1. Introduction
The term Industry 4.0 (I4.0) or the Fourth Industrial Revolution signifies the advent of new and sophisticated production models. These models are underpinned by innovative technologies that facilitate the digital transformation of processes, products, services, and business models [1]. I4.0, originating from a German initiative, represents the fusion of manufacturing and information technology (IT). This integration gives rise to “smart” factories that are characterized by their high efficiency in resource utilization and their ability to rapidly achieve management objectives [2].
I4.0 encompasses a suite of technologies such as networking, the availability of vast data volumes, the capacity for customized production, interconnected microsensor networks, intelligent visualization of information for remote operations, and automation. These technologies are not only applicable in traditional factories but also in the shipbuilding industry [3]. The concept of I4.0 facilitates the evolution towards a digital and automated manufacturing ecosystem, as well as the digitization of the value chain [4]. In essence, I4.0 redefines the performance model of manufacturing processes by incorporating a holistic perspective that includes economic, energy, environmental, functional, and social dimensions [1].
I4.0 also represents a production system that integrates digital technologies into manufacturing and assembly processes. This encompasses the industrial design of products, the manufacturing and assembly operations, the planning of products, and the organization of work. The convergence of these digital technologies fosters digital continuity across the entire life cycle of a product [5].
There is a growing enthusiasm among global manufacturing companies for smart manufacturing and smart factories. This concept is advancing rapidly through the integration of information and communications technology (ICT) with automation solutions across the full spectrum of the production process [6]. Notably, the construction value chain in shipbuilding is significantly influenced by the need for close collaboration with customers, suppliers, subcontractors, and other parties involved. Despite the myriad benefits offered by innovative technologies, companies within the shipbuilding sector have struggled to incorporate these advancements, falling behind their counterparts in the automotive and mechanical engineering industries [4].
Considering these circumstances, the necessity for additional research is evident. Shipbuilding companies are confronting escalating challenges due to fierce global economic competition, leading to slender profit margins and constrained research and development (R&D) investments [4].
1.1. Industry 4.0 and Shipbuilding
The concept of I4.0 was introduced to the public in 2011 at the Hannover Fair, marking the evolution of cyber-physical systems (CPSs) into cyber-physical production systems (CPPS). In the era of I4.0, CPPS allows production systems to make intelligent decisions through real-time communication and collaboration between “manufacturing things”. This enables the flexible production of high-quality personalized products at mass efficiency [7,8].
The shipbuilding process, from the initiation of contracts and design to the actual construction, is conducted concurrently. The industry is characterized by its reliance on intensive labor and is a complex assembly process. To construct a vessel, significant amounts of labor, production costs, time, and resources are necessary [5]. Ships are composed of millions of similar yet predominantly unique intermediate products. Initially, during the production phase, the resemblance among these intermediate products is substantial, making automation feasible for certain processes. However, as production advances, this similarity diminishes significantly. Consequently, the automation levels in shipyards are typically low and skewed towards the early stages of production [6].
It is crucial to underscore the integration of design and manufacturing processes in shipbuilding. This integration is achieved through decentralized design in real time, where manufacturing information is collected, optimized, and integrated by an intelligent system. This collaborative approach aims to enhance the alignment of ship design and manufacturing processes, thereby optimizing the entire product life cycle [3]. Digital continuity, I4.0, and product life cycle management (PLM) represent significant challenges for companies that span the entire product life cycle, including those in the aerospace, shipbuilding, and automotive industries [5].
Particularly in the context of vessel design and manufacturing, the transition to new methods, processes, and enterprise information systems presents unique challenges for the management of product information. I4.0 and PLM systems represent pivotal digital continuity initiatives within the shipbuilding industry [5]. Technologies such as additive manufacturing (AM) and augmented reality (AR) are poised to play a pivotal role in ship maintenance and manufacturing strategies, leveraging the informativeness potential of both [3]. The shipbuilding industry currently confronts challenges related to its survival and sustainability. One strategy for addressing these challenges is the active integration of smart manufacturing technology [6].
However, shipbuilding presents a peculiar challenge: the shipbuilding industry exhibits distinct characteristics that set it apart from other sectors that are adopting I4.0 solutions [5]. Shipbuilding projects are characterized by their project-based nature, complexity, and individuality, necessitating a substantial level of specialized expertise [4]. Despite the disparate physical locations where each component of the ship is produced, these sites are part of the same manufacturing project, thereby constituting a form of distributed manufacturing. This complexity demands a strategic progression aligned with the principles of I4.0 to advance [1].
1.2. Related Work and Literature
To evaluate I4.0’s application in shipbuilding, the literature has been extensively surveyed. Several studies conducted similar literature reviews on I4.0’s application in the shipbuilding sector [6]. These are presented in a broader context as reference for accomplishing the objectives of this research [9]. Among them, four studies (Appendix A) specific to the shipbuilding industry can serve as notable exemplars [10,11,12,13].
After evaluating the results of these four literature reviews, the results reveal that in essence, all of them are aiming to provide a definition of the term I4.0 for the shipbuilding industry as well as its key technologies and concepts from a technical point of view [4]. It is important to highlight that the four literature reviews examined do not address the topic as comprehensively as this paper does and do not incorporate the viewpoint of a shipbuilding company [14]. Therefore, it can be inferred that the existing reviews have not been sufficiently beneficial for shipbuilding companies or have provided only limited assistance [14]. Consequently, to the best of our knowledge, our contribution represents the first to offer a comprehensive exploration of the key concepts of I4.0 within the specific context of the shipbuilding industry [4].
1.3. Research Questions
This paper seeks to explore the status of the research in domains of I4.0 in the shipbuilding industry [2]. Existing research tends to concentrate on discrete aspects of I4.0 without offering a thorough and in-depth examination of the concepts and technologies necessary to integrate I4.0 into the design, construction, maintenance, and other stages throughout the entire life cycle of a ship [15].
In this context, the following research questions arise [4].
What are the different research approaches used to study I4.0 related to shipbuilding?
What is the status of research in the domains of I4.0 application to the shipbuilding industry?
What are the research challenges with I4.0’s application in shipbuilding?
This review is organized as follows. Section 2 outlines the methodology employed for the systematic literature review (SLR). Section 3 discusses the current state of the art of the material collected. Section 4 presents the descriptive findings of the review. Section 5 describes the proposed Shipbuilding 4.0 framework based on the review findings and outlines the scope for future research. Section 6 concludes the review, summarizes the research contributions, and highlights the research limitations [2].
2. Materials and Methods
This systematic review adhered to the applicable guidelines set forth by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement. The SLR is recommended as a suitable method for the summarizing of existing knowledge as well as identifying and highlighting research gaps [4]. The research gaps should be such that if worked upon, they will help strengthen the field of study [2]. In the present work, we carry out a review on I4.0, considering only articles that effectively address topics relevant to I4.0 in the shipbuilding industry [16]. The structured review methodology adopted a seven-step process, as presented in the following [2].
Once the RQs were defined (step 1 of 7), the next step was to survey articles. The articles were collected using the Scopus and Web of Science (WoS) databases (step 2 of 7) on 17 November 2023 [16]. The searches were performed with the keywords “I4.0” and “shipbuilding.” Additionally, they were limited to publications from 2010 to 2023 and being in English. The engineering, computer science, and science technology areas were chosen (step 3 of 7) [16].
Due to several I4.0 synonyms used in the literature and to obtain a comprehensive and relevant set of articles, the authors used multiple search strings [17]. The literature review considered the following keywords and searched the terms [14] “smart manufacturing” (or “I4.0” or “smart factory”) and “shipbuilding” (or “shipyard”). Presented below is the full search string used to identify the relevant literature for this critical review.
WOS: I4.0 or smart manufacturing or smart factory (topic) and shipbuilding or shipyard (topic) and preprint citation index (exclude–database) and 2023 or 2010 or 2012 or 2013 or 2014 or 2015 or 2016 or 2017 or 2018 or 2019 or 2020 or 2021 or 2022 (publication years) and English (languages) and article or meeting or review article (document types).
Scopus: (TITLE-ABS-KEY (I4.0 or (smart and manufacturing) OR (smart and factory)) AND TITLE-ABS-KEY (shipbuilding or shipyard)).
The initial search in the Scopus database returned 120 articles, and in WoS returned 137 articles. Joining the articles from the two databases, excluding duplicates, we reached a sample of 178 articles (step 4 of 7) [16].
2.1. Inclusion and Quality Assessment Criteria
The search conducted on the Scopus and WoS databases alone is insufficiently precise for a thorough literature review. To achieve this, the following inclusion and exclusion criteria were established for the initial triage of the compiled database:
Relevance to the shipbuilding industry (inclusion criterion).
Availability of the full text in English (inclusion criterion).
Classification as a primary study (inclusion criterion).
Identification as a duplicate study (exclusion criterion).
After analyzing the review as per the abovementioned inclusion (step 5 of 7) and exclusion criteria (step 6 of 7), the review articles were filtered to 152 to focus on technical content only [15].
The subsequent step involved the introduction of quality assessment criteria to evaluate the technical merit of the papers. For this purpose, the quality assessment in line with the approach advocated by Mauro and Kana serves as an appropriate model, and entails addressing the following quality indicators (QIs):
Are the objectives of the study explicitly articulated?
Are the scope, context, and experimental design adequately delineated?
Do the study variables appear to be reliable and valid?
Is the research methodology sufficiently detailed?
Are the RQs of the study adequately addressed?
Are negative results and identified limitations adequately discussed?
Are the credibility, validity, and reliability of the main findings appropriately evaluated?
Are the conclusions suitably related to the study’s scope and reliable?
Each paper is then assigned a score based on these indicators: 1 point if the indicator is fully met, 0.5 points if met to a partial extent, and 0 points if not met at all. Using this scoring system, a paper can achieve a maximum of 8 points, and to pass the QI screening, it must score a minimum of 4 points. This method ensures that only high-quality papers are retained as inputs for the review [15]. After quality assessment, 68 final articles were selected for data synthesis, where information relating to the content, approach, application phase, and applied technologies was collected and recorded [18].
2.2. Content and Research Type Classification
The above-described method delivers a final database of high-quality papers related to I4.0 in the shipbuilding industry. It is then necessary to classify their content to suitably answer the RQs [15]. This study proposes four categorizations, three primarily related to the I4.0 topic described and one on the research approach [15]. Concerning the I4.0-related categorization, the first criterion covers the life cycle, the second covers the content category, the third the technology category, and the fourth the research approach [15].
The life cycle phases considered here are the general ones for a ship (and generally for an industrial product), namely, design, production, operation, and retirement. In addition, a specific category covers papers dealing with the whole ship’s life cycle [15]; in summary: design, production/manufacturing, operation—shipyard, operation—ships, retirement, life cycle (includes works dealing with more than one of the above-mentioned phases or making general considerations on the whole ship’s life cycle).
The papers were categorized in six research content categories. The distribution of categories includes concepts, technologies, value chain, smart factory, smart manufacturing, smart work, and data.
The technologies were also classified into nine categories of I4.0 [19]: big data and analytics, autonomous robots, simulation, the industrial Internet of Things (IoT), cybersecurity, the cloud, AM, AR, and horizontal and vertical system integration.
Papers were also classified by research approaches [17]. Three research methodologies were considered for classification: review, conceptual, and empirical. Empirical papers concentrate on observable or quantifiable I4.0 activities and processes, employing a range of methodological approaches. Conversely, conceptual papers explore the concepts, applications, theories, benefits, and challenges of I4.0 without gathering primary data or analyzing secondary data. The remaining papers engaged with the topic using empirical research methods, which included case studies, simulations, prototypes, experimentation, and surveys.
2.3. Research Protocol
To achieve the objectives of this research and as per the method mentioned above, a research protocol was developed, which is summarized in Appendix B. This protocol includes the steps of collection, selection, and analysis of articles [16].
3. Current State of the Art
The subsequent phase is dedicated to the analysis of the identified scientific publications to present the state of the art of I4.0-related technologies within the shipbuilding industry. It is evident from the previous analysis that the database for the ongoing investigation encompasses a diverse spectrum of scientific publications, including peer-reviewed journal articles on the relevant research topics [4].
3.1. Concepts
The review identifies that the shipbuilding companies have high interest in digitization and use of new technologies [20]. Digital shipbuilding or Shipbuilding 4.0 is a hot topic in present academic and industry sectors [10]. I4.0 is revolutionizing ship design, manufacturing, and operations in a smart product-through-life process [21].
The review identifies various concepts that are important to digital shipbuilding: the concept of a digital twin (DT) system for ship design and production processes [22], using DT in a shipbuilding project [23], a digital twin for a flexible manufacturing system (FMS) utilizing a particular module from an enterprise resource planning (ERP) system to virtualize the physical entity, with production data being input based on trials conducted within the FMS [24], and the approach to the digital thread (DTH) in shipbuilding and using it for the DT [25]. Pang proposed a DT and DHT framework for an I4.0 Shipyard [26]. Application of lean tools and methods as well as I4.0 technologies are covered in practice and in the literature [27]. A framework for smart shipyard maturity level assessment [6] and a conceptual architecture model of a smart shipbuilding factory [28] are also proposed in the literature.
3.2. Value Chain
There was limited literature on the components of the value chain. The adoption of I4.0 technologies in the shipbuilding supply chain shows that the main interest reserved for integrating resilience capabilities and innovative technologies is to enhance market position, knowledge, and information sharing and supply chain design and integration [29]. Some literature focuses on the sustainability of the supply chain, such as Ramirez-Peña et al., who reviewed the sustainability of the shipbuilding supply chain [11], Ramirez-Peña et al. discussed the sustainability in the aerospace, naval, and shipbuilding industries [30]. Strandhagen et al. highlighted the sustainability challenges in shipbuilding supply chains and impacts of I4.0 technologies [31]. Seven key technologies of I4.0 contributing most to the sustainability of shipbuilding are identified by Rmirez [32].
The review identified some frameworks of the shipbuilding supply chain, such as per the performance model in I4.0 [1] and a framework of a shipbuilding risk supply chain (Ship-RISC) that offers a simulation of suppliers and sub-tier suppliers [33]. The concept of digital transformation includes the embrace and fusion of diverse innovative information and communication technologies to create more effective, adaptable, nimble, and eco-friendly solutions for industrial systems. This transition also entails novel organizational structures and gives rise to fresh business paradigms. An examination of the collaborative elements necessary across the different facets of I4.0 is carried out [34].
3.3. Smart Factory
The review identified that functional and industrial product structures and process structure concepts are discussed for the shipbuilding industry [5], as well as the means of collecting and managing big data in shipbuilding [35].
It is found from the literature review that autonomous ship and relevant technologies are covered, such as smart autonomous ship and shore architecture [36], a review of research on ship automatic berthing control [37], and advanced navigation assistance systems for safe navigation [38].
Several studies researched planning systems, which include the design and development of a production planning system for heavy shipyards [39], a simulation-based planning system for shipbuilding [40], and a simulation of a digital twin workshop for planning of an intelligent manufacturing workshop in ocean engineering [41].
A few more studies focused on smart designing and engineering, such as an intelligent hull form design optimization concept [42], hybrid evolutionary algorithm and morphing (HEAM) to enable optimal hull designs more rapidly [43], an algorithm that automatically creates a hierarchical structure of blocks [44], a data-driven performance evaluation method for structural design [45], an automated ship design and optimization concept by HEAM [46], and a simulation tool to support ship design [47].
3.4. Smart Manufacturing
It is found from the literature review that robotic welding systems, smart pipe workshops, and pre-outfitting workshops are mainly discussed. For robotic welding systems, a customized interface for a robotic welding application is developed [48] and an automated robotic welding system adapted for shipbuilding is covered [49]. The research on smart pipe workshops includes a smart pipe system for Shipyard 4.0 [50], design and empirical validation of industrial CPS architecture for intelligent I4.0 shipyard pipe workshops [11], and design and implementation of a monitoring pipe system [51]. For the pre-outfitting workshop, an IoT platform concept is discussed [52], as well as a monitoring platform of a visualization system of the cutting and subassembly processes [53]. A new artificial intelligence (AI) deep-learning system for detecting painting defects in an actual shipyard production line is proposed in [54]. Another manuscript puts forward a model and technological framework for the creation of an intelligent cyber-physical manufacturing system (Smart-CPMS). The evolution from current manufacturing systems to the Smart-CPMS is regarded as the future wave of manufacturing advancement within the context of Industry 4.0 [55].
Several studies covered the topic of manufacturing support activities, such as an industrial IoT information system for decision support for mobile or static operators and supervisors [56], requirements for the creation of flexible and safe human–robot collaborative (HRC) tools in shipbuilding workplaces [57], identification and traceability technologies in Industry 5.0 shipbuilding scenarios [58], a methodology that enables objective process progress measurement [59], and an amalgamation of IoT and machine learning (ML) technologies for the purpose of overseeing the manufacturing system [60].
3.5. Infrastructure and Technologies
The review identifies various I4.0 technologies that are used to assist shipbuilding activities and decisions. The survey identifies AR, simulation and optimization, big data, AM, and cybersecurity as the emerging technologies of I4.0 for shipbuilding within the reviewed literature [2]. Current research has pinpointed the key characteristics of I4.0 as IoT, big data, AI, cloud computing, simulation, and cybersecurity. The studies suggest devices mainly intended for executing AI tasks in industrial settings. Additionally, the relationship between the industrial IoT and the artificial IoT is introduced and elaborated upon [61].
For AR, a few studies cover this topic, such as technologies to design an industrial AR system for developing applications for an I4.0 shipyard [62,63,64], collaborative application [65,66], and reviews on AR in relation to the shipbuilding industry [12,67,68].
For simulation and optimization, Park et al. discussed an integrated offshore drilling platform simulation for virtual onboard experience [69] and Kwark et al. discussed the optimization of long-term planning with a constraint satisfaction problem algorithm with ML [70].
For big data, AM, and cybersecurity, a hypernetwork-based design lesson-learned knowledge (DLK) proactive feedback approach is discussed [71]. How big data analytics create a significant impact in the shipping industry is also discussed [72]. The studies cover information about R&D work in the field of implementing AM in shipbuilding [13], as well as the cyber risk of CPSs onboard digitized contemporary and future ships [73].
4. Results
In the following sections, we present the results from our SLR [4].
4.1. Year of Publication
There is an upward trend observed from the year 2015 to 2022, as shown in Figure 1a. It is observed that 50 out of 68 total papers were published in the years 2020–2023. The number of papers published from 2020 onward has increased drastically, which shows researchers’ growing interest in the emerging technology since 2020 [2].
4.2. Journals
The credibility and fame of a journal have a significant impact on how people perceive the publication (see Figure 1b) [2].
There were 46 journal articles included. These articles were published in 29 journals. Five journals accounted for 39% of all publications: (i) Applied Science (8.7%), (ii) IEEE Access (8.7%), (iii) International Journal of Naval Architecture and Ocean Engineering (8.7%), (iv) Sustainable (6.52%), (v) and Sensors (6.52%). In detail, the journals Applied Science, IEEE Access, and International Journal of Naval Architecture and Ocean Engineering had four publications each and Sustainable and Sensors had three publications each. The types of the selected 68 papers are illustrated in Table 1 [16].
4.3. Content Categories of Publications
The selected 68 papers were categorized in seven research categories, as shown in Figure 2a. The distribution of categories indicates that more attention has been paid to smart manufacturing (22.1% of papers). This was followed by studies that propose general theories and concepts in I4.0 (20.6% of papers), then I4.0 technologies (17.6% of papers), use of big data, simulations, AM, IoT, virtual reality, etc., and value chain (14.7% of papers), smart factory (16.2% of papers), and smart work and data (8.8% of papers). Technologies and smart work and data together will be termed “infrastructure” during the discussion and thereafter.
The distribution of research categories indicates increased interest from researchers in all research categories during the years 2015–2022 (Figure 2b).
4.4. Life Cycle and Technology Categorization of Publications
Figure 3 presents the filtered papers with the life cycle phase and I4.0 technologies discussed in the paper. These topics cover a wide spectrum of I4.0 applications in the shipbuilding field, including traditional ship life cycles, such as ship design, production, or manufacturing, and more information about operation and retirement, which focus on technology-specific issues. Among the selected articles, there are 27 papers of non-technology-oriented content. The topics of the remaining 41 papers cover a wide spectrum of I4.0 technologies in the shipbuilding field, including big data and analytics, autonomous robots, simulation, horizontal and vertical system integration, the industrial IoT, cybersecurity, the cloud, AM and AR. Within the empirical research, a significant number of studies focus on the technologies of big data and analytics, AR, and industrial IoT. As illustrated in Figure 3, big data and analytics are used more in the design phase, whereas AR is used more in the operations of both the shipyard and the ships.
4.5. Research Approach Categories of Publications
The data presented in Figure 4 provide insights into the various research methodologies employed to investigate the different categories within I4.0 research. Most studies employed an empirical research approach (58.8%), while the remaining opted for a conceptual approach (32.4%) and review (8.8%) to explore I4.0. The empirical approach involves the use of case studies, simulations, experiments, and other methods to test and validate concepts, theories, and applications. Among the papers that are not conceptual in nature, 30.9% utilized a case study methodology. Simulations and experiments were employed in 8.8% and 13.24% of the studies, respectively. This suggests that the case study approach is the most favored by researchers for illustrating technologies or interface layers. Despite the growing use of simulation, experimentation, and prototyping in research, their presence is not yet substantial. Future research would benefit from an increased application of these methodologies to further enhance the understanding and practical application of I4.0 concepts [2].
4.6. Keyword Statistics
To understand the overall keyword distribution, the keywords of the 178 papers will be used in Section 4.6 and Section 4.7. The most used keywords (Figure 5) in all selected papers were extracted. “Shipbuilding” was the most frequently used keyword (18.5%), followed by “ships” (15.9%), “shipyard” (15.9%), and “I4.0” (12.7%). The other keywords used were “lifecycle”, “design process”, “ship design”, and “manufacture” (see Figure 5) [2].
4.7. Keyword Co-Occurrence
There were 524 non-contextual keywords (such as article, survey, case study, literature review, article, etc.) that were excluded. Similar-meaning keywords were replaced by a single keyword (e.g., “ship design process”, “design process”, and “maritime industry”, “marine industry” were replaced by each other) [74].
The co-occurrence network (Figure 6) constructed by the keywords that appeared in at least two documents was illustrated, with the node size representing the occurrence frequency. Interestingly, the keyword “shipbuilding” occurred with the highest frequency, while “shipyard”, “ships”, and “I4.0” also appeared noticeably in different clusters [74].
To gain a more nuanced understanding of the variations in keyword usage across the documents, networks were constructed for terms that appeared in more than three documents. There were 27 keywords generated, and they formed different cluster structures [74]. With more keywords, a clearer trend can be observed in Figure 7. The two largest clusters depicted reflect the life cycle and design aspects of I4.0-related shipbuilding. Noticeably, “engineering education” emerged with “life cycle”, “manufacturing”, “industrial revolutions”, and “AI” in the life cycle cluster. Another significant note that should be pointed out is that IoT appeared with “ship design”, “supply chain”, “engineering-to-order”, and “Shipyard 4.0” in the design cluster [74].
4.8. Clusters Found from Keywords Co-Occurrence Networks
The co-occurrence of keyword clusters revealed a high degree of overlap, but with varying distributions. Clusters derived from co-occurrence networks did not emerge as autonomous topics, and their boundaries were blurred. This observation underscores the relatively nascent nature of the general topic of I4.0 in relation to shipbuilding. Research streams in this area have not yet been clearly defined. However, the keyword analysis from published articles did provide insights into the emerging topics and trends among authors. As “shipbuilding” is the most common keyword and this research is focused on I4.0-related shipbuilding, this keyword is removed from further analysis.
To delve deeper into the disparities in keyword usage across various documents, networks were constructed for terms that appeared in more than four documents. There are again 18 keywords (Figure 8) generated, and they formed different cluster structures [74].
The first cluster, depicted in red, was the largest, and the most frequent keywords of “AR”, “IoT”, “manufacture”, “manufacturing”, “shipbuilding industry”, “ships”, and “shipyard” exhibited the main technologies of I4.0 related to shipbuilding, with the focus on a larger scale of infrastructure [74].
The second cluster is illustrated in green, with the keywords “design”, “design process”, “digital transformation”, “marine industry”, and “ship design” appearing most frequently, representing the collaboration process between engineering and manufacturing systems [74]. Cluster 2 specialized in the collaboration among the processes to improve the collaboration performance.
The third cluster represented the application of technology to support the data integration within value chain, with keywords such as “DT”, “I4.0”, “life cycle”, “product design”, and “supply chain” [74]. Cluster 3 handled sustainability at different scales and levels within the value chain, with technical proposals and platforms and digitization frameworks toward sustainable transition [74].
The last cluster contained only one keywords—“industrial revolutions” [74]. This cluster served as the background for I4.0-related shipbuilding.
It can be concluded that the network of keywords lacks a coherent structure, with a lack of supporting concepts within the same cluster. However, the position of I4.0-related terms in relation to shipbuilding still falls within a technical cluster, albeit not fully aligned with the application aspect. This suggests that the keywords from publishers do not fully capture the intricate intrinsic characteristics of the shipbuilding domain [74].
5. Proposed Framework and Research Agenda
Based on the review of the literature, we propose a digital shipbuilding framework comprising five main components: concepts, value chain, smart factory, smart manufacturing, infrastructure, and technologies (see Figure 9) [2].
As depicted in our conceptual framework (Figure 9), at the foundation of the framework, we have identified what we term “infrastructures and technologies” and “concepts” of I4.0 [75]. Concepts encompass the fundamental ideas and theoretical foundations of digital shipbuilding, providing guiding principles and objectives for the entire framework. Infrastructure and technologies cover the infrastructure and technologies that support digital shipbuilding and the novel approaches to work enabled by emerging technologies, referred to as smart work, which is a subset of infrastructure as well [75]. Value chain focuses on the entire value creation process in shipbuilding, from raw material procurement to the final product delivery. Smart factory involves the application of I4.0 technologies in shipyards to improve production efficiency and flexibility, focusing on the way products are designed, manufactured, traded etc. Smart manufacturing is focused on the application of smart manufacturing technologies to the actual production process of shipbuilding, such as the manufacturing processes through cutting-edge technologies. To be highlighted is that Shipbuilding 4.0 is supported by lean theory and sustainable concepts [2].
One of the primary aims of our research is to investigate the state of the future research agenda by applying an SLR [4]. This section also presents a critical discussion of the identified research agenda with reference to the shipbuilding industry and introduces its fit with the proposed model as per shipbuilding requirements [14]. After a further investigation on I4.0’s application in the shipbuilding industry, the comprehensive literature search for this identified the relevant research agendas for future perusal [4,76]. It has to be noted that due to the different complexity of the components of the model, their description in the following subsections may vary in length in order to capture the relevant value of the component to the shipbuilding industry [14].
5.1. Concept
5.1.1. Shipbuilding 4.0
There is a keen interest in shipyard digitization and the integration of new technologies within the shipbuilding industry. Shipyards that possess a high technological index are able to offer a more diverse and expansive range of market opportunities [20]. The principles of I4.0 will fundamentally transform various aspects of the shipbuilding industry, including design, manufacturing, operation, shipping, services, production systems, maintenance, and value chains. For this transformation to be realized, the ship must evolve into a smart ship, which is produced through a smart shipbuilding process [10]. Ang et al. underscore smart design, manufacturing, and operation as the strategic direction for the shipbuilding industry in the era of I4.0. This includes designing for enhanced energy efficiency, the development of more intelligent ships, and the implementation of smart operations throughout the ship’s life cycle. I4.0 will revolutionize the design, manufacturing, and operations of ships through a smart product life cycle process in the near future [21].
5.1.2. Digital Twin and Digital Thread
The DT should be acknowledged as the cornerstone of the Shipyard 4.0 model. Its potential for streamlining the shipbuilding process and its role in achieving high standards of environmental protection and workplace safety make the DT a key enabler for this purpose [23]. Iwańkowicz and Rutkowski proposed the concept of a DT system for the entire ship design and production process and tested it using the example of the construction process of a simplified ship [22]. A DT has also been proposed as a natural step from model-based system engineering(MBSE) [26].
The monitoring of individual processes can be achieved through a DTH, which is a prerequisite for the development of a DT. Jagusch et al. have presented an approach to map the DHT in shipbuilding and utilize it for the creation of a DT [25]. The DTH has been recognized as an alternative to traditional 2D drawings, enabling shipbuilders to design and construct ships more rapidly and with greater efficiency. DTH provides shipbuilders with the capability to connect and synchronize their employees and suppliers with the shipyard, production planning, customer orders, requirements, and 3D models, ensuring comprehensive alignment across all aspects of the design process [26].
The substantial amount of data generated and collected by a DT presents challenges in terms of handling, processing, and storage. The DT–DTH framework proposed integrates two components that rely on the connectivity between the twin and thread for information flow and exchange, driving innovation and enhancing the optimization of operational processes and the traceability of information in the physical world, particularly within the context of Shipyard 4.0 [26].
5.1.3. Digital Shipyard
Woo et al. developed a new diagnostic framework for smart shipyard maturity assessment, which was applied to eight shipyards in South Korea to diagnose their smartness maturity [6]. Yi et al. presented a conceptual architecture design model for a civil smart shipbuilding factory. This included the functional definition of a smart shipyard, an initial conceptual model of a smart shipyard, an information network platform model, a smart production process model, and a smart implementation process model [28]. Navantia is updating its entire inner workings to keep up with Shipyard 4.0 [50]. Sedef Shipyard, the leading shipyard in Turkey, has started to implement a few projects within the scope of I4.0 transformation [77].
The application of I4.0 principles to shipyards is giving rise to Shipyard 4.0. These challenges can be categorized into three main groups: the vertical integration of production systems, the horizontal integration of new value creation networks, and the re-engineering of the entire production chain, which entails changes that impact the entire life cycle of each component of a ship [50].
5.1.4. Digital Shipbuilding and Lean Application
Schulze and Dallasega conducted an analysis and comparison of the application of lean tools and methods, as well as I4.0 technologies, in both practice and the literature. They found that companies in the construction, shipbuilding, and machine and plant manufacturing sectors utilize lean tools and I4.0 technologies to varying extents in an effort to minimize losses [27]. Beifert et al. explored the current limitations of lean manufacturing within the shipbuilding sector and its suppliers, examining the integration of modular manufacturing into a lean production process. They also discussed the potential benefits of adopting I4.0 methodologies [78]. Kunkera et al. studied the extent to which the application of the lean tool “value stream mapping” (VSM) has improved the shipbuilding sales process. They found significant cost savings in the sales process, as well as substantial growth in sales and the shipyard’s income [79].
The integration of lean principles with I4.0 has been discussed in the literature, but it has not been extensively researched, and empirical evidence in the engineering-to-order (ETO) context is sparse. Consequently, several practices from both domains are applied in practice to minimize losses, yet the reviewed literature suggests few successful implementations. Future research should aim to gather more empirical data on the application of lean and I4.0 practices to mitigate losses in companies with an ETO strategy and to provide best practices and guidelines [27].
5.1.5. Competence and Others
The integration of I4.0 relies on emerging technologies such as the IoT, big data, robotic automation of processes, 3D printing and AM, drones, and AI within the manufacturing industry. However, the adoption of these I4.0 technologies is impeded by a significant gap in workforce capability and capacity. Joseph Peter Kosteczko et al. have proposed the three workforce pillars of digital shipbuilding: career pathway mapping and curriculum development, outreach and workforce development, and R&D [80].
The growth in the Internet of Services (IoS) is linked to the increase in information and the management of big data. Rodrigo Pérez Fernández argued that it is necessary to provide CAD tools to carry out design for the IoS [81]. A case study of enabling technology and strategies for technology acquisition reveals three modes of technology acquisition, namely, alliances and licensing, monitor, acquire and merger, and competition [82].
5.2. Value Chain
5.2.1. Supply Chain
The shipbuilding industry exhibits a keen interest in embracing the transformative changes encapsulated by I4.0. The supply chain plays a pivotal role in any transition aimed at enhancing sustainability. The insights derived from these efforts are positioning the shipbuilding industry on the trajectory towards I4.0 [83]. The integration of I4.0 technologies within the shipbuilding sector has a profound impact on the resilience capabilities of the supply chain. Drawing on the resource-based view theory and dynamic capabilities, the primary focus is on integrating resilience capabilities with innovative technologies to strengthen market positioning, enhance knowledge and information sharing, and optimize supply chain design and integration [29].
5.2.2. Sustainability of Shipbuilding
Ramirez-Peña et encouraged research that is focused on the sustainability of the shipbuilding supply chain [11]. Nine digital solutions are posited to facilitate sustainable operations in the shipbuilding industry: optimizing ship design for enhanced energy efficiency, fostering efficient knowledge and information sharing, fostering closer collaboration with suppliers, enhancing information visibility and data accessibility, improving work conditions and productivity, streamlining manufacturing logistics, enabling continuous design optimization, enabling on-demand spare parts production, and enhancing the end-of-life management of ships [31].
Another study identified seven key technologies of I4.0 that significantly contribute to the sustainability of shipbuilding. The IoT generates a wealth of real-time data that form the foundation for subsequent analysis through big data analytics and simulation. The impact of simulation technology is particularly noteworthy. Cybersecurity is deemed indispensable, particularly in military projects, and serves as a safeguard for all blockchain operations, completing the circle of the most prominent technologies [32].
Environmental considerations, sustainability, and energy efficiency are pivotal for the advancement of technologies. However, they have not received adequate attention, and companies should prioritize the means to expand research in these areas. This is essential for the implementation of sustainable policies across all levels, leveraging the interconnectedness provided by the supply chain [11]. Green shipbuilding technologies contribute to mitigating threats to human health, environmental hazards, and property risks by diminishing emissions into the air, water, and land, conserving energy, and enhancing economic and social benefits. These technologies encompass a range of measures that collectively reduce emissions within the shipbuilding sector [84].
5.2.3. Digital Supply Chain
Lean, agile, resilient, and green (LARG) paradigms are recognized as the performance model for the shipbuilding supply chain in the context of I4.0. Twelve enabling technologies have been identified as the pivotal elements for enhancing LARG paradigms within the shipbuilding supply chain [1]. Diaz et al. conceptualized a framework, Ship-RISC, which provides a simulation framework for interactions between suppliers and sub-tier suppliers. This framework leverages real-time data using an I4.0 approach to generate both descriptive and prescriptive analytics, thereby supporting risk management assessment and decision-making processes [33].
The integration of enabling technologies should be executed in two distinct phases. The initial phase is aimed at establishing a sustainable shipbuilding supply chain that enhances economic, energy, and environmental aspects through the deployment of the following technologies: autonomous robots, AM, cloud computing, cybersecurity, and AR [1]. The second phase involves the convergence of functional and social aspects through the implementation of horizontal and vertical integration, big data, blockchain, simulation, IoT, and AI, thereby reinforcing resilient and agile paradigms. This approach ensures total visibility and connectivity across the shipbuilding supply chain [1].
5.2.4. Horizontal Integration
Given the unique characteristics of the shipbuilding industry and the intricate nature of shipbuilding projects, horizontal integration throughout value networks is a natural outcome of the growing number of project participants involved across the entire value chain, including contractors, customers, designers, subcontractors, and suppliers. The adoption of I4.0 technologies can contribute to establishing a conducive construction environment for enhanced collaboration and communication, such as through the utilization of a centralized, cloud-based collaboration platform [4].
Within the factory, technologies are employed to facilitate horizontal integration with external suppliers, thereby enhancing the delivery of raw materials and finished products within the supply chain. This integration has a direct impact on operational costs and delivery time [75]. Initially, horizontal integration, supported by value chain technologies, entails the real-time exchange of information about production orders with suppliers and distribution centers. Furthermore, when digital platforms equipped with analytical capabilities are interconnected with meteorological systems, potential delivery delays can be mitigated. These platforms can also facilitate customer interaction by monitoring product delivery and addressing specific customer needs. Additionally, digital platforms can connect different factory locations within a company, enabling the sharing of real-time operational information between them [75].
I4.0 is anticipated to reduce operating costs through comprehensive end-to-end digital integration. Further research is necessary to assess the diverse I4.0 solutions that enhance customer service, optimize supply chains, and foster sustainable practices such as remanufacturing and recycling. With the guidance of intelligent devices and smart production systems, I4.0 holds the potential to minimize production waste, overproduction, the movement of goods, and energy consumption. Future studies should concentrate on formulating frameworks for integrating smart production networks to maximize their collective benefits, including the sharing of resources like raw materials, power plants, and the workforce [2].
5.3. Smart Factory
5.3.1. Product Structure and Data Fusion
Digital continuity, I4.0, and PLM represent significant challenges for companies that span the entire product life cycle, such as those in the aerospace or shipbuilding industries. Functional and industrial product structure and process structure concepts are being introduced into the shipbuilding industry, and a process-oriented approach is being proposed based on the generation of a product structure for design and manufacturing, an engineering bill of material (eBOM), and a manufacturing bill of material (mBOM), as well as a process structure bill of process (BOP). This approach aims to ensure digital continuity through the use of advanced PLM tools [5].
Given the integration and autonomous navigation of vessels through information and communications technology (ICT), which involves the convergence of IoT, AI, and big data, it is imperative to acquire cutting-edge technology and vessel quality to participate in the revitalized shipbuilding industry. Research into the collection and management of big data in shipbuilding, tailored to the diverse data types and communication methods prevalent in the industry, is necessary. Furthermore, the exploration of how to utilize these big data to adapt to the rapidly evolving changes within the industry is also a critical area of inquiry [35].
5.3.2. Smart Design and Engineering
The automated ship design and optimization concept of HEAM is introduced to enable a more intelligent and efficient ship design process [43,46]. This enables optimal hull designs to be produced more rapidly with no user intervention [43]. An intelligent hull form design optimization concept was also discussed, which aims to upgrade hull form design into a smart design process by combining with I4.0 concepts [42]. An algorithm that automatically creates a hierarchical structure of blocks was also proposed based on the shipyard assembly process using geometric information extracted from a neutral computer-aided design format [44].
Other key topics include the application of CAutoD to realize the I4.0 concept for smart design of future ships and the design of smart ships throughout their life cycle. The concepts of morphing and free-form deformation are integrated into an evolutionary algorithm to automate the design and optimization process of the hull form [85]. A simulation tool that facilitates ship design is introduced, and a ranking of various solutions is conducted to reflect the importance of attributes in defining each evaluation criterion. This ranking also aids in defining alternative layouts for power plant generation [47].
The significance of data analysis in transforming data into actionable knowledge is crucial for shipbuilding design and engineering. A data-driven performance evaluation method should be implemented to enable the investigation of relationships between different parameters, the development of predictive algorithms, the calculation of cost indices, and the ranking of numerous structural design alternatives [45].
5.3.3. Planning System
To create an integrated production planning system, requirement analysis, architecture design, implementation, and testing were employed [39,40].
Shipbuilding planning processes are restructured through the implementation of an integrated planning and scheduling system. Additionally, a process-centric discrete event system simulation can be employed to further enhance the quality of planning [40]. Production planning is a critical component of production management within manufacturing enterprises. Since the advent of computerization, modern production planning has evolved, initially with material requirement planning (MRP), and has since advanced to encompass ERP, advanced planning and scheduling (APS), and supply chain management (SCM) [39].
5.3.4. Smart Product and Autonomous Ship
Im et al. proposed a smart autonomous ship and shore architecture, where information between the smart autonomous ship and a data center is converged and organically integrated and operated through the application of advanced technologies to both the ship and a shore-based data center [36].
Research and engineering practices related to ship automatic berthing control both domestically and internationally have been integrated with the “Shipbuilding I4.0 and e-Navigation” project of the International Maritime Organization (IMO), aiming to align the development of ship automatic berthing trends with unified models, intelligent control, the entire berthing process, precise measurements, and practical engineering implementations [37].
In response to the rising number of ship collision incidents attributed to human errors and in alignment with the principles of I4.0, Jeon et al. developed advanced navigation assistance systems designed to facilitate safe navigation. These systems provide users with intuitive information by overlaying camera, radar ARPA, and AIS data on a single image display. This advancement is expected to contribute to the advancement of autonomous and unmanned ship technology [38].
5.3.5. End-to-End Integration
Factory-level requirements necessitate the intelligent implementation of step-by-step processes, including product development, production planning, process control, quality control, facility management, and logistics management [86]. The integration of cyber components and operational technology (OT)–IT networks, both vertically (as part of CPSs), is fundamental for the realization of a smart factory [76]. Moreover, the connectivity layer, which facilitates OT integration and enables OT–IT integration, should also receive increased attention [76].
End-to-end digital integration of engineering throughout the entire value chain is achieved through the deployment of technologies such as information systems, CPSs, and mobile computing. This integration ensures an integrated approach to digital engineering during all stages of the shipbuilding project, including tendering, briefing, design, planning, construction, and use and maintenance [4]. At the factory level, this integration encompasses not only enterprise information systems like PLM, ERP, SCM, and manufacturing execution systems (MESs) but also factory energy management systems (FEMSs) [86].
Within the smart factory, smart working technologies are utilized to support workers’ tasks, thereby enhancing their productivity and flexibility in meeting the demands of the manufacturing system [75]. These technologies aim to create optimal working conditions for employees, which in turn boosts productivity and provides remote access to shop floor information [75].
Smart products consider the external value-added aspects of the products, where customer information and data are integrated with the production system. Technologies related to product offerings are a key component of smart products [75]. The ISA-95 models have been expanded by incorporating the product domain into the model. The cloud is positioned at the apex of the model, facilitating horizontal integration [76].
In the I4.0 paradigm, humans and machines are viewed as an integrated socio-technical system. I4.0 also encompasses the remote control of operational activities related to smart products through mobile devices, which enhances decision-making processes and improves the visibility of information within the process. These aspects of smart working and smart products are integral to the concept of a smart factory [75].
Lean production has been instrumental in mass production systems, focusing on enhanced product quality to meet customer expectations. I4.0 and lean production methodologies can mutually reinforce each other. The integration of various I4.0 technologies, which enhance the computing capabilities of an organization, or the adaptation of these new technologies, requires careful analysis to understand their impact on lean manufacturing practices. Research on the practical implementation of lean manufacturing within smart production systems is necessary. These studies could involve developing conceptual frameworks that integrate lean principles into fully functional CPSs [2].
5.4. Smart Manufacturing
5.4.1. Robotic Welding System and Pipe Workshop
Morgado-Estevez et al. developed an automated robotic welding system tailored for shipbuilding in large shipyards, with the system specifically designed for use at the Navantia shipyard [49]. Additionally, the development of a customized interface for a robotic welding application at Navantia has been developed [48].
The concept of a smart pipe is characterized as an object capable of periodically transmitting signals, thereby enabling the provision of enhanced services within a shipyard. Various technologies have been evaluated, with passive and active radio frequency identification (RFID) technologies being identified as the most suitable for creating such a system. Additionally, promising indoor positioning results were achieved in a pipe workshop, including the implementation of multi-antenna algorithms and Kalman filtering [50].
An active ultrahigh-frequency (UHF) RFID real-time monitoring pipe system, which incorporates fingerprinting and received signal strength (RSS) stabilization techniques, demonstrates the capability to automatically detect and present significant events in a pipe workshop to operators [51]. The viability of employing Bluetooth 5 in a pipe workshop has been validated through both simulated models and practical experiments, utilizing an in-house developed three-dimensional ray launching (3D RL) simulation tool [87].
5.4.2. Pre-Outfitting Workshop
The development of a monitoring platform is essential for the establishment of a visualization system that is integral to realizing I4.0 in shipbuilding. A visualization system for the cutting and subassembly processes has been validated and confirmed to demonstrate the practicality of the monitoring platform [53]. Furthermore, a novel vision AI deep-learning system for the detection of painting defects was developed and tested in an actual shipyard production line. Experiments were conducted to optimize and evaluate the system’s performance [54].
The integration of the IoT within a factory setting presents a vast array of opportunities, given its potential for synergy with other technologies. One such application is the modeling and simulation of processes and plants [52]. Research has been conducted to implement an interactive approach utilizing the IoT on a closed power-loop test bench. This test bench is equipped with advanced sensors and is specifically designed to evaluate high-power thrusters prior following their installation on high-speed craft. The IoT platform demonstrates the effectiveness of the decision-making support tool in enhancing the design of propulsion systems and enhancing their efficiency in comparison to traditional systems [88].
5.4.3. Manufacturing Support
The supporting system integrates sensor data acquisition with concepts for developing context-aware systems, including context modeling and contextual data information provision. This integration enables the development of an industrial IoT context-aware information system, which provides decision support for both mobile and static operators and supervisors [56]. The development of flexible and safe HRC tools for use in shipbuilding workplaces enables seamless interaction and collaboration between operators and other resources during block assembly processes [57].
Additionally, an indoor positioning system (IPS) was implemented at the Sedef Shipyard, and the most appropriate technology was identified through the evaluation of various IPS technology options [89]. A methodology for selecting auto-identification technologies for Industry 5.0 factories was utilized to identify the primary components of a ship during its construction and repair. Meticulous selection and evaluation of the tags enable product identification and tracking, even in areas with a high concentration of metallic objects [58].
The methodology employed enables the objective measurement of process progress based on data, with shipyard ship block assembly plants serving as an example. This approach facilitates the measurement of overall ship block progress performance. IoT-based performance collection methods for mounting and welding activities, which constitute a significant portion of the work in ship block assembly, have been identified as capable of automatically measuring actual work volume without human intervention. In the context of collecting welding performance data, an ML-based performance measurement method can analyze sensor data from a welding machine to assess performance [59].
5.4.4. Vertical Integration
Smart manufacturing encompasses technologies for product processing within the production system. It is the foundational and primary objective of I4.0, with the smart factory representing its extension. Frank et al. further subdivided the technologies related to smart manufacturing into six main categories: (i) vertical integration, (ii) virtualization, (iii) automation, (iv) traceability, (v) flexibility, and (vi) energy management [75].
Vertical integration and networked manufacturing systems are achieved through the integration of IT systems, processes, and data flows within a company. This integration is facilitated by the use of digitization and virtualization technologies or by integrating automation technologies while considering the OT and machine-level requirements [4]. The OT segment encompasses the physical components [76]. Machine-level requirements refer to the automation of factory facilities, such as production, logistics, inspection, and equipment information networking, which should be implemented [86].
Factory vertical integration involves the integration of advanced ICT systems that connect all levels of the company, from the shop floor to middle and top management. This integration aims to reduce the reliance on human intervention in decision-making processes. The journey towards vertical integration involves programmable logic controllers (PLCs), supervisory control and data acquisition (SCADA) systems, MESs, and ERP systems. When these systems are seamlessly integrated, the information about production orders flows in both directions—from the ERP to MES and then to the SCADA system, and vice versa—enabling the efficient deployment of enterprise resources in manufacturing. Vertical integration enhances transparency and control over the production process, thereby supporting improved decision-making on the shop floor [75].
5.5. Smart Infrastructure and Technologies
5.5.1. Augmented Reality
Industrial automation and robotics (IAR) hardware and software can be employed for smart manufacturing and shipbuilding. The primary use cases of IAR in shipbuilding and IAR communications architecture are being implemented by Navantia at its shipyard in Ferrol, Spain [62]. Furthermore, when multiple IAR clients access the IAR service concurrently, the cloudlet is significantly faster than the fog computing system, with some instances showing a speed advantage of more than fourfold [63].
The various factors that impact the design of an system for an I4.0 shipyard encompass diverse scenarios such as workshops and the ship itself [64]. Vidal-Balea et al. have developed an industrial AR collaborative application. This application is based on the Microsoft HoloLens smart glasses and is designed to assist and guide shipyard operators during their training and assembly tasks [65]. Molina Vargas et al. presented various concrete AR applications in maritime engineering, production, design, operation, and maintenance [12].
A collaborative industrial AR system for training and guidance in assembly processes was developed. The DT system was assessed to identify limitations in handling large volumes of data, which can lead to rendering delays that are almost non-existent in the industrial IoT layer [66]. The usefulness of AR in different sectors was presented, such as in smart factories, shipyard building, online shopping, surgery, and education [67]. The current state of the art in the application of commercial and academic AR and mixed reality (MR) solutions to shipbuilding offers a comprehensive review of the latest advancements in the integration of AR and MR within the shipbuilding domain. It also includes valuable insights and best practices for future developers [68].
5.5.2. Big Data
A hypernetwork-based context-aware deep learning knowledge (DLK) proactive feedback approach is presented to anticipate potential design quality issues during the design process. This approach provides relevant DLK and assists designers in minimizing the recurrence of previous quality problems in design for manufacturing (DFM). Experimental results indicate that the proposed approach is effective and demonstrates positive performance in DFM [71].
Big data analytics holds the potential to significantly transform the shipping industry. The applications and challenges associated with implementing big data in the industry must be addressed [72]. DT-based models and platforms are developed using the foundational modeling capabilities of Maya and the scene-rendering features of Unity 3D. To tackle the integration of multisource heterogeneous data in the ship operation process, an enhanced Bayesian neural network (BNN)-based algorithm is integrated into the DT-based models. This integration allows for the extraction and aggregation of the collected data accordingly [90].
5.5.3. Simulation and Optimization
An integrated offshore drilling platform simulation has been developed to provide a virtual onboard experience. This simulation includes a virtual driller’s cabin for handling drilling equipment, a well control simulator to manage kicks, a dynamic positioning system (DPS) simulator to control the platform’s motion, a walk-through simulator for monitoring operations from a worker’s perspective on the platform, and dynamic analyses of the heave compensation system. All systems share data in real time, enabling the virtual offshore drilling platform to effectively demonstrate the diverse scenarios that arise during drilling operations [69].
Berth planning in shipyards is currently being carried out using an heuristic method that incorporates specific rules, such as berth priority and proximity to the delivery date. To optimize the berth planning, a constraint satisfaction technique is employed. Additionally, a workload prediction model has been developed using supervised learning with a deep neural network. The proposed methods have been tested with actual shipyard data, which indicate improved results [70].
5.5.4. Other Core Technologies
The core technologies of infrastructure are composed of what are known as new ICT, which include, the IoT, cloud services, CPSs, AM, big data, and analytics. These technologies are considered foundational, as they are present across all dimensions and various technologies within those dimensions of the proposed framework. They enable interconnectivity and provide the intelligence necessary for the new manufacturing systems [75].
It is important to note and further investigate within the shipbuilding industry that the CPSs that enable the entire factory are adaptable. These systems are formed by integrating physical systems. In the context of manufacturing, this includes machines like CNC machines, lathes, mills, or grinders, with processing units such as computers [2]. Kavallieratos and Katsikas evaluated the cyber risks associated with CPSs on board modern and future digitized ships. The results of this assessment were intended to inform the design of a security architecture for cyber-enabled ships [73]. Ziółkowski and Dyl provided insights into the implementation of AM in shipbuilding, highlighting the potential benefits, opportunities, and associated threats [13].
AI plays a pivotal role in supporting smart manufacturing in various ways. Machines equipped with AI can automatically detect product nonconformities at earlier stages of the production process, thereby enhancing quality control and reducing production costs. Moreover, AI complements systems like ERP by predicting long-term production demands [75].
6. Conclusions
I4.0 offers shipbuilding companies the opportunity to streamline complexity, foster information exchange, and boost productivity and quality. The reviewed studies reveal that digital shipbuilding strategies are being implemented in shipyards to varying degrees.
This study conducted an SLR on 68 selected publications through an appropriate review methodology to investigate the current state of research on I4.0 application in shipbuilding, identifying key research categories and methodologies. The study’s main contribution is offering theoretical insights and managerial guidance for supporting the digital transformation of the shipbuilding industry. The adoption of I4.0 can help the shipbuilding industry evolve into a technology-driven sector, though further research and practice are necessary to fully realize its potential in this complex environment.
6.1. Theoretical and Managerial Contribution
The study’s primary contribution is the proposal of a Shipbuilding 4.0 framework that integrates five main components: concepts, value chain smart factory, smart manufacturing, infrastructure, and technologies. The concept encompasses the fundamental ideas and theoretical foundations of digital shipbuilding, providing guiding principles and objectives for the entire framework. The value chain focuses on the entire value creation process in shipbuilding, from raw material procurement to the final product delivery. The smart factory concept involves the application of I4.0 technologies in shipyards to improve production efficiency and flexibility. Smart manufacturing is focused on the application of manufacturing technologies to the actual production process of shipbuilding. Infrastructure and technologies cover the infrastructure and technologies that support digital shipbuilding.
This framework offers a holistic view of I4.0 adoption patterns in the shipbuilding sector, contrasting previous models that presented idealized stages with empirical evidence. The framework serves as a theoretical contribution to the understanding of I4.0 application in the shipbuilding industry, offering practical guidelines for practitioners and policymakers in the field of sustainable shipbuilding development. The framework underscores sustainability as a target element of digital shipbuilding, encouraging managers to persevere through I4.0 adoption challenges to achieve a sustainable shipbuilding environment.
It also suggests avenues for further research, including the development of measurement constructs for I4.0 applications. The proposed framework guides practitioners to leverage modern technologies to drive shipbuilding organizational trends and make real-time decisions, offering customized vessels with shorter lead times and optimizing resource usage and energy consumption.
6.2. Limitations and Outlook
The study’s interpretation should consider two key points: its limited scope to English-language papers from 2010 onwards, which may have overlooked relevant studies, and the exclusion of practical publications and case studies from non-peer-reviewed sources, which could have provided valuable insights. The study’s methodology has limitations related to potential subjectivity in paper analysis and categorization, though efforts were made to address this using a specific protocol and a framework with categorization from related literature.
The reviewed studies indicate that current research primarily focuses on the technical aspects of I4.0 technologies, with shipbuilding companies being hesitant to adopt new I4.0 technologies due to high investment costs and uncertainty about the benefits. The study’s findings and limitations present opportunities for further research and address the challenges of I4.0 adoption in the shipbuilding industry.
Conceptualization, X.Z. and D.C.; methodology, X.Z. and D.C.; software, X.Z.; validation, X.Z. and D.C.; formal analysis, X.Z.; investigation, X.Z.; resources, X.Z.; data curation, X.Z.; writing—original draft preparation, X.Z.; writing—review and editing, X.Z. and D.C.; visualization, X.Z. and D.C.; supervision, D.C.; project administration, X.Z. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Author Xiaowei Zhang was employed by the company COSCO Shipping Heavy Industry Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Enlarge this image.Figure 2. (a) Distribution of research categories; (b) distribution of research categories during 2015–2022.
Enlarge this image.Figure 8. Co-occurrence of keywords network—keywords of 4 minimum after removing keyword of “shipbuilding”.
Reference type.
| Reference Type | Title |
|---|---|
| Book | 2 |
| Collected work | 1 |
| Conference proceedings | 1 |
| Contribution in… | 18 |
| Journal article | 46 |
Appendix A. Four Studies as Notable Exemplars
| Authors | Year | Title | Abstract |
| Ramirez-Pena, Magdalena; Abad Fraga, Francisco J.; Salguero, Jorge; Batista, Moises | 2020 | Assessing sustainability in the shipbuilding supply chain 4.0: A systematic review | This article aims to gain a comprehensive understanding of the current state of the art in shipbuilding that is compatible with the key enabling technologies of I4.0. This is achieved through a detailed examination of each technology, utilizing a systematic review of the scientific literature to categorize these technologies [ |
| Vargas, D.G.M.; Vijayan, K. K.; Mork, O. J. | 2020 | Augmented reality for future research opportunities and challenges in the shipbuilding industry: A literature review | This paper seeks to analyze the most recent research and the most advanced industrial AR applications within the shipbuilding and maritime sectors. The objective is to ascertain how these advancements can foster new research opportunities and contribute to the concurrent development of the learning factory concept at the Norwegian University of Science and Technology (NTNU) in Ålesund [ |
| Ziółkowski, M.; Dyl, T. | 2020 | Possible applications of additive manufacturing technologies in shipbuilding: A review | This paper offers insights into ongoing R&D efforts related to the integration of AM in the shipbuilding industry. It explores the potential benefits, opportunities, and associated threats associated with the implementation of AM technology [ |
| Stanic, Venesa; Hadjina, Marko; Fafandjel, Niksa; Matulja, Tin | 2018 | Toward Shipbuilding 4.0—An Industry 4.0 changing the face of the shipbuilding industry | The objective of this article is to conduct a comprehensive review of the current academic and industrial advancements in what is known as the Shipbuilding 4.0 (or Shipping 4.0, Maritime 4.0, Shipyard 4.0) wave within the shipbuilding sector. The analyzed publications were assessed across various topics and their impact on the industrial aspects of society [ |
Appendix B. Research Protocol
| 1. Databases | Scopus and WoS | ||
| 2. Search Criteria and Screening | WoS | Scopus | |
| 2.1 Search Terms | (industry 4.0 OR Smart manufacturing or smart factory) and (Shipbuilding or shipyard) | 232 | 131 |
| 2.2 Year of Publication | 2010 to 2023 | 207 | 129 |
| 2.3 Language | English | 190 | 126 |
| 2.4 Subject Area(s) | Engineering, computer science, science technology other topics | ||
| 2.5 Document Type(s) | Articles, meeting, reviews, book chapters | ||
| 2.6 Date of Search | 17 November 2023 | 137 | 120 |
| 3. Exclusion Criteria | 3.1 Total after elimination of duplicate records | 178 | |
| 3.2 Article does not address I4.0 issues in shipbuilding (scope) after reading title and keywords | 152 | ||
| 4. Selection | 4.1 After first reading according to quality index | 68 | |
| 5. Analysis | 5.1 Descriptive analysis | ||
| 5.2 Content analysis (second reading) | |||
| 6.Classification | |||
| 6.1 Research content | Concept | ||
| Technology | |||
| Value chain | |||
| Smart factory | |||
| Smart manufacturing | |||
| Smart work and data, etc. | |||
| 6.2 Research approach | Review | ||
| Conceptual | |||
| Empirical: | |||
| Case study | |||
| Simulation | |||
| Prototypes | |||
| Experimentation | |||
| Survey | |||
| 6.3 Pillars of Technology of I4.0 | Big data and analytics | ||
| Autonomous robots | |||
| Simulation | |||
| Horizontal and vertical system integration | |||
| The industrial IoT | |||
| Cybersecurity | |||
| The cloud | |||
| AM | |||
| AR | |||
| 6.2 Life cycle phase | Design | ||
| Production/manufacturing | |||
| Operation—shipyard | |||
| Operation—ships | |||
| Retirement | |||
| Life cycle | |||
References
1. Ramirez-Peña, M.; Sánchez Sotano, A.J.; Pérez-Fernandez, V.; Abad, F.J.; Batista, M. Achieving a sustainable shipbuilding supply chain under I4.0 perspective. J. Clean. Prod.; 2020; 244, 118789. [DOI: https://dx.doi.org/10.1016/j.jclepro.2019.118789]
2. Kamble, S.S.; Gunasekaran, A.; Gawankar, S.A. Sustainable Industry 4.0 framework: A systematic literature review identifying the current trends and future perspectives. Process Saf. Environ. Prot.; 2018; 117, pp. 408-425. [DOI: https://dx.doi.org/10.1016/j.psep.2018.05.009]
3. Zutin, G.C.; Barbosa, G.F.; de Barros, P.C.; Tiburtino, E.B.; Kawano, F.L.F.; Shiki, S.B. Readiness levels of Industry 4.0 technologies applied to aircraft manufacturing-a review, challenges and trends. Int. J. Adv. Manuf. Technol.; 2022; 120, pp. 927-943. [DOI: https://dx.doi.org/10.1007/s00170-022-08769-1]
4. Oesterreich, T.D.; Teuteberg, F. Understanding the implications of digitisation and automation in the context of Industry 4.0: A triangulation approach and elements of a research agenda for the construction industry. Comput. Ind.; 2016; 83, pp. 121-139. [DOI: https://dx.doi.org/10.1016/j.compind.2016.09.006]
5. Rubio, L.R.; Mariscal, A.M.; Alvarez, E.P.; Mas, F. A Process-Oriented Approach for Shipbuilding Industrial Design Using Advanced PLM Tools. Proceedings of the IFIP International Conference on Product Lifecycle Management;; Springer: Cham, Switzerland, 2023; pp. 144-152.
6. Woo, J.H.; Zhu, H.; Lee, D.K.; Chung, H.; Jeong, Y. Assessment Framework of Smart Shipyard Maturity Level via Data Envelopment Analysis. Sustainability; 2021; 13, 1964. [DOI: https://dx.doi.org/10.3390/su13041964]
7. Wu, K.; Xu, J.; Zheng, M. Industry 4.0: Review and proposal for implementing a smart factory. Int. J. Adv. Manuf. Technol.; 2024; 133, pp. 1331-1347. [DOI: https://dx.doi.org/10.1007/s00170-024-13839-7]
8. Xu, X.; Lu, Y.; Vogel-Heuser, B.; Wang, L. Industry 4.0 and Industry 5.0—Inception, conception and perception. J. Manuf. Syst.; 2021; 61, pp. 530-535. [DOI: https://dx.doi.org/10.1016/j.jmsy.2021.10.006]
9. Winkelhaus, S.; Grosse, E.H. Logistics 4.0: A systematic review towards a new logistics system. Int. J. Prod. Res.; 2020; 58, pp. 18-43. [DOI: https://dx.doi.org/10.1080/00207543.2019.1612964]
10. Stanić, V.; Hadjina, M.; Fafandjel, N.; Matulja, T. Toward Shipbuilding 4.0—An Industry 4.0 Changing the Face of the Shipbuilding Industry. Brodogradnja; 2018; 69, pp. 111-128. [DOI: https://dx.doi.org/10.21278/brod69307]
11. Ramirez-Peña, M.; Abad Fraga, F.J.; Salguero, J.; Batista, M. Assessing Sustainability in the Shipbuilding Supply Chain 4.0: A Systematic Review. Sustainability; 2020; 12, 6373. [DOI: https://dx.doi.org/10.3390/su12166373]
12. Molina Vargas, D.G.; Vijayan, K.K.; Mork, O.J. Augmented Reality for Future Research Opportunities and Challenges in the Shipbuilding Industry: A Literature Review. Procedia Manuf.; 2020; 45, pp. 497-503. [DOI: https://dx.doi.org/10.1016/j.promfg.2020.04.063]
13. Ziółkowski, M.; Dyl, T. Possible Applications of Additive Manufacturing Technologies in Shipbuilding: A Review. Machines; 2020; 8, 84. [DOI: https://dx.doi.org/10.3390/machines8040084]
14. Mittal, S.; Khan, M.A.; Romero, D.; Wuest, T. A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). J. Manuf. Syst.; 2018; 49, pp. 194-214. [DOI: https://dx.doi.org/10.1016/j.jmsy.2018.10.005]
15. Mauro, F.; Kana, A.A. Digital twin for ship life-cycle: A critical systematic review. Ocean. Eng.; 2023; 269, 113479. [DOI: https://dx.doi.org/10.1016/j.oceaneng.2022.113479]
16. Lemstra, M.A.M.S.; de Mesquita, M.A. Industry 4.0: A tertiary literature review. Technol. Forecast. Soc. Chang.; 2023; 186, 122204. [DOI: https://dx.doi.org/10.1016/j.techfore.2022.122204]
17. Nosalska, K.; Piątek, Z.M.; Mazurek, G.; Rządca, R. Industry 4.0: Coherent definition framework with technological and organizational interdependencies. J. Manuf. Technol. Manag.; 2020; 31, pp. 837-862. [DOI: https://dx.doi.org/10.1108/JMTM-08-2018-0238]
18. Lepasepp, T.K.; Hurst, W. A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing. Future Internet; 2021; 13, 264. [DOI: https://dx.doi.org/10.3390/fi13100264]
19. Rüßmann, M.; Lorenz, M.; Gerbert, P.; Waldner, M.; Engel, P.; Harnisch, M.; Justus, J. Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries. BCG Global [Online], April 9. 2015; Available online: https://www.bcg.com/publications/2015/engineered_products_project_business_industry_4_future_productivity_growth_manufacturing_industries (accessed on 17 September 2023).
20. Dolz, M.; Martinez, X.; Sá, D.; Silva, J.; Jurado, A. Composite materials, technologies and manufacturing: Current scenario of European Union shipyards. Ships Offshore Struct.; 2023; pp. 1-16. [DOI: https://dx.doi.org/10.1080/17445302.2023.2229160]
21. Ang, J.; Goh, C.; Saldivar, A.; Li, Y. Energy-Efficient Through-Life Smart Design, Manufacturing and Operation of Ships in an Industry 4.0 Environment. Energies; 2017; 10, 610. [DOI: https://dx.doi.org/10.3390/en10050610]
22. Iwańkowicz, R.; Rutkowski, R. Digital Twin of Shipbuilding Process in Shipyard 4.0. Sustainability; 2023; 15, 9733. [DOI: https://dx.doi.org/10.3390/su15129733]
23. Kunkera, Z.; Opetuk, T.; Hadžić, N.; Tošanović, N. Using Digital Twin in a Shipbuilding Project. Appl. Sci.; 2022; 12, 12721. [DOI: https://dx.doi.org/10.3390/app122412721]
24. Magalhães, L.C.; Magalhães, L.C.; Ramos, J.B.; Moura, L.R.; de Moraes, R.E.N.; Gonçalves, J.B.; Hisatugu, W.H.; Souza, M.T.; de Lacalle, L.N.L.; Ferreira, J.C.E. Conceiving a Digital Twin for a Flexible Manufacturing System. Appl. Sci.; 2022; 12, 9864. [DOI: https://dx.doi.org/10.3390/app12199864]
25. Jagusch, K.; Sender, J.; Jericho, D.; Flügge, W. Digital thread in shipbuilding as a prerequisite for the digital twin. Procedia CIRP; 2021; 104, pp. 318-323. [DOI: https://dx.doi.org/10.1016/j.procir.2021.11.054]
26. Pang, T.Y.; Pelaez Restrepo, J.D.; Cheng, C.-T.; Yasin, A.; Lim, H.; Miletic, M. Developing a Digital Twin and Digital Thread Framework for an ‘Industry 4.0’ Shipyard. Appl. Sci.; 2021; 11, 1097. [DOI: https://dx.doi.org/10.3390/app11031097]
27. Schulze, F.; Dallasega, P. Lean and Industry 4.0 mitigating common losses in Engineer-to-Order theory and practice: An exploratory study. Flex. Serv. Manuf. J.; 2023; pp. 1-41. [DOI: https://dx.doi.org/10.1007/s10696-023-09503-z]
28. Yi, Z.; Mi, S.; Tong, T.; Li, H.; Lin, Y.; Wang, W.; Li, J. Intelligent initial model and case design analysis of smart factory for shipyard in China. Eng. Appl. Artif. Intell.; 2023; 123, 106426. [DOI: https://dx.doi.org/10.1016/j.engappai.2023.106426]
29. Centobelli, P.; Cerchione, R.; Maglietta, A.; Oropallo, E. Sailing through a digital and resilient shipbuilding supply chain: An empirical investigation. J. Bus. Res.; 2023; 158, 113686. [DOI: https://dx.doi.org/10.1016/j.jbusres.2023.113686]
30. Ramirez-Peña, M.; Mayuet, P.F.; Vazquez-Martinez, J.M.; Batista, M. Sustainability in the Aerospace, Naval, and Automotive Supply Chain 4.0: Descriptive Review. Materials; 2020; 13, 5625. [DOI: https://dx.doi.org/10.3390/ma13245625]
31. Strandhagen, J.W.; Buer, S.-V.; Semini, M.; Alfnes, E.; Strandhagen, J.O. Sustainability challenges and how Industry 4.0 technologies can address them: A case study of a shipbuilding supply chain. Prod. Plan. Control.; 2022; 33, pp. 995-1010. [DOI: https://dx.doi.org/10.1080/09537287.2020.1837940]
32. Ramirez, M.; Pérez, V.; Gómez, A.; Montaño, R.; Batista, M. Supply chain production planning of a manufacturing project system 4.0: Case study: Shipbuilding. IOP Conf. Ser. Mater. Sci. Eng.; 2021; 1193, 12051. [DOI: https://dx.doi.org/10.1088/1757-899X/1193/1/012051]
33. Diaz, R.; Smith, K.; Acero, B.; Longo, F.; Padovano, A. Developing an Artificial Intelligence Framework to Assess Shipbuilding and Repair Sub-Tier Supply Chains Risk. Procedia Comput. Sci.; 2021; 180, pp. 996-1002. [DOI: https://dx.doi.org/10.1016/j.procs.2021.01.363]
34. Camarinha-Matos, L.M.; Fornasiero, R.; Ramezani, J.; Ferrada, F. Collaborative Networks: A Pillar of Digital Transformation. Appl. Sci.; 2019; 9, 5431. [DOI: https://dx.doi.org/10.3390/app9245431]
35. Park, S.; Huh, J.-H. Study on PLM and Big Data Collection for the Digital Transformation of the Shipbuilding Industry. J. Mar. Sci. Eng.; 2022; 10, 1488. [DOI: https://dx.doi.org/10.3390/jmse10101488]
36. Im, I.; Shin, D.; Jeong, J. Components for Smart Autonomous Ship Architecture Based on Intelligent Information Technology. Procedia Comput. Sci.; 2018; 134, pp. 91-98. [DOI: https://dx.doi.org/10.1016/j.procs.2018.07.148]
37. Qiang, Z.; Im, N.-K.; Zhongyu, D.; Meijuan, Z. Review on the Research of Ship Automatic Berthing Control. Offshore Robotics; Springer: Singapore, 2022; pp. 87-109.
38. Jeon, M.; Park, J.; Woo, J. Development of HHI’s Advanced Navigation Assistance System for Safe Voyage. IFAC-Pap.; 2019; 52, pp. 111-113. [DOI: https://dx.doi.org/10.1016/j.ifacol.2019.12.292]
39. Cho, S.; Lee, J.M.; Woo, J.H. Development of production planning system for shipbuilding using component-based development framework. Int. J. Nav. Archit. Ocean. Eng.; 2021; 13, pp. 405-430. [DOI: https://dx.doi.org/10.1016/j.ijnaoe.2021.05.001]
40. Lee, Y.G.; Ju, S.; Woo, J.H. Simulation-based planning system for shipbuilding. Int. J. Comput. Integr. Manuf.; 2020; 33, pp. 626-641. [DOI: https://dx.doi.org/10.1080/0951192X.2020.1775304]
41. Li, J.; Yin, W.; Yang, B.; Chen, L.; Dong, R.; Chen, Y.; Yang, H. Modeling of Digital Twin Workshop in Planning via a Graph Neural Network: The Case of an Ocean Engineering Manufacturing Intelligent Workshop. Appl. Sci.; 2023; 13, 10134. [DOI: https://dx.doi.org/10.3390/app131810134]
42. Ang, J.H.; Goh, C.; Jirafe, V.P.; Li, Y. Efficient Hull Form Design Optimisation Using Hybrid Evolutionary Algorithm and Morphing Approach. Proceedings of the International Conference on Computer Applications in Shipbuilding; Singapore, 26–28 September 2017.
43. Ang, J.H.; Goh, C.; Choo, C.T.; Lee, Z.M.; Jirafe, V.P.; Li, Y. Evolutionary Computation Automated Design of Ship Hull Forms for the Industry 4.0 Era. Proceedings of the 2019 IEEE Congress on Evolutionary Computation (CEC); Wellington, New Zealand, 10–13 June 2019; pp. 2347-2354.
44. Son, Y.-B.; Nam, J.-H. Creation of hierarchical structure for computerized ship block model based on interconnection relationship of structural members and shipyard environment. Int. J. Nav. Archit. Ocean. Eng.; 2022; 14, 100455. [DOI: https://dx.doi.org/10.1016/j.ijnaoe.2022.100455]
45. Bilen, U.; Helvacioglu, S. Data Driven Performance Evaluation in Shipbuilding. Brodogr. Int. J. Nav. Archit. Ocean. Eng. Res. Dev.; 2020; 71, pp. 39-51. [DOI: https://dx.doi.org/10.21278/brod71403]
46. Cho, S.; Lee, J.M.; Woo, J.H. Ship Design with a Morphing Evolutionary Algorithm: 2020 conference proceedings. Proceedings of the 2020 IEEE Congress on Evolutionary Computation (CEC); Glasgow, UK, 19–24 July 2020.
47. Gianni, M.; Bucci, V.; Marinò, A. System simulation as decision support tool in ship design. Procedia Comput. Sci.; 2021; 180, pp. 754-763. [DOI: https://dx.doi.org/10.1016/j.procs.2021.01.323]
48. Galindo, P.L.; Morgado-Estévez, A.; Aparicio, J.L.; Bárcena, G.; Soto-Núñez, J.A.; Chavera, P.; Abad Fraga, F.J. Development of a Customized Interface for a Robotic Welding Application at Navantia Shipbuilding Company. Iberian Robotics Conference; Springer: Cham, Switzerland, 2018; pp. 43-52.
49. Morgado-Estevez, A.; Galindo, P.L.; Aparicio-Rodriguez, J.-L.; Diaz-Cano, I.; Rioja-del-Rio, C.; Soto-Nuñez, J.A.; Chavera, P.; Abad-Fraga, F.J. Towards Automated Welding in Big Shipbuilding Assisted by Programed Robotic Arm Using a Measuring Arm. Robot 2017. Volume 2: Third Iberian Robotics Conference; Ollero, A.; Sanfeliu, A.; Montaño, L.; Lau, N.; Cardeira, C. Springer: Cham, Switzerland, 2018; pp. 53-63. ISBN 978-3-319-70835-5
50. Fraga-Lamas, P.; Noceda-Davila, D.; Fernández-Caramés, T.M.; Díaz-Bouza, M.A.; Vilar-Montesinos, M. Smart Pipe System for a Shipyard 4.0. Sensors; 2016; 16, 2186. [DOI: https://dx.doi.org/10.3390/s16122186] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27999392]
51. Fraga-Lamas, P.; Fernandez-Carames, T.M.; Noceda-Davila, D.; Diaz-Bouza, M.A.; Castedo, L. Enabling automatic event detection for the pipe workshop of the shipyard 4.0. Proceedings of the 2017 56th FITCE Congress; Madrid, Spain, 14–15 September 2017; pp. 20-27.
52. Munín-Doce, A.; Díaz-Casás, V.; Trueba, P.; Ferreno-González, S.; Vilar-Montesinos, M. Industrial Internet of Things in the production environment of a Shipyard 4.0. Int. J. Adv. Manuf. Technol.; 2020; 108, pp. 47-59. [DOI: https://dx.doi.org/10.1007/s00170-020-05229-6]
53. Aoyama, K.; Yotsuzuka, T.; Tanaka, Y.; Tanabe, Y. “Monitoring Platform” of Monitoring and Visualizing System for Shipyard: Application to Cutting and Subassembly Processes. Practical Design of Ships and Other Floating Structures; Springer: Singapore, 2021; pp. 321-337.
54. Ma, H.; Lee, S. Smart System to Detect Painting Defects in Shipyards: Vision AI and a Deep-Learning Approach. Appl. Sci.; 2022; 12, 2412. [DOI: https://dx.doi.org/10.3390/app12052412]
55. Tran, N.-H.; Park, H.-S.; Nguyen, Q.-V.; Hoang, T.-D. Development of a Smart Cyber-Physical Manufacturing System in the Industry 4.0 Context. Appl. Sci.; 2019; 9, 3325. [DOI: https://dx.doi.org/10.3390/app9163325]
56. Alexopoulos, K.; Sipsas, K.; Xanthakis, E.; Makris, S.; Mourtzis, D. An industrial Internet of things based platform for context-aware information services in manufacturing. Int. J. Comput. Integr. Manuf.; 2018; 31, pp. 1111-1123. [DOI: https://dx.doi.org/10.1080/0951192X.2018.1500716]
57. Zacharaki, N.; Dimitropoulos, N.; Makris, S. Challenges in human-robot collaborative assembly in shipbuilding and ship maintenance, repair and conversion (SMRC) industry. Procedia CIRP; 2022; 106, pp. 120-125. [DOI: https://dx.doi.org/10.1016/j.procir.2022.02.165]
58. Fraga-Lamas, P.; Varela-Barbeito, J.; Fernandez-Carames, T.M. Next Generation Auto-Identification and Traceability Technologies for Industry 5.0: A Methodology and Practical Use Case for the Shipbuilding Industry. IEEE Access; 2021; 9, pp. 140700-140730. [DOI: https://dx.doi.org/10.1109/ACCESS.2021.3119775]
59. Choi, T.; Seo, Y. A Real-Time Physical Progress Measurement Method for Schedule Performance Control Using Vision, an AR Marker and Machine Learning in a Ship Block Assembly Process. Sensors; 2020; 20, 5386. [DOI: https://dx.doi.org/10.3390/s20185386]
60. Shahbazi, Z.; Byun, Y.-C. Smart Manufacturing Real-Time Analysis Based on Blockchain and Machine Learning Approaches. Appl. Sci.; 2021; 11, 3535. [DOI: https://dx.doi.org/10.3390/app11083535]
61. Barton, M.; Budjac, R.; Tanuska, P.; Gaspar, G.; Schreiber, P. Identification Overview of Industry 4.0 Essential Attributes and Resource-Limited Embedded Artificial-Intelligence-of-Things Devices for Small and Medium-Sized Enterprises. Appl. Sci.; 2022; 12, 5672. [DOI: https://dx.doi.org/10.3390/app12115672]
62. Blanco-Novoa, O.; Fernandez-Carames, T.M.; Fraga-Lamas, P.; Vilar-Montesinos, M.A. A Practical Evaluation of Commercial Industrial Augmented Reality Systems in an Industry 4.0 Shipyard. IEEE Access; 2018; 6, pp. 8201-8218. [DOI: https://dx.doi.org/10.1109/ACCESS.2018.2802699]
63. Fernández-Caramés, T.M.; Fraga-Lamas, P.; Suárez-Albela, M.; Vilar-Montesinos, M. A Fog Computing and Cloudlet Based Augmented Reality System for the Industry 4.0 Shipyard. Sensors; 2018; 18, 1798. [DOI: https://dx.doi.org/10.3390/s18061798] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29865266]
64. Fraga-Lamas, P.; Fernandez-Carames, T.M.; Blanco-Novoa, O.; Vilar-Montesinos, M.A. A Review on Industrial Augmented Reality Systems for the Industry 4.0 Shipyard. IEEE Access; 2018; 6, pp. 13358-13375. [DOI: https://dx.doi.org/10.1109/ACCESS.2018.2808326]
65. Vidal-Balea, A.; Blanco-Novoa, O.; Fraga-Lamas, P.; Vilar-Montesinos, M.; Fernández-Caramés, T.M. Creating Collaborative Augmented Reality Experiences for Industry 4.0 Training and Assistance Applications: Performance Evaluation in the Shipyard of the Future. Appl. Sci.; 2020; 10, 9073. [DOI: https://dx.doi.org/10.3390/app10249073]
66. Vidal-Balea, A.; Blanco-Novoa, O.; Fraga-Lamas, P.; Vilar-Montesinos, M.; Fernández-Caramés, T.M. A Collaborative Industrial Augmented Reality Digital Twin: Developing the Future of Shipyard 4.0. International Summit Smart City 360°; Springer: Cham, Switzerland, 2022; pp. 104-120.
67. Sharma, A.; Mehtab, R.; Mohan, S.; Mohd Shah, M.K. Augmented reality—An important aspect of Industry 4.0. Ind. Robot. Int. J. Robot. Res. Appl.; 2022; 49, pp. 428-441. [DOI: https://dx.doi.org/10.1108/IR-09-2021-0204]
68. Fernández-Caramés, T.M.; Fraga-Lamas, P. Augmented and Mixed Reality for Shipbuilding. Springer Handbook of Augmented Reality; Springer: Cham, Switzerland, 2023; pp. 643-667.
69. Park, K.-P.; Ham, S.-H.; Ku, N.; Jo, A.; Suh, H.-W. Development of offshore drilling platform simulation for virtual onboard experience. Int. J. Nav. Archit. Ocean. Eng.; 2022; 14, 100431. [DOI: https://dx.doi.org/10.1016/j.ijnaoe.2021.100431]
70. Kwak, D.H.; Cho, Y.I.; Choe, S.W.; Kwon, H.J.; Woo, J.H. Optimization of long-term planning with a constraint satisfaction problem algorithm with a machine learning. Int. J. Nav. Archit. Ocean. Eng.; 2022; 14, 100442. [DOI: https://dx.doi.org/10.1016/j.ijnaoe.2022.100442]
71. Ji, Y.; Jiang, Z.; Li, X.; Huang, Y.; Liu, J. A hypernetwork-based context-aware approach for design lesson-learned knowledge proactive feedback in design for manufacturing. Adv. Eng. Inform.; 2022; 54, 101794. [DOI: https://dx.doi.org/10.1016/j.aei.2022.101794]
72. Zaman, I.; Pazouki, K.; Norman, R.; Younessi, S.; Coleman, S. Challenges and Opportunities of Big Data Analytics for Upcoming Regulations and Future Transformation of the Shipping Industry. Procedia Eng.; 2017; 194, pp. 537-544. [DOI: https://dx.doi.org/10.1016/j.proeng.2017.08.182]
73. Kavallieratos, G.; Katsikas, S. Managing Cyber Security Risks of the Cyber-Enabled Ship. J. Mar. Sci. Eng.; 2020; 8, 768. [DOI: https://dx.doi.org/10.3390/jmse8100768]
74. Gladysz, B.; Tran, T.; Romero, D.; van Erp, T.; Abonyi, J.; Ruppert, T. Current development on the Operator 4.0 and transition towards the Operator 5.0: A systematic literature review in light of Industry 5.0. J. Manuf. Syst.; 2023; 70, pp. 160-185. [DOI: https://dx.doi.org/10.1016/j.jmsy.2023.07.008]
75. Frank, A.G.; Dalenogare, L.S.; Ayala, N.F. Industry 4.0 technologies: Implementation patterns in manufacturing companies. Int. J. Prod. Econ.; 2019; 210, pp. 15-26. [DOI: https://dx.doi.org/10.1016/j.ijpe.2019.01.004]
76. Apilioğulları, L. Digital transformation in project-based manufacturing: Developing the ISA-95 model for vertical integration. Int. J. Prod. Econ.; 2022; 245, 108413. [DOI: https://dx.doi.org/10.1016/j.ijpe.2022.108413]
77. Cil, I.; Arisoy, F.; Kilinc, H.; Ozgurbuz, E.; Cil, A.Y.; Uysal, E. Challenges and Trends in Shipbuilding Industry: Digitization of SEDEF Shipyard in Turkey. Proceedings of the IEEE 2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT); Ankara, Turkey, 21–23 October 2021; pp. 799-804.
78. Beifert, A.; Gerlitz, L.; Prause, G. Industry 4.0—For Sustainable Development of Lean Manufacturing Companies in the Shipbuilding Sector. Proceedings of the International Conference on Reliability and Statistics in Transportation and Communication; Riga, Latvia, 18–21 October 2017; Springer: Cham, Switzerland, 2018; pp. 563-573.
79. Kunkera, Z.; Tošanović, N.; Štefanić, N. Improving the Shipbuilding Sales Process by Selected Lean Management Tool. Machines; 2022; 10, 766. [DOI: https://dx.doi.org/10.3390/machines10090766]
80. Kosteczko, J.P.; Smith, K.; Johnson, J.; Diaz, R. Virginia Digital Shipbuilding Program (VDSP): Building an Agile Modern Workforce to Improve Performance in the Shipbuilding and Ship Repair Industry. Proceedings of the 2020 ASEE Virtual Annual Conference Content Access; Virtual, 22–26 June 2020.
81. Fernández, R.P. How the industry 4.0 could affect the shipbuilding world. J. Marit. Res.; 2020; 17, pp. 18-27.
82. Ellingsen, O.; Aasland, K.E. Digitalizing the maritime industry: A case study of technology acquisition and enabling advanced manufacturing technology. J. Eng. Technol. Manag.; 2019; 54, pp. 12-27. [DOI: https://dx.doi.org/10.1016/j.jengtecman.2019.06.001]
83. Ramirez-Peña, M.; Abad Fraga, F.J.; Sánchez Sotano, A.J.; Batista, M. Shipbuilding 4.0 Index Approaching Supply Chain. Materials; 2019; 12, 4129. [DOI: https://dx.doi.org/10.3390/ma12244129]
84. Noordin, N.; Salleh, Z. Green Shipbuilding Technology for Boustead Naval Shipyard Sdn Bhd Towards Sustainable Shipbuilding Development. Advanced Maritime Technologies and Applications; Ismail, A.; Dahalan, W.M.; Öchsner, A. Springer International Publishing: Cham, Switzerland, 2022; pp. 99-110. ISBN 978-3-030-89991-2
85. Ang, J.H.; Goh, C.; Li, Y. Smart design for ships in a smart product through-life and industry 4.0 environment. Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC); Vancouver, BC, Canada, 24–29 July 2016; pp. 5301-5308.
86. Lee, J.; Jun, S.; Chang, T.-W.; Park, J. A Smartness Assessment Framework for Smart Factories Using Analytic Network Process. Sustainability; 2017; 9, 794. [DOI: https://dx.doi.org/10.3390/su9050794]
87. Fraga-Lamas, P.; Lopez-Iturri, P.; Celaya-Echarri, M.; Blanco-Novoa, O.; Azpilicueta, L.; Varela-Barbeito, J.; Falcone, F.; Fernandez-Carames, T.M. Design and Empirical Validation of a Bluetooth 5 Fog Computing Based Industrial CPS Architecture for Intelligent Industry 4.0 Shipyard Workshops. IEEE Access; 2020; 8, pp. 45496-45511. [DOI: https://dx.doi.org/10.1109/ACCESS.2020.2978291]
88. Giallanza, A.; Aiello, G.; Marannano, G.; Nigrelli, V. Industry 4.0: Smart test bench for shipbuilding industry. Int. J. Interact. Des. Manuf.; 2020; 14, pp. 1525-1533. [DOI: https://dx.doi.org/10.1007/s12008-020-00739-9]
89. Cil, İ.; Arisoy, F.; Özgürbüz, E.; Cil, A.Y.; Kılınç, H. Indoor Positioning Technology Selection Using a Combined AHP and PROMETHEE Method at SEDEF Shipyard. J. ETA Marit. Sci.; 2022; 10, pp. 108-123. [DOI: https://dx.doi.org/10.4274/jems.2022.47550]
90. Wang, K.; Hu, Q.; Liu, J. Digital Twin-Driven Approach for Process Management and Traceability towards Ship Industry. Processes; 2022; 10, 1083. [DOI: https://dx.doi.org/10.3390/pr10061083]
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Chen, Daoyi 2 1 Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China; COSCO Shipping Heavy Industry Co., Ltd., Shanghai 200135, China
2 Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China