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Background: Against the backdrop of the transformation and upgrading of the manufacturing industry, lean manufacturing has emerged as a systematic and advanced production paradigm that has deeply permeated the entire value chain of enterprises. Objective: However, there is a lack of systematic and effective lean technology paradigms in aspects such as lean practice processes and improving manufacturing process efficiency. Moreover, a comprehensive analysis of the current status and development strategies of lean technologies in discrete manufacturing enterprises has yet to be conducted to address issues such as the fragmentation of lean technology applications and the ambiguity of implementation strategies in discrete manufacturing enterprises. Methods: This paper conducts an extensive review of the literature on lean technologies and transformation methods in discrete manufacturing enterprises. A multi-stage data analysis approach (including data identification, screening, eligibility assessment, classification, and comprehensive analysis) is utilized to analyze 369 highly relevant documents. Results: The main contributions of this study are as follows: (1) A comprehensive review of existing lean manufacturing technologies and methods is provided, classifying, comparing, and summarizing the current status of lean technology and strategy applications, and delineating nine categories of lean technology application directions. (2) A “5P” theoretical framework (Philosophy, Process, People, Problem-solving, and Product) is proposed, redefining a lean technology framework that covers the value streams of discrete manufacturing. (3) Future application trends of lean technologies in discrete manufacturing are summarized and predicted, and an implementation strategy for lean technologies tailored to small and medium-sized discrete manufacturing enterprises, along with six lean technology development strategies, are proposed. The results indicate that many enterprises have derived diversified methods based on their own circumstances, which compensate for the deficiencies of the original lean models. Discussion and Conclusions: This paper organizes these methods to serve as a reference for future research on the lean technology system. The proposed strategies include formulating lean transformation strategies for discrete manufacturing enterprises, enhancing proactive lean capabilities, adapting to passive lean factors, and creating value for the enterprises’ reasonable lean needs from three levels: strategic philosophy, objective principles, and tool technologies. This research will play a guiding role in promoting the coordinated development of lean implementation and achieving high-quality development in discrete manufacturing enterprises.
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1. Introduction
Discrete manufacturing is a mode of production in which multiple components are processed through a series of discontinuous processes and ultimately assembled into a product. It is typically driven by orders, characterized by independent production steps and a diverse range of product types, encompassing industries such as aircraft, shipbuilding, weaponry, machine tools, furniture, etc. And it usually makes extensive use of advanced technologies such as CNC machine tools and automated assembly lines.
Small and medium-sized enterprises (SMEs) in the discrete manufacturing sector constitute the backbone of the industry. The European Union (EU) defines SMEs as any organization employing fewer than 250 people [1].
There are nearly 5.6 million small and medium-sized enterprises (SMEs) in the United States with fewer than 500 employees, accounting for 99.7 per cent of all types of employer firms. In addition, there are approximately 15 million self-employed businesses, (U.S. Small Business Administration (SBA)) [2]. There are a total of 5.089 million SMEs in Japan, accounting for 99.7% of the total number of enterprises. In Germany, the number of SMEs can reach 99 per cent, contributing 54 per cent of the GNP and providing about 62 per cent of employment. The number of SMEs in the EU reaches 99 per cent [2]. Due to the different scales of small and medium-sized discrete manufacturing enterprises, they face issues such as incoherent information systems, a lack of targeted improvement methods and procedures, an emphasis on partial assessments, a one-sided pursuit of equipment and personnel efficiency, and high work-in-process inventory. The application of lean technology has become a crucial means of accelerating the transformation of discrete manufacturing enterprises.
Furthermore, as an important component of the global manufacturing industry, to survive in today’s fiercely competitive global market affected by economic uncertainties, discrete manufacturing enterprises must possess efficient lean operating models [3]. Womack, Jones, and Roos believed that lean principles can be applied to any industry [4]. Countries have launched corresponding policies to encourage lean transformation and high-quality development of the manufacturing industry. For instance, the U.S. government introduced the Small Business Investment Act, ‘manufacturing back’, and other policies; Japan, to encourage research and development, provides tax incentives, capital subsidies, as well as internationalization of the layout; and the European Union has implemented the ‘Green New Deal’ program to encourage enterprises to adopt environmentally friendly technologies and production methods. Germany has put forward Industry 4.0 and skills training policies to provide advanced technology and talent security support for lean manufacturing. South Korea has proposed the ‘Manufacturing Innovation 3.0’ strategy, while the United Kingdom has proposed an ‘Industrial Strategy’ [5]. These policies have furnished a robust institutional foundation for the transformation and upgrading of discrete manufacturing.
Research on lean technologies in discrete manufacturing enterprises holds significant importance not only for the development of China’s manufacturing industry, living environment, and socio-economy but also for the global manufacturing industry, ecology, and economy. The following data support these assertions:
Firstly, the study of lean technologies can enhance production efficiency and flexibility. For instance, the “Made in China 2025” strategy aims to propel the manufacturing industry towards intelligent, green, and service-oriented development, providing a favorable policy environment for lean manufacturing [6]. The core logic of lean technology research in discrete manufacturing lies in treating data as an asset and achieving the transformation from data to information, knowledge, and ultimately wisdom. This helps enterprises realize the value of data mining and the service and optimization of production and manufacturing scenarios [7,8].
Secondly, China actively promotes the concepts and technologies of green and lean manufacturing, making significant contributions to the global effort to combat climate change. In 2023, global renewable energy installations increased by 510 gigawatts, with China contributing over 50% of this growth [9]. In 2022, China’s manufacturing value-added accounted for 30.2% of the world’s total, becoming a crucial driver of global industrial economic growth. By 2024, China’s actual manufacturing value-added had reached seven times that of 2000, with its share of global manufacturing output rising from 8.5% to 30.9%. China is actively advancing economic and trade cooperation with other countries and regions, creating more opportunities for the export of discrete manufacturing products [10].
Lastly, Chinese discrete manufacturing enterprises prefer to employ a combination of lean technologies to tackle various challenges faced by the enterprises, aligning with the majority of scholars’ views that lean production constitutes a portfolio of techniques [11]. By adopting their respective combinations of lean technologies, Chinese discrete manufacturing industries have enhanced their operational efficiency while actively promoting economic and trade cooperation with other countries and regions [12]. The export destinations of Chinese manufacturing products are widespread across the globe, encompassing Southeast Asian nations, North America, Europe, the Middle East, Africa, and other areas, thereby creating more export opportunities for discrete manufacturing enterprises [13].
This study focuses on the application and development of lean technologies in discrete manufacturing. Through an in-depth analysis of the current status and existing problems of lean technologies in discrete manufacturing enterprises, it aims to provide theoretical guidance and practical suggestions for the transformation and upgrading of small and medium-sized discrete manufacturing enterprises. The specific arrangement of the article is as follows: Section 2 introduces the methods and processes of data acquisition and analysis. Section 3 elaborates on the results of the literature data, introduces relevant concepts of lean manufacturing, and presents the current status of lean tool application and the directions of lean technology application in discrete manufacturing enterprises. Section 4 conducts a discussion and proposes an implementation framework and development strategies for lean technologies tailored to small and medium-sized discrete manufacturing industries.
2. Materials and Methods
This study adopts a three-stage structured data collection method to systematically review the research context of lean technologies in the discrete manufacturing field. In the first stage of data identification, a compound search query was constructed based on the Web of Science Core Collection database, using key search terms such as “lean manufacturing”, “lean technologies”, and “lean technology in discrete manufacturing enterprises”. A search strategy was formulated by combining Boolean logical operators, resulting in the retrieval of 2421 original documents. The literature mainly originated from sources such as Elsevier, Springer, EBSCO Host Academic Search Premier, Inderscience, World Scientific, Academic Journals, and the American Society for Engineering Management. We conducted multiple rounds of data screening. Firstly, duplicate records (84 in total) were effectively eliminated through data cleaning and deduplication using COOC14.9 bibliometric software, ultimately yielding an initial sample of 2337 documents.
In the second stage, during eligibility assessment and classification, the research team implemented a multi-level screening mechanism: Firstly, GraphPad Prism 8.0 software was used to create a flowchart for literature screening, during which 1932 irrelevant documents were excluded. Subsequently, a quality assessment was conducted through in-depth reading of the full texts, and the remaining 405 documents were subjected to dual-blind review using the JBI evidence-based research evaluation tool to assess dimensions such as methodological rigor and data reliability. After three rounds of iterative screening, 369 high-quality documents were selected for inclusion in the analysis system. For ease of verification, we coded these 369 documents. The specific steps are shown in Figure 1.
In the third stage, we conducted a bibliometric visualization analysis, revealing the evolution path of the knowledge map in this field by combining COOC software and Citespace. Through reading and analyzing the 369 documents, we manually extracted the main technological and research content from each document, listing all the lean tools used in discrete manufacturing. In cases where combined lean tools were encountered, we classified them according to the research focus.
3. Results
3.1. Basic Information of the Literature
We reviewed the specific information of 369 articles, among which 113 articles pertained to the automotive industry, 23 to aerospace, and 14 to furniture manufacturing, while 18 were review papers and 85 case studies. The remaining articles covered topics such as components, production lines, simulation models, algorithm development, and workshop scheduling.
Moreover, journals with a higher volume of publications are included in Table 1. We plotted the annual publication volume and cumulative volume of the literature, as shown in Figure 2. We categorized the data by industry. Since 2020, the number of publications on lean technologies in the automotive industry (yellow line) has fluctuated significantly, while the number of publications in the aerospace industry (green line) has gradually declined since 2020. The number of publications in the furniture industry (red line) has remained relatively stable. Additionally, research related to case studies, the automotive industry, algorithm development, and production lines accounts for a larger proportion, while research on human–machine collaboration, dynamic capabilities, and the full life cycle accounts for the smallest proportion, as shown in Figure 3.
Furthermore, based on the visualization results, we created a title word cloud and a matrix diagram (Figure 4), which reveal that scholars primarily focus on areas such as lean manufacturing, Industry 4.0, Discrete Event Simulation (DES), Value Stream Mapping (VSM), lean product development, lean Six Sigma, simulation, the automotive industry, and continuous improvement. In contrast, research on lean philosophy, behavioral studies, fuzzy logic, and the furniture industry is less prevalent.
Subsequently, we further utilized the keywords from the articles to create a confusion bubble chart (as shown in Figure 5) to analyze the co-occurrence of these keywords. The chart indicates that research related to lean manufacturing frequently involves topics such as Value Stream Mapping, sustainable manufacturing, Six Sigma, simulation, continuous improvement, Industry 4.0, Kanban, Discrete Event Simulation (DES), circular economy, and the automotive industry. Topics associated with Industry 4.0 include optimization, Value Stream Mapping, lean thinking, lean Six Sigma, lean product development, and the automotive industry. DMAIC is often studied in conjunction with lean Six Sigma and serves as a common tool within this framework.
In the research direction clustering map (shown in Figure 6), we can discern that the research directions centered on engineering also encompass business economics, computer science, mathematics, management science, automation control systems, and telecommunications, while materials science-focused research involves physics, chemistry, and forestry.
Based on the institutional clustering diagram (as shown in Figure 7), we can observe that IK Gujral Punjab Technical University, National University of Singapore, Accurate Institute of Management and Technology, King Saud University, and Indian Institute of Technology Kanpur have close ties among themselves. Similarly, the University of Federal Santa Catarina, Anna University, Cranfield University, Islamic Azad University, Linking University, and the University of Melbourne exhibit strong connections. Furthermore, the University of Nova Lisboa; the University of Minho; the University of Beira Interior; and the Centre for Maritime, Mechanical, and Aerospace Sciences and Technology form a tightly knit group. Lastly, the University of Derby, the National Institute of Technology, Northumbria University, Cadi Ayyad University, and the University of Verona also demonstrate close institutional links.
We conducted a visual analysis of the frequency of lean techniques and tools used in the literature, as shown in Figure 8. Value Stream Mapping (VSM) is often combined with Kanban, 5S, Lean Implementation, Discrete Event Simulation (DES), Sustainable Manufacturing, Lean Manufacturing, the Furniture Industry, Process Improvement, Line Balancing, and Barriers. Lean Six Sigma frequently associates with Leadership, DMAIC, FMEA, WBS, Implementation, and POKA-YOKE. Industry 4.0 is commonly linked with Product Development, Lean Production, Lean Thinking, and Circular Economy. Meanwhile, Industry 4.0 appears in a cluster associated with the Automotive Industry, which is often related to Sustainability, Supply Chain Management, and Smart Manufacturing. Finally, Simulation is typically associated with Optimization, Continuous Improvement, Overall Equipment Effectiveness (OEE), and Product Planning.
3.2. Overview of Lean Manufacturing
There exists a profound complementary and evolutionary relationship between Industry 4.0 and Lean Production, and their integration is reshaping the value realization pathways of modern manufacturing. Since the official introduction of Industry 4.0 at the Hannover Messe in April 2013, the rapid transformation of digital technologies has provided an intelligent foundation for Lean Manufacturing, enabling enterprises to break through the limitations of traditional experience. For instance, through methods such as machine learning, it allows for the precise identification of waste and the continuous optimization of enterprise value streams. Simultaneously, Lean Manufacturing can provide value-oriented guidance for Industry 4.0 practices, preventing inefficiencies arising from the mere accumulation of technologies. The relationship between Industry 4.0 and Lean Manufacturing can be understood as a mutually reinforcing and complementary one, both jointly driving the development of the manufacturing industry. The term “Lean” was first used in the context of the Toyota Production System (TPS), pioneered by Japanese engineers Taiichi Ohno and Shigeo Shingo during the International Motor Vehicle Program in 1988. This system serves as the classic paradigm for Lean production. In 1977, Sugimori et al. introduced this methodology into academia, describing TPS as comprising two systems: the Just-In-Time (JIT) production system and a human-centered system that emphasizes employee participation and the elimination of activities that waste employee effort. The concept of “waste” has been extensively discussed in academia, including the seven types of “muda” proposed by Ohno, the eight types of waste proposed by Liker [14], and the nine types of waste proposed by Imai [15] and SJ Pavnaskar [16]. Subsequently, Womack et al. popularized the concept of Lean in “The Machine That Changed the World” [4] and systematically proposed Lean Thinking in 1996, which encompasses five principles: value, value stream, flow, pull, and continuous improvement. This has gained widespread recognition. In 1998, Åhlström proposed eight principles of Lean production, including the elimination of waste, zero defects, pull scheduling, multifunctional teams, postponement, team leadership, vertical information systems, and continuous improvement [17]. Sherif Mostafa added a customer-centric principle to Lean management based on Åhlström’s work [18]. In academia, Lean has been defined in various ways. It can be considered an approach, a process [19], a set of principles, a set of tools or techniques [20], a methodology, a concept, a philosophical system, a system, a project, a production paradigm, or a model [21,22,23]. Currently, the challenge for discrete manufacturing enterprises in transformation lies in constructing an organization or system that integrates lean thinking and digital technology, and achieving the goal of continuous improvement within the enterprise by enhancing employees’ digital and intelligent capabilities.
3.3. Lean Manufacturing Tools and Their Applications
3.3.1. Lean Manufacturing Tools
Christian F. Fricke categorized 29 lean elements (as part of lean manufacturing) into four categories: philosophy, process, people, and problem-solving, referred to as the 4P [3,14]. These categories were used to establish lean awareness and assess the state of lean implementation in the surveyed companies. We have reviewed 369 pieces of the literature on lean tools utilized in discrete manufacturing enterprises and classified these tools into five categories. Building upon the foundation of philosophy, process, people, and problem-solving, we propose the addition of “product” as a fifth category, as shown in Table 2. Some scholars consider Lean Six Sigma as a lean tool as well [24], and we include it here for completeness.
Discrete manufacturing enterprises ultimately output products, yet the study of products is not systematically reflected in the 4P model. Relevant product design methodologies and manufacturing processes may be mentioned under “Philosophy” and “Process”, but without systematic elaboration. However, as the key output of discrete manufacturing enterprises, products encompass knowledge domains such as green manufacturing, cellular manufacturing, and life cycle management, which lay the theoretical foundation for the 5P model. Furthermore, certain digital technologies related to product design and manufacturing can reconstruct the product production value stream and enhance the value of the product throughout its life cycle, from raw materials to recycling and disposal. The 5P framework proposed in this study represents a dimensional evolution of the traditional 4P system, adding different dimensional deconstructions of the Toyota Production System (TPS). Given that discrete manufacturers’ functional innovations ultimately deliver integrated products requiring technology-intensive design–manufacture convergence, we systematically incorporate product-centric methodologies—including Quality Function Deployment (QFD), Cellular Formation Methodology (CFM), Cellular Manufacturing, FIFO principles, and Design for Manufacturing & Assembly (DFMA)—into the lean architecture. The theoretical advancements manifest through three paradigms: methodological hybridization (e.g., TRIZ-driven technical contradiction resolution), toolchain evolution (e.g., Prognostics and Health Management (PHM) deployment), and agile–lean synergy (e.g., integrated iterative design protocols).
3.3.2. Current Application Status of Lean Tools in Discrete Manufacturing Enterprises
We have reviewed and categorized the lean tools and techniques utilized in discrete manufacturing from 369 academic articles and will now introduce the primary lean tools featured in these articles in sequence.
(1). Value Stream Map
As a crucial tool in lean production, the Value Stream Map (VSM), also known as process mapping, plays a significant role in process visualization and cross-functional collaboration. It is a simple and user-friendly symbolic process modeling tool that specifies activities, cycle times, downtime, and delays, aiming to enhance efficiency by identifying bottlenecks and non-value-added activities in production or logistics [25,26,27]. The implementation steps of VSM are shown in Figure 9.
Traditionally, Value Stream Mapping (VSM) has been applied to the physical production processes of workshops within highly repetitive manufacturing environments. Typically, VSM is created using pen and paper; however, modeling more complex systems, such as multiple production lines, requires a platform that supports hierarchical modeling. The advent of digital technology has facilitated the evolution of VSM, as exemplified in the literature such as 5, 66, 245, and 188. Several software tools, including Process Simulator, Simul8, and VisioSim, support the simulation of flowcharts created using Microsoft Visio. Additionally, other software, such as Arena, SimCad, and Extend, provide specific VSM templates.
VSM comprises three steps: (1) Collecting and verifying data on waste generation and flow as the data input step for VSM. (2) Encompassing three stages: mapping the volume and components of waste generation, as well as conducting horizontal and vertical performance analyses. (3) Developing actual and future state maps, which have proven effective.
Static Value Stream Mapping (VSM) lacks real-time dynamic capabilities and necessitates the use of additional tools for supplementary analysis. However, even when traditional VSM is integrated with these tools, it remains a form of static analysis, as evidenced in the literature such as 86, 93, 362, 138, 146, 212, and 9. Currently, scholars are combining lean tools to improve manufacturing processes such as production and decision making, considering more flexible methodologies based on comprehensive models, and integrating the theory of dynamic capabilities throughout the manufacturing process. The representative literature in this field includes 292, 301, 3, 248, and 67 (Table 3). It is evident that scholars are assessing the impact of external environments, such as the market and economy, and advocating for the application of VSM to evolve in conjunction with a company’s inherent capabilities to keep pace with the times.
(2). Kanban
Kanban, a Japanese term meaning “instruction card”, conveys information such as production orders, material requirements, and transportation arrangements during the production process. As a core control tool in lean production, Kanban is crucial for process visualization and waste elimination [28]. It can be described as a visual lean management tool that enables Just-in-Time (JIT) production and reduces waste. By introducing an efficient pull mechanism based on available resource capacity rather than inventory replenishment, Kanban accelerates throughput in multi-variety, small-batch production environments [29]. In the literature we have collected, there is relatively little research on Kanban. From the existing literature, it can be seen that Kanban research is generally combined with technologies such as RFID and ERP systems, as evidenced in references 13, 14, 57 and 303 (Table 4). In addition, some scholars have derived models based on the traditional Kanban, such as the Scrumban framework proposed by Massimo Bertolini, which aims to simplify management sequences. Currently, due to the impact of machine learning and artificial intelligence, future Kanban systems will evolve towards intelligence, distribution, and humanization, transforming from a tool for controlling individual production units into an adaptive coordination engine at the overall supply chain level.
(3). WIP
Work-in-process (WIP, the acronym for Working In Progress), in the context of Enterprise Resource Planning (ERP), refers to work-in-process inventory or items currently on the production line, also known as shop floor production management. The issue of WIP in discrete manufacturing is not new and warrants in-depth exploration. According to lean production theory, WIP is caused by overproduction and batch production [30]. Addressing the WIP problem depends on various measures, such as job design, time and motion studies, continuous improvement activities, quick setup techniques [31], ConWIP [32], layout strategies that facilitate one-piece flow, assembly line balancing techniques, and tools like kanban. As an important concept in lean manufacturing theory, WIP management is of great significance in resource optimization for discrete enterprises. In future research, WIP management should strengthen collaborative innovation in technology and human–ecological aspects to address inventory and waste issues in more complex environments, thereby reducing non-value-added activities and saving costs.
(4). Takt Time
Takt Time refers to the time required for a production line or manufacturing equipment to meet customer demand and serves as a powerful tool in Discrete Manufacturing (DM) due to its ability to deliver outputs in accordance with customer preferences [33,34]. As a critical link between the market and production in DM, Takt Time holds strategic value in demand matching and resource optimization. However, the traditional Takt Time struggles to meet the needs of current discrete manufacturing enterprises, such as diverse customer demands, high volatility in customized production, and imbalances between processes. To address these issues, Takt Time requires dynamic adjustment, utilizing flexible Takt Time algorithms or other lean techniques (such as Value Stream Mapping (VSM) to identify bottleneck processes and Single Minute Exchange of Die (SMED) for quick tool changes) to enhance the balance of manufacturing production lines based on specific process problems within the enterprise. During this process, human factors engineering should be introduced to optimize task allocation and labor intensity, thereby sharing high-intensity or highly repetitive tasks.
(5). “Push” and “Pull”
In the production process, there are generally two strategies. First is the “push” strategy, which typically supports Make-To-Stock (MTS) and relies on a demand forecasting system. Second is the “pull” strategy, which drives production through actual (internal or external) demand orders. The industrial application of the “pull” system originated from the use of Kanban technology in the Toyota Production System (TPS). Other techniques are also applied, such as Constant Work-In-Process (Conwip) or Polca (Pair-wise Overlapping Loop with Authorization). Lean production often adopts the “pull” method, while Enterprise Resource Planning (ERP) systems typically use the “push” method. These two methods complement each other and jointly support the efficient operation of manufacturing enterprises [35].
In the “push-pull” system, the Work-In-Process (WIP) and inventory levels are generally maintained at a constant level. When planning tasks, the push method can maximize off-site manufacturing capacity, while the pull method can achieve on-time delivery. If the pull strategy results in an extension of the Customer Lead Time (CLT), it may lead to the failure of lean manufacturing initiatives. Factors that affect the performance of the pull system include material procurement, increased risks, prolonged delivery times for customer orders, poor material fluidity, and external influences (such as machine failures). Both methods need to consider the logical constraints and resource availability of the off-site construction process.
(6). Continuous Improvement
The 5S methodology encompasses Seiri, Seiton, Seiso, Seiketsu, and Shitsuke. As crucial tools in lean production, 5S/6S play an irreplaceable role in establishing order and instilling a cultural ethos. Many enterprises leverage 5S to enhance productivity in their equipment workshops [36]. Building upon the 5S framework, 6S incorporates an additional focus on safety, representing another approach originating from Japanese on-site production management. Both methodologies aim to improve production efficiency and product quality through continuous improvement.
In practical applications, traditional 5S and 6S implementations often devolve into mere “superficial tidiness”, particularly in flexible production environments characterized by discrete manufacturing and customization. Issues arise such as excessive emphasis on visual management at the expense of value stream optimization; cleaning standards that contradict improvements in core process flows; conflicts between traditional “set-in-order” principles and dynamic material flows; incompatibility between traditional 5S principles and the spatial requirements of new technologies like Automated Guided Vehicles (AGVs) and collaborative robots; and the simplification of the newly added “safety” element in 6S to mere signage posting, neglecting systematic risk assessments. To address these challenges, enterprises can combine the use of other lean tools (such as Value Stream Mapping, VSM) and, based on digital technologies, develop workshop digital systems tailored to different types of work.
(7). Total Productive Maintenance
Total Productive Maintenance (TPM) aims to drive overall improvement in enterprises through equipment maintenance, encompassing measures such as enhancing equipment stability, reducing failure rates, increasing production efficiency, and lowering costs. TPM includes a comprehensive framework across eight key areas: autonomous maintenance, preventive maintenance, planned maintenance, skills training, quality management, equipment management, environmental management, and safety management. This framework is designed to help enterprises comprehensively enhance their production capabilities. Current research reveals four main deficiencies. Firstly, the periodic maintenance strategy in TPM theory is unable to support contemporary production environments. Traditional TPM emphasizes “total productive maintenance by all employees”, but in practice, it faces challenges such as low participation from employees and departments, and superficial analysis of the root causes of equipment failures. Secondly, enterprises that still rely on paper-based inspection checklists for traditional TPM find that the maintenance logic of TPM lags significantly behind emerging technologies such as digital twins and collaborative robots. Thirdly, the construction of TPM requires continuous investment, and its return on investment is not proportional. Fourthly, traditional inspection standards neglect ergonomic design considerations. Therefore, the direction of TPM innovation should address these issues and construct an intelligent maintenance system that integrates humans and machines in the digital age.
(8). Production smoothing
Under the conditions of multi-variety mixed-model production, scientifically organizing the production sequence of products on the assembly line can achieve a comprehensive balance of product variety, output, working hours, and equipment load. The core objective is to improve production efficiency, reduce waste, and ensure product quality [35].
In a mixed-model production environment, production smoothing offers numerous advantages in terms of enhancing efficiency, reducing waste, and improving product quality. However, it also presents challenges in planning, forecasting, investment, and change management, as well as the potential for over-optimization. For example, over-reliance on historical data can hinder the ability to respond to sudden market changes. Additionally, maintaining a smooth production rhythm may lead to neglecting energy efficiency optimization. Discrete manufacturing enterprises need to weigh these factors when considering the adoption of production smoothing
(9). Pre-Production Planning
The Production Preparation Process (PPP) involves creating simulated products or workflow procedures to validate the rationality of designs. This process aids in rapidly predicting outcomes that would otherwise only be achievable through prolonged continuous improvement, thereby reducing the risk of sunk costs associated with post hoc improvement efforts. However, the various stages of the traditional PPP (process design—tooling preparation—material procurement—equipment debugging) are severely fragmented. Process planning overly relies on the personal experience of engineers and lacks a scientific validation system. In the context of multi-variety, small-batch production scenarios, the traditional PPP lacks flexibility. These issues indicate that the linear process formed during the Industry 3.0 era of the traditional PPP is struggling to meet the demands for agility, precision, and sustainability in the digital age. Discrete manufacturing enterprises should undergo a fundamental transformation from being “experience-driven” to “data-driven”, from “physical trial and error” to “virtual validation”, and from “cost centers” to “value engines”.
(10). Supply Chain Management
Supply chain management represents an integrated management philosophy and approach, which involves the efficient planning, organization, coordination, and control of the entire supply chain, from raw material procurement to final product delivery. Pursuing sustainability within the supply chain (SC) is one of the top priorities for business leaders across all manufacturing industries [37]. According to the literature, research on supply chains in discrete manufacturing enterprises primarily focuses on the integration of lean manufacturing, sustainability, green design, and other factors, as exemplified by the representative literature cited in Table 4 [38]. We have observed that enterprises place greater expectations on suppliers to improve in five areas: products, processes, organizational management, communication, and relationships. Among these, product and process improvements are of utmost importance to them, as they aim to achieve a more efficient and continuously improving value creation process by optimizing logistics, information flow, and capital flow (or workflow). Additionally, discrete manufacturing enterprises tend to prioritize the prediction of supply chain risks (SCRM) [39]. As shown in Table 5.
(11). DMAIC and DMADV
The American Society for Quality (ASQ) defines DMAIC as Define-Measure-Analyze-Improve-Control and DMADV as Define-Measure-Analyze-Design-Verify, both of which are methodologies within the Six Sigma framework. DMAIC focuses on improving existing processes, while DMADV is dedicated to designing new products or services [40]. The process sequence of DMAIC is fixed, whereas the sequence of DMADV can be adjusted according to the specific requirements of the project. A notable difference between the two is that DMADV includes a design phase, while DMAIC aims to improve existing processes [41]. The basic idea is to integrate other methodologies, such as combining TRIZ theory to design lean warehousing methods (Literature 83). DMAIC, along with RCA (Root Cause Analysis) tools and methods like fishbone diagrams, has been used to address assembly issues (Literature 200). DMAIC has also been combined with three hybrid simulation paradigms (SD, DES, and AB) to simulate existing real factory environments (Literature 305). The representative literature is listed in Table 5. However, both methods have their respective drawbacks, and careful consideration must be given when determining which method to use. DMAIC is particularly suitable for improving existing processes, while DMADV is more appropriate for designing new products or processes from scratch. A comprehensive understanding of these methods and their respective advantages and disadvantages is crucial for making informed decisions. As shown in Table 6.
(12). Lean Six Sigma
Lean aims to eliminate waste, while Six Sigma strives to reduce errors. Six Sigma is a rigorous, data-driven methodology that encompasses two subprocesses: DMAIC and DMADV (refer to the previous section) [42]. Many scholars employ the Lean Six Sigma Framework (LSSF) to evaluate, justify, and implement future lean initiatives [43,44]. LSS is recognized as one of the most effective business transformation strategies in strategic operations management [45]. Currently, LSS practices in discrete manufacturing enterprises primarily focus on the mechanical aspects of this methodology, such as the application of the DMAIC method in manufacturing (Table 5), without incorporating a more strategic perspective and softer elements [42]. Some scholars argue that these “softer” elements include training, communication, continuous improvement, vision creation, goal adjustment, employee motivation, employee empowerment, leadership commitment, and a supportive culture, among others [46,47,48]. Currently, scholars have turned their attention to researching these “softer elements”, such as Industry 4.0 strategies [49,50], Green Lean Six Sigma (GLSS) formed by green concepts [51,52], sustainable manufacturing [53], Voice of the Customer (VOC), cause-and-effect diagrams and Pareto charts [54], Best-Worst Method (BWM) [55,56], Multi-Criteria Decision Making (MCDM) [40], and brainstorming, among others, to enhance its “soft nature”.
3.4. Directions for the Application of Lean Techniques in Discrete Manufacturing Enterprises
The directions for the application of lean techniques that we have summarized are as follows, encompassing a full life cycle perspective, derivation of fundamental lean models, development of novel information systems, integration of lean tools, discrete simulation technology, application of lean thinking, management reform, enhancement of human–machine collaboration, and utilization of emerging software technologies. These will be introduced in sequence below:
3.4.1. Technology Application Direction I—Life Cycle Perspective
Life cycle assessment (LCA) is a method used to evaluate the environmental impact of a product or service throughout its entire life cycle, from “cradle to grave”, encompassing raw material extraction through to waste recovery. The United States Environmental Protection Agency (EPA) introduced the Lean and Environmental Toolkit in 2007, aimed at assisting lean practitioners in improving environmental performance [57]. Scholars have extensively applied the concept of sustainability [58], and currently, academia has explored lean production methods from a full-life-cycle perspective, proposing theories such as the Lean VSM4.0 model [59], the Manufacturing Sustainability Index (MSI) concept [60], and the step-by-step method for Sustainable Setup Flow Mapping (3SM). The additional representative literature is presented in Table 6. Research indicates that enterprise management methods based on a life cycle perspective are more effective in enhancing lean benefits, for example, quantifying energy consumption improvements in machines for discrete component production, and studying sustainability capabilities based on life support systems and total quality management principles [61]. As shown in Table 7.
3.4.2. Technology Application Direction II—Deriving Basic Lean Models
During the period from 1990 to 2000, Lean was applied in the field of operations management, encompassing production, manufacturing, logistics, supply chains, and products. In 2000, “Lean Six Sigma” emerged. In 2006, Lean was introduced into the service industry, leading to concepts such as Lean Service, Lean Healthcare, and Lean Office. In 2013, Karim and Arif-Uz-Zaman developed a Lean implementation methodology based on five Lean principles. In 2017, Lean was integrated with Industry 4.0, resulting in Lean 4.0 [62]. Currently, more scholars tend to propose comprehensive integrated frameworks or models [63]. The key literature is presented in Table 7, and the integration approaches can be broadly categorized into derivation from Lean toolset combinations (200, 299), derivation through integration with other theories (216, 265, 281, 315, 316, 328, 98), derivation utilizing software technology (359, 23), derivation based on functions and objectives (236, 139), and derivation of evaluation models (78, 82, 126). As shown in Table 8.
3.4.3. Technology Application Direction III—Developing New Information Systems
Toyota Production System (TPS), Ford Production System (FPS), and Chrysler Operating System (COS) are classic methods for implementing lean management [64]. Currently, scholars are focusing on developing production systems tailored to individual enterprises. We have summarized these into seven functional information systems, categorized as follows.
(1). Decision-making systems
The functional orientation of Decision Support Systems (DSS) includes integrating multi-source data (such as market forecasts, supply chain status, and equipment efficiency), with the aim of providing dynamic decision support to management, thereby optimizing resource allocation and strategic planning. Currently, DSS primarily relies on various algorithms and models for decision making, with specific technical details provided in Table 8. Scholars have combined different algorithms and software to establish decision system frameworks, such as Markov algorithms, heuristic algorithms, and augmented Lagrangian relaxation methods. These frameworks primarily address combinatorial optimization problems involving nonlinear inequalities in the manufacturing process. As shown in Table 9.
(2). Technical systems
Technical systems primarily focus on manufacturing process optimization and technological innovation, with main functions including supporting complex product design, simulating processes, and resolving technical contradictions. Examples include developing automated DES systems using MTConnect and enhancing field service efficiency through off-site manufacturing OSM technology. The representative literature on these topics is listed in Table 10.
(3). Monitoring systems
The functions of monitoring systems include real-time data acquisition, anomaly detection in data, etc. Currently, scholars employ methods such as multivariate Statistical Process Control (SPC) based on Partial Least Squares (PLS) and Bayesian optimization. As shown in Table 11.
(4). Management systems
Management systems are designed to enhance organizational collaboration and efficiency within enterprises, serving as comprehensive systems that encompass production planning, personnel scheduling, performance evaluation, and more. Currently, the research technologies and methods related to management systems in academia mainly include systems leveraging intelligent technologies and digital shop floors (DSFs), as well as shop floor management systems (SFMs). As shown in Table 12.
(5). Distribution scheduling system
The distribution scheduling system aims to optimize logistics routes, allocate inventory, and balance distribution efficiency. Currently, scholars use improved algorithms to conduct research on related issues. For example, B.O. Xin utilized A-BPSO to balance workload problems. L. Shi developed a sustainable hybrid shop floor flow using the Dynamic Scheduling Unit (DSU) of a Multi-Agent System (MAS). Other relevant algorithm studies are listed in Table 13.
(6). Production system
Production systems focus on production process planning and efficiency issues. Current research is primarily based on mathematical theories and algorithmic studies, such as artificial neural networks, Markov algorithms, fuzzy information axiomatics, and weighted fuzzy information axiomatics. In terms of system construction approaches, there are methods for order-oriented production planning and methods based on Total Productive Maintenance (TPM) systems. As shown in Table 14.
(7). Evaluation systems
Discrete manufacturing enterprises place greater emphasis on evaluating aspects related to their production performance, production capacity, process level, decision-making capabilities, etc. For instance, G. Ante proposed a tree structure of Key Performance Indicators (KPIs) for describing the Performance Measurement System (PMS) of lean production systems. M. Elnadi developed an initial model for assessing PSS lean. M.B. Baskir combined Bayesian models with QFD-AHP in an IT2F environment to eliminate ambiguity in lean decision making based on conceptual changes. Other specific methods are listed in Table 15.
3.4.4. Direction of Technical Application IV—Combined Lean Tools
From the literature obtained, it can be seen that current enterprises adopt the approach of combining lean tools to achieve lean improvements in specific parts or processes of their operations. The advantages of combining tools mainly include complementarity, synergy, and adaptability, where different tools can be paired to leverage their individual strengths. For example, B. Durakovic developed a method incorporating three operations research techniques (process planning, line balancing, and equipment selection) to achieve optimal lean results. M.L. Junior used Overall Equipment Effectiveness (OEE) as a comparative metric, reflecting the improvements brought about by the implementation of lean production concepts and principles combined with technologies such as Automated Guided Vehicles (AGVs). Other application methods are listed in Table 16.
3.4.5. Technology Application Direction V—The Discrete Event Simulation
The Discrete Event Simulation (DES) method involves the use of computers to conduct simulation experiments on discrete event systems [65]. It is a method to digitally replicate the behaviors and performances of processes, systems, and facilities in the real world [66]. Guilherme Luz Tortorella argues that advanced lean manufacturing (LA) implementations should adopt highly complex and demanding infrastructure technologies, such as cloud computing systems, additive manufacturing, rapid prototyping, and 3D printing [67]. Research has found that a significant proportion of lean practices utilize computer simulation techniques (56, 44, 272, 80, 87, 65). Therefore, we believe that DES technology can be regarded as an important direction for future lean implementations in discrete manufacturing enterprises. However, in some cases, relying solely on this method is insufficient, particularly when aiming to improve simulation performance or expand functionality (284, 79, 327), as illustrated in Table 17.
3.4.6. Technology Application Direction VII—Application of Lean Thinking
Drawing on the three levels proposed by Shah and Ward, namely, philosophy, principles, and tools [68], discrete manufacturing enterprises have begun to emphasize the application of lean thinking in their transformation processes. Currently, research on lean thinking is primarily manifested at the enterprise strategic level, exploring how to utilize a series of tools, techniques, or methods to achieve the goal of enhancing lean practices. For example, Aries Susanty used SmartPLS3.0 software to process data obtained from questionnaires through Partial Least Squares (PLS) regression, investigating the impact of Lean Manufacturing (LM) practices on Operational Performance (OP) and Business Performance (BP). V. Saddikutti proposed achieving an appropriate dynamic combination of lean and traditional Supply Chain (SC) practices through coordinated demand-driven production. N.M. Bastos discussed reconfiguring and improving electronic component assembly lines by applying lean thinking principles. D. Ramesh Kumar collected 50 non-value-added activities and 27 lean manufacturing strategies from the literature field, conducting a mapping study between non-value-added activities and lean manufacturing strategies through critical thinking. Costel-Ciprian Raicu proposed a method based on a hybrid development strategy integrating different core areas such as lean, Scrum, feature-driven development, and VDI, and introduced a canvas-type model to achieve rapid delivery of headlights. The other representative literature is listed in Table 18.
3.4.7. Technology Application Direction VII—Change Management
Liker proposed 14 principles of lean production management [14], which have been widely accepted in the academic community [69]. Small and medium-sized discrete manufacturing enterprises are characterized by flexible structures, centralized decision making, unified culture, resistance to change, and simple planning [70]. In the literature, we find that small and medium-sized discrete manufacturing enterprises face issues such as inadequate contingency measures, human factors, lack of strategic vision, and ineffective management during the implementation of lean management. Some scholars often utilize agile project management methods such as Scrum [71] to explore these aspects as well. For example, Salah Ahmed Mohamed Almoslehy proposed methods for effectively managing risks during the sustainable management of complex product development processes in competitive environments such as Industry 4.0. Varun Tripathi proposed an orthogonal array for intelligent workshop management based on the relationship between production sustainability and constraints. Ewa Skorupińska introduced a series of quality management methods, including Concurrent Engineering (CE), Total Quality Management (TQM), Statistical Process Control (SPC), Quality Function Deployment (QFD), and Failure Mode and Effects Analysis (FMEA). The other representative literature is listed in Table 19.
3.4.8. Technology Application Direction VIII: Strengthening Human–Machine Collaboration
The enhancement of enterprise value cannot be separated from humane management strategies. Respecting employees, strengthening internal communication, and fostering employees’ sense of responsibility and belonging are all “key factors” for enterprises to obtain lean production value. Meanwhile, in the production process of enterprises, improving human–machine collaboration capabilities is of utmost importance. V.L. Bittencourt regards humans as a crucial link in the process. Regular training for employees on standardized operations, paying attention to their physical and mental health development, and improving employee efficiency are also incorporated into the scope of lean research by other scholars. For example, Aditya Kumar Sahu studied employees’ behavioral reasoning perspectives on the implementation of Lean Manufacturing Practices (LMPs) based on Behavioral Reasoning Theory (BRT). A. Assunção considered ergonomic risk factors (EAWS) and proposed a design for job rotation plans based on genetic algorithms. M. Pantano investigated and evaluated the three design elements of human–cyber–physical systems and proposed a conceptual framework for human–robot collaboration. Alexander Kurt Möldner verified that the analytical technique of multiple linear regression models and humane lean practices have a positive impact on the inputs of manufacturing enterprises. The specific literature is listed in Table 20.
3.4.9. Technology Application Direction IX: Utilization of Emerging Software Technologies
Emerging software technologies offer numerous benefits to discrete manufacturing enterprises, enabling them to achieve better resource allocation, effectively reduce labor costs, shorten downtime, and facilitate precise decision making to ensure smooth project progression. Currently, within the product manufacturing cycle, discrete manufacturing enterprises have widely adopted various systems, such as Computer-Integrated Manufacturing Systems and Product Data Management (encompassing CIMS, PDM, etc.), Computer-Integrated Production Systems, Manufacturing Execution Systems, Master Production Schedules, Process Control Systems, and Management Information Systems (including CIPS, MAS, MPS, PCS, MIS, etc.), Intelligent Warehouse Management Systems (e.g., WMS, IBP, OTWB, RPR, etc.), Sales and Quality Management Systems (e.g., MRO, EMS, SCRM, DMP, etc.), and Quality Traceability and Recycling Systems (e.g., EAM, CPC, etc.). However, the results of the current literature searches indicate that the majority of research focuses on optimizing the functionality of specific aspects of these systems, as detailed in Table 21.
4. Discussion
4.1. Lean Transformation Strategy for Small and Medium-Sized Discrete Manufacturing Enterprises
The core of pursuing lean management for small and medium-sized discrete manufacturing enterprises lies in value creation. This can be considered from three levels of lean technology research: Philosophy-Driven Strategy, Goal-Oriented Principle, and Technical Tools. We believe that due to limited resources, small and medium-sized enterprises should adopt a more proactive lean strategy, root it in the corporate development culture, identify a development strategy suitable for themselves, and set achievable short-term and long-term goals. By leveraging lean thinking and technologies, they can complete the lean transformation of the enterprise. Based on the above literature analysis, we have summarized six lean measures for the development of small and medium-sized discrete manufacturing enterprises (Development of Dynamic Lean Management Mechanisms, Exploring the New Lean Paradigm, Exploring Sustainable Lean, Endogenous Technological Innovation, Activation of Human-Centered Values, and Restructuring of Business Models), which will be elaborated in detail in the next section. As shown in Figure 10.
Furthermore, enterprises are influenced by external factors such as policies, environments, cultures, and peers, which collectively drive technological progress and facilitate the development of lean tools. Therefore, we believe that in the process of implementing lean management, small and medium-sized discrete manufacturing enterprises should not only actively learn, explore, reflect, and adapt to meet their reasonable lean needs and achieve the goal of value creation but also leverage both internal and external factors to reciprocally nourish the corporate culture and lean philosophy, forming a virtuous cycle of lean value.
4.2. Research Directions of Lean Technologies for Small and Medium-Sized Discrete Manufacturing Enterprises
4.2.1. Development of Dynamic Lean Management Mechanisms
The concept of dynamic capabilities, initially proposed by Teece et al., refers to the ability of enterprises to integrate, build, and reconfigure internal and external capabilities in response to rapidly changing environments. Currently, there are more academic studies on dynamic capabilities (DCs) for lean practices [72,73,74]. Dynamic capabilities can enhance organizational agility. While implementing lean tools for improvement, small and medium-sized discrete manufacturing enterprises should also focus on the application of dynamic capabilities across all manufacturing elements of the enterprise, such as management, operations, maintenance, and others (e.g., demand, orders, projects, supply chains, strategies, risks, costs, personnel training, etc.) [75,76]. Furthermore, by leveraging the core viewpoints of the dynamic capabilities theory, which encompasses resource integration, learning capabilities, pioneering impetus, and knowledge management, research on the dynamic capability mechanism should be expanded. This will enable enterprises to maintain their competitiveness in the ever-changing external environment.
4.2.2. Exploring the New Lean Paradigm
The production process has never reached a state of perfection; rather, it is through continuous improvement and optimization that it can progressively approximate the ideal state. We have observed that in discrete manufacturing enterprises, the primary path for implementing lean management is through the expansion of its foundational model, which encompasses diversified principles and methodologies. Notably, the current discrete manufacturing industry has transcended traditional boundaries, turning its focus towards areas such as thinking innovation, cultural development, humanistic care, and health promotion. In light of this, we believe that the development of the lean model is equally influenced by external factors, such as policy orientations (e.g., Lean 4.0), economic conditions, and the ecological environment.
The lean design philosophy has silently permeated all aspects of enterprise operations, becoming a significant force driving their transformation and upgrading. In the future, tailoring and adopting corresponding lean management methods based on the specific circumstances of enterprises (such as internal and external environments, market dynamics, production status, corporate cultural heritage, corporate values, and long-term goals) will be an important direction for research. This endeavor aims to assist enterprises in achieving more efficient, flexible, and sustainable development in the complex and ever-changing market environment.
4.2.3. Exploring Sustainable Lean
Lean manufacturing and green manufacturing are often discussed together in academic circles. Green manufacturing, as a modern manufacturing mode that takes into account both environmental impact and resource efficiency, focuses on maximizing resource utilization, minimizing environmental impact, and achieving synergistic optimization of economic and social benefits for enterprises throughout the entire product life cycle. In recent years, numerous scholars have proposed related concepts such as green lean manufacturing and sustainable lean manufacturing, encompassing areas like product carbon footprint management, clean production methods, and lean combustion technologies. In the future, when applying lean technologies, small and medium-sized discrete manufacturing enterprises will pay more attention to the perspective of environmental benefits, continuously deepening the connotation and expanding the scope of the “lean” concept, thereby promoting the emergence of more innovative theories and concepts.
4.2.4. Endogenous Technological Innovation
Digital technology provides a powerful impetus for discrete manufacturing enterprises to enhance quality and efficiency. Discrete manufacturing, as the cornerstone of the economic system, serves as the backbone supporting social development. Seizing the opportunity presented by the new round of technological revolution and industrial transformation, and vigorously developing digital technology, represents a strategic choice with a significant multiplier effect on economic growth. Therefore, small and medium-sized discrete manufacturing enterprises must accelerate their digital transformation; deeply explore the potential of new technologies; closely follow the trend of the digital economy; and continuously innovate and upgrade their products, technologies, and business models to inject new vitality into their sustained development.
On the journey of transformation, enterprises should focus on the key issues that urgently need to be addressed, striving to make breakthroughs in these areas. At the same time, they should promote the digital transformation of their core businesses, avoiding the blind pursuit of comprehensive and in-depth digitization, and instead emphasizing practical results and precise implementation. By optimizing resource allocation, enterprises can ensure that digital transformation effectively enhances their competitiveness and market position.
4.2.5. Activation of Human-Centered Values
Small and medium-sized discrete manufacturing enterprises need to address the digital talent gap through external recruitment, hiring on a contract basis, and internal training. On the one hand, they can implement a gradual talent introduction strategy, and on the other hand, they can flexibly “outsource” external digital talents based on the internal digitalization needs of the enterprise. It is essential to fully tap into employees’ potential, enhance their digital literacy, and attract interdisciplinary talents. In addition, emphasis should be placed on human–machine collaboration, which can significantly boost production efficiency, ensure product quality, safeguard employee safety, strengthen the enterprise’s competitiveness, enable rapid response to unexpected situations, achieve risk management and standardization, as well as facilitate flexible production.
4.2.6. Restructuring of Business Models
The rapid development of digital technology has shattered the boundaries of traditional industries, fostering a symbiotic digital ecosystem. In the face of profound structural changes in the digital economy era, the discrete manufacturing industry must place greater emphasis on business model innovation and pursue a path of transformation and upgrading, a consensus that has been reached within the academic community. The interplay between digital transformation and business model innovation has also emerged as a focal point of discussion among scholars. By leveraging technology, enterprises have enhanced their resource integration capabilities and gained acute market insights, enabling them to delve deeply into latent demands and thereby propel innovative activities in products and services, achieving precise bidirectional value transfer with the market.
Internally, the deep integration of digital technology with manufacturing processes has achieved value creation through cost reduction and efficiency enhancement. Externally, enterprises are building digital platforms to construct new value co-creation networks and implementing “disintermediation” profit-sharing mechanisms, continually expanding the scope of existing business models to achieve differentiated competition and lay a solid foundation for their long-term development.
5. Conclusions
Through an extensive literature review and in-depth analysis, we have identified the challenges for future research. Based on these findings, we draw the following conclusions: (1). We have determined that the focus of lean technology in discrete manufacturing industries currently lies in two main areas: digitization and methodological strategies. However, digital technology primarily relies on the integration of computer programs and existing information systems, with innovations mainly based on algorithm improvements targeting specific functions or deficiencies. On the other hand, there is currently a lack of systematic research on lean strategies in discrete manufacturing enterprises, and there are still gaps in laws and regulations related to lean manufacturing. (2). Currently, research on lean manufacturing technology primarily focuses on nine aspects: (a) exploring future trends of lean manufacturing in the context of the ecological environment; (b) developing innovative foundational lean models; (c) leveraging new technologies to develop novel information systems; (d) combining the use of various lean tools; (e) widely using simulation technology to explore the effects of lean processes; (f) applying lean thinking in manufacturing processes; (g) researching lean practices at the enterprise management level; (h) addressing human–machine collaboration and employee health issues in lean practices; (i) utilizing lean information systems. (3). Based on the literature data, in this study, we have constructed a framework for implementing lean technologies in small and medium-sized discrete manufacturing enterprises and proposed six strategies for their implementation. These strategies include investigating the dynamic mechanisms of lean management, exploring new paradigms of lean, conducting sustainable lean research, tapping into new technologies within the enterprises, focusing on human values, and integrating new business models.
6. Research Outlook
Based on the current research findings, we have high hopes for the future development of lean techniques in discrete manufacturing. With the continuous advancement of digitization and technological innovation, lean techniques will play an even more critical role in improving production efficiency, optimizing resource allocation, and enhancing enterprise competitiveness. At the research level, we anticipate seeing more studies on the systematic application of lean techniques in discrete manufacturing enterprises, accelerating the filling of institutional gaps in legal regulations and standard systems to provide compliance guarantees for technology implementation. Meanwhile, we predict that research on lean production technologies will continue to deepen, particularly in exploring future trends within the context of sustainable development, with a focus on human–machine collaborative innovation, environmental protection, and resource conservation and recycling. Additionally, we will pay attention to the specific implementation of lean techniques at the enterprise management level, addressing issues of human–machine collaboration and employee health, improving employee satisfaction and enterprise cohesion, leveraging lean information systems to enhance enterprise informatization levels, and supporting digital transformation. Finally, the six strategic implementation directions we have proposed will provide important references and guidance for small and medium-sized discrete manufacturing enterprises to adopt lean techniques, comprehensively supporting their lean transformation, jointly promoting the continuous innovation and application of lean techniques, and contributing to the sustainable development of discrete manufacturing.
X.Y.: Writing—review and editing, Writing—original draft, Methodology, Investigation, Formal analysis, Conceptualization. L.F.: Visualization, Software, Resources, Investigation. L.Z.: Writing—review and editing, Software, Formal analysis. J.L.: Supervision, Project administration, Funding acquisition. All authors have read and agreed to the published version of the manuscript.
Data will be made available on request.
We are very grateful to Lei Fu for methodology and collecting the data resources; Ling Zhu for processing the data in this paper; Jiufang Lv for her guidance, thesis project management, and funding; and, finally, the leaders and colleagues of the School of Home and Industrial Design, Nanjing Forestry University.
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
The following abbreviation is used in this manuscript:
| SME | Small or medium-sized enterprise |
Footnotes
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Figure 1 The literature screening methodology flowchart.
Figure 2 The annual publication and cumulative literature of the discrete manufacturing lean research literature 2003–2024.
Figure 3 Proportion of various types of the literature.
Figure 4 Research situation of the lean literature titles in the discrete manufacturing industry (word cloud and matrix diagram).
Figure 5 Confusion bubble chart of research content in the lean literature for the discrete manufacturing industry.
Figure 6 Clustering of research directions.
Figure 7 Institutional clustering diagram.
Figure 8 Lean tool clustering map.
Figure 9 The implementation steps of VSM.
Figure 10 Lean technology implementation framework for small and medium-sized discrete manufacturing industries.
Ranking and publication volume of the top six journals.
| Journal | Publication Volume Ranking |
|---|---|
| International Journal Of Lean Six Sigma | 32 |
| Journal Of Cleaner Production | 24 |
| Sustainability | 22 |
| Production Planning & Control | 13 |
| International Journal Of Production Research | 12 |
| Journal Of Manufacturing Technology Management | 11 |
The 5P Lean technology model.
| 5P | Tools, Techniques or Methodologies | Lean Six Sigma |
|---|---|---|
| Philosophy | Vision Statement, Mission Statement, World Class Manufacturing (WCM), 6R | TQM |
| Process | Value Stream Mapping, Takt Time, Pull System, Supermarket, Replenishment | DMAIC |
| People | Training Shop Floor Employees, Training Administrative Employees, Training | |
| Problem Solving | Continuous Improvement(Kaizen), Root Cause Analysis(Fish Bone Diagram), | DMADV |
| Product | Quality Source, Quality, Batch Reduction, Cell Manufacturing, Continuous | Pareto Chart |
The value stream mapping tool representative literature. (The references cited here and all the following tables are included in the
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 86 | Qingqi Liu | Fuzzy VSM | Overcoming Uncertainty |
| 93 | Ashkan Keykavoussi | VSM with Current State Mapping (CSM) and Future State Mapping (FSM) | Identifying Waste and Value |
| 362 | ChiaNan Wang | Kanban, VSM, Pareto Chart, Supplier Input Customer Output, Arena simulation | Improving processes |
| 138 | Apafaian Dumitrita Ioana | One Piece-Flow+VSM | Improve production performance |
| 146 | T Buser | VSM and Value Stream Analysis (VSA), a six-phase methodology | Controlling process efficiency |
| 212 | Fikile Poswa | Simulated Value Stream Mapping (SVSM) VSM, SQCDP (Safety, Quality, Delivery, Cost, and Productivity), Delphi method | Decision making |
| 292 | Hongying Shan | Dynamic value stream mapping system | Increased capacity |
| 301 | MB Kumar | Future-state VSM, fuzzy AHP | Increase efficiency |
| 3 | P Solding | Simulation dynamic VSM of systems | Analysing complex systems |
| 248 | Zhuoyu Huang | Dynamic Value Stream Mapping (DVSM) | Understand production processes |
| 67 | Timo Busert | Combined ERP systems | Calculate RL and TQs |
| 5 | Quan Yu | LCA+Discrete Event Simulation (DES) = SMM flowcharts | Improve communication efficiency |
| 66 | Emad Alzubi | Ombined VSM with computer software | Handling distributed systems |
| 245 | William de Paula Ferreira | HS-VSMframework | Process Improvement |
| 188 | Yangguang Lu | framework based on DEVS+Flexible Simulation (FS)by Internet of Things (IoT) | Increase efficiency |
The Kanban tools represent literature.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 21 | W Su | based on RFID | Management information |
| 22 | C Karrer | Enterprise Resource Planning (ERP) | Provide kanban systems |
| 57 | Daryl J. | Engineer-To-Order (ETO) manufacturing, kanban | Use in high-mix, low-volume environments |
| 303 | Massimo Bertolini | “scrumban” framework | simplifies order management |
The supply chain representative literature.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 94 | Gunjan Yadav | Sustainable Supply Chain Management (SSCM) | Supply Chain Management |
| 228 | Fazal Hussain Awan | Green Supply Chain Management (GSCM) | Examining the mediating role of performance and management |
| 285 | Gonzalo Maldonado-Guzmán | green supply chains (GSC) | Impact of operational performance |
| 119 | Assadej Vanichchinchai | (LM) and supply chain relationships (SCR) | Analyzed differences |
| 272 | B Abdelilah | Structural equation modeling | Proven agile supply chain capability |
The DMAIC and DMADV tool representative literature.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 141 | Narottam | Applied DMAIC | Enhance profitability and bottom-line results |
| 124 | P Sivaraman | DMAIC methodology | Improve engines |
| 144 | A Baptista | DMADV | Improve mass production of piping systems |
| 200 | Krishna Priya | DMAIC with tools such as RCA (Root Cause Analysis) and fishbone diagrams | Address assembly issues |
| 305 | Ali Ahmed | DMAIC with three hybrid simulation paradigms (SD, DES, and AB) | Simulate existing real-factory environments |
| 83 | Fatima Zahra Ben Moussa | DMAIC with TRIZ | Design lean warehousing methods |
The representative literature on the whole life cycle perspective.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 331 | Rogério Lopes | LeanDfX methodology | model optimisation |
| 34 | Raul Garcia-Lozano | Designs methods for detachability, multifunctionality, dematerialisation, increasing materials from renewable sources and recycled materials | Proposed Strategy |
| 5 | M Paju | SMM framework based on life cycle assessment, sustainable design and sustainable production management. | Proposed Strategy |
| 164 | K Mathiyazhagan | Practices of Indian industrial leaders through the lens of sustainability. | Improving Efficiency |
| 208 | M Schutzbach | A sustainable management system. | Improving Efficiency |
| 270 | R Henao | A ‘Hourglass’ model and the second is a ‘trade-off’ approach. | Improving Efficiency |
| 296 | Benedictus Rahardjo | Smart and Sustainable Manufacturing System (SSMS). | Improving Efficiency |
| 320 | Amirkeyvan Ghazvinian | Lean, Agile, Resilient, Green and Sustainable (LARGS) paradigm. | Integration of approach to vender sselection |
The representative literature on deriving basic lean models.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 200 | J L García-Alcaraz | Second-order structural equation model | Analyzing the continuous flow |
| 299 | S Narula employed | The Best-Worst Method (BWM) | Mapping of priorities |
| 216 | M R Galankashi | A multi-objective mathematical model | Optimisation of production schedules |
| 265 | A Saha | Fermate Fuzzy Sets (FF), Delphi, a double normalized MARCOS method based on FF | Optimal Warehouse Location |
| 281 | D Mendes | Model for Sustainable Operational Maintenance Management (MMSO) | Enhances the effectiveness of management |
| 315 | D Ramesh Kumar | SENIM model | Eliminate non-value-added activities |
| 316 | Tasnim | IDEF0 (Integrated DEFinition method for Function and Organization modeling) | Address sustainability issues in SMEs |
| 328 | W A Chitiva Enciso | Hesitant Fuzzy Linguistic Term Sets, AHP, Multi-Criteria Decision Making (MCDM) | Assessing lean manufacturing performance |
| 98 | Sl Kumar D | Total Interpretive Structural Modeling (TISM) method | Facilitat the adoption of lean concepts |
| 359 | T Tantanawat | DES, Standardized Work Sequence Diagrams (SWSD), 4M (B4M) visual tools | Establish standardized work |
| 23 | A Bai | Model for Numerical Control (NC) job shops | Lean production implementation |
| 348 | C Yu Lin | Technology-Organization-Environment (TOE) model | Explore the influences of LM |
| 236 | R Wu | Based on consumer and risk assessment models | A decision-making model (EPDF) |
| 139 | AP Velasco Acosta | A Demand-Driven Material Requirements Planning (DDMRP) model | planning and execution purposes |
| 78 | J M. Müller | The SCOR model | Assessment of quality management |
| 82 | P Cocca | Multi-criteria methods into lean assessment models | Evaluating effectiveness |
| 126 | A Boutayeb | Outlining the conflict between technical and social/organisational objectives | Assessment organizations |
The representative literature on decision-making systems.
| No | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 19 | Xiaoying Yang | The Augmented Lagrangian Relaxation method and heuristic algorithms | Solve the combinatorial optimization problem with nonlinear inequality constraints |
| 69 | Eduard Shevtshenko | Using the VAC/EPC representation | Sustainable partner selection mechanism |
| 71 | Xinbao Liu | A discrete-time Markov decision process | Enhance the profitability of product-service systems |
| 129 | TIto | Decision Support System (DSS) framework | Implemented in lean manufacturing for parts assembly |
| 167 | A Mendes | A Decision Support System | Help organizations identify waste |
| 267 | E Santos | Using Excel Microsoft 365 and base on a Many-Objective Approach to Stock Optimization in Multi-Storage Supply Chains | Improve inventory control |
| 275 | S S Khan | A Knowledge-Based System (KBS) | Make a decision |
The representative literature on technical systems.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 12 | J. Michaloski | Using MTConnect and some extended functionalities | Developed a prototype system for automated DES |
| 51 | Daria Battini | A system for modeling lean part feed systems | Increased efficiency in the use of parts |
| 70 | Matthew Goh | Offsite manufacturing (OSM) techniques | Improved efficiency of field operations |
| 367 | Vinod Ramakrishnan | Forecasting techniques based on artificial neural networks | Forecasting future demand |
| 366 | Ö Dönmez | The Automated Valet Parking System (AVPS) | Creating new function |
| 191 | D Mezzogori | Workload Control (WLC) | Reduced queuing and waiting times |
| 197 | MM Abagiu | A novel Automatic Defect Detection System (ADDS) | Creating new function |
The representative literature on monitoring systems.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 107 | R Sanchez Marquez | Multivariate SPC methods based on partial least squares regression | theoretical comparison |
| 114 | Amir Hejazi | A performance measurement model to quantify the effects | Effects of implementing lean |
| 297 | Jonny Herwan | Bayesian Optimization (BO) | A hybrid monitoring and optimization process |
| 295 | Fansen Kong | A unified information field analysis model | Estimate operators’ cognitive load |
The representative literature on management systems.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 97 | Diamantino Torres | through intelligent technology and the functionality of the Digital Shop Floor (DSF) | Analyse workshop management efficiency |
| 102 | Flávio Gaspar | Shop Floor Management (SFM) | Testing Utility |
| 64 | Yi-Shan Liu | Muther’s systematic layout planning procedure, combined with the principles of continuous flow | generate alternative designs for unit layout |
The representative literature on distribution scheduling system.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 11 | R. Logendran | Developed three tabu search-based algorithms | Address the two-machine group scheduling problem |
| 37 | Bo Xin | An A-BPSO algorithm | Balance the workload |
| 133 | L. Li | A production material allocation method | Achieves precise matching of manufacturing and material resources |
| 155 | L. Shi | A Dynamic Scheduling Unit (DSU) with a Multi-Agent System (MAS) | Develop a sustainable hybrid flow shop |
| 356 | Guiliang Gong | An Improved Memetic Algorithm (IMA) | Solve the Flexible Job Shop Scheduling Problem |
| 287 | C. Singhtaun | The branch and cut algorithm from the COIN-OR CBC library | Improve production line balancing efficiency |
| 310 | Laxmi Narayan Pattanaik | A hybrid model of machine cells, using the NSGA-II metaheuristic approach | minimize inter-cell part movements |
The representative literature on production system.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 153 | Neven Hadžić | Utilizing finite state methods and Markov modeling | Improving and designing lean production |
| 54 | Izvorni znanstveni članak | A lean production control system based on the Glenday sieve, artificial neural networks, and simulation modeling | Effectively planning and executing production schedules |
| 325 | C. Saavedra Sueldo | Metaheuristic simulations | Handling issues in dynamic environments. |
| 274 | O. Ateş | Utilizing the fuzzy information axiom and the weighted fuzzy information axiom | Identified the most efficient unit feeding method |
| 63 | V.G. Cannas | A production planning method for order-oriented (ETO) environments is proposed | Improve efficiency |
| 291 | Daniel Medyński | developed the e-Lean system based on Total Productive Maintenance (TPM) software | Digitising tools |
The representative literature on evaluation systems.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 60 | G. Ante | A tree structure of Key Performance Indicators (KPIs) | Describing the Performance Measurement System (PMS) |
| 157 | M. Elnadi | Developed an initial model to evaluate the lean attributes of PSS | Measure the lean level of Product Service Systems (PSS) |
| 172 | Ana Cornelia Gavriluţă | The Job Observation method | Assess the performance of production systems |
| 192 | A. Hayashi | Established a Continuous Integration (CI) documentation system | Be used to evaluate inventory management |
| 258 | A. Wu | Extracted characteristic factors from the product manufacturing process | An evaluation model for excellent process levels |
| 294 | M.B. Baskir | Combines Bayesian models with QFD-AHP in the Interval Type-2 Fuzzy (IT2F) environment | Eliminating ambiguities in lean decision-making |
| 340 | Funlade Sunmola | Adopted the SCOR model | Evaluate the lean level |
| 352 | Sonu Rajak | The Grey Decision-Making Trial and Evaluation Laboratory (DEMATEL) method | Discover the influence of each obstacle on others |
| 354 | M. Katsigiannis | Using hybrid simulations | Evaluate the impact of Lean Manufacturing (LM) on production facilities |
| 48 | Victor Emmanuel de Oliveira Gomes | The MAPS (Modeling to Assist in Process improvement through Simulation) method, | Avoids errors in estimating improvement benefits |
| 22 | Nancy Diaz-Elsayed | Discrete-Event Simulation(DES) | Evaluate lean and green strategies |
The representative literature on combined lean tools.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 234 | Jalal Possik | Poka Yoke and 5S | Modelling of industrial environments |
| 233 | B Durakovic | Approach to three operations research techniques (process planning, line balancing and equipment selection) | Awarded ‘Best Lean’ |
| 83 | Fatima Zahra Ben Moussa | Based on lean warehousing methods, following the DMAIC methodology and the Algorithmic Resolution of Innovative Problems (ARIZ). | Addressed warehousing issues |
| 125 | G Yadav | Fuzzy Analytic Hierarchy Process (FAHP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) | Carry out decision-making |
| 105 | ML Junior | Using OEE as a comparative indicator, combined with technologies such as AGVs | Reflects improvements |
| 246 | Varun Tripathi | Integrates VSM, TPM, IOT, CI, FL, AI, ATS, CPS | Enhanced production for Industry 4.0 |
The representative literature on the discrete event simulation.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 56 | J. Michaloski | A finite-state model to simplify the integration of machine tools and DES | Helps machine tools to distribute parts |
| 44 | Sebastian Greinacher | Based on simulation (DES) and Design of Experiments (DoE) | Determine suitable improvement strategies |
| 272 | Jongsawas Chongwatpol | Discrete event simulation and incorporated RFID | Reduce waste |
| 80 | Omogbai Oleghe | Hybrid system dynamics-discrete event simulation modeling | Increase efficiency |
| 87 | Ivan Arturo Renteria-Marquez | Discrete event simulation software | Increase efficiency |
| 65 | Aleksandr Korchagin | ARENA software (based on DES modeling) | Demonstrate the efficiency of lean practices |
| 284 | Sinem Buyuksaatci Kiris | Use of simulation and Data Envelopment Analysis (DEA) | Address multi-objective decision-making problems |
| 79 | David Grube | Use of physical object connections embedded on Digital Twin Modules (DTM) | Implementation of discrete event simulation |
| 327 | Yuxi Wei | Discrete Event Simulation (DES) and Agent-Based Modeling (ABM) methods | Compare the planning outcomes of offsite construction |
The representative literature on application of lean thinking.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 219 | Aries Susanty | Questionnaires with the SmartPLS software | ascertain the impact on operational performance and business performance |
| 224 | V Saddikutti harmonized | Demand-driven production by dynamically integrating lean tools | Achieve lean outcomes |
| 196 | NM Bastos | Assembly line using lean thinking principles | Reconfigured an electronic component |
| 233 | D Ramesh Kumar | 50 non-value-added activities and 27 lean manufacturing strategies were collected from the field of literature through critical thinking | Influencing Elements of a Lean Strategy |
| 307 | D Bianco | Lean organisations have a culture of problem solving and innovation that can be sustained in times of business crisis | The Importance of a Lean Culture |
| 171 | Thomas Schmitt | Design for Modularity (DfM), improving the standardisation of parts | Helped reduce assembly time and costs |
| 198 | Costel-Ciprian Raicu | A hybrid strategy approach based on Lean, Scrum, Function Driven Development and VDI, and a canvas-type model | Rapid delivery of headlamps |
| 204 | Kaustav Kundu | WLC’s approach to implementing lean technologies in an MTO-MTS environment | Increase efficiency |
| 66 | Lluís Cuatrecasas-Arbós | Close workstation layouts, further batch size reduction, job analysis, shortening the process, and keeping the new process fluid | Proposes lean strategies |
| 364 | RA Sasso | Integrates Circular Economy (CE) and Lean Management (LM) | Responding positively to globalisation |
The representative literature on change management.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 189 | Salah Ahmed Mohamed Almoslehy | Combining Lean and Agile design paradigms | A methodology for effective risk management |
| 244 | M Amejwal | Production process management (PFM) | The implementation of smart shop floor management methods |
| 202 | Varun Tripathi | An orthogonal array for smart shop floor management | Production sustainability and constraints. |
| 318 | Ewa Skorupińska | Concurrent Engineering (CE), Total Quality Management (TQM), Statistical Process Control (SPC), Quality Function Deployment (QFD), and Failure Mode and Effects Analysis (FMEA) | Presented a range of quality management methods |
| 59 | Satie Ledoux Takeda Berger | Using computational simulation to model four different strategies | Providing a management strategy research methodology |
| 321 | Varun Tripathi | Using Lean, Green and Smart Manufacturing concepts | To improve sustainability of shop floor operations management |
| 232 | Varun Tripathi | Using Lean and Smart Manufacturing in Industry 4.0 | A cleaner production management |
The representative literature on strengthening human–machine collaboration.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 239 | Aditya Kumar Sahu | Behavioural Reasoning Theory (BRT) | LMP implementation |
| 240 | Amal Benkarim | Found seven HRM practices (i.e., job security, communication, fairness, supervisor/manager support, training, occupational health and safety, and respect) | Solving the difficulty of integrating CPS with Lean Tools |
| 73 | V.L.Bittencourt’s | Integrate the human factor with existing models | Presenting a point of view |
| 130 | D Andronas | a hybrid workstation design approach | safe and efficient human-machine collaboration |
| 262 | A Assuncao | biomechanical risk factors (EAWS) and proposed a scheme for designing work rotation plans based on genetic algorithms | Solving the Human-Machine Collaboration Problem |
| 131 | M Pantano | proposed a conceptual architecture for human-robot collaboration | evaluated the three design elements of human-cyber-physical systems |
| 117 | Alexander Kurt Moldner | multiple linear regression modelling analysis techniques | verified the impact |
| 361 | Weibing Zhong | a deep learning-based pose recognition framework | enhanced user engagement and personalised experience |
The representative literature on utilization of emerging software technologies.
| No. | Author | Tools, Techniques, or Methodologies | Goal |
|---|---|---|---|
| 206 | A Moussa | using radar observation for real-time processing of average processing units | a tailored lean detection strategy |
| 266 | J Mendes Monteiro | an action research strategy | train employees using virtual reality |
| 58 | Jorge González-Reséndiz | VSM-based modelling and analysis of discrete processes using ARENA software | model validation |
| 88 | J Yudhatama | using LINGO 17.0 software | analyse waste and reduce production time |
| 156 | Miriam Pekarcikova | uses simulation software Tx Plant Simulation | create simulation models |
| 175 | S. Vijay | simulation software ARENA | standardise processes |
| 201 | Ryan Pereira | 3D scanning technology (3DS) for collaboration | visualisation and product analysis |
| 302 | Chun-Ho Wu | a MySQL-based data-driven framework | reduce defect rates |
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