Supply chain disruption refers to a breakdown, often caused by an unforeseen incident or risk, in a supply chain's production or distribution process. Contemporary supply chains are globalized, ...complex, and extended, exhibiting an increased vulnerability to a multitude of risks and disruptions. However, the current trend of real-time data exchange through smart technologies, also known as industry 4.0, provides significant opportunities to reshape the conventional business operations and effectively cope with unanticipated supply chain breakdowns. Yet, limited attention has been paid to the role played by industry 4.0 technologies in mitigating supply chain risks and any resulting disruptions. By bringing together these inter-related yet often separate concepts, we devise a novel model that addresses this knowledge gap. In this article, we empirically test our model on a sample of 302 responses received from senior managers of the Australian food processing industry. We found that supply-demand mismatch, process risks, and transportation risks are currently the major sources of supply chain disruptions. Specifically, supply-demand mismatch appears to be the most severe and attention seeking risk, followed by process and transportation risks. We also reveal that industry 4.0 technologies significantly mitigate supply-demand mismatch and process risks and any resulting supply chain disruptions. Contrary to our expectations, however, the impact of industry 4.0 technologies on transportation risks is found to be positive but nonsignificant. This is the first empirical article to assess the extent to which the three critical supply chain risks may undermine firm performance and to explore the moderating effect of industry 4.0 technologies. We draw managers' attention to the detrimental impact of supply chain disruptions and the significance of industry 4.0 technologies in dealing with adversities.
Industry 4.0 (I4.0), also known as the fourth industrial revolution, describes the digitalization of manufacturing industries. The transition to I4.0 is crucial for manufacturing firms to sustain ...competitive advantage and seize new opportunities. Most research has focused on the technological aspects of I4.0 in the form of product and process innovations. Despite I4.0's rising attention from both researchers and practitioners, little research exists about I4.0 business model (BM) innovation, even though BM innovations can be more successful than product or process innovations. To address this research gap, we analyze 32 case studies of I4.0 BM innovators. We develop a taxonomy to characterize I4.0 BMs and derive 13 patterns of I4.0 BMs by applying the taxonomy to the case studies. Three super-patterns are identified: integration, servitization, and expertization. Integration innovates a BM with new processes and integrates parts of the supply chain. New combined products and services are the basis for servitization. Expertization is a hybrid of product- and process-focused BMs, which includes consulting services and multi-sided platforms. This study contributes to research with a framework for describing, analyzing, and classifying BMs for I4.0. The findings deepen the understanding of how I4.0 impacts ecosystem roles, BMs, and service systems. Archetypal patterns show how firms can leverage I4.0 concepts and build a conceptual basis for future research. The taxonomy supports practitioners in evaluating the I4.0-readiness of their existing BM. The patterns additionally illustrate opportunities for becoming an I4.0 firm.
•We extend the technically-driven Industry 4.0 research with business models.•We develop a taxonomy for describing, analyzing, and classifying business models.•13 archetypal patterns show how Industry 4.0 impacts business models.•Three areas of Industry 4.0 business models emerge:•Integrating parts of the value chain, servitization, and consulting or platforms.
In recent years, there have been great advances in industrial Internet of Things (IIoT) and its related domains, such as industrial wireless networks (IWNs), big data, and cloud computing. These ...emerging technologies will bring great opportunities for promoting industrial upgrades and even allow the introduction of the fourth industrial revolution, namely, Industry 4.0. In the context of Industry 4.0, all kinds of intelligent equipment (e.g., industrial robots) supported by wired or wireless networks are widely adopted, and both real-time and delayed signals coexist. Therefore, based on the advancement of software-defined networks technology, we propose a new concept for industrial environments by introducing software-defined IIoT in order to make the network more flexible. In this paper, we analyze the IIoT architecture, including physical layer, IWNs, industrial cloud, and smart terminals, and describe the information interaction among different devices. Then, we propose a software-defined IIoT architecture to manage physical devices and provide an interface for information exchange. Subsequently, we discuss the prominent problems and possible solutions for software-defined IIoT. Finally, we select an intelligent manufacturing environment as an assessment test bed, and implement the basic experimental analysis. This paper will open a new research direction of IIoT and accelerate the implementation of Industry 4.0.
The evolution of intelligent manufacturing has had a profound and lasting effect on the future of global manufacturing. Industry 4.0 based smart factories merge physical and cyber technologies, ...making the involved technologies more intricate and accurate; improving the performance, quality, controllability, management, and transparency of manufacturing processes in the era of the internet-of-things (IoT). Advanced low-cost sensor technologies are essential for gathering data and utilizing it for effective performance by manufacturing companies and supply chains. Different types of low power/low cost sensors allow for greatly expanded data collection on different devices across the manufacturing processes. While a lot of research has been carried out with a focus on analyzing the performance, processes, and implementation of smart factories, most firms still lack in-depth insight into the difference between traditional and smart factory systems, as well as the wide set of different sensor technologies associated with Industry 4.0. This paper identifies the different available sensor technologies of Industry 4.0, and identifies the differences between traditional and smart factories. In addition, this paper reviews existing research that has been done on the smart factory; and therefore provides a broad overview of the extant literature on smart factories, summarizes the variations between traditional and smart factories, outlines different types of sensors used in a smart factory, and creates an agenda for future research that encompasses the vigorous evolution of Industry 4.0 based smart factories.
Pallets and forklifts equipped with Radio-Frequency Identification (RFID) technology can be a suitable option for bridging the information gap between cutting and bending stages in sheet metal ...production. However, a decision on how tagged pallets can be assigned to their content needs to be made. In this paper, a reactive and a proactive approach for the near-automatic identification of parts on pallets after cutting are discussed and their performance is evaluated through a series of simulations. In both approaches, the nesting information along with the measured net weight of the pallets are used to determine the parts on top of each pallet. The influence of the alternative solutions problem on the performance is investigated for both approaches. It is concluded that the actual decision on the approach selection depends on the time that is required for each recalculation and each pre-allocation. Those times are workshop dependent and, therefore, a decision should be made for each workshop specifically.
Supply chains are experiencing significant advances in digital technologies, particularly those associated with industry 4.0. An example of such technology is blockchain. Blockchain is a disruptive ...technology characterised by anonymity and identity, consensus mechanism, decentralisation, overall performance and expectancy, reliability of systems and data, and information transparency. Blockchain offers supply chain opportunities to strengthen end-to-end visibility and traceability, leading to enhanced levels of transparency. Supply chains are increasingly exploring blockchain technology and transparency, with many focusing on system development. This paper explores transparency in blockchain-based supply chains to understand the principles underlining its design. A systematic review of literature is used, accompanied by data-driven analysis. The results present the principles within a framework for transparency by design in blockchain-based supply chains. Limitations and areas for future work are also presented.
In developing nations, small and medium-sized enterprises (SMEs) in the manufacturing sector do not or are slowly adopting industry 4.0 (I4.0) concepts, making them less competitive. Yet, they serve ...as the backbone of emerging economies, thus creating a performance gap between them and their international competitors. As a result, more efforts will be required to provide adequate tools for SMEs to implement I4.0, especially in emerging economies and Africa. This study tackles the disconnect in sub–Saharan African manufacturing SMEs through an empirical research approach using the focus group discussion method, which is the first to collect opinions, reinforcing the current literature on I4.0 technologies, prospects, and challenges to implementation. Our findings underpin six critical drawbacks for implementing I4.0 by manufacturing SMEs in emerging economies and proffer mitigation strategies to establish Sub-Saharan Africa as a competitive, innovation-driven manufacturing powerhouse at the centre of Industry 4.0 development and adoption.
Machine tools play a pivotal role in the manufacturing world since their performance significantly affects the product quality and production efficiency. In the era of Industry 4.0, machine tools are ...expected to have a higher level of accessibility, connectivity, intelligence, adaptivity, and autonomy. With the rapid development and application of various Industry 4.0 technologies, digitalisation and servitisation of machine tools have become a new research trend. However, few review articles on the development of machine tools in the context of Industry 4.0 have been reported. To understand the current status of digitalisation and servitisation of machine tools, this paper provides a systematic literature review combining both bibliometric and qualitative analysis. Our review results provide a comprehensive and in-depth understanding of recent advancements of digitalisation and servitisation of machine tools, including the key enabling technologies, methods, standards, architectures, and applications. Furthermore, we propose a novel conceptual framework of Cyber-Physical Machine Tool (CPMT) as a systematic approach to achieving digitalisation and servitisation of next-generation machine tools. Finally, major research issues, challenges, and future research directions are discussed. This work will help researchers and industrial practitioners spark new ideas for developing the next-generation machine tools in the era of Industry 4.0.
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Research limitations/implications
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.
Practical implications
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.
Originality/value
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.
Digital assets are highly vulnerable and always prone to malicious intervention. Identification of causes of such intervention for timely support and assistance remains a key challenge for businesses ...to remain functional and thrive with the competition. A framework is proposed in this paper for identifying cyber risk, threat, and countermeasure, based on breach databases and textual information processing. Alongside, a multi-objective optimisation of a mixed-integer non-linear problem (MINLP) is made post linearisation to find out a suitable trade-off between cyber risk and investment. The model helps in effective decision-making by finding the proneness of suppliers (as nodes) in the sequence of reducing vulnerability and pairing of categorised factors. The web scrapping and historical databases are processed to extract relationships among categorised factors using natural language processing (NLP). Pareto optimal pairs are obtained to explain the application of the current contribution in terms of risk-cost trade-off. It helps in forming preventive strategies with a suitable amount of investment and the required order of precedence or susceptibility.
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