The purpose of this paper is to conduct a state-of-the-art review of the ongoing research on the Industry 4.0 phenomenon, highlight its key design principles and technology trends, identify its architectural design and offer a strategic roadmap that can serve manufacturers as a simple guide for the process of Industry 4.0 transition.
The study performs a systematic and content-centric review of literature based on a six-stage approach to identify key design principles and technology trends of Industry 4.0. The study further benefits from a comprehensive content analysis of the 178 documents identified, both manually and via IBM Watson’s natural language processing for advanced text analysis.
Industry 4.0 is an integrative system of value creation that is comprised of 12 design principles and 14 technology trends. Industry 4.0 is no longer a hype and manufacturers need to get on board sooner rather than later.
The strategic roadmap presented in this study can serve academicians and practitioners as a stepping stone for development of a detailed strategic roadmap for successful transition from traditional manufacturing into the Industry 4.0. However, there is no one-size-fits-all strategy that suits all businesses or industries, meaning that the Industry 4.0 roadmap for each company is idiosyncratic, and should be devised based on company’s core competencies, motivations, capabilities, intent, goals, priorities and budgets.
The first step for transitioning into the Industry 4.0 is the development of a comprehensive strategic roadmap that carefully identifies and plans every single step a manufacturing company needs to take, as well as the timeline, and the costs and benefits associated with each step. The strategic roadmap presented in this study can offer as a holistic view of common steps that manufacturers need to undertake in their transition toward the Industry 4.0.
The study is among the first to identify, cluster and describe design principles and technology trends that are building blocks of the Industry 4.0. The strategic roadmap for Industry 4.0 transition presented in this study is expected to assist contemporary manufacturers to understand what implementing the Industry 4.0 really requires of them and what challenges they might face during the transition process.
Ghobakhloo, M. (2018), "The future of manufacturing industry: a strategic roadmap toward Industry 4.0", Journal of Manufacturing Technology Management, Vol. 29 No. 6, pp. 910-936. https://doi.org/10.1108/JMTM-02-2018-0057Download as .RIS
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Copyright © 2018, Emerald Publishing Limited
The term Industry 4.0 is the reminiscent of the fourth industrial revolution which is upon us. The first three industrial revolutions spanned almost 200 years. The first industrial revolution, which occurred at the end of seventeenth century, was driven by the advent of steam engines, water power and mechanization. The second industrial revolution was driven by the assembly lines, pioneered by Henry Ford who first officialized mass production almost a century ago. The third industrial revolution, which occurred in the 1970s, was driven by the use of computer and automation in manufacturing processes. The term Industry 4.0 stem from its German equivalent “Industrie 4.0” which was introduced in 2011 at the Hannover Fair. Industry 4.0 immediately became the focus of the government in Germany, and many other European countries. In general, Industry 4.0 is interpreted as the application of the cyber physical systems within industrial production systems, which can be an equivalent to what has been introduced as industrial internet by General Electric in the North America (Posada et al., 2015). Industry 4.0 might be in near future, yet, most design principles and technologies that enable Industry 4.0 have already been used in practice, and they have been an active area of research for almost a decade. Scholars believe that Industry 4.0 is an upcoming phenomenon, whether it is wanted or not. Similar to the internet that challenged the consumer world with uncertainty in 1990s, and later emerged as a dominating and vital technological phenomenon, Industry 4.0 is a potential hit rather than a hype. Thus, all manufacturers need to ready themselves to embrace this potential industrial revolution to remain competitive in the turbulent and hyper-competitive market.
Technological innovations and changes in business environments affect both firms’ short-term performance and long-term sustainability. When future directions and options in technology are obscure and uncertain, firms need to formulate an appropriate technology strategy to support their planning for interacting with upcoming future technological developments such as Industry 4.0 (Ivanov et al., 2016; Lee et al., 2013). From both strategic and technologic perspectives, the transitioning toward Industry 4.0 requires a comprehensive strategic roadmap that visualizes every further step on the route toward an entirely digital manufacturing enterprise (Sarvari et al., 2018). Contemporary firms use technology roadmappping extensively as a framework for supporting research and development of future technologies that could sustain a competitive advantage (Lee et al., 2013). Roadmapping is an important method that has become integral to creating and delivering strategy and innovation in many organizations. It is, therefore, obvious that an accurate technological and strategic roadmap is indispensable for securing success in the digital transformation process needed by Industry 4.0 (Vogel-Heuser and Hess, 2016).
The present study aims to offer an integrative framework that can be used as a stepping stone by academicians and practitioners toward development of a detailed strategic roadmap for successful transition from traditional manufacturing into Industry 4.0. It is obvious that having a deep understanding of the particularities of Industry 4.0 is a prerequisite for development of the strategic and technological roadmap. Thus, the present study first reviews the design principles and technology trends that are the building blocks of Industry 4.0 and explores their potential technical and economic benefits for production processes, and further continues with introducing challenges that contemporary manufacturers may face while transitioning into the Industry 4.0.
2. Systematic review of Industry 4.0 literature
Industry 4.0 is currently a top priority for many organizations, research centers and universities, yet, the majority of experts in the academia believe that the Industry 4.0 term itself is unclear, and manufacturing firms are facing difficulties when it comes to understanding this phenomenon, and identifying the steps required for the transition toward Industry 4.0. Accordingly, scholars such as Gilchrist (2016), Liao et al. (2017), Santos et al. (2017), Ustundag and Cevikcan (2017) and Vogel-Heuser and Hess (2016) believe that Industry 4.0 can be defined based on its design principles and technology trends. The present study follows this categorization and tries to initially explain and define Industry 4.0 based on its design principles and technology trends. Design principles of Industry 4.0 are what that explicitly address the issue of Industry 4.0 vagueness by providing a systemization of knowledge and describing the constituents of this phenomenon (Hermann et al., 2016). These design principles enable manufacturers to foresee the adaptation progress of Industry 4.0, and grant them the “how to do” knowledge in developing appropriate procedure and solutions required for Industry 4.0 transition. Alternatively, technology trends simply refer to the advanced digital technological innovations that, collectively, enable the rise of the new digital industrial technology, known as Industry 4.0 (Gilchrist, 2016; Liao et al., 2017).
To identify the key design principles and technology trends of Industry 4.0, this study benefited from a systematic and content-centric literature review based on the multiple-stage approach introduced by Webster and Watson (2002). Figure 1 provides a schematic presentation of steps undertaken to conduct this systematic review.
To obtain a comprehensive set of papers, an initial advanced search was constructed through the combination of the operator “and” in between each of the four terms of “the fourth industrial revolution,” “the 4th industrial revolution,” “Industry 4.0” and “Industrie 4,” the two terms of “Internet of Things” or “cyber-physical systems.” Terms “Internet of Things” and “cyber-physical systems” were added as the keywords given most of previous scientific works on Industry 4.0 consider Cyber-Physical Systems (CPS) or Internet of Things (IoT) as the key building blocks of the fourth industrial revolution (Liao et al., 2017). The initial systematic search used two electronic databases, namely, Web of Science and SCOPUS to identify the key academic papers. In addition, the initial systematic search via the abovementioned search string benefited from Google search engine to identify key industrial/commercial/formal reports on Industry 4.0. These initial search policies, collectively, resulted in the identification of 536 documents that might explain the properties and particularities of Industry 4.0.
Next, the content of each of the 536 documents was carefully reviewed and the following exclusion criteria (adapted from Liao et al., 2017) were utilized while handpicking the most related documents:
a paper has only its title, abstract and keywords in English but not its full text;
a paper to be reviewed is without full text;
a paper is not an academic, professional or formal article (e.g. contents, forewords, personal viewpoints, informal web-based contents, etc.);
the definition about Industry 4.0 is not related to IoT and/or CPS at all;
the document does not focus on the review, survey, discussion or problem solving of Industry 4.0;
Industry 4.0 (or the alternate terms) is only used as a part of the document’s future research direction, future perspective or future requirement for the large-scale policy making;
the document uses Industry 4.0 and IoT/CPS only as a cited expression; and
the document uses Industry 4.0 and IoT/CPS only in keywords and/or references.
The use of the exclusion criteria resulted in the identification of 158 related documents. In stage 2, the backward review of citation for documents identified in previous stage was conducted. This means the reference section of the of each of the 158 documents identified in section 1 was carefully reviewed and documents that had the notion of either of “the fourth industrial revolution”, “the 4th industrial revolution”, “Industry 4.0” and “Industrie 4.0” were identified, which resulted in the identification of 18 new documents. Next, the content of each of these 18 documents was carefully reviewed and the eight exclusion criteria introduced were applied to them, resulting to the removal of seven documents at this stage. Accordingly, 11 new documents were identified as highly related to the Industry 4.0.
In stage 3, Google Scholar and Web of Science services were used with the aim of recognizing the documents that cited the 169 (158+11) documents identified in steps 1 and 2. In doing so, the full title of each of the 169 documents identified in previous steps was used as the search term in Google Scholar and Web of Science, and the list of papers that cited each of them as the reference was identified. This resulted in a list of 1,341 unique papers. The title of these 1,341 papers were carefully reviewed and papers that had the notion of either of “the fourth industrial revolution,” “the 4th industrial revolution,” “Industry 4.0” and “Industrie 4.0” in their title were identified, which resulted in the identification of 98 new documents. In total, 53 of these 98 documents were already in the pool of 169 handpicked documents. Thus, the content of 45 remaining paper was assessed based on the eight exclusion criteria. As a result, nine more documents were identified as highly related to the Industry 4.0, and were further added to the final pool of related documents, leading to the final pool of 178 documents.
At Stage 4, the content of each of the 178 documents was assessed qualitatively. In addition, a qualitative-quantitative analysis of the documents identified was conducted via IBM Watosn sentiment and context tool with the aim of highlighting the similarities within the documents in terms of sentiments, keywords, taxonomy and concepts related to the Industry 4.0. Based on the procedure employed in Stage 4, the key design principles and technology trends of Industry 4.0 were identified as in Figure 2, in which the inner circle represents design principles and outer circle includes technology trends.
The use of the search string and the exclusion criteria explained above resulted in the final pool of 178 documents that directly addressed Industry 4.0 phenomenon and some of its challenges, issues or trends. Out of these 178 documents, 89 documents were non-academic articles (e.g. IBM’s catalog on Industry 4.0 and IoT Connected Manufacturing, or the EU’s Digital Transformation Monitor report on Industry 4.0), 43 documents were journal article, 16 documents were book/book section, and 30 documents were conference paper. To ensure the rigidity and reliability of the content analysis, and to better understand how academia defines Industry 4.0 based on its design principles and technology trends, only journal articles and books/book sections that were published by reputable academic publishers (including Elsevier, IEEE Xplore, SAGE Publications, John Wiley & Sons and Taylor & Francis) were selected from the pool of the 178 documents (Stage 6 in Figure 1), leading to the selection of 28 articles/books/book-sections to be assessed as Table I. The results of content analysis of these selected documents are presented as Table I, from which it is obvious that Industry 4.0 is an integrative system of value creation that is comprised of 12 design principles and 14 technology trends. Before moving toward introducing the Industry 4.0 architecture and explaining how its components interact with each other, each of the design principles and technology trends identified are first explained briefly.
2.1 Technology trends
The IoT enables physical objects to communicate with each other and further to share formation and to coordinate decisions (Al-Fuqaha et al., 2015). There is much fuzziness around the definition of IoT given scholars of various disciplines, stakeholders, business alliances and standardization bodies have approached this paradigm based on their specific interests, finalities and backgrounds (Atzori et al., 2010). IoT in the Industry 4.0 context is commonly referred to as Industrial Internet of Things (IIoT), which addresses the industrial application of IoT (Wang, Wan, Zhang, Li and Zhang, 2016). IIoT not only refers to the network of the physical objects in industry but also includes the digital representations of products, processes and manufacturing infrastructure such as 3D models or physical behavior models of machines (Jeschke et al., 2017). IIoT offers better visibility and insight into the firm’s operations and assets through integration of machine sensors, middleware, transportation equipment, healthcare equipment, software and backend cloud computing and storage systems (Gilchrist, 2016). IIoT builds on the philosophy that smart machines excel humans at accurately and consistently capturing and communicating data. Industrial reports indicate that IIoT holds a great potential for enabling predictive maintenance services, green manufacturing initiatives, product quality efficiency, energy optimization and design optimization.
Internet of Services (IoS) is concerned with the systematical use of the internet for new ways of value creation through materialization of Product-as-a-Service (PaaS) business model. Manufacturers of consumer products are nowadays striving to establish a direct link to consumers and to strengthen their competitive position by offering supplementary services and cultivating additional sources of revenue (Becker et al., 2014), and IoS provides the technological infrastructure required. PaaS business model has been enabled by IoS infrastructure such as sensor-based products that continuously feed information about product usage and condition to the manufacturer, who can then leverage the data for a variety of purposes, from charging the consumer based on the level of usage of the product to delivering proactive and preventive maintenance (Leminen et al., 2012). The Otis Elevator Company for example supplies its elevators with sensors, which send data into their cloud. This company further analyzes the data and sells a predictive maintenance service package. Tesla, Inc., as another example, delivers sensor ready upgradeable automobiles that can receive purchasable system upgrades via internet, leading to extra revenue for Tesla, Inc.
Internet of People (IoP) refers to a complex socio-technical system where the humans and their personal devices are not seen merely as end users of applications, but become active elements of the internet (Conti et al., 2017). The infrastructure necessary for IoP is formed around the combination of the social devices (SDs) and People as a Service (PeaaS). In such environment SDs enhance humans’ personal devices (e.g. smartphones) proactive capabilities to coordinate their interactions with other devices linked to the IoT, whereas PeaaS offers humans’ personal devices with serving capabilities that let individuals execute their intentions using their devices, such as providing their context and sociological profile online (Miranda et al., 2015). For the first time in the history of humankind, people are willing to put their lives online and make a public virtual communication about how they are feeling and what their interests are. These data reflecting actual human sentiment are available within people’s social media posts and internet activities. With comprehensive data gathering, computation and simulation in the IoP environment, companies will be able to better forecast market trends thanks to a deeper understanding of consumer buying patterns and what triggers a purchase, and produce real-time actionable results.
Internet of Data (IoD) can be regarded as the extension of the IoT in the digital world (Fan et al., 2012), which very recently has received attention from scholars. IoD will be primarily concerned with the means of effective data transfer, storage, management and processing in the IoT environment where countless objects produce a staggering amount of data (Anderl, 2014). IoD would allow all the data entities to be identified and inventoried in the system, and data activities and data vitalization results to be collected in virtual tags. This, in turn, enables organizations to benefit from data tracing, data identification, data vitalization and further collect valuable business intelligence thanks to the big data analytics. IoD, therefore, can be regarded as conceptual equivalent of database management systems that can serve as a building block of IIoT, IoS and IoP (Anderl et al., 2018) given it enables the acquisition, storage and management of open data, social data, and crowd and sensor data (Motta et al., 2014).
Cloud computing is not a completely new concept, yet there is no universal or standard definition of cloud computing. This paradigm evolved based on the recent advancements in hardware, virtualization technology, distributed computing and service delivery over the internet (Oliveira et al., 2014). The application of cloud computing provides manufacturers with cloud-based software application, web-based management dashboard and cloud-based collaboration, and enables the integration of distributed manufacturing resources and establishment of a collaborative and flexible infrastructure across geographically distributed manufacturing and service sites (He and Xu, 2015). This will, in turn, lead to the cloud manufacturing as the next generation manufacturing paradigm (Ooi et al., 2018).
Big data technologies refer to a new generation of technologies and architectures that enable organizations to economically extract value through discovering, capturing and analyzing very large volumes of a wide variety of data. Big data analytics enables contemporary organizations to better gain value from the massive amounts of information they already have, and identify what is likely to happen next and what actions should be taken to achieve the optimal results (LaValle et al., 2011). The concept of big data has been around for many years. However, firms nowadays move toward big data analytics to instantly identify insights and upcoming trends for immediate decisions, and sustain competitiveness (Hu et al., 2014). In particular, big data analytics would enable manufacturers to improve their asset efficiency and performance, enhance product customization, better administrate predictive maintenance and prevent asset breakdowns, and streamline production processes and supply chain management initiative more effectively (Babiceanu and Seker, 2016; Wang, Gunasekaran, Ngai and Papadopoulos, 2016). IBM cognitive manufacturing service is an example of the industrial application of big data analytics.
Blockchain also known as distributed ledger technology is the foundation of cryptocurrencies such as Bitcoin and Ethereum, but its capabilities extend far beyond that. Blockchain is immutable, transparent and redefines trust, as it enables transparent, secure, trustworthy and swift public or private solutions (Underwood, 2016). The scientific community believes that blockchain technology is critical to Industry 4.0 because cryptocurrencies allow countless smart devices to perform transparent, secure, fast and frictionless financial transactions, fully autonomous without human intervention in the IoT environment (Devezas and Sarygulov, 2017; Sikorski et al., 2017). The application of blockchain is not limited to the financial services, and it can be used for any type of digitized transfer of information. Industry 4.0 develops on the foundation of automation, and blockchain can operate as the ledger to develop trusted and autonomous relationship among different components of smart factories, suppliers and even customers. For example, putting blockchain between IIoT, cyber-physical production systems, and supply partners can enable machineries within the smart factory to securely and autonomously place an order for their replacement parts to further optimize the processes.
Augmented reality (AR) has been regarded as a highly promising technology that allows for visualization of computer graphics placed in the real environment (Yew et al., 2016). Thanks to the ever-increasing advancement of computer software and hardware design and development, the AR is commonly used in the description, planning and real-time operation monitoring, fault diagnostic and recovery, and training related to industrial products and processes (Doshi et al., 2017; Khan et al., 2011). Industrial reports indicate that modern manufacturers have implemented AR in support of employee training, simplification of maintenance tasks, quality management and control practices, and product design among others (Elia et al., 2016).
Automation and industrial robotics are clearly on the rise, in manufacturing and increasingly in everyday environments. International Federation of Robotics reports that by the end of 2016 robot sales increased by 16 percent to 294,312 units, a new peak, thanks to the key role of electrical/electronics industry and automobile industry as the main users of industrial robotics (IFR, 2017). Demand for industrial robots has increased due to the ongoing trend toward automation among manufactures. Industrial robotics and automation promise numerous benefits such as reduced part cycle time, lower defect rate, higher quality and reliability, reduced waste and better floor space utilization, making it indispensable to world-class manufacturers (Esmaeilian et al., 2016).
Cybersecurity is a key element of Industry 4.0 given all internet-facing organizations are at risk of attack. The Stuxnet can never be forgotten, the notorious malware that infested control systems at the nuclear plants and manipulated the speed of centrifuges, causing them to spin out of control. There is no doubt that Industry 4.0 will be challenged by the traditional cybersecurity issues along with its very own unique security and privacy issues (Thames and Schaefer, 2017). Within Industry 4.0 environment, “things” are connected through the internet or amongst themselves to create a fully interconnected industrial networked environment across the supply chain. It is obvious that the tremendous number of interconnected things in the Industry 4.0 context requires secure, safe and reliable communication so that any decisions and actions made are based on dependable and properly authorized information (Mehnen et al., 2017).
Additive manufacturing denotes the manufacturing technique in which parts are built by melting thin layers of powder and adding one layer of material, either plastic or metal, on top of another, based on the geometry suggested by Computer-Aided Design (CAD) modules (Esmaeilian et al., 2016). Additive manufacturing, 3D printing technology in particular, enables manufacturers to produce prototypes and proof of concept designs, which simplify and speed up the processes of new product design and manufacturing (Gilchrist, 2016). Industry 4.0 will bring customers and suppliers closer together, and it will become a common procedure for customers to directly send production orders to the production partner in real-time. In such circumstances, additive manufacturing can support the “smart factory” idea through improved speed to production, manufacturing design freedom, supply chain reductions, rapid prototyping and small-scale production experiments (Lasi et al., 2014).
Simulation and modeling techniques aim for simplification and economic favoring of the design, realization, tests and running a live operation of manufacturing systems (Kocian et al., 2012). In the smart factories, simulation and modeling will be necessary for leveraging real-time data to mirror the physical world in a virtual model, which can include machines, products and humans (Rüßmann et al., 2015). Simulation and modeling not only enable manufacturers to prevent errors at an early stage that might otherwise result in substantial costs for plant operators, but they can be used to optimize a manufacturing plant during ongoing daily operation (Gilchrist, 2016). For example, manufacturers nowadays can simulate the machining of parts using data from the physical machine leading to the reduction of setup time for the actual machining process by as much as 80 percent (Rüßmann et al., 2015). Industrial reports reveal that world-class manufactures see a much greater potential for simulation in the future in an attempt for virtual testing of complete production systems.
CPS is a collection of transformative technologies that enables connection of the operations of physical assets and computational capabilities (Lee et al., 2015). CPS is controlled and monitored by computer-based algorithms, and is tightly integrated with its users (objects, humans and machines) via internet. Gilchrist (2016) explains that since CPS can be just about anything that has integrated computation, networking and physical processes, a human operator in a production line is a CPS and so is a smart factory. As another example, a smart production line can be regarded as a CPS in which machinery, operators, materials and even work in progress can communicate with each other and further monitor the production information or pass it to another networked node in which computation, analysis and decision making will be performed and feedback will be provided if and when needed.
Semantic technologies can provide a common standard for communication and a standardized language for exchange of information among different components of Industry 4.0 (Janev and Vraneš, 2011). Semantic technologies achieve this standard via offering an abstraction layer above existing IoT technologies and infrastructure that connects data, content and processes. Although IIoT provides an environment in which physical devices are embedded into electronic systems and discover, monitor, control and interact with each other over various network interfaces, however, IIoT lacks a universal application protocol, which prevents the integration of machinery from various manufacturers, and different components of the smart factory into a single application (Thuluva et al., 2017). Under these circumstances, integration of semantic web with Web of Things (WoT) technologies can provide standardized knowledge representation formalisms such as Resource Description Framework or Web Ontology Language. This feature, in turn, facilitates interoperability among assets and their services across domains, and facilitates communications among heterogeneous components of Industry 4.0.
2.2 Design principles
Service orientation in the context of Industry 4.0 mostly refers to the concepts of Manufacturing as a Service (MaaS) and PaaS. MaaS business model refers to the collective use of a networked manufacturing infrastructure to produce goods. Interconnectivity between manufacturers and the widespread of IoT and cloud computing has offered a new manufacturing ecosystems by allowing companies to communicate their manufacturing needs and capacities automatically. In this environment, complex manufacturing tasks can be accomplished collaboratively by several manufacturing services from different companies. This means, instead of the physical product, the production capacity of manufacturers can be regarded as the primary good (Tao and Qi, 2017). In the PaaS business model, products are delivered as a service or virtualized experience, and instead of a single upfront payment, customers subscribe to the product and pay a recurring fee on a perpetual per-outcome basis. This business model is particularly enabled by the IoS technologies that can be built into products (e.g. goods, software and infrastructure) to monitor when and how they are used.
Smart product refers to a new generation of physical products that, thanks to the different types of sensors embedded to them, can communicate with the environment, and collect, store and transfer data, during their life cycles (Schmidt et al., 2015). This means that in the manufacturing stage, smart products at the production line can communicate valuable information regarding where they are being manufactured, what their current state is, and what steps are required for them to reach their desired state. At the consumption stage, smart products, along with IoS infrastructure, facilitate the materialization of PaaS business model (Gilchrist, 2016).
Smart factory denotes a highly productive manufacturing environment of connected and intelligent machines and materials where waste, defect, and, downtime are minimized (Diederik et al., 2014). In this environment, process efficiency is optimized through machinery and equipment automation and self-optimization. Smart factory is indeed a dynamic integrated cyber-physical-human manufacturing system in which the physical resources are implemented as smart things that communicate with each other and with human resources via IIoT and IoP and WoT infrastructure (Wang, Wan, Zhang, Li and Zhang, 2016).
Interoperability can be simply defined as the capability of systems to transact with other systems. In the Industry 4.0 context, interoperability is the ability of all components such as human resources, the smart products, the smart factories, and any relevant technologies to connect, communicate, and operate together via the IIoT, IoS, IoP and WoT (Gilchrist, 2016). A more detailed consideration reveals that interoperability in Industry 4.0 can be defined in four different levels of operational, semantic, systematical and technical interoperability (Lu, 2017). It is critical to note that interoperability differs from data standardization as it is concerned with the meaning of the contents of the data and how different components of a system can communicate and understand the meaning of the data, and make a decision based on it in support of flexibility.
Modularity is another design principle of Industry 4.0, which is concerned with shifting from linear manufacturing and planning, rigid systems and inflexible production models toward an agile system that can adapt to an ever-changing circumstances and requirements (Gilchrist, 2016). Modularity involves the entire production and manufacturing levels (Ghobakhloo and Azar, 2018), and builds on agile supply chain, flexible material flow systems, modular decision-making procedures and flexible processes (Perales et al., 2018). Modularity is complemented by product personalization, which is another design principle of Industry 4.0. Product personalization is indeed a more customer-oriented implication of mass customization (Yang et al., 2017). The introduction of modern technology trends such as responsive CPS, IoT, open product architecture, automation and additive manufacturing has enabled product reconfiguration based on the continually changing customer preferences, mostly identified via assessment and prediction of consumers’ behavior (Jiang et al., 2016). This means manufacturers not only should meet customers’ existing demands and preferences, but also benefit from IoP, simulation and big data analytics to forecast upcoming market trends and customers’ needs (Lu, 2017; Wang, Gunasekaran, Ngai and Papadopoulos, 2016).
Decentralization enables different components of the smart factory to work independently and make decisions autonomously in a way they remain aligned with the path toward the single ultimate organizational goal (Gilchrist, 2016). Self-regulating systems and intelligent control mechanisms such as CPS are among the key enablers of decentralization (Lasi et al., 2014). Companies profit from decentralization thanks to the simplified planning and coordination of different processes. For example, the synchronization of eKanban with the components of a smart warehouse (e.g. automated guided vehicle or RFID tagged robots) can significantly reduce the complexity of central planning by providing the freedom of decision making (MPDV, 2015).
Virtualization enables the replication of a “digital twin” of the entire value chain (smart warehouse, smart factory, all related equipment and machinery, and even smart products) by merging sensor data acquired from the physical world into virtual or simulation-based models (Moreno et al., 2017). The virtual twin of the smart factory, for example, would enable process engineers and designers to enhance existing processes or optimize the functionality of production lines in complete isolation, without disrupting the physical processes in the smart factory they have virtualized (Gilchrist, 2016). Alternatively, the digital twin of a smart product would enable manufacturers to have a complete digital footprint of their existing or new products all throughout their lifecycle, from design and development to the end of the product. This not only would enable a better understanding of the performance of the product at the consumption stage, but also allows companies to virtually evaluate the system that builds the product (Schleich et al., 2017; Tao et al., 2018). Virtualization is heavily dependent upon the real-time capability. In general, Industry 4.0 is centered on cumulative, real-time, real-world data across an array of dimensions such as smart warehouse, smart factory, smart product and smart business partners, meaning that real-time capability is deeply supported by internet of everything (Lee et al., 2015; Qi and Tao, 2018; Zhang et al., 2017). Real-time capability is not just about collecting data, as it involves real-time data analysis, real-time decision making according to the new findings (Moeuf et al., 2018), and even real-time cyber-security attack detection (Thames and Schaefer, 2017).
System integration refers to the process of bringing together the component sub-systems into one system in a way the system is able to deliver the intended functionality. Moving forward toward Industry 4.0 requires the vertical integration of layer upon layer of systems and technologies, some dating back several decades (Posada et al., 2015). Smart factories as the heart of Industry 4.0 cannot work on a standalone basis, and vertical networking of smart factories, smart products and other smart production systems is indeed a necessity. The integration is not limited to the manufacturing systems and technologies. Industry 4.0 relies on horizontal integration for connecting all functions and data across the value chain at the global scope. This integration across business partners and customers facilitates the establishment and maintenance of networks that create and add value (Rüßmann et al., 2015).
Corporate social responsibility represents a form of corporate self-regulation that integrates into to the existing business model. In the manufacturing environment, corporate social responsibility mostly involves areas such as environmental and labor regulations. Within the fourth industrial revolution, robotics and industrial automation will heavily influence job opportunities in many future-oriented corporations, and scholars argue that Industry 4.0 is most likely to act as a job killer. It is believed that the magnitude of this negative impact on jobs will depend on the worker’s skill level, and low to middle-skill workers will be negatively affected the most. It is also believed that technology has always ended up creating more jobs than it wipes out, thus, Industry 4.0 is also expected to create numerous job opportunities, particularly related to computer engineering, informatics and mathematics. Therefore, in a more proactive note, companies that aim for the Industry 4.0 should emphasize the development of skills for their future workforce (Choi, 2017). From the environmental sustainability perspective, Industry 4.0 offers immense opportunities for the realization of sustainable manufacturing as it enables the efficient coordination of the product, material and energy all throughout the product life cycles; sustainable design of products; sustainable design of processes and materialization of the resource efficiency; a higher efficiency of workers thanks to the IIoT infrastructure; and deployment of the green-leagile business model (Stock and Seliger, 2016).
3. Industry 4.0 architecture
There are different perspectives toward defining the architecture of Industry 4.0. Based on the design principles and technology trends introduced, and several definitions that exist within the literature, this study introduces the architecture for Industry 4.0 as Figure 2. Industry 4.0 is a dynamic and integrated system for exerting control over the entire value chain of the lifecycle of products. Vertical and horizontal integration and fusion of the physical and the virtual worlds is at the heart of Industry 4.0, and technology trends such as CPS, IIoT, IoS and IoP, Blockchain and WoT enable such level of integration at a global scale. CPSs are central to the Industry 4.0 vision given they offer the highest levels of control, surveillance, transparency and efficiency in the production process (Hofmann and Rüsch, 2017) and the realization of smart products and PaaS concept (Gilchrist, 2016). CPSs communicate over the internet of everything infrastructures, and IIoT and IoS in particular to enable the so-called “smart factory.” The smart factory follows the idea of decentralized production system, in which machines, processes, human beings and resources communicate with each other in real-time as naturally as in a social network (Hofmann and Rüsch, 2017). Given the manner in which machines, devices and human resources interact, communicate and learn from each other within the smart factory context, cloud technologies, IoD and big data analytics are vital for collecting, storing and more importantly analyzing a huge stream of process, production and supply chain data. Data mining for relevant or pertinent information acquisition, coupled with virtualization would enable manufacturers to maintain a competitive edge in operations management and offer a higher production efficiency thanks to the early anomalies and system failures detection. As another component of smart factory setting, AR holds a massive potential, as it facilitates industrial maintenance, workforce training, process management and control, and broadens the boundaries of innovation. These components, in conjunction with the application of industrial robotics and additive manufacturing would enable manufacturers to transform their production strategy from mass customization to mass personalization (Wang et al., 2017) (Figure 3).
4. Industry 4.0 roadmap
Industry 4.0 is no longer a “future trend” and many leading organizations have taken it at the center of their strategic agenda, and those manufacturers that are able to catch up will benefit from the competitive advantages that are available to the early adopters. The digital transition required by Industry 4.0 not only challenges companies’ capacity to innovate, but also requires new strategies and organizational models, and organization-wide changes in physical infrastructure, manufacturing operations and technologies, human resources and management of practices (Gilchrist, 2016). This fundamental transition can look overwhelming to smaller manufacturers, and it is likely for typical manufactures to avoid embarking on the journey to digitization for fear of not having every single technological block and design principle in place. Organizations need to develop a strategic roadmap to better time, visualize and understand each move and decisions that they need to make to facilitate the transition toward Industry 4.0. This study develops and introduces a basic form of the strategic roadmap for Industry 4.0 transition as Figure 4, which is derived from existing and documented best practices within strategic management, marketing, management information system, supply chain management and manufacturing technology management background.
The first phase of the roadmap is the strategic management of Industry 4.0 transition, which starts with defining Industry 4.0 short-term, medium-term and long-term strategies. These strategies should be defined in a time-based plan and describe where the company is, where it needs to go and how to get there, based on the Industry 4.0 pre-set visions and plans (Schumacher et al., 2016). Digitization and Industry 4.0 transition require committed leadership and fundamental resource allocation. Appointing an Industry 4.0 transition team that manages and leads the digital transformation, and integration of existing systems and infrastructure is of prime importance (Müller et al., 2018; Ustundag and Cevikcan, 2017). Not all organization have the adequate IT maturity to embrace Industry 4.0, and not all manufacturers with working IoT-enabled production or service systems are large enough to handle horizontal integration and sustain their competitive position in the globalized and hyper-competitive market (Gilchrist, 2016; Leyh et al., 2016). This means Industry 4.0 necessitates numerous Acquisitions and Mergers (M&A) at the global scale, and manufacturers of any size that aim for digitization should carefully plan for potential M&A opportunities ahead of time. The company and the Industry 4.0 transition team in particular, should decode the transition procedure into a detailed project plan, specify the characteristics of work in each phase of transition and further conduct the comprehensive analysis of costs and benefits associated with each phase (Galpin and Herndon, 2014). In doing so, functional needs and priorities required by each of the Industry 4.0 transition phases should be identified, and the inter- and intra-organizational changes that are associated with each phase of the transition should be identified, managed and facilitated (Kagermann, 2015).
The Industry 4.0 is revolutionizing the rules of business, as well as the consumer market who is looking for smart products that can be offered as services, and are more personalized than ever before. Nevertheless, many traditional manufacturers continue to operate under marketing strategies that have already passed their effectiveness. Manufacturers that are transitioning into Industry 4.0 need to devise new marketing strategies, and the assessment of their level of digital market maturity is the first step for this purpose (Bettiol et al., 2018). To fully capitalize on opportunities offered by digitization, manufacturers need to develop capabilities in blockchain technologies and data analytics in IoP environment. At the more micro level, and with the advent of IoP platforms (such as social media listening tools) and big data analytics platforms (e.g. predictive analytics tools), companies need to establish an up-to-date, real-time and accurate 360-degree view of potential customers to determine future market trends and customer demands (IBM, 2018). This capability, in turn, would enable modern marketing strategies such as market sensing and learning strategy as well as data driven marketing, which coupled with blockchain-based platforms and smart contracts, can ensure the success of the PaaS business model in the Industry 4.0 environment (Kim and Laskowski, 2018; Tao and Qi, 2017).
It is well agreed that Industry 4.0 results in a fundamental change in the division of work between humans and machines. Experts believe that competent employees are among the most important success factors in shaping digitization. Industry 4.0 merges the real and virtual worlds thanks to the modern technology trends such as CPS, IIoT, IoD, IoS, robotics, simulation, cybersecurity and WoT, which demand for a high level of technological understanding and relevant qualifications from the employee side (Gilchrist, 2016). From the human resource management perspective, the first step for the success of Industry 4.0 transition is the assessment of human resources competencies for Industry 4.0. Companies need to carefully assess the skillset in their workforce and recognize the digital skills amongst current employees, and further identify the skillsets that the company currently lacks (Hecklau et al., 2016). Although current employees may not have the entire competency needed to operate in a digitized factory, however, they are well-experienced about the company’s procedures, norms and workplace culture. Even if the Industry 4.0 transition requires a complete overhaul to the company’s operations and manufacturing processes, the existing employees have a significant advantage, and the decision should be to train them for the necessary skills and, professionally adapt them to the upcoming technologies and procedures. For example, all employees should go through purposeful computer and IT training programs given their real working world will progressively integrate with the virtual one. Yet, some aspect of Industry 4.0 transition requires advanced expertise such as computer engineering skills, and not every skills can be taught in onsite training. Manufacturers, therefore, need to perform detailed cost-benefit analysis of different human resource development initiatives, and aim for employing new talents in the process of digitization whenever needed. Industry 4.0 is a dynamic trend of automation and digitization, and its technology blocks progress continuously at an exponential rate, meaning manufacturers should look for new recruits that are multi-skilled and flexible enough to adapt to any technology that may emerge as a requirement for Industry 4.0 (Shamim et al., 2016).
Industry 4.0 necessitates the digitization of products, processes and systems, together with their interconnectedness, which not only concern separate functional areas such as the shop floor or IT department, but also takes place all throughout the entire value chain (Leyh et al., 2016). The design principles and technology trends of Industry 4.0 such as horizontal and vertical integration, IIoT, IoD, CPS, interoperability, simulation and blockchain indicate that the fourth industrial revolution is all about IT. IT governance is typically the weakest aspect of corporate governance (Wu et al., 2015), and as the first step of IT maturity strategy for Industry 4.0, manufacturers should ensure that IT governance team is in place to strategize, budget, execute, control and report on IT advancement projects and operations in accordance with the requirements of Industry 4.0 transition. IT governance team should perform a detailed analysis of existing IT infrastructure (e.g. networks, computer hardware and software, sensors, controllers and actuators) and identify the most meaningful approach for using them in support of Industry 4.0 transition. IT governance team should further identify different business segments that need networking and integration, and when the existing IT infrastructure does not fully support the digitization of these business segments, formulate and implement necessary IT development strategies (Savtschenko et al., 2017). The key to the success of Industry 4.0 transition is the ability for the entire component of the smart factory to communicate to one another at the field level, in real-time and with an intelligent functionality that collects data, interprets it, and offers meaningful insights to the management system (Gilchrist, 2016). However, in reality, component of a smart factory such as machinery, equipment and industrial robots come from different vendors, have different technological characteristics, and operate on different communication protocols such as Modbus, PROFIBUS or PROFINET (Kjellsson et al., 2009). At a more micro level, IT governance team needs to ensure that existing and the newly added IT infrastructure are harmonized and integrated in a way all the components of the smart factory are interconnected and interoperable (Chen et al., 2018).
Smart manufacturing system, characterized by connectivity, integration, transparency, proactivity and agility, aims for the shift from traditional automation to a fully connected and flexible manufacturing system that thrives on a constant stream of data from interconnected production systems and operations to learn and adapt to the ever-changing demands (Kang et al., 2016). The movement toward development of smart manufacturing systems starts with incorporating IIoT for establishing smart connections across the factory and vertical integration of machines, physical assets, databases, processes and control systems, as well as the people who are interacting with them (Da Xu et al., 2014; Gilchrist, 2016). This includes the application of smart intra-factory logistics via autonomous mobile units and different types of production or process controllers (e.g. SCADA, PLC or DCS) to acquire machine and process level data (Colombo et al., 2014). These types of control systems that communicate with each other and with a central control system can be further developed to take the form of a manufacturing execution system (MES). At one layer above that, the integration of MES and intelligent enterprise resource planning (ERP) facilitates the complete information transparency as well as connectivity to business data in real-time (Wang, Gunasekaran, Ngai and Papadopoulos, 2016). The intelligent ERP, coupled with data mining procedures, would enable digital twin models that provide a digital representation of the past and current behavior of a single object up to the entire manufacturing system, the feature that can significantly contribute to the development of smart factory (Coronado et al., 2018).
The fourth industrial revolution has severely affected the supply chain interactions, which is mainly due to the exponential growth of sensible data and the widespread of digitalized processes (Wu et al., 2016; Zhong et al., 2016). With the emergence of mass-personalization business model, the production models of future will move toward a highly specialized and highly customized production schemes that require the reconfiguration and integration of the entire supply network for a more dynamic way of manufacturing (Tien, 2011). As Industry 4.0 spreads more across the value chain, supply partners even need to integrate the digital twins of their operations with the aim of creating digital supply network (Parrott and Warshaw, 2017), which significantly relies on IT alignment across value chain (Ghobakhloo and Tang, 2015). To accomplish this goal, business partners need to first develop the supply chain application system integration to achieve real-time communication across the supply chain (Ghobakhloo et al., 2014). They further need to benefit from WoT and blockchain technologies to remove information silos, and ensure data consistency, interoperability and security across different platforms within the entire supply chain while protecting intellectual property of each partner (Korpela et al., 2017; Raggett, 2016; Underwood, 2016). By doing so, members of a supply network can integrate the flow of information, materials, activities, finances and even knowledge (managerial and manufacturing), and move toward the establishment of smart (digital) supply chain that supports Industry 4.0 transition (Rai et al., 2006; Vanpoucke et al., 2017).
The development of smart manufacturing system and smart supply chain management, collectively, allows the integration of data from operations and business systems, as well as from supply partners and customers sides. This level of integration, in turn, offers a holistic view of upstream and downstream supply chain processes, generating additional value within the entire supply network.
5. Concluding remarks
Scholars believe that the academic literature lacks a clear and common definition of the Industry 4.0, as its concept is still quite fuzzy, among both researchers and practitioners (Hofmann and Rüsch, 2017). To address this concern, this paper conducted a state-of-the-art review of the ongoing research on Industry 4.0 phenomenon, highlighted its key design principles and technology trends and offered a strategic roadmap that can serve manufacturers as a simple guide for the process of Industry 4.0 transition. Industry 4.0 is no longer a hype, as more software, hardware and system developers are glimpsing the opportunities of Industry 4.0 and launching their products and services in this field. IBM, for example, has developed the capability-rich IBM® Watson IoT™ platform that enables manufacturers to implement cognitive manufacturing, adapt to the Industry 4.0 and digitize their processes. Metal additive manufacturing has also turned into a reality in 2018 given Audi AG, for example, benefits from Selective Laser Melting systems from SLM Solutions for the metal additive manufacturing of components for their exclusive automobiles. Augmented and virtual reality application are already in use in a wide range of industries, and according to International Data Corporation the value of augmented and virtual reality spending is poised to double every year through 2021 (IDC, 2017). When it comes to the fourth industrial revolution, the big question is how a manufacturer makes its transition from where they are now to where they want to be through the digitization processes. The answer would be through a comprehensive strategic roadmap that carefully identifies and plans every single steps they need to take, as well as the timeline, and the costs and benefits associated. The strategic roadmap presented in this study is merely a holistic view of common steps that contemporary manufacturers need to undertake in their quest for transitioning toward Industry 4.0. It is obvious that there is no one-size-fits-all strategy that suits all businesses or industries, meaning the Industry 4.0 roadmap for each company is idiosyncratic, and should be devised based on company’s core competencies, motivations, capabilities, intent, goals, priorities and budgets.
It is believed that the benefits of transitioning into Industry 4.0 could outweigh the associated costs, particularly for world-class manufactures that have the necessary experience and manpower to create and implement underlying technology trends, and have adequate support from stakeholders to invest heavily in new technologies. Yet, it cannot be ignored that there are many challenges associated with the fourth industrial revolution, examples of which are financial capability, data security issues, maintaining the integrity of the production process, IT maturity and knowledge competencies. The promise of Industry 4.0 is real, but for companies that are mature enough to embrace it, and have devised a comprehensive transition strategy. Many companies leapfrogging into the Industry 4.0 technologies tend to ignore Pareto’s well-known 80/20 rule that addressing the behavioral root causes of process problems is what that offers the lion’s share of performance improvement, not simply investing in the digitization of processes. In addition, not all manufacturers are ready for the full digital transformation. In reality, majority of manufacturers, smaller ones in particular, are competent enough to only digitize certain areas of their operations, such as digitizing their warehousing or supply chain interactions. Although transitioning toward Industry 4.0 requires the removal of functional silos, openness to change, supportive culture, supply chain integration, and data transparency across the entire value chain, unfortunately, it is difficult, rather impossible, for a network of typical manufactures to achieve them in a short run. Strategic and operations management scholars are, therefore, recommended to investigate how typical manufacturers can achieve organizational, operational, technical and legal readiness in preparation for the Industry 4.0.
Another interesting avenue for future research would be to develop a comprehensive strategic roadmap of Industry 4.0 transition for small- and medium-sized enterprises (SMEs). Large companies that produce in high volumes will realize considerable efficiency gains from the Industry 4.0 and the underlying digitization, but SMEs are generally characterized by resource limitation, higher levels of flexibility, higher degree of specialization and inclination toward niche markets. That being said, the impact of the fourth industrial revolution on manufacturing SMEs can be considered largely positive, if the authorities, governmental agencies and international associations, for example, assists smaller firms with the process of digital transformation.
The exclusion criteria used while performing the review of existing literature resulted in the removal of non-English documents. Thus, many of the existing research published in other languages, German language in particular, have been ignored in this study. In addition, the strategic roadmap presented in this study has been developed based on the existing research and empirical evidence in the context of strategic management, marketing, supply chain management, manufacturing technology management and IT resource management. Yet, this strategic roadmap is merely a holistic view of common steps that contemporary manufacturers need to undertake in their quest for Industry 4.0 transition, and its generalizability to different manufacturing settings, and service industry in particular, is expected to be limited.
Results of content analysis of key academic documents in the context of Industry 4.0
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