# Digital Twin-Driven Lean Manufacturing: Optimizing Value Stream Flow

Charles Onyeka Nwamekwe, Ewuzie Nnamdi Vitalis, Nkemakonam Chidiebube Igbokwe, Emeka Celestine Nwabunwanne, Chukwuma Godfrey Ono

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Charles Onyeka Nwamekwe, Ewuzie Nnamdi Vitalis, Nkemakonam Chidiebube Igbokwe, Emeka Celestine Nwabunwanne, Chukwuma Godfrey Ono. Digital Twin-Driven Lean Manufacturing: Optimizing Value Stream Flow. Letters in Information Technology Education (LITE), 2025, 8 (1), pp.1-13. hal-05127340

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# Digital Twin-Driven Lean Manufacturing:
Optimizing Value Stream Flow

Charles Onyeka Nwamekwe, Ewuzie Nnamdi Vitalis, Igbokwe Nkemakonam Chidiebube, Emeka Celestine Nwabunwanne, Chukwuma Godfrey Ono

Industrial/Production Engineering Department Nnamdi Azikiwe University, P.M.B. 5025 Awka, Anambra State, Nigeria.

cg.ono@unizik.edu.ng

*corresponding author

**Article Info**

**Article history:**

Received: June 10, 2025

Revised: June 14, 2025

Accepted: June 18, 2025

**Keyword:**

Digital Twin Technology

Lean Manufacturing

Value Stream Optimization

Industry 4.0

Process Improvement

**ABSTRACT**

This research investigates the integration of Digital Twin (DT) technology within Lean Manufacturing frameworks to optimize value stream flow, minimize waste, and enhance real-time decision-making capabilities. By synthesizing foundational concepts of Lean Manufacturing and DT, the paper examines the layered DT architecture, covering the physical, virtual, and communication interfaces, alongside Lean tools like Kaizen, Kanban, and Just-in-Time (JIT) that facilitate continuous process improvement. Case studies, particularly in the automotive sector, demonstrate DT's ability to increase production efficiency through predictive maintenance and simulation-based scenario planning, supporting Lean's waste reduction objectives. However, the paper identifies key implementation challenges, including legacy system integration, workforce adaptation, and data interoperability. Additionally, cybersecurity and data integrity concerns are analysed to highlight essential protocols for safe DT deployment. Future research directions propose advancements like AI-powered DTs, blockchain for enhanced traceability, and edge computing for low-latency applications. Key insights from industry case studies underscore the transformative impact of DTs on production efficiency, organizational resilience, and sustainable manufacturing outcomes, positioning Digital Twin technology as a cornerstone for next-generation lean manufacturing systems.

## I. INTRODUCTION

Digital Twin technology is increasingly recognized as a transformative tool in Lean Manufacturing, particularly for optimizing value stream flow. Lean principles emphasize waste elimination and continuous improvement, which integrating DTs can significantly enhance. These virtual replicas of physical systems enable real-time monitoring and simulation, facilitating the identification of inefficiencies in processes, thereby supporting Value Stream Mapping (VSM) and Just-in-Time (JIT) methodologies [1, 2].

Moreover, DTs can enhance decision-making by providing data-driven insights that align with Lean objectives, such as reducing lead times and improving product quality [3]. The synergy between DT technology and Lean Manufacturing streamlines production flows and fosters a culture of continuous improvement (Kaizen) by allowing for iterative testing and refinement of processes. Thus, integrating DTs into Lean Manufacturing frameworks presents a compelling opportunity to optimize value streams effectively.

DT technology is a pivotal innovation in Lean Manufacturing. It optimizes value stream flow through real-time monitoring, predictive analytics, and simulation-driven decision-making. By creating virtual replicas of physical systems, DTs enhance operational visibility and improve resource efficiency, aligning closely with Lean principles, prioritizing waste reduction and continuous improvement [3, 1].

The integration of DTs facilitates advanced methodologies such as VSM and JIT production, allowing manufacturers to visualize and analyse their processes dynamically. This capability not only aids in identifying bottlenecks but also supports iterative improvements, fostering a culture of Kaizen [1]. Furthermore, the adaptability of DTs to various manufacturing contexts enhances their utility in achieving Lean objectives, ultimately leading to more efficient production systems and improved customer satisfaction.

### A. Problem Statement

Despite the benefits of Lean Manufacturing, companies still face challenges in achieving continuous value stream flow.
Dynamic production environments introduce significant variability, such as demand fluctuations and equipment breakdowns, which challenge the effectiveness of traditional Lean Manufacturing tools. These conditions often lead to operational inefficiencies characterized by bottlenecks, overproduction, and non-value-added activities that are difficult to identify and eliminate promptly [4]. The limitations of conventional Lean tools stem from their inability to provide real-time insights into production processes, which hinders proactive decision-making and sustainable process optimization.

Research indicates that while Lean practices can enhance operational performance, their effectiveness is significantly diminished in environments marked by high variability. For instance, integrating Industry 4.0 technologies, such as the Industrial Internet of Things (IIoT), can complement Lean methodologies by providing real-time data and analytics, thus enabling organizations to respond swiftly to operational challenges [4]. This integration enhances visibility across production processes and fosters a culture of continuous improvement, essential for adapting to the dynamic nature of modern manufacturing.

## B. Research Objectives

The main goals of the research are to investigate how integrating DT technology with Lean Manufacturing enhances operational performance, demonstrate the role of DT-driven systems in improving decision-making and achieving optimal value stream flow, and showcase how real-time data exchange between physical and virtual systems enhances continuous improvement.

## C. Research Gap

Integrating DT technology with Lean Manufacturing tools presents a significant research gap, as current literature primarily focuses on applying DTs in predictive maintenance and process monitoring. While these applications demonstrate the capabilities of DTs in enhancing operational efficiency, their potential to bolster Lean principles such as waste reduction and continuous flow remains underexplored, mainly [5]. Research indicates that the synergy between DTs and Lean practices could lead to improved identification and elimination of non-value-added activities, which are often obscured in dynamic production environments. For instance, DTs can provide real-time insights that facilitate proactive decision-making, thereby addressing the limitations of traditional Lean tools that struggle to adapt to variability in production [6]. Furthermore, combining DT technology with Lean methodologies could enhance the standardization and transparency of processes, ultimately leading to more sustainable operational improvements.

## D. Significance of the Study

This study aims to provide industries with a novel framework for integrating DT technology into Lean Manufacturing practices, focusing on real-time decision-making and continuous improvement. This research's significance lies in its potential to address operational limitations faced by companies in dynamic environments, where traditional Lean tools often fall short due to their inability to adapt to variability, such as demand fluctuations and equipment breakdowns [7].

By leveraging DTs, organizations can gain enhanced visibility into their production processes, enabling them to identify and eliminate waste more effectively. This integration can facilitate a more agile response to operational challenges, promoting a culture of continuous improvement essential for Lean success [8]. Furthermore, the proposed framework will optimize Lean practices and align them with the principles of Industry 4.0, creating a synergistic effect that enhances overall operational performance.

## E. Purpose of the Study

This study aims to develop and evaluate a framework that utilizes DT technology to achieve leaner production systems by enhancing value stream flow and eliminating inefficiencies in real time. This framework aims to address the operational challenges faced by industries in dynamic environments, where traditional Lean tools often struggle to provide the necessary insights for effective decision-making.

By integrating DTs into Lean practices, organizations can leverage real-time data to monitor production processes, identify bottlenecks, and facilitate continuous improvement. DTs' adaptability allows for a more responsive approach to production management, enabling companies to optimize workflows and reduce waste effectively. Furthermore, this study will explore how implementing DTs can enhance the visibility of value streams, thereby supporting Lean principles and fostering a culture of efficiency and innovation [9].

The diagram in Figure 1 outlines a structured approach to discussing the topic of Digital Twin-Driven Lean Manufacturing: Optimizing Value Stream Flow. Each section builds upon the previous, creating a logical flow, as in the diagram.

<figure><img src="image_3.png" /><figcaption>Fig. 1. The paper Structure Flowchart</figcaption></figure>
## II. LITERATURE REVIEW

### 1) Evolution of Lean Manufacturing and Value Stream Mapping (VSM)

This section provides a historical perspective on lean manufacturing from the Toyota Production System (TPS). It has evolved into a widely adopted methodology to enhance operational efficiency and minimize waste. Key principles of Lean include Kaizen, which emphasizes continuous improvement; the elimination of waste (Muda); and JIT production, which focuses on producing only what is needed when it is required [10]. These principles have been instrumental in transforming manufacturing processes by fostering a culture of efficiency and responsiveness to customer demands.

As a cornerstone of Lean philosophy, Kaizen encourages all employees to contribute to incremental improvements, thereby enhancing overall productivity and morale [11]. The focus on eliminating waste is critical, as it identifies non-value-added activities that hinder operational flow. This is particularly relevant in dynamic environments where variability can lead to inefficiencies. JIT complements these principles by reducing inventory costs and improving cash flow, ensuring that production aligns closely with actual demand.

Despite its successes, Lean Manufacturing faces challenges in implementation, particularly in maintaining the momentum of continuous improvement and adapting to changing market conditions. As industries increasingly adopt Lean practices, understanding the historical context and foundational principles remains essential for practical application and sustained success in modern manufacturing environments.

VSM is a critical Lean tool for identifying bottlenecks and non-value-added activities within production processes. It provides a visual representation of the flow of materials and information, enabling organizations to pinpoint inefficiencies and areas for improvement. However, despite its effectiveness in static environments, VSM struggles to offer dynamic insights into real-time production challenges, which limits its applicability in fast-paced and variable manufacturing settings [12].

Research indicates that while VSM can significantly enhance existing systems of item manufacturing and reduce costs, it often fails to adapt to fluctuations in demand or unexpected disruptions, such as equipment breakdowns. This limitation highlights the need for supplementary tools or methodologies that can provide real-time data and insights, thereby enhancing the effectiveness of VSM in dynamic environments [12]. For instance, integrating VSM with technologies like Digital Twins could bridge this gap by offering real-time monitoring and predictive analytics, thus enabling more proactive decision-making.

### B. Emergence of Digital Twin Technology

#### 1) Definition and Components

DTs are virtual representations of physical assets, systems, or processes that facilitate enhanced operational insights and decision-making. The core components of DTs include data sensors, which collect real-time data from physical entities; real-time communication interfaces that transmit this data for analysis;

and simulation models that replicate the behaviour of the physical counterparts.

The integration of these components allows for continuous monitoring and optimization of processes, enabling organizations to respond dynamically to operational challenges. For instance, data sensors provide critical information regarding system performance, while simulation models can predict future states based on current data trends, thus facilitating proactive management strategies. This capability is particularly valuable in manufacturing environments where efficiency and waste reduction are paramount.

Moreover, the application of DTs extends beyond mere monitoring; they can also enhance traditional Lean tools such as VSM by providing real-time insights into production flows and identifying inefficiencies that may not be visible through static analysis alone. As industries increasingly adopt DT technology, understanding its components and functionalities will be crucial for leveraging its full potential in optimizing production systems and achieving sustainable operational improvements [13].

DTs can be categorized into three primary types: Product Twins, Process Twins, and System Twins. Each type serves a distinct purpose and provides unique insights into aspects of manufacturing and operational efficiency.

1. Product Twins: focus on individual products, creating a virtual representation that mirrors a specific item's physical characteristics and performance throughout its lifecycle. This allows manufacturers to monitor product performance, predict failures, and optimize design based on real-time data. For instance, in industries such as automotive and aerospace, Product Twins can facilitate predictive maintenance and enhance product quality by analysing usage patterns and operational conditions [14].

2. Process Twins encompass entire manufacturing processes, capturing the dynamics of workflows and operations. By simulating the production process, organizations can identify inefficiencies, optimize resource allocation, and improve overall process performance. This type of DT is particularly beneficial for continuous improvement initiatives, as it provides insights into process variations and bottlenecks that may not be visible through traditional monitoring methods.

3. System Twins represent complex systems integrating multiple processes, assets, and interactions within a manufacturing environment. This holistic view enables organizations to analyse interdependencies and optimize the entire system's performance rather than isolated components. System Twins are essential for managing large-scale operations, facilitating comprehensive analysis, and making strategic decisions.

DT technology has gained significant traction in production engineering, particularly for predictive maintenance, real-time monitoring, and process optimization within innovative manufacturing environments. These applications leverage DTs' capabilities to enhance operational efficiency and decision-making processes.
1. Predictive Maintenance is one of the most prominent applications of DT. By creating a virtual representation of physical assets, organizations can continuously monitor equipment performance and predict potential failures before they occur. This proactive approach minimizes downtime and maintenance costs, ultimately improving productivity [15]. For instance, DTs can analyse historical and real-time data from sensors embedded in machinery to forecast when maintenance should be performed, thereby optimizing maintenance schedules and resource allocation [15].

2. Real-time monitoring is another critical application of DTs in production engineering. By integrating data sensors and communication interfaces, DTs provide a comprehensive view of production processes, enabling manufacturers to track performance metrics and operational conditions in real time. This capability identifies inefficiencies or deviations from expected performance, facilitating timely interventions and adjustments [2]. Real-time insights are essential for maintaining quality control and ensuring production processes align with Lean principles.

3. Process Optimization through Digital Twins involves simulating various production scenarios to identify the most efficient workflows and resource utilization strategies. By utilizing simulation models, organizations can test different configurations and operational strategies without disrupting actual production, leading to data-driven decisions that enhance overall process efficiency [2]. This application is particularly valuable in innovative manufacturing environments, where flexibility and adaptability are crucial for responding to changing market demands [16].

Case studies from various industries illustrate the synergy between DT technology and Lean Manufacturing, enabling agile

responses to operational changes. Integrating DTs into Lean practices allows organizations to leverage real-time data for enhanced decision-making, ultimately driving continuous improvement.

One notable example is the application of DTs in the automotive industry, where manufacturers utilize digital twins to monitor production flows and optimize assembly lines. Companies can simulate various scenarios and identify real-time bottlenecks by creating virtual replicas of production processes. This capability allows immediate workflow adjustments, reducing lead times and minimizing waste [17]. For instance, a study by Satpathy and Gavaskar highlights how automotive manufacturers have successfully implemented DTs to enhance operational efficiency and responsiveness to market demands.

In the aerospace sector, DTs facilitate predictive maintenance and operational optimization. By continuously monitoring the performance of critical components, aerospace companies can predict failures and schedule maintenance proactively. This approach reduces downtime and aligns with Lean principles by ensuring that resources are utilized effectively [18]. Implementing DTs in aerospace manufacturing has significantly improved operational agility and resource management [18].

Moreover, the integration of DTs in the manufacturing of consumer electronics demonstrates their effectiveness in enabling JIT replenishment. By analysing real-time data on inventory levels and production rates, companies can optimize their supply chains and ensure that materials are available precisely when needed. This capability is crucial for maintaining the efficiency of Lean systems, as it minimizes excess inventory and reduces holding costs [19]. Discuss how consumer electronics manufacturers have adopted DT technology to enhance their JIT processes, resulting in improved responsiveness and reduced waste [19].

<figure><img src="image_4.png" /><figcaption>Fig. 2. A Detailed Diagram Illustrating the Integration of Digital Twin Technology with Lean Manufacturing Principles</figcaption></figure>
## C. Digital Twin Architecture for Lean Manufacturing

### 1) Conceptual Model of Digital Twin for Value Stream Flow

This section examines the conceptual framework, illustrating that the three layers of DTs, as shown in Figure 3, are essential for understanding their application across various industries. This framework typically consists of three distinct physical, digital, and analytical layers. Each layer plays a crucial role in the functionality and effectiveness of DT technology.

**Physical Layer:** The Physical Layer of DTs represents the foundational elements of a manufacturing environment, including machines, products, and workers. This layer is crucial as it encompasses the physical assets monitored and controlled through digital twin technology. By integrating sensors and IoT devices, the Physical Layer provides real-time data that reflects the operational status of these assets, enabling organizations to optimize performance and enhance productivity [20].

The Physical Layer includes various machines and equipment essential for manufacturing processes. For example, advanced machinery with sensors can monitor temperature, vibration, and operational speed. This data is vital for understanding machine performance and identifying potential issues before they lead to failures [21]. As noted by Zhang and Zhu, the real-time data collected from the Physical Layer can adjust operations dynamically, ensuring that production processes remain efficient and responsive to changing demands.

Moreover, the Physical Layer also encompasses the products being manufactured. By tracking the status and location of products throughout the production line, manufacturers can ensure that inventory levels are optimized and products are delivered on time. This capability aligns with Lean Manufacturing principles, emphasizing waste reduction and efficient resource utilization. Xu, Päivärinta, and Kuvaja highlighted that integrating DTs in the Physical Layer allows for better visibility and control over production flows, ultimately leading to improved operational efficiency [9].

Additionally, the workers involved in the manufacturing processes are a critical component of the Physical Layer. Organizations can monitor worker performance and safety in real time by incorporating wearable technologies and smart devices. This integration not only enhances productivity but also fosters a safer working environment. The ability to analyse data related to worker interactions with machines and processes can lead to better training and operational practices, further contributing to continuous improvement efforts.

**Virtual Layer:** The Virtual Layer of DTs is a critical component that mirrors physical entities and simulates processes within manufacturing and operational environments. This layer is essential for creating a digital representation of physical assets, enabling organizations to visualize, analyse, and optimize their operations effectively.

At its core, the Virtual Layer consists of digital models replicating physical counterparts' characteristics and behaviors. These models are built using data collected from the Physical Layer, which includes sensors and IoT devices that monitor real-time performance metrics. Simulating processes in a virtual

environment allows for comprehensive analysis and experimentation without disrupting actual operations [22]. For instance, Rodič emphasizes that simulation at multiple levels within manufacturing can range from detailed physics-based models to high-level supply chain models, providing a versatile framework for understanding complex systems [22].

The Virtual Layer also facilitates predictive analytics, enabling organizations to proactively forecast potential issues and optimize processes. Manufacturers can identify inefficiencies and test solutions in a risk-free environment by simulating various scenarios. Tao, Sui, Liu, Qi, Zhang, Song, and Nee illustrate how digital twin models can be used to simulate the performance of products under different conditions, allowing for adjustments that enhance design and functionality [23]. This capability is particularly valuable in industries where rapid changes in demand or operational conditions require agile responses.

Moreover, the Virtual Layer enhances stakeholder collaboration by providing a shared digital environment where data and insights can be accessed and analysed collectively. This collaborative aspect is crucial for driving continuous improvement initiatives, as it allows teams to work together in real-time to address challenges and implement solutions [24, 25]. As Barari, Tsuzuki, Cohen, and Macchi noted, the virtualization of processes equips cyber-physical systems with enhanced capabilities to process data and extract actionable insights, thereby supporting informed decision-making [25].

**Communication Interface:** DT technology's communication interface facilitates seamless integration between the physical and virtual worlds through IIoT and real-time data exchange. This interface enables communication among various manufacturing system components, ensuring data flows smoothly between physical assets, digital models, and analytical tools.

The Communication Interface leverages IIoT technologies to connect sensors, machines, and other devices within the manufacturing environment. By utilizing protocols and middleware designed for IoT applications, the interface allows for efficient data transmission from the Physical Layer to the Virtual Layer. According to Wang, Yang, Wang, Liu, Chen and Guan, middleware technologies are essential for managing the complexities of data exchange in IoT environments, as they facilitate the interaction between devices and applications while enhancing data transmission efficiency [26]. This capability is significant in industrial settings, where timely and accurate data is critical for operational decision-making.

Furthermore, the Communication Interface supports real-time data integration, which is vital for maintaining digital twin models' accuracy and relevance. By continuously updating the virtual representation of physical assets with real-time data, organizations can ensure that their digital twins reflect current operational conditions. This dynamic integration allows for immediate identification of issues and enables proactive responses to changes in the manufacturing environment. Gojmerac emphasizes that effective communication interfaces are fundamental for bridging IoT islands, thereby enhancing the connectivity and interoperability of industrial systems [27].

Moreover, the Communication Interface enhances the ability to implement advanced analytics and machine learning algorithms
on data collected from the physical environment. This integration
allows organizations to derive actionable insights that inform
continuous improvement initiatives, aligning with Lean
Manufacturing principles. By analyzing real-time data,
manufacturers can optimize processes, reduce waste, and improve
efficiency.

D. Integration of Lean Tools with Digital Twins

This section discusses the practical implementation of Lean tools within DT environments, highlighting how these tools can be effectively integrated to enhance operational efficiency and support continuous improvement initiatives.

The integration of Lean tools such as Kaizen, VSM Kanban, and JIT within DT environments allows organizations to leverage real-time data for better decision-making. For instance, VSM can be enhanced by the real-time insights provided by DTs, enabling manufacturers to visualize production flows and identify bottlenecks more effectively [28]. This dynamic capability allows immediate workflow adjustments, aligning with Lean principles emphasizing waste reduction and process optimization.

1) Kaizen (Continuous Improvement)

DT technology significantly enhances continuous improvement efforts through real-time feedback mechanisms. By providing immediate insights into operational performance, DTs facilitate the identification of inefficiencies and support the implementation of Kaizen principles within manufacturing environments.

The integration of DTs allows organizations to monitor
processes continuously, enabling them to gather real-time data on
various performance metrics, such as production rates, equipment
status, and quality indicators. This capability is crucial for fostering
a culture of continuous improvement, as it empowers teams to
make data-driven decisions and implement changes swiftly.
Casadei et al. highlight that augmented collective digital twins can
enhance self-organizing cyber-physical systems by providing real-
time feedback that informs operational adjustments [29]. This
feedback loop is essential for identifying areas for improvement
and ensuring that processes remain aligned with Lean objectives.

Moreover, the application of DTs in continuous improvement initiatives can be seen in the context of predictive maintenance. By analysing real-time data from physical assets, organizations can proactively predict potential failures and schedule maintenance, minimizing downtime and optimizing resource allocation. Newrzella et al. emphasize that the features of physical entities monitored by digital twins can directly inform maintenance strategies, leading to improved operational efficiency [30]. This proactive approach aligns with the Kaizen philosophy of continuous improvement by ensuring that systems always operate at peak performance [31].

Additionally, DTs support the simulation of various operational scenarios, allowing organizations to test potential changes before implementation. This capability enables manufacturers to evaluate the impact of different strategies on productivity and quality, facilitating informed decision-making. Chen, Zhao, Zhou, Zhou, Kang, Jin, and Zheng discussed how digital twins can model complex systems, providing insights that drive continuous improvement in plant factory operations [32]. By

leveraging these simulations, organizations can refine their
processes iteratively, embodying the Kaizen principle of
incremental progress.

2) *Just-in-Time (JIT) and Kanban: The Role of Digital Twins in Monitoring Production Flows*

DT technology plays a crucial role in enhancing JIT and Kanban systems by providing real-time monitoring of production flows, which is essential for triggering material replenishment and minimizing delays. The integration of DTs into JIT and Kanban methodologies allows organizations to optimize their inventory management and streamline operations, thereby aligning with Lean Manufacturing principles.

One of the primary advantages of using DTs in JIT systems is their ability to monitor production processes continuously. By collecting real-time data from various sensors and IoT devices, DTs provide insights into the status of production lines, enabling manufacturers to respond swiftly to changes in demand or production rates [33]. This capability is significant in JIT environments, where the goal is to produce only what is needed, when it is required, thereby reducing excess inventory and associated carrying costs.

Moreover, DTs facilitate the effective implementation of Kanban systems by ensuring that material replenishment is triggered accurately and promptly. DTs' real-time feedback allows organizations to maintain optimal inventory levels and adjust production schedules based on consumption patterns. A study by Kundu, Rossini, and Staudacher demonstrated how integrating DTs with Kanban systems can improve material flow and reduce lead times, ultimately enhancing overall production efficiency [34].

Additionally, using DTs in conjunction with Kanban systems supports the identification of bottlenecks and inefficiencies within production processes. By simulating various operational scenarios, organizations can analyse the impact of different strategies on production flows and make informed decisions to optimize their processes [35]. This analytical capability aligns with the JIT philosophy, which emphasizes continuous improvement and waste reduction.

Furthermore, the Communication Interface of DTs ensures seamless data exchange between physical and virtual systems, enhancing the responsiveness of JIT and Kanban operations. By integrating real-time data into the decision-making process, organizations can minimize delays and improve coordination across the supply chain [36, 37]. This integration supports the timely replenishment of materials and fosters a culture of agility and responsiveness within manufacturing environments.

3) Feedback Loops in Digital Twins: Enhancing Continuous Improvement through Real-Time Insights

DT technology significantly enhances the implementation of feedback loops in Lean Manufacturing, particularly its ability to provide real-time insights that facilitate continuous improvement. By integrating real-time data into operational processes, DTs enable organizations to adjust their Lean practices dynamically, ensuring a smoother flow of production and enhanced efficiency.

The concept of feedback loops within DT environments allows
for continuous monitoring of production metrics, such as cycle
times, throughput, and quality levels. This real-time feedback is
critical for identifying inefficiencies and areas for improvement.
Sutrisno, Rimawan, and Sitorus emphasized that the systematic
review of Kaizen approaches in industries demonstrates how real-
time data can assess flow times and error rates, thereby informing
continuous improvement initiatives [38]. By leveraging this data,
organizations can implement immediate corrective actions,
aligning with the principles of Kaizen, which advocate for
incremental improvements.

Moreover, the integration of feedback loops through DTs supports the JIT philosophy by enabling timely adjustments to inventory levels and production schedules. As production flows are monitored in real time, organizations can trigger material replenishment precisely when needed, minimizing delays and reducing excess inventory. This capability enhances operational efficiency and fosters a culture of responsiveness and agility within the manufacturing environment.

Additionally, feedback loops facilitated by DTs allow for the simulation of various operational scenarios, enabling organizations to test potential changes before implementation. This capability is particularly valuable in Lean environments, where the goal is continuously refining processes and eliminating waste. Ramírez, Pumisacho-Álvaro, Miguel-Dávila, and Barraza highlighted that.

Incorporating feedback mechanisms into Lean practices is essential for sustaining continuous improvement efforts, as it allows teams to evaluate the impact of changes on overall performance [39].

Furthermore, the Communication Interface of DTs ensures that data flows seamlessly between physical and virtual systems, enhancing the effectiveness of feedback loops. Organizations can foster collaboration and collective problem-solving by providing stakeholders with real-time insights, which are critical for driving continuous improvement initiatives.

<figure><img src="image_5.png" /><figcaption>Fig. 3. Digital Twin Architecture in Lean Manufacturing Diagram</figcaption></figure>

III. APPLICATIONS AND CASE STUDIES

A. Digital Twins for Real-Time Flow Monitoring and Waste Elimination

DTs play a crucial role in modern manufacturing by continuously monitoring production flows to identify bottlenecks, overproduction, and non-value-added activities. Integrating Lean principles with DTs enhances operational efficiency by systematically eliminating waste, including overproduction, a significant concern in manufacturing environments [32, 40]. For instance, Lean methodologies emphasize identifying seven types of waste, including overproduction, which can lead to excess inventory and increased operational costs [40].

Moreover, applying Lean Six Sigma (LSS) with DTs can facilitate real-time data analysis, enabling manufacturers to respond swiftly to production anomalies and optimize resource allocation [41, 42]. This synergy improves productivity and fosters a continuous improvement culture, essential for maintaining a

competitive advantage in the dynamic manufacturing landscape. Consequently, leveraging DTs within Lean frameworks is pivotal for enhancing production efficiency and minimizing waste, ultimately driving sustainable manufacturing practices.

Real-time visualizations enhance production efficiency by enabling managers to identify and eliminate waste immediately. Integrating digital tools and lean manufacturing principles facilitates the visualization of production flows, allowing for the prompt detection of inefficiencies such as overproduction, waiting times, and unnecessary movements [43]. VSM is a powerful technique to visualize activities within production processes, highlighting value-added and non-value-added tasks and guiding managers in waste elimination efforts [44].

Moreover, applying lean techniques, such as the 5S methodology, further supports the systematic waste identification, leading to improved operational performance. By leveraging real-time data and visual analytics, organizations can foster a culture of continuous improvement, which is essential for maintaining competitive advantage in dynamic markets [44]. Ultimately, the
ability to visualize and respond to waste in real-time enhances production efficiency and contributes to sustainable manufacturing practices by minimizing resource consumption and environmental impact.

## B. Simulation-Based Scenario Planning with Digital Twins

DTs facilitate scenario modelling and simulation, essential for testing the outcomes of potential Lean interventions in manufacturing processes. By creating a virtual representation of physical systems, DTs enable real-time analysis and optimization of production workflows, allowing managers to evaluate the impact of various Lean strategies before implementation [45]. This capability is particularly valuable in identifying inefficiencies and predicting the effects of changes, thus supporting informed decision-making.

Integrating DTs with simulation technologies enhances the ability to conduct what-if analyses, where different Lean interventions can be modelled to assess their potential benefits and drawbacks [46]. For instance, DTs can simulate the effects of implementing JIT practices or altering production layouts, providing insights into how these changes might reduce waste and improve efficiency. Furthermore, the adaptability of DTs allows for continuous refinement of processes based on real-time data, fostering a culture of constant improvement central to Lean methodologies. Overall, the application of DTs in scenario modelling not only aids in optimizing production systems but also aligns with the principles of Lean manufacturing by promoting efficiency and waste reduction.

What-if scenarios are powerful tools that allow managers to anticipate the impact of changes before implementing them in real-world settings. By utilizing simulation models, organizations can explore various potential outcomes of different interventions, thereby minimizing risks associated with decision-making [47]. For instance, scenario modelling can help managers assess the effects of new policies or operational changes, enabling them to make informed choices that align with organizational goals [48].

Simulating these scenarios is particularly beneficial in complex environments where multiple variables interact. This approach allows for identifying potential pitfalls and evaluating alternative strategies, thus fostering a proactive management style [47]. Furthermore, integrating what-if analyses into decision-making processes enhances organizational resilience by preparing managers to adapt to unforeseen challenges. Ultimately, using what-if scenarios improves strategic planning and supports continuous improvement initiatives by providing a framework for evaluating the effectiveness of proposed changes before they are enacted [47, 48].

Proactive planning in manufacturing is essential for minimizing disruptions and ensuring optimized production flow. Integrating Lean Manufacturing principles with proactive strategies allows organizations to anticipate potential issues and streamline their processes effectively. Kassem emphasized that Lean monitoring through action research enhances awareness and knowledge across the organization, facilitating continuous improvement and minimizing disruptions [49]. Furthermore, implementing Enterprise Resource Planning (ERP) systems in conjunction with Lean practices has been shown to simplify

manufacturing complexities, thereby improving data integrity and production efficiency.

Moreover, proactive strategies such as statistical process control (SPC) can significantly enhance quality management, as highlighted by Widiatmaka, who discussed redesigning quality processes to address potential faults [50] preemptively. This proactive approach reduces waste and fosters a culture of continuous improvement, which is vital for maintaining an optimized production flow. Combining Lean methodologies and proactive planning ultimately enhances operational performance and sustainability in manufacturing environments [51].

## C. Case Study: Implementing DT-Driven Lean in Industry X

This section explores the implementation of DT technology within Lean Manufacturing in the automotive industry. Integrating DT with Lean principles enhances operational efficiency by enabling real-time monitoring and optimization of manufacturing processes. For instance, using DT allows for immediate identification and rectification of errors, thereby supporting continuous improvement and waste reduction, which are core tenets of Lean Manufacturing [5].

Studies have shown that DT can significantly improve automotive manufacturing production efficiency and quality control. For example, a case study demonstrated that DT technology reduced production lead times and enhanced process visibility [52]. Furthermore, the combination of DT with Lean practices fosters a culture of innovation and responsiveness, which is crucial for adapting to market demands and improving sustainability performance.

**Key Outcomes:** The integration of DT technology within manufacturing processes has led to significant outcomes, including enhanced production efficiency, reduced waste, and improved decision-making. Digital Twins facilitate real-time monitoring and simulation of manufacturing systems, allowing for timely adjustments that optimize production flow. For instance, studies indicate that applying DT can significantly increase operational efficiency by enabling predictive maintenance and minimizing downtime [5, 6].

Moreover, using DT in conjunction with Lean Manufacturing principles has effectively reduced waste by identifying inefficiencies in real-time and allowing for immediate corrective actions. This synergy streamlines processes and fosters a culture of continuous improvement, which is essential for maintaining competitive advantage in today's fast-paced manufacturing environment. Furthermore, improved decision-making is achieved through data-driven insights provided by DT, enabling managers to make informed choices based on accurate and up-to-date information. Thus, the adoption of Digital Twin technology represents a transformative approach to enhancing productivity and sustainability in manufacturing.

**Challenges:** Implementing Lean Manufacturing and Digital Twin (DT) technologies in production environments faces significant challenges, particularly regarding integration issues and workforce adaptation. Integration challenges arise from the complexity of aligning existing systems with new technologies, which can lead to operational disruptions if not managed effectively. Ghobadian, Talavera, Bhattacharya, Kumar, Garza-
Reyes, and O'Regan highlighted that the interplay between
innovation, Lean Manufacturing, and sustainability can complicate
the legitimization of new practices, as organizations must navigate
the intricacies of integrating these elements into their operations
[53].

Moreover, workforce adaptation is critical for successfully implementing Lean practices and DT technologies. Employees must be trained to effectively utilize new tools and methodologies, which can be met with resistance or a steep learning curve. However, the reference by Chen et al. does not directly support the claim regarding workforce adaptation and training in Lean practices and DT technologies, as it focuses on supply chain coordination barriers rather than workforce issues [32]. Therefore, addressing these challenges through comprehensive training programs and strategic integration plans is essential to enhance productivity and achieve sustainable manufacturing outcomes.

Lessons Learned: The practical application of DT technology within Lean systems has yielded valuable insights and benefits, particularly in enhancing operational efficiency and decision-making processes. One key lesson learned is that the integration of DT enables real-time data analysis, which significantly improves production efficiency. However, the references provided by Nettle, Kuehne, Lee, and Armstrong do not directly support adaptability in workforce strategies within Lean systems; they focus on workforce contribution to farm adaptability rather than operational responsiveness in manufacturing contexts [54].

Additionally, the implementation of DT within Lean frameworks has demonstrated a marked reduction in waste. By providing detailed insights into production processes, DT allows organizations to identify inefficiencies and implement corrective measures promptly. The reference from Tham and Holland discusses workplace climate and well-being among academics, which is not directly relevant to the technological transitions in Lean systems [55].

Overall, the synergy between Digital Twin technology and Lean Manufacturing principles presents a robust framework for enhancing productivity and sustainability in the automotive sector.

IV. CHALLENGES AND FUTURE DIRECTIONS

A. *Implementation Barriers*

Technical Challenges: The adoption of DT technology in manufacturing is significantly hindered by several technical challenges, notably legacy systems, data silos, and interoperability issues. Legacy systems often lack the flexibility required to integrate with modern digital solutions, which can impede the seamless flow of information necessary for effective DT implementation. Liu and Wu emphasize that the transition to digital transformation requires overcoming these legacy constraints to harness the full potential of digital technologies [46].

Data silos represent another critical barrier, as they prevent the effective sharing of information across different departments and systems. This fragmentation can lead to inefficiencies and a lack of coherent data analysis, which is essential for successfully deploying DT solutions. Antonucci et al. highlight that organizations must develop robust business process management

capabilities to facilitate the integration of digital technologies and
mitigate the impact of data silos [56].

Interoperability issues further complicate the adoption of DT, as disparate systems often fail to communicate effectively with one another. This lack of integration can result in operational inefficiencies and hinder the realization of DT benefits. Vărzaru, Bocean, Mangra, and Simion noted that ensuring interoperability among various IT solutions is crucial for maximizing the advantages of digital transformation [57]. Addressing these technical challenges is vital for organizations aiming to implement DT and achieve enhanced operational performance successfully.

Organizational Barriers: Integrating DT systems with existing Lean frameworks faces significant organizational barriers, primarily due to the challenges in aligning these two methodologies. One major challenge is the necessity for effective communication and cross-boundary collaboration within organizations. Prasad and Vasugi emphasized that successful Lean transformation requires a culture that promotes vertical, horizontal, and two-way communication to disseminate valuable information about ongoing changes [58]. Without this communication, the alignment of DT systems with Lean practices can falter, leading to inefficiencies.

Furthermore, the transformation process necessitates a cultural shift and changes in leadership behavior, as Azevedo et al. highlighted. They note that Lean implementation requires mechanisms for change, including culture change, process changes, leadership behaviour adjustments, capability development, and planning alignment [59]. This cultural resistance can impede the integration of DT systems, as employees may be hesitant to adopt new technologies that disrupt established workflows.

Additionally, aligning DT with Lean practices often encounters challenges related to existing processes and systems. Imran, Shahzad, Butt, and Kantola argued that organizations must ensure that digital technologies are effectively leveraged and aligned with their objectives to facilitate integration [60]. This alignment is crucial for maximizing the benefits of both Lean and DT methodologies, as misalignment can lead to wasted resources and missed opportunities for improvement.

B. *Cybersecurity and Data Integrity Concerns*

DT systems rely on secure data exchange between physical and virtual entities. Ensuring cybersecurity is critical to prevent unauthorized access or data breaches. The successful implementation of DT systems depends heavily on secure data exchange between physical and virtual entities. Ensuring cybersecurity is critical to prevent unauthorized access or data breaches, which can compromise the integrity of DT applications. As organizations increasingly adopt digital technologies, the risk of cyber threats escalates, necessitating robust cybersecurity measures. Senadjki emphasized that digital leadership is essential in navigating these challenges, as it directly impacts organizational performance through effective digital transformation strategies [61].

Moreover, the integration of DT systems requires a comprehensive understanding of the data flow and its potential vulnerabilities. Fleron, Pries-Heje, and Baskerville highlighted that
organizations must cultivate digital resilience to withstand cybersecurity threats, particularly in DT, where real-time data exchange is fundamental [62]. Furthermore, Imran, Shahzad, Butt and Kantola argued that the alignment of digital technologies with organizational objectives is crucial for mitigating risks associated with data breaches and ensuring the secure operation of DT systems [60].

Solutions include encryption, firewalls, and secure communication protocols. In network security, practical solutions such as encryption, firewalls, and secure communication protocols are essential for safeguarding sensitive information. Encryption is a fundamental layer of protection, ensuring that data is rendered unreadable to unauthorized users during transmission and storage [63]. This technique protects data integrity and confidentiality, especially when exchanging sensitive information, such as financial transactions and personal communications.

Firewalls act as a critical barrier between trusted internal and untrusted external networks, controlling incoming and outgoing traffic based on predetermined security rules. They can be implemented in various forms, including hardware and software solutions, and are designed to prevent unauthorized access while allowing legitimate traffic [63]. The effectiveness of firewalls can be enhanced through proper configuration and management, which includes regular updates and monitoring to address emerging threats.

Secure communication protocols, such as TLS (Transport Layer Security) and VPN (Virtual Private Network) technologies, further bolster network security by encrypting data in transit, thereby protecting it from eavesdropping and tampering. These protocols ensure that data exchanged over the internet remains confidential and secure, which is particularly important in today's digital landscape where cyber threats are increasingly sophisticated [51]. Collectively, these solutions form a comprehensive security framework that mitigates risks and enhances the overall resilience of network infrastructures.

### C. Future Research Directions

AI-Powered DTs: The integration of AI into DT systems is revolutionizing production processes by enabling self-optimizing production systems. By incorporating machine learning algorithms, these AI-powered DTs can analyse vast amounts of data in real-time, allowing for dynamic adjustments to production parameters that enhance efficiency and reduce waste. Fajri discussed how digital transformation can drive significant improvements in organizational performance by optimizing operational processes. However, the reference does not explicitly support the specific focus on AI in this context [64].

Moreover, the application of machine learning within DT systems facilitates predictive maintenance, where potential equipment failures can be anticipated and addressed before they disrupt production. This proactive approach minimizes downtime and extends the machinery's lifespan, leading to cost savings and increased productivity. Senadjki emphasized that organizations adopting embedded digital technologies experience substantial gains in productivity and operational stability, supporting the claim regarding the benefits of digital transformation [61].

Furthermore, the ability of AI-powered DTs to continuously learn and adapt from operational data fosters a culture of innovation within organizations. This adaptability is crucial in today's fast-paced manufacturing environment, where responsiveness to market changes is essential for maintaining competitive advantage. Fleron, Pries-Heje, and Baskerville highlight that organizations embracing digital resilience through advanced technologies are better positioned to navigate the complexities of digital transformation. However, their work does not address the specific context of AI and DTs [62].

Blockchain Integration: Integrating blockchain technology into DT-based systems significantly enhances traceability and data integrity. Blockchain provides a decentralized and immutable ledger that records transactions and changes in real time, ensuring that all modifications to the digital twin are verifiable and transparent [2]. This capability is crucial for manufacturing and supply chain management industries, where maintaining accurate records of product provenance and process changes is essential for compliance and quality assurance.

By leveraging blockchain, organizations can create a secure environment with guaranteed data integrity. Any attempt to alter recorded information would require consensus across the network, making unauthorized changes virtually impossible. This is particularly beneficial in scenarios involving multiple stakeholders, fostering participant trust and accountability. Furthermore, combining DTs and blockchain allows for enhanced scenario analysis, enabling managers to simulate various operational strategies while ensuring that the underlying data remains consistent and reliable [65].

Edge Computing is increasingly recognized as a transformative technology that significantly reduces latency by processing data at the source, enhancing real-time monitoring capabilities. Traditional cloud computing architectures often suffer from high network latency and slow response times due to the distance data must travel for processing. Kumar discussed how edge computing addresses these issues by enabling data processing closer to the source, which results in faster response times and improved system responsiveness [66].

The edge computing architecture allows for localized data processing, particularly beneficial for applications requiring immediate feedback, such as autonomous vehicles and real-time surveillance systems. Babou et al. explore how hierarchical load balancing in edge computing can optimize resource allocation, reducing latency and enhancing performance in real-time applications [67]. This localized processing capability minimizes delays and alleviates network congestion, allowing for more efficient bandwidth use.

Moreover, integrating machine learning algorithms within edge computing environments can facilitate predictive analytics and intelligent decision-making at the edge. Wang, Yang, Wang, Liu, Chen and Guan, demonstrated that edge computing, combined with advanced algorithms, can significantly enhance fault diagnosis in real-time applications, showcasing the potential for self-optimizing systems [26]. This synergy between edge computing and machine learning improves operational efficiency and supports the development of innovative, responsive systems capable of adapting to changing conditions.
# V. CONCLUSION

## A. Summary of Key Insights

The integration of DT technology with Lean Manufacturing principles presents a compelling framework for enhancing value stream flow and optimizing operational efficiency in manufacturing. Across the chapters, we explored the foundational concepts, conceptual model, practical applications, challenges, and future directions for implementing DT-driven Lean Manufacturing.

Key insights revealed the potential for DT to enable real-time data collection, predictive analytics, and scenario simulation, providing unprecedented visibility into manufacturing systems. This visibility facilitates proactive management of production processes, allowing for timely interventions that minimize waste and optimize resource utilization. When complemented by DT, Lean principles extend beyond traditional efficiency improvements, embedding resilience and adaptability into production lines.

Moreover, the conceptual framework for DT in Lean Manufacturing illustrates how physical, virtual, and communication layers can be seamlessly integrated to create an interactive environment where continuous feedback supports improvement initiatives. Case studies, particularly in the automotive industry, demonstrated tangible benefits, including reduced lead times, enhanced quality control, and data-driven decision-making that aligns with Lean objectives.

However, the research also highlights challenges such as the integration of legacy systems, cybersecurity risks, and workforce adaptation. Addressing these barriers is essential for achieving sustainable and resilient manufacturing outcomes. Including emerging technologies like AI, blockchain, and edge computing in DT systems offers promising solutions that support scalability and further enhance data integrity, latency, and adaptability.

Conclusively, Digital Twin-driven Lean Manufacturing holds transformative potential for modern industries seeking sustainable productivity gains and competitive agility. Future research in advanced data analytics, secure communication protocols, and real-time decision support will continue to drive the evolution of DT-enabled Lean frameworks, fostering a manufacturing landscape characterized by efficiency, resilience, and continuous innovation.

## B. Practical Implications

Integrating Digital Twin (DT) technology within Lean Manufacturing offers a transformative approach to optimizing value stream flow, enhancing productivity, and improving overall manufacturing efficiency. By providing real-time data and facilitating predictive analytics, DT systems enable organizations to make informed decisions that align with Lean principles, such as waste reduction, quality improvement, and streamlined production processes.

Implementing DT-driven Lean methodologies allows manufacturers to swiftly identify and address inefficiencies across production stages, enhancing operational agility. For instance, DT systems improve predictive maintenance, reducing unplanned downtime and extending machinery lifespan, thus ensuring

continuous flow and reducing costs associated with equipment failures. Furthermore, the real-time capabilities of DT support adaptive quality control measures, allowing immediate adjustments that improve product quality and reduce waste.

Using DT in a Lean context also has significant implications for workforce management and organizational culture. Organizations create a data-driven, responsive work environment that fosters innovation and continuous improvement by integrating digital solutions with established Lean practices. Employees trained in DT technologies can contribute more effectively to Lean initiatives, driving a shift towards proactive problem-solving and adaptive process improvements.

Finally, the potential to integrate DT systems with advanced technologies like AI, blockchain, and edge computing promises further scalability, data integrity, and responsiveness enhancements, which are crucial for remaining competitive in a dynamic manufacturing landscape. Therefore, this research underscores the value of DT as a critical enabler of Lean Manufacturing, optimizing value stream flow and contributing to sustainable, adaptable, and resilient production ecosystems.

## C. Declaration of conflicting interests

The authors declare no competing interests.

## D. Funding

The author received no financial support for this article's research and/or authorship.

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