasting, synchronisation of material flows, reduction of development times, and other benefits described. This momentum strengthens supplier–customer collaboration and reliability in both directions, creating a continuous two-way flow.

On the other hand, customer and employee involvement, which is a key to continuous improvement and operational flexibility, is enhanced by collaborative tools (Virtual, Augmented, Smart Work, CRM, etc.), contributing to greater employee and customer engagement, that is, to the co-creation of value.

Finally, the operational constructs linked (PULL, FLOW, etc.) to the effectiveness and efficiency of internal processes see their benefits enhanced by the arguments presented, transforming the principles of LM into an intelligent, highly adaptive, and, therefore, resilient system.

As the Lean 4.0 paradigm, a combination of LM and I4.0, is a relatively new field, this study opens numerous avenues for future research. These future lines of research could be grouped into two perspectives, depending on the essence of their objectives.

From the perspective of broadening and deepening the impact of I4.0 technologies (independent variable) on LM practices (dependent variable), delving into how and why new AI algorithms, Digital Twins, and human–machine interactions can favour or amplify the mentioned effect.

From the perspective of studying and analysing the moderating effect of I4.0 technologies, it is remarkable how several possibilities for exploration are offered by providing empirical support to the deductions set forth in Table 3.

As mentioned above, the Lean 4.0 domain offers a wide range of possibilities for study, given the doctrinal and operational scope of the concept itself.

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