The Internet of Things (IoT) is playing a significant role in the transformation of traditional factories into smart factories in Industry 4.0 by using network of interconnected devices, sensors, and ...software to monitor and optimize the production process. Predictive maintenance using the IoT in smart factories can also be used to prevent machine failures, reduce downtime, and extend the lifespan of equipment. To monitor and optimize energy usage during part manufacturing, manufacturers can obtain real-time insights into energy consumption patterns by deploying IoT sensors in smart factories. Also, IoT can provide a more comprehensive view of the factory environment to enhance workplace safety by identifying potential hazards and alerting workers to potential dangers. Suppliers can use IoT-enabled tracking devices to monitor shipments and provide real-time updates on delivery times and locations in order to analyze and optimize the supply chain in smart factories. Moreover, IoT is a powerful technology which can optimize inventory management in smart factories to reduce costs, improve efficiency, and provide real-time visibility into inventory levels and movements. To analyze and enhance the impact of internet of thing in smart factories of industry 4.0, a review is presented. Applications of internet of things in smart factories such as predictive maintenance, asset tracking, inventory management, quality control, production process monitoring, energy efficiency and supply chain optimization are reviewed. Thus, by analyzing the application of IoT in smart factories of Industry 4.0, new ideas and advanced methodologies can be provided to improve quality control and optimize part production processes.
•Main characteristics of Industry 4.0.•The different technical, economic and social challenges and barriers facing a wide adoption of Industry 4.0.•Blockchain supporting Industry 4.0 strategy.•How ...blockchain can push forward the development of Industry 4.0.
Industry 4.0 mainly refers to factories in which machines use smart and autonomous systems enhanced by the Internet of Things (IoT), Artificial Intelligence (AI), machine learning and cyber-physical systems. But what reveals the true potential of Industry 4.0 is the established connection and communication between computers and machines that allow making decisions without any human intervention. The network formed by these interconnected machines, and the generated considerable amount of data, marks the actual value of Industry 4.0. Looking at the main features of Industry 4.0, we find many similarities with blockchain technology. The key drivers for Industry 4.0 and blockchain are data management, connectivity and digitization.
Additionally, decentralization and distribution are considered as shared architectural features. This paper seeks to open new horizons on the potential of blockchain technology as an empowering tool for the 4th industrial era. We are first investigating the fundamentals of Industry 4.0, its challenges, barriers and limitations, and finally exploring areas where blockchain technology can bring new features and add value to the deployment of Industry 4.0.
•Industry 4.0 concept can help enhance operational efficiencies.•A supply chain view is a must for achieving industry 4.0 benefits.•Organizational and culture integration is needed before supply ...chain digitization.•Industry 4.0 implementation across the supply chain requires a phased approach.
The true potential of the industry 4.0, which is a byproduct of the fourth industrial revolution, cannot be actually realized. This is, of course true, until the smart factories in the supply chains get connected to each other, with their systems and the machines linked to a common networking system. The last few years have experienced an increase in the adoption and acceptance of the industry 4.0′s components. However, the next stage of smart factories, which will be the smart supply chains, is still in its period of infancy. Moreover, there is a simultaneous need to maintain a focus on the supply chain level implementation of the concept that industry 4.0 puts forth. This is important in order to gain the end to end benefits, while also avoiding the organization to organization compatibility issues that may follow later on. When considering this concept, limited research exists on the issues related to the implementation of industry 4.0, at the supply chain level. Hence, keeping in mind this lack of literature and research available, on a phenomenon that will define the future of business and industry, this study uses an exploratory approach to capture the implementation of industry 4.0 concepts across multiple tiers of the supply chain. Based on this research, the study proposes a multistage implementation framework that highlights the organizational enablers such as culture, cross-functional approach, and the continuous improvement activities. Furthermore, it also highlights the staged implementation of the advanced tools, starting from the focal organization with the subsequent integration with the partner organizations.
Smart manufacturing has attracted significant attention from both researchers and manufacturing experts owing to its capability of accomplish the goals of Industry 4.0. It significantly contributes ...to build highly effective smart factories, increase yield, cut down human error by utilizing autonomous machines or robots, optimize overall electrical consumption, fulfill users’ customized demands, etc. Hence, we reviewed the four industrial revolutions, including the development of new technologies with time, adoption of the fourth industrial revolution by five leading goods manufacturing leaders (Germany, U. S. A., Japan, China, and Taiwan), their distinct strategies for smart manufacturing, and the technologies associated with smart manufacturing systems (such as virtual reality, augmented reality, mixed reality, additive manufacturing, big data analytics, Industrial Internet of Things, and artificial intelligence). Next, we discussed the influence of artificial intelligence on six aspects of the production line with practical case studies: quality checks, maintenance, reliable design, reduced environmental impact, factory integration, and post-production support. Furthermore, we also discussed inspection methods (such as conventional as well as artificial intelligence-based automated optical inspection) and the challenges in implementing smart manufacturing systems before future prospects in the global market are projected.
•Initiation of Industry 4.0 and adoption of smart manufacturing by key goods producing countries.•Review of seven advanced technologies associated with the smart manufacturing operation.•Application of artificial intelligence from the design to final product inspection during smart manufacturing process.•Three major challenges associated with the smart manufacturing technologies.
Increasing complexity in manufacturing processes poses new challenges for industrial maintenance. In addition, advanced machine monitoring and lifetime forecasting options expand the tools and ...maintenance strategies available. Today, maintenance strategy selection is performed sequentially usually based on prioritised machines and components. These selections are optimized locally for each machine isolated, not considering the context of other machines within the value-adding network. To overcome these challenges, this paper presents an approach for an integrated maintenance strategy selection in one-step by an integrated model considering possible machine failures and the context of other machines within the value-adding network in parallel.
Smart factories are complex systems where many different components need to interact and cooperate in order to achieve common goals. In particular, devices must be endowed with the skill of learning ...how to react in front of evolving situations and unexpected scenarios. In order to develop these capabilities, we argue that systems will need to build an internal, and possibly shared, representation of their operational world that represents causal relations between actions and observed variables. Within this context, digital twins will play a crucial role, by providing the ideal infrastructure for the standardisation and digitisation of the whole industrial process, laying the groundwork for the high-level learning and inference processes. In this paper, we introduce a novel hierarchical architecture enabled by digital twins, that can be exploited to build logical abstractions of the overall system, and to learn causal models of the environment directly from data. We implement our vision through a case study of a simulated production process. Our results in that scenario show that Bayesian networks and intervention via do-calculus can be effectively exploited within the proposed architecture to learn interpretable models of the environment. Moreover, we evaluate how the use of digital twins has a strong impact on the reduction of the physical complexity perceived by external applications.
Using predictive maintenance, more efficient processes can be implemented, leading to fewer maintenance costs and increased availability. The development of a predictive maintenance solution ...currently requires high efforts in time and capacity as well as often interdisciplinary cooperation. This paper presents a standardized model to describe a predictive maintenance use case. The description model is used to collect, present, and document the required information for the implementation of predictive maintenance use cases by and for different stakeholders. Based on this model, predictive maintenance solutions can be introduced more efficiently. The method is validated across departments in the automotive sector.
•The PrOM framework merges terminological components of ontologies to increase semantic interoperability in production.•Production ontologies can be merged via terminological and structural matching, ...domain knowledge, and interactive debugging.•PrOM is adaptable, enabling the customization of the matching process and the framework’s architecture by domain engineers.
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Current trends, such as individualization, increasing complexity, and specialization, require digitization in engineering and production. However, digitization by itself often leads to so-called data silos, which cannot be leveraged effectively when designing and operating smart factories due to the heterogeneity of the information available. This paper presents a framework for (semi-)automatically merging the highly reusable terminological components of production ontologies in an a posteriori way. The framework combines translations, domain-specific vocabularies, and inconsistency checks with syntactic, terminological, and structural analyses to integrate knowledge representations formalized in the Web Ontology Language. Integrating heterogeneous knowledge representations in the production domain can improve support systems for engineers, increase awareness of interdependencies, and enable unambiguous communication in smart factories.
•Unleash human creative potential to complement the robotic and virtual world of Advanced Manufacturing.•Factory Operators transitioning to the role of a “Maker” and working alongside automated ...production systems.•Argues for the design of work organization models that ensure an enhanced role for humans.•Considers safety and productivity issues under Industry 4.0.
Many authors have advocated a human–centric view of Industrie 4.0 automation, in which the human operator works with augmented powers and capabilities such as super-strength, artificial intelligence and virtual reality, although their roles are still in the manufacture of the product. A human-automation symbiosis in the production process, with robotic and IOT tools helping to extend and meld the capabilities of the Operator to accomplish the tasks of production is a proposed goal. The approach taken in the present work does not, however, envisage a super-human outcome. Rather, the goal is the unleashing of human creative potential, as a complement to the robotic and virtual world of the automated production system.
Agile manufacturers typically run small scale operations (20 people or less), make short runs of many products and are continuously involved in their design – both at detailed and conceptual levels. This can be a strength in terms of international competitiveness where knowledge of the production process, the product end user needs, and the machinery and tooling itself combine to produce innovation. SME manufacturers are actually designers. Their employees think about design as they manufacture. We foresee that the Operator may transition to a Maker – a person who works alongside the automated production system but with a different role - one which is essentially creative, rather than assisting or monitoring non-discretionary work flow steps or processes.
Nowadays, Internet-of-Things (IoT), big data, and cloud computing technologies allow increasing the throughput and quality of manufacturing systems, bringing to the rise of the Industry 4.0 paradigm. ...The aim is to leverage the ICT technologies to achieve a flexible customised production with reduced time while avoiding resources waste. In this framework, Zero-Defect Manufacturing (ZDM) concept plays a crucial role in guaranteeing the minimisation of defects and errors in industry processes by trying to act at the first time properly. Due to its importance, this topic has received greater attention during the last two years by the technical literature. Given the increasing number of research works and the several approaches suggested by researcher interested into the topic, this study aims at providing a literature overview of the current trends in the ZDM field from 2018 to 2020. The focus of the work is to provide a state-of-the-art about ZDM strategies, i.e. Detection, Repair, Prediction and Prevention, by analyzing the related most significant works, thus providing a single-strategy analysis and the corresponding most frequently used methods. A brief bibliometric study corroborates the general research patterns and the relevant aspects emerging from each single-strategy analysis. Finally, based on the conducted study, we point out the shortcomings present in the current technical literature to target the future research directions in the ZDM field.
•We perform a systematic review of the technical literature in the appraised time period for each of the ZDM strategies, namely, Detection, Prediction, Prevention and Repair.•We describe their advantages and aims for each of these latter and the most significant critical findings of the related works.•A bibliometric analysis of all the selected works is conducted so to highlight the importance of the topic for the industry during the last years.•A discussion of the shortcoming and of the open challenges are given.
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