1 Introduction

The manufacturing industry is undergoing a profound shift propelled by several megatrends, broadly categorized into three major themes: mass personalization, sustainable production, and urbanization. These megatrends encompass various factors such as individualization, climate change, emissions, and resource scarcity. Urban production has emerged as a popular response to these trends, providing multiple advantages such as reduced lead times, decreased costs, increased customization by involving customers in product and service design, reduced transportation emissions, and improved integration of work and home life. Nevertheless, there remain several challenges to be tackled, including logistics, environmental impact, and spatial limitations. Addressing these obstacles will be critical to realizing the full potential of urban production [1,2,3].

In today’s complex and rapidly changing environment, conventional operations research methods may not be sufficient to address the growing challenges of sustainability and personalization. However, scholars have recognized that Industry 4.0 and the digitization of the industrial sector offer promising opportunities for sustainable development. By embracing cutting-edge technologies such as the Internet of Things (IoT), big data analytics, and artificial intelligence, we can achieve economic, environmental, and social sustainability. By integrating these technologies, we can optimize resource utilization, reduce energy consumption, and improve the quality of life for people. Therefore, Industry 4.0 presents a potential solution to sustainability and personalization challenges, as it offers new tools and methods for addressing these issues [4].

Smart manufacturing is a prime example of manufacturing technology that has undergone significant evolution to address the growing diversity of customer needs and maximize the degree of personalization. The emergence of Industry 4.0 has further facilitated the attainment of product personalization, making it increasingly feasible to meet customers' unique demands has brought about a range of technological advancements that present significant opportunities. Among these opportunities are product individualization, improved customer experience, and enhanced resource and energy efficiency [5]. By leveraging Industry 4.0 technologies, organizations can tailor their products to meet the specific needs of individual customers, delivering an enhanced level of personalization. Moreover, these technologies can help organizations gain insights into their customers’ preferences and behaviours, enabling them to provide a superior customer experience. Finally, Industry 4.0 technologies can also facilitate efficient use of resources and energy, reducing waste and minimizing environmental impact [6]. Ching et al. conducted a literature review and concluded that Industry 4.0 has a significant impact on sustainable manufacturing, with 15 highly interconnected functions playing a critical role. These functions encompass a wide range of areas, including business model innovation, customer-oriented manufacturing, employee productivity, manufacturing agility, manufacturing productivity and efficiency, new employment opportunities, resource and energy efficiency, reduced manufacturing costs, and the provision of a safe and smart working environment. By focusing on these interconnected functions, organizations can leverage Industry 4.0 to achieve sustainable manufacturing, reducing environmental impact and improving social responsibility while enhancing overall business performance [7].

Integrating the principles and technologies of smart factory (SF) is a critical measure towards unlocking the production potential in urban areas while addressing the associated challenges. This concept is commonly known as urban smart factory (USF), which prioritizes Human-centricity, Sustainability, and Resilience. The integration of SF principles and technologies enables the USF to achieve these essential objectives. By focusing on human-centricity, the USF ensures that human factors, such as safety, comfort, and efficiency, are considered in the factory design and operations. This approach fosters a positive working environment for the workforce, leading to increased productivity and reduced employee turnover. Additionally, the USF emphasizes sustainability, considering the environmental impact of the factory operations, including the use of renewable energy sources and waste minimization. Furthermore, the USF prioritizes resilience, enabling it to withstand disruptions such as natural disasters, pandemics, or cyber-attacks. By implementing advanced technologies such as Internet of Things (IoT), digital twin, and artificial intelligence (AI) the USF can quickly detect and respond to disruptions, minimizing downtime and loss of revenue [8].

To achieve the goals of manufacturing systems, it is crucial to utilize maturity assessment models that can evaluate progress through iterative improvements and facilitate the attainment of higher levels of maturity. Numerous maturity models have been developed by academics, research institutions, and consulting firms to evaluate the implementation of smart manufacturing and Industry 4.0. Upon conducting a literature review, it has been identified that there is a pressing need for an assessment model that can appraise the maturity and preparedness of implementing smart manufacturing principles and technologies in urban factories. Thus, we propose a maturity assessment model for USF that evaluates the implementation maturity of SF technologies and principles in USF attributes derived from its significant characteristics and disciplines. This study contributes to the growing body of research on Industry 4.0 maturity models, providing a tailored approach for evaluating the maturity level of USF. By utilizing our proposed model, stakeholders in the USF ecosystem can better understand their current level of maturity and develop targeted improvement strategies to enhance their smart manufacturing capabilities. While the implications of our research may extend to various regions, it is important to note that our study does not specifically target regional assessments.

The structure of this paper is as follows: firstly, it presents an overview of the current debates on urban production, sustainable and smart manufacturing, and urban smart factories. Secondly, it outlines the specific attributes of urban smart factories, derived from the characteristics of USF and urban factory resources. Thirdly, a comprehensive review of existing Industry 4.0 and smart factory maturity models is conducted to highlight the need for a dedicated maturity assessment model for USF. Subsequently, the paper details the development of the proposed USF maturity assessment model. To validate the effectiveness and suitability of the proposed model, two case studies are presented. Finally, the paper concludes by summarizing the academic contributions made, reflecting on research limitations, and providing recommendations for future research directions.

2 Literature Review

2.1 Urban Production

Previous studies have shed light on the significant impact of demographic changes, sustainability, and individualization on the manufacturing industry [9,10,11,12]. Demographic changes encompass urbanization, population growth, demographic aging, and age distribution. Urbanization is a key driver of demographic changes, with projections indicating that more than 60 percent of the global population will reside in urban areas by 2050 [13]. This shift is expected to have profound implications for the manufacturing sector, including changes in demand, shifts in the labor force, and the need for new technologies and infrastructure.

The advantages of urban production have been discussed in the literature, including improved delivery times, reduced costs, increased personalization, shorter lead times, reduced transportation emissions, and the integration of working and living spaces [1,2,3]. Urban production is particularly well-suited for small or low-material products, customized production, and industries requiring a high degree of flexibility [11]. However, challenges such as spatial constraints, environmental impacts, logistics, and transportation must also be considered to ensure the success of urban production.

The concept of the Factory-City-System highlights the interdependence between cities and factories in urban production [14, 15]. This concept identifies eight key resources, referred to as urban factory resources, which are shared by both cities and factories: Energy, Materials, Human, Space, Knowledge, Mobility, Image & Appearance, and Justice & Culture. These resources play a critical role in the success of urban production as every activity in the process directly affects or depends on them.

By understanding the mutual impacts of cities and factories through the utilization of these urban factory resources, the complex dynamics of urban production become clearer. The literature emphasizes that urban production offers advantages such as proximity to customers, business partners, and suppliers, as well as access to a larger and more diverse market [1,2,3]. However, it is important to recognize that not all products and industries are suitable for urban production, and challenges such as spatial constraints, environmental considerations, logistics, and transportation need to be carefully addressed.

In summary, previous studies have emphasized the significance of demographic changes, sustainability, and individualization in shaping the manufacturing industry. Urban production offers benefits in terms of improved efficiency, reduced costs, and integration of living and working spaces. However, the suitability of urban production depends on factors such as product type and industry, and challenges related to spatial constraints, environmental impacts, logistics, and transportation must be considered. Understanding the interdependence of cities and factories through the utilization of urban factory resources provides insights into the complexities of urban production.

2.2 Sustainable Manufacturing

Previous studies have highlighted the concept of sustainable development to address current needs while ensuring the ability of future generations to meet their own needs [16]. Sustainable industrialization and responsible production are integral parts of the global sustainable development goals (SDGs), with urban production playing a crucial role in achieving several of these goals. Urban production contributes significantly to SDG 8 (decent work and economic growth), SDG 9 (industry, innovation, and infrastructure), and SDG 12 (responsible consumption and production), while making a noteworthy contribution to SDG 7 (affordable and clean energy) and SDG 11 (sustainable cities and communities) [17]. These findings underscore the importance of effective implementation of urban production in promoting sustainable development.

Sustainable manufacturing aims to incorporate manufacturing processes and systems that produce high-quality products and services while efficiently using resources (energy and materials), ensuring the safety of stakeholders, and minimizing negative environmental and social impacts throughout their lifecycles [18]. The concept of sustainability is often approached through the triple bottom line (TBL) perspective, which involves balancing profit, planet, and people by considering economic, social, and environmental factors [19, 20]. Achieving sustainability in manufacturing requires a balance among these three factors to ensure long-term sustainability [21].

Sustainable manufacturing encompasses three levels of focus: products, processes, and systems. At the product level, sustainable manufacturing adopts approaches such as the 6Rs (reduce, reuse, recycle, recover, redesign, remanufacture) or the 3Rs (reduce, reuse, recycle) to maximize product value and minimize waste. At the process level, sustainable manufacturing aims to lower energy consumption, reduce waste, improve product shelf life, eliminate health hazards, and enhance manufacturing quality through recycling, reuse, and remanufacturing. A sustainable manufacturing system consists of three essential components: information, management, and culture/procedures [22]. These components are critical for ensuring the sustainability of manufacturing processes and systems by incorporating environmental, social, and economic considerations.

In summary, previous studies have emphasized the importance of sustainable development and its connection to urban production. Urban production contributes significantly to various SDGs, highlighting its role in promoting sustainable development. Sustainable manufacturing strives to achieve a balance among economic, social, and environmental factors, considering the triple bottom line perspective. It addresses sustainability at the product, process, and system levels, aiming to maximize product value, minimize waste, and incorporate environmental, social, and economic considerations. The incorporation of information, management, and culture/procedures is crucial for achieving sustainability in manufacturing processes and systems.

2.3 Smart Manufacturing

Previous studies have extensively examined Industry 4.0, a manufacturing model emphasizing personalization, digitalization, and real-time interaction. However, a comprehensive analysis of the findings and outcomes from the literature reveals several key insights. The integration of the cloud, Internet of Things (IoT), Internet of Services (IoS), and smart factories as the four key components of Industry 4.0 enables seamless connectivity and cyber-physical integration [23, 24]. These components adhere to six principal design principles, including interoperability, virtualization, decentralization, real-time capability, modularity, and service orientation. Notably, IoT and IoS play vital roles in establishing cyber-physical systems, facilitating communication between these systems and individuals. Leveraging data collected through sensors, virtual plant models and simulations are developed to enhance decision-making and optimize manufacturing processes. Moreover, the design principles of smart factories promote flexibility, efficiency, and cost reduction [25,26,27].

The literature on Industry 4.0 provides a comprehensive understanding of this manufacturing paradigm. It encompasses the concept of smart factories (SFs), which incorporate various pillars of Industry 4.0, such as additive manufacturing, augmented reality, IoT, big data analytics, autonomous robots, simulation, cyber-security, vertical and horizontal integration, and cloud computing [28]. SFs are designed to operate autonomously and dynamically, leveraging real-time data and advanced technologies to optimize production processes, reduce costs, and enhance product quality. By integrating these technologies, SFs establish efficient communication and collaboration among machines, products, and individuals, fostering a highly interconnected and adaptive production environment. Additionally, the literature emphasizes the critical role of flexibility in achieving competitive advantage in manufacturing. Various dimensions of flexibility, including product, process, collaboration, supply chain, and strategy, contribute to the adaptability of manufacturing systems [29]. Industry 4.0, driven by the integration of advanced technologies such as autonomous robotics, simulation, IoT, cybersecurity, cloud computing, additive manufacturing, augmented reality, big data, and analytics, paves the way for the development of cyber-physical systems. These systems, capable of real-time communication and interaction, form the foundation for the future of smart factories. Notably, the emergence of digital twin technology, where model updating occurs based on real-time information and simultaneous knowledge feedback exists, holds significant potential in revolutionizing not only manufacturing but also urban planning and management [30,31,32].

2.4 Urban Smart Factory

Previous studies have explored the integration of Industry 4.0 and urban production, shedding light on the transformative effects on manufacturing paradigms. This integration is transforming the business models, manufacturing processes, and product development by facilitating product co-creation and enabling factories to interact with the surrounding urban environment. As a result, factories are becoming more responsive to the needs of their customers and the local community, while also taking advantage of the benefits that come with urbanization, such as access to infrastructure, talent, and resources [33, 34]. The circular problem-solving approach of smart urban production underscores the importance of spatial proximity to customers, even with the availability of digital communication tools. As urban production factories place a growing emphasis on problem-solving, their need to locate near pools of skilled workers, including IT experts, becomes more pronounced. Additionally, the proximity of urban production factories to knowledge institutions enhances their ability to deliver tailor-made solutions to customers, a capacity that cannot be fully replicated using digital means [35].

Urban Smart Factory (USF) prioritizes achieving product and service personalization, improving employee well-being, collaborating with local communities, promoting sustainability, and strengthening resilience using smart manufacturing technologies [8]. The literature provides a comprehensive understanding of the integration between Industry 4.0 and urban production, highlighting key aspects and interconnections. Personalization emerges as a vital factor in enhancing customer experience and satisfaction. Focused on meeting evolving customer needs, requires optimizing the trade-off between cost, variety, and quantity. Collaborative design processes involving customers from the outset and incorporating their feedback result in superior products, customer satisfaction, and loyalty. The optimization of design interfaces and modular product development are crucial for achieving maximum levels of personalization [33, 35,36,37]. The influence of customers on product development has created a lack of transparency throughout the manufacturing value chain [38], presenting significant challenges for manufacturers. To address this issue, manufacturers must first develop cyber-physical systems that ensure design manufacturability. This step is critical for achieving the optimal trade-off between quality, cost, and lead time in the new product design process. By leveraging these systems, manufacturers can more effectively incorporate customer feedback while ensuring efficient and effective product development. The concept of mass personalization can be effectively implemented through an open architecture platform that features three distinct modules: common, customized, and personalized. This is achieved by employing product modularity which allows for the creation of tailored solutions for each customer. To meet market demands, personalized and customized modules with lower usage rates can be produced on-demand, while common modules and customized modules that are frequently used should be mass-produced to ensure efficient production and delivery. Therefore, the use of an open architecture platform and product modularity can enable manufacturers to offer personalized products and services to customers while maintaining cost-effective production processes [39,40,41].

Sustainability plays a significant role in manufacturing processes, necessitating the adoption of circular economy principles to reduce waste and minimize environmental impact. Communicating sustainability efforts to customers raises awareness and encourages sustainable purchasing decisions. A circular economy is characterized by a closed-loop system that utilizes circular processes, such as reusing, refurbishing, remanufacturing, and recycling, to convert waste into resources [1].

The integration of urban smart factories into smart cities can significantly contribute to reducing income inequality. As cities become smarter and employ innovative solutions, there is a correlation with lower levels of income inequality. Moreover, individuals with lower incomes have a stronger preference for reducing income inequality, which reinforces the impact of a city’s smartness on reducing inequality [42]. Resilience is a critical aspect of USF that enables them to anticipate and adapt to changes caused by both internal and external disruptions. To foster USF resilience, it is crucial to have a thorough understanding of the current situation and analyse past occurrences. Establishing metrics for comparison and maintaining flexibility and adaptability are also important factors in enhancing resilience [8, 43, 44].

3 Methodology

To enhance the effectiveness of smart manufacturing technologies and key principles in urban smart factories (USFs) towards achieving sustainable outcomes, a well-defined maturity assessment model was developed in this study. The model aimed to accurately evaluate the maturity of smart manufacturing technologies and key principles in USFs based on their primary objectives, such as human-centricity, sustainability, and resilience. The proposed maturity assessment model classified the maturity level into four categories: outstanding, developed, piloted, and beginner. These categories were determined based on the average scores achieved in the assessment. The dimensions and categories of the model were derived from the detailed attributes of urban factory resources and USF characteristics, ensuring relevance and specificity to the context. To establish the assessment criteria, a thorough literature review of smart manufacturing principles and technologies was conducted. This step ensured that the assessment criteria captured the essential elements required for evaluating the level of maturity in smart manufacturing practices. In order to validate the effectiveness of the proposed model, a comparison was made with existing Industry 4.0 maturity models to assess their suitability for evaluating USFs. Additionally, two case studies were presented to demonstrate the applicability and appropriateness of the proposed model in assessing USFs. The methodology employed in this study provided a comprehensive and objective approach for evaluating the maturity of smart manufacturing technologies and key principles in USFs. This enabled stakeholders to identify areas for improvement and formulate targeted improvement strategies, ultimately enhancing the sustainability and resilience of USFs.

4 Maturity Assessment Model

4.1 Detailed Attributes of USF

In this section, we present an analysis of the attributes of the USF, which can be derived from its core characteristics such as human-centricity, sustainability, and resilience, as well as its associated disciplines and urban factory resources. The sustainability and resilience-associated attributes of urban smart factories are derived from the characteristics of USF and the urban factory resources identified by the concept of the Factory-City-System [14, 15]. A total of fifty-two attributes were identified and categorized, each of which plays a significant role in shaping the USF. In the following sections, we provide a detailed overview of each attribute, highlighting its importance and potential impact on the USF.

4.2 Human-Centricity

A human-centric approach is integral to the Urban Smart Factory (USF), prioritizing employee well-being, customer satisfaction, and fostering a symbiotic relationship with local communities and citizens. To provide a comprehensive understanding of the purpose and importance of human-centricity, we have referenced relevant literature, identified research gaps, and highlighted the direction for future research. The core characteristics of human-centricity in the USF, including employees, customers, and citizens, have been extensively explored in the literature [8]. However, there is a need for further research to investigate the specific strategies and frameworks for implementing human-centric practices within the USF context. Addressing these gaps contributes to enhancing employee satisfaction, customer experience, and community engagement in the USF. In order to achieve customer satisfaction, the USF must focus on three key attributes: personalized marketing for both existing and potential customers, personalized product co-creation involving both design and manufacture, and personalized service delivery. Furthermore, a symbiotic relationship with the local community and citizens is critical for the USF's success, and can be fostered through complementary infrastructure services, community development activities, and open innovation initiatives. By prioritizing these attributes, the USF can create a positive impact on society while also promoting sustainable practices and economic growth. Table 1 provides an overview of the three disciplines that comprise human-centricity in the context of the USF.

Table 1 Detailed attributes of USF associated with the human-centricity [39]
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4.3 Sustainability

Sustainability is a crucial aspect of the USF, encompassing the three key dimensions of environment, society, and economy. In presenting the core attributes of sustainability, we have referred to relevant literature sources and highlighted the gaps and research directions in this area. The environmental dimension focuses on energy and resource efficiency, waste reduction, and pollution mitigation. The social dimension emphasizes employee well-being, community engagement, and social responsibility. The economic dimension relates to the optimization of manufacturing profitability and the development of sustainable business models [8, 19,20,21,22]. Table 2 provides a summary of the three key disciplines that comprise the concept of sustainability within the context of the USF: environment (planet), society (people), and economy (profit).

Table 2 Detailed attributes of USF associated with the sustainability [39]
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4.4 Resilience

The concept of resilience is critical for the USF to effectively respond to internal and external disruptions. We have derived the attributes associated with internal and external disruptions in the USF, considering the characteristics of USF and the urban factory resources identified by the concept of the Factory-City-System [14, 15]. Table 3 outlines the seven attributes associated with internal disruption and the eight attributes associated with external disruption. By addressing these attributes, the USF can develop a resilient system that can withstand unexpected challenges and continue to operate effectively, ultimately leading to a sustainable and profitable model for the USF.

Table 3 Detailed attributes of USF associated with the resilience [39]
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4.5 Characteristics, Disciplines, and Attributes of USF

Table 4 summarizes the 52 attributes that were derived from the USF's core characteristics, associated disciplines, and urban factory resources identified by the concept of the Factory-City-System [14, 15]. The utilization and implementation of SF technologies is a critical enabler for achieving the USF's objectives, which include product and service personalization, employee well-being, collaboration with local communities, sustainability, and resilience. In order to evaluate the maturity of the USF, it is crucial to assess the deployment of key SF technologies and principles across its various attributes to achieve its goals and optimize its features. In order to develop a comprehensive maturity assessment model, it is important to investigate existing models and frameworks that can guide the assessment process. This topic will be further explored in the following session.

Table 4 Characteristics, disciplines, and detailed attributes of USF [39]
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4.6 Industry 4.0/SF Maturity Models

4.6.1 Maturity Models

A maturity assessment model is essential for evaluating the step-by-step continual improvement at all levels of manufacturing systems, including conditions, attitudes, resources, and technologies. Several assessment models have been proposed for Industry 4.0 and SF by industry associations, consulting firms, government agencies, and academics. Maturity models can be divided into three types: descriptive, prescriptive, and comparative. Although these models may appear to represent different phases of a model's evolution, they actually represent successive phases of the model evaluation. The primary purpose of a descriptive model is to provide insight into the current state of the domain. Once a solid understanding of the current situation has been gained, the model can then be developed into a prescriptive one, as significant, repeatable improvements can only be achieved with a solid knowledge of the situation. Finally, the model should be applied across as many organizations as possible to gather enough data to make valid comparisons. By doing so, a comprehensive understanding of the domain can be obtained, leading to continuous improvement [45].

Considering the lack of a dedicated assessment model for evaluating the maturity of USFs, we conducted a comprehensive review of existing maturity models in the broader context of Industry 4.0 and smart factories. This review served two key purposes: first, to identify potential models that could be adapted or extended to suit the evaluation of USFs, and second, to understand the limitations and gaps in existing models that necessitated the development of a new model.

In the context of Industry 4.0, a maturity assessment model typically has a defined scope, maturity levels, and attributes. The maturity levels reflect the steps that an organization needs to complete in order to fully implement Industry 4.0 practices. To facilitate understanding, the attributes of maturity models are often divided into dimensions, categories, and assessment criteria [46]. To provide an overview of existing maturity assessment models for Industry 4.0, we conducted a brief literature review and compiled a list of models, which is presented in Table 5 [47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74]. Through a detailed analysis of these models, we can extract valuable insights into the different dimensions and categories of Industry 4.0 implementation, as well as the evaluation criteria utilized to assess their maturity levels. This examination can provide us with a better understanding of the various factors that influence the successful implementation of Industry 4.0 technologies and processes.

Table 5 Maturity Models—General Information [47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74]
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4.6.2 Public Availability of Information

In this study, we conducted a comprehensive analysis of 17 maturity models, focusing on their public availability. The analysis involved evaluating the maturity levels and attributes of each model and assigning specific scores based on predefined criteria. We assessed the availability of information for each model across several dimensions, including the number and description of maturity levels, dimensions, categories, and criteria. For most criteria, a score of 10 was assigned, except for the description of dimensions and description of categories, which were assigned a score of 20 to emphasize their significance.

These criteria were carefully chosen to gauge the overall information available for each model and provide a fair assessment of their public accessibility. The maximum score attainable for each maturity model in terms of public information availability was set at 100. Among the 17 models included in our study, we found that four models achieved a perfect score, indicating a high level of information availability and transparency.

The comparison and results of our analysis are presented in Table 6, which provides a clear overview of the maturity models' performance in terms of public information availability. By conducting this analysis, we aimed to provide valuable insights into the extent to which these models are documented and accessible to the public.

Table 6 Maturity Models—comparison for information availability [39]
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The Industry 4.0 Readiness Model (IMPULS) was proposed by the IMPULS foundation of the VDMA (Mechanical Engineering Industry Association) located in Frankfurt, Germany. The model aims to assess the readiness of mechanical engineering companies to implement the Industry 4.0 concept [47,48,49,50]. The Industry 4.0 Maturity Index was proposed by the National Academy of Science and Engineering (Acatech) to help manufacturing companies assess their strengths and weaknesses in the context of Industry 4.0. The index considers six developmental stages, as described in various sources [46, 48, 52]. The Smartness Assessment Framework was developed in 2015 through a collaborative effort between the Ministry of Trade, Industry, and Energy of the Korean government, the Korea Smart Manufacturing Office (KOSMO), and the Korea Productivity Center (KPC). The framework comprises five maturity levels, four dimensions, ten categories, and 95 criteria, as described in sources [55, 56]. The Singapore Smart Industry Readiness Index (SIRI) was launched in November 2017 by the Economic Development Board of Singapore and global companies specializing in testing, inspection, certification, and training, such as TÜV SÜD. The index was developed based on the Reference Architecture Model for Industry 4.0 (RAMI 4.0) to assist industrial companies in realizing the benefits of Industry 4.0 [59, 60].

4.7 Comparison of Existing Maturity Models

To deepen our understanding of maturity levels, we conducted a comparative analysis of the top ten maturity models with the highest scores in terms of their maturity levels and dimensions. Our findings, which are summarized in Table 7, indicate that the maturity levels of these models ranged from four to six. These maturity levels were defined based on Industry 4.0 and SF characteristics and goals, and their titles were chosen to reflect these definitions. Notably, the highest levels of maturity were exhibited by terms such as “top performers,” “adaptability,” “autonomy,” “expert,” “intelligent,” “digital leader,” and “smart digitalist” [47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74].

Table 7 Maturity levels(scales) of the first ten maturity models with the higher score [39]
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Table 8 presents the dimensions and scores of the maturity models that scored the highest. The dimensions that received the most mentions were organization, strategy, operation, technology, process, and product. In two of the maturity models, namely leadership, customers, culture, and resources, there was duplication. In addition to these dimensions, some were closely associated with different terms, such as employee and labour, supply chain and supply-demand matching, legal considerations and governance, business model, and business strategy driven by digital technology [49, 53, 55, 56, 59, 62, 63, 70, 72,73,74].

Table 8 Dimensions of the first ten maturity models with higher scores [39]
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4.8 Existence of USF Attributes

In order to comprehensively identify and delineate the intricate attributes of the Urban Smart Factory (USF), our analysis delved into its multifaceted disciplines, overarching goals, and symbiotic relationships with urban factory resources. Throughout our analysis, a discerning observation emerged: the majority of the evaluated maturity models, though valuable, fall short of encompassing the distinct and nuanced features that define the USF landscape.

As we meticulously evaluated the landscape in Table 9, it became apparent that while some of the considered maturity models align with attributes encapsulating the essence of human-centricity—an intrinsic element of the USF—only a sparse few models incorporate the broader spectrum of attributes emanating from the USF's profound connection to its environment and society. This comprehensive attribute spectrum is fundamental to capturing the intricate fabric of the USF's sustainability characteristics.

Table 9 Existence of the detailed attributes of USF in the existing maturity models [39]
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Furthermore, upon closer examination, it was evident that only a mere two maturity models ventured to incorporate attributes from the economic discipline, and an even scarcer representation encompassed attributes central to the essence of resilience—an indispensable aspect of the USF's operational paradigm.

This conspicuous gap in attribute representation across existing models illuminates a critical necessity—a tailored and meticulously crafted maturity assessment model that resonates harmoniously with the USF’s intrinsic nature, disciplines, and multifaceted attributes. The development of such a bespoke model holds paramount importance in accurately gauging the USF’s maturity journey, enabling robust evaluation across its distinct dimensions and attributes. By tailoring a model specific to the USF’s realm, we ensure not only the precision of its maturity evaluation but also the catalysis of its holistic growth and sustained success.

The imperative of a customized model that seamlessly interweaves the distinct attributes of the USF stands vividly, and our proposed USF maturity assessment model uniquely rises to meet this profound requirement.

4.9 USF Maturity Assessment Model

4.9.1 Goal and Scope

Numerous maturity models have been developed by research organizations, universities, and consulting firms to evaluate the implementation of smart manufacturing and Industry 4.0, each with distinct scopes, specific domains, and criteria. However, none of the existing models fully account for the unique characteristics and technologies of the USF. Thus, a customized maturity model is essential to comprehensively assess the maturity of USF attributes and technologies. The USF maturity assessment model comprises various attributes, such as perspectives, dimensions, categories, and criteria. In this section, we will delve into the development of the USF maturity assessment model, including a detailed discussion of its attributes.

4.9.2 Model Development

The development of the USF maturity assessment model aims to optimize the features and objectives of the Urban Smart Factory (USF) by evaluating the implementation of Smart Factory (SF) technologies and principles within the USF context. The SF technologies and principles serve as the criteria for the model, while the four perspectives used to construct the maturity model are derived from strategic performance management and USF characteristics. Drawing on strategic management principles, the USF maturity assessment model starts with the performance perspective. The remaining three perspectives of the model are based on USF characteristics, namely Human-centricity, Sustainability, and Resilience.

The USF maturity assessment model encompasses eleven dimensions, as illustrated in Fig. 1. These dimensions include three overarching categories: "Strategic," "Financial," and "Operational." Under the "Human-centricity" perspective, the "Human well-being" is further divided into three categories: "Employee well-being", "Customer satisfaction", and "Citizens symbiotic relationship". The "Sustainability" perspective aligns with the three dimensions of the Triple Bottom Line approach, namely "Environment," "Society," and "Economy." Lastly, the "Resilience" perspective consists of two dimensions: "Internal disruption" and "External disruption". Through the development of this comprehensive maturity assessment model, the USF can evaluate and improve its performance across various dimensions, ensuring alignment with its strategic goals and characteristics.

Fig. 1
figure 1

Roots, perspectives, and dimensions of USF assessment model (adapted from [39])

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Figure 2 illustrates the suggested 8 categories under the performance perspectives, namely leadership, competitiveness, return on investment (ROI), time-to-market, productivity, quality, overall equipment effectiveness (OEE), and space.

Fig. 2
figure 2

Categories of performance perspective (adapted from [39])

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The USF maturity assessment model comprises 52 detailed attributes across the human-centricity, sustainability, and resilience perspectives. Eleven categories have been proposed for the human-centricity perspective, as illustrated in Fig. 3. Figure 4 presents the proposed 26 categories for the sustainability perspective, and Fig. 5 displays the 15 categories associated with resilience. In summary, this section has explained the proposed USF maturity assessment model, comprising 11 dimensions and 60 categories across the four perspectives.

Fig. 3
figure 3

Categories of Human-centricity perspective (adapted from [39])

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Fig. 4
figure 4

Categories of Sustainability perspective (adapted from [39])

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Fig. 5
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Categories of Resilience perspective (adapted from [39])

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The SF principles and key technologies discussed previously are considered as the criteria for the USF maturity assessment model, as outlined below.

SF principles

SF technologies

1. Modularity

2. Interoperability

3. Decentralization

4. Virtualization

5. Real-time capability

6. Flexibility

7. Smart Sensor

8. IoT and Cybersecurity

9. CPS and Digital Twins

10. Information Systems and Big Data

11. Industrial AI

12. Automation, Autonomous Robot and HRC

13. XR (including VR, AR, MR)

14. Cloud and Edge computing

15. 3D printing

The following summarizes the description of criteria levels in the USF maturity assessment model:

  • Performance-Leadership

  • Level 1- Management's strategy does not exist.

  • Level 2- Management's strategy does exist but is Not Implemented

  • Level 3- Management's strategy does exist and is Partially Implemented

  • Level 4- Management's strategy does exist and is Fully Implemented

  • N/A—The targeted principle or technology is Not Applicable

  • Performance-Competitiveness

  • Level 1- Leaders have no knowledge (of the SF principle/technology implementation for competitiveness)

  • Level 2- Leaders are fully aware (of the SF principle/technology implementation for competitiveness)

  • Level 3- Leaders are fully aware but not eager to investing in the SF principle/technology for competitiveness.

  • Level 4- Leaders are fully aware and committed to investing in the SF principle/technology for competitiveness.

  • N/A—The targeted principle or technology is Not Applicable

  • Performance-ROI

  • Level 1- Not Implemented

  • Level 2- Implemented / No update.

  • Level 3- Implemented and updated annually.

  • Level 4- Implemented and updated quarterly with improvement strategies.

  • N/A—The targeted principle or technology is Not Applicable.

  • Performance-Time-to-Market, Performance-Productivity, Performance Quality, Performance OEE, Performance Space

  • Level 1- The targeted principle or technology is Not Implemented

  • Level 2- The targeted principle or technology is Partially Implemented

  • Level 3- The targeted principle or technology is Largely Implemented

  • Level 4- The targeted principle or technology is Fully Implemented

  • N/A—The targeted principle or technology is Not Applicable

  • Human-centricity, Sustainability, and Resilience

  • Level 1- The targeted principle or technology is Not Implemented

  • Level 2- The targeted principle or technology is Partially Implemented

  • Level 3- The targeted principle or technology is Largely Implemented

  • Level 4- The targeted principle or technology is Fully Implemented

  • N/A—The targeted principle or technology is Not Applicable

The USF maturity assessment model is comprised of four maturity levels: outstanding, developed, piloted, and beginner. Table 10 displays two criteria that determine the average maturity level (μ) of the perspectives, as well as the required level for each perspective to be achieved at each maturity level.

Table 10 Maturity levels of USF maturity assessment model [39]
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5 Case Study

As discussed previously, existing Industry 4.0 and Smart Factory maturity assessment models are not suitable for evaluating the maturity of USF. Hence, a custom-made maturity assessment model was proposed to assess the maturity of USF characteristics. In this section, the validity and effectiveness of the proposed model are investigated through the evaluation of two case studies from different business sectors, and the results are presented. In each of the case studies, the researchers involved in a modern USF research project conducted two different evaluations. The first evaluation used the smart assessment framework developed by the Korean government, KOSMO, and the KPC. The second evaluation utilized the maturity assessment model developed specifically for the USF.

First, we will provide an overview of the two case studies evaluated in this study. Then we will present the consolidated results for each case and compare the outcomes of the "Maturity Assessment Model for USF" with the designated targets to determine its appropriateness and effectiveness.

5.1 Case Studies Overview

The first case study evaluated a Korean biotech and healthcare company that was planning to establish a new production site in Hanam-si, Gyeonggi-do. The production facility was a ten-story building with five underground floors, covering an area of more than 10,000 square meters. The building area exceeded 5,800 square meters [75].

The second case study focuses on a Korean automotive manufacturer and their new urban factory called E-Forest. The company describes E-Forest as an innovative SF that integrates people, nature, and technology to create customer value. The factory aims to innovate manufacturing systems by emphasizing the core values of auto-flexion, intelligence, and humanity. The E-Forest pilot plant was launched in South Korea, and the company announced the establishment of a new urban factory in Singapore to serve as a hub for manufacturing technology, research, and training. The case study evaluates the maturity of the USF characteristics of this new urban factory using the proposed USF maturity assessment model [76,77,78].

5.2 First Case Study—Biotech Company High-Tech Center Project

As previously mentioned, the first evaluation was conducted using the KPC "Smartness Assessment Framework." The results of the evaluation for the dimensions of maturity level are presented in Fig. 6. The results indicate that the company has the highest level of focus on leadership and strategy among all the dimensions. Logistic management attained the second-highest level, while production planning and facility management attained the lowest level among all the ten categories.

Fig. 6
figure 6

Biotech company high-tech center project evaluation results—Smartness Assessment Framework [39]

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Researchers who were part of the project on inbound logistics and machinery layout optimization for the new facility evaluated the USF using the proposed maturity assessment model. As shown in Fig. 7, the evaluation results indicate that the company has considered smart manufacturing technologies and key principles from all four perspectives: performance, human-centric, sustainability, and resilience.

Fig. 7
figure 7

Biotech company high-tech center project evaluation results—Perspectives Maturity Level—Maturity Assessment Model for USF [39]

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The results depicted in Fig. 8 highlight a noticeable gap between the various dimensions of the SF. Specifically, the analysis indicates that the company has prioritized the implementation of principles and key technologies related to operational and strategic performance within the SF. However, there appears to be a significant lack of attention given to the symbiotic relationship with citizens dimension. This suggests that while the company has made strides in certain aspects of the SF, there is room for improvement in fostering a mutually beneficial relationship with the community it serves.

Fig. 8
figure 8

Biotech company high-tech center project evaluation results –Dimensions Maturity Level—Maturity Assessment Model for USF [39]

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Figure 9 presents the evaluation results for the different categories, revealing that infrastructure and resource sharing achieved the lowest level of performance. On the other hand, the company's talent recruitment efforts were highly successful, closely followed by its open innovation initiatives, which also received a high level of performance evaluation. These results suggest that the company has been successful in attracting and retaining top talent and implementing innovative practices, but there may be room for improvement in terms of infrastructure and resource sharing.

Fig. 9
figure 9

Biotech company high-tech center project evaluation results—Categories Maturity Level—Maturity Assessment Model for USF [39]

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5.3 Second Case Study—New Urban Factory of a Korean Automotive Manufacturer

The "smartness assessment framework" was used as the basis for the first evaluation, and the results for the maturity level dimensions are presented in Fig. 10. Among the ten categories evaluated, quality control achieved the highest level of maturity. Facility automation, facility management, and performance are closely followed as the second highest performing categories. In contrast, process control had the lowest level of maturity compared to all other dimensions evaluated. These results suggest that the company has made considerable progress in improving quality control and facility automation, but process control may require more attention to reach the same level of maturity.

Fig. 10
figure 10

New urban factory of a Korean automotive manufacturer evaluation results—Categories Maturity Level—Smartness Assessment Framework [39]

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The proposed maturity assessment model was utilized to evaluate the new urban factory of a Korean automotive manufacturer by a researcher who participated in the advisory project for the factory. The results of the evaluation, as shown in Fig. 11, indicate that the performance perspective attained the highest level of maturity when compared to all other perspectives. The human-centricity perspective achieved the second-highest level of maturity, followed by resilience and sustainability perspectives. These findings suggest that the company has made notable progress in terms of performance, with a focus on improving productivity and efficiency.

Fig. 11
figure 11

New urban factory of a Korean automotive manufacturer—Overall & Perspectives Maturity Level–Maturity Assessment Model for USF [39]

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Figure 12 displays a clear gap between the different dimensions evaluated. The analysis shows that the company has made the most progress in strategic performance, which achieved the highest level of maturity among all eleven dimensions. In contrast, environmental sustainability received the least attention and had the lowest level of maturity. Social sustainability and the symbiotic relationship with citizens dimensions also attained a low level of maturity. These results indicate that the company has been successful in implementing strategies that improve its strategic performance. However, more emphasis is required in developing sustainability and community-focused initiatives.

Fig. 12
figure 12

New urban factory of a Korean automotive manufacturer evaluation results—Dimensions Maturity Level–Maturity Assessment Model for USF [39]

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The evaluation results for the different categories are displayed in Fig. 13. However, several categories have an unknown level (0) due to information confidentiality. Among the categories with available information, workplace safety and inbound logistics achieved the highest level of maturity compared to all other 60 dimensions evaluated. These results suggest that the company has made notable progress in ensuring a safe work environment and improving the efficiency of its inbound logistics operations.

Fig. 13
figure 13

New urban factory of a Korean automotive manufacturer evaluation results—Categories Maturity Level–Maturity Assessment Model for USF [39]

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5.4 Case Study Analysis

Two case studies were conducted in the biotechnology and automotive manufacturing sectors to assess the effectiveness and appropriateness of the proposed maturity assessment model. The smart assessment framework was utilized to evaluate the urban factories in these industries, and the proposed maturity assessment model was employed to evaluate the maturity level of the urban factories across different dimensions. These case studies provide valuable insights into the practical application of the proposed model and demonstrate its ability to evaluate the maturity level of urban factories in diverse industries. Through the evaluation of these urban factories, the study successfully identifies areas of strength and weakness, which can be utilized to improve overall performance and promote sustainable development.

In the first case study, the smartness assessment framework was employed, revealing weaknesses and improvement opportunities in facility management and product development. However, leadership and strategy achieved the highest level of maturity among all ten evaluated categories. The study showcased the biotech company's commitment to promoting employee well-being through investments in self-development expenses and ensuring a safe workplace. Additionally, the company demonstrated a strict focus on minimizing environmental impacts, particularly in new plants, indicating significant steps toward promoting sustainability and social responsibility. This is summarized in Table 11.

Table 11 Biotech company evaluation results sorted—Categories Maturity Level–Maturity Assessment Model for USF [39]
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The study also found that the company's efforts towards achieving social sustainability goals included public non-profit services, promoting gender equality, providing job offers for local people, and collaborating with local research institutes and universities for open innovation. In terms of economic sustainability, the study identified the establishment of an assembly shop near an airport as a positive step. Furthermore, the company's investments in talent development and partnerships for supply chain management were recognized as significant efforts in the resilience dimension. These results highlight the company's commitment to sustainable development and social responsibility across various dimensions. The evaluation results of the proposed maturity assessment model for the second case study yielded insightful findings. However, due to confidentiality concerns regarding some information from the new urban factory of a Korean automotive manufacturer, some categories had unclear levels in the developed model. It is important to note that information availability plays a vital role in accurately evaluating the maturity level dimensions, and this aspect should be considered in future studies.

The analysis of the case studies leads to the following summary of results, highlighting the uniqueness of the proposed maturity assessment model compared to existing models:

  • The developed maturity assessment model can be effectively utilized by manufacturers planning to establish factories in urban areas to evaluate their readiness and develop improvement roadmaps.

  • Urban factories can benefit from the proposed model to assess their current status and identify categories and dimensions with lower maturity levels.

  • Defining assessment criteria, including the principles and key technologies of the smart factory, in advance is crucial to ensure consistent and accurate evaluations. Regular updates should also be made to reflect the latest technological innovations.

The proposed maturity assessment model was applied to evaluate the new urban factory of a Korean automotive manufacturer, revealing that 12 categories had unclear maturity levels, while 14 categories achieved maturity levels below two. Table 12 illustrates that twenty-four categories have achieved a maturity level exceeding level two, and among them, four categories are approaching level three. The new urban factory of the Korean automotive manufacturer prioritizes the well-being of its employees, customers, and citizens, evident in the implementation of innovative technologies such as the Chairless Exoskeleton (CEX) and Vest Exoskeleton (VEX), product and service personalization, and manufacturing showrooms and sky-tracks. Additionally, the factory has adopted a battery-as-a-service (BaaS) business model and established collaborations with universities to achieve economic sustainability. Furthermore, digital twins for the manufacturing value chain and partnerships for supply chain management have been developed to enhance resilience [76,77,78].

Table 12 New urban factory of a Korean automotive manufacturer evaluation results (sorted)—Categories Maturity Level—Maturity Assessment Model for USF [39]
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The proposed maturity assessment model provides a holistic approach to evaluating the readiness of urban factories and identifying areas for enhancement to achieve the goal of sustainable, resilient, and human-centric urban factories. Unlike other maturity models, this model enables the comprehensive evaluation of different dimensions and categories, allowing for the identification of areas that require improvement, even if the numerical value of the maturity level is not high.

The case studies conducted using the proposed maturity assessment model offer insights into manufacturers' intentions and efforts toward human-centricity, sustainability, and resilience. While these case studies do not involve operational factories, the assessment results can guide manufacturers in improving their roadmap and adjusting their strategies. They can identify strong categories and criteria to enhance other categories, ultimately facilitating targeted improvements. Manufacturers can identify areas requiring improvement based on their specific perspectives. It is also essential to determine weight factors for each perspective, dimension, and category, which can be achieved through an analytic hierarchy process using a multi-criteria decision approach.

6 Conclusion

This study presents an innovative maturity assessment model tailored explicitly for Urban Smart Factories (USFs), underscoring its novel contribution to the field. While conventional maturity models within the spectrum of Industry 4.0 and smart factories have their merits, they often lack the granularity needed to comprehensively evaluate the maturity of USFs. The novelty of our research lies in the development of a dedicated model that centers on the integration of smart manufacturing technologies and key principles, with a profound emphasis on the attributes distinctive to USFs.

Our proposed maturity assessment model transcends the limitations of traditional models, which predominantly address technological aspects. Instead, it encompasses the intricate interplay between human-centricity, sustainability, and resilience—core attributes that define the essence of USFs. By addressing this research gap, we provide a comprehensive framework that empowers stakeholders to holistically assess the maturity levels of smart manufacturing technologies and principles within the context of USFs. The model's practical implications resonate widely. Manufacturers seeking to establish factories in urban locales gain access to a robust tool for gauging their readiness and devising a roadmap for effective implementation. Existing urban factories, too, can leverage the model to evaluate their current positioning across various dimensions and identify avenues for growth. The model serves as a strategic guide, identifying specific categories necessitating targeted investment for maximal impact.

In acknowledging the study's limitations, we recognize that the availability of USFs with distinct characteristics posed challenges to the model's generalizability. To fortify the model's efficacy, future research should encompass a broader array of case studies to validate its applicability. Engaging a more diverse range of experts in the multiple-criteria decision analysis process will enhance the precision of weight factor assignments, thus securing a robust evaluation framework.

As the urban smart manufacturing landscape evolves, the development of USF maturity assessment models retains its significance. The emergence of experts steeped in smart manufacturing technologies and principles will propel the refinement of the model. Future research avenues include refining the model by deriving more nuanced attributes of the USF, meticulously calculating weight factors for each criterion and category, and exploring its applicability in varied contexts beyond urban settings.

By steadfastly pursuing these research trajectories, our proposed USF maturity assessment model will be fortified, offering an indispensable compass to manufacturers in adeptly implementing smart manufacturing principles and technologies. This endeavor aligns with our commitment to further the primary objectives of the USF and contribute to the advancement of sustainable and resilient urban manufacturing paradigms, solidifying the distinct novelty of our work.