literature review of industry 4.0 and related technologies

Browse Econ Literature

• Working papers
• Software components
• Book chapters
• JEL classification

More features

• Subscribe to new research

RePEc Biblio

Author registration.

• Economics Virtual Seminar Calendar NEW!

Some searches may not work properly. We apologize for the inconvenience.

Literature review of Industry 4.0 and related technologies

• Author & abstract
• 110 Citations
• Related works & more

Corrections

(Marmara University Engineering Faculty)

Suggested Citation

Download full text from publisher.

Follow serials, authors, keywords & more

Public profiles for Economics researchers

Various research rankings in Economics

RePEc Genealogy

Who was a student of whom, using RePEc

Curated articles & papers on economics topics

Upload your paper to be listed on RePEc and IDEAS

New papers by email

Subscribe to new additions to RePEc

EconAcademics

Blog aggregator for economics research

Cases of plagiarism in Economics

About RePEc

Initiative for open bibliographies in Economics

News about RePEc

Questions about IDEAS and RePEc

RePEc volunteers

Participating archives

Publishers indexing in RePEc

Privacy statement

Found an error or omission?

Opportunities to help RePEc

Get papers listed

Have your research listed on RePEc

Open a RePEc archive

Have your institution's/publisher's output listed on RePEc

Get RePEc data

Use data assembled by RePEc

Literature review of Industry 4.0 and related technologies

Chat with Paper

Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet.

A review of indirect tool condition monitoring systems and decision-making methods in turning: critical analysis and trends., segmentation-based deep-learning approach for surface-defect detection, from artificial intelligence to explainable artificial intelligence in industry 4.0: a survey on what, how, and where, significant applications of big data in industry 4.0, internet of things (iot): a vision, architectural elements, and future directions, rfid technologies: supply-chain applications and implementation issues, cyber-physical systems, why research in sustainable supply chain management should have no future, business intelligence: an analysis of the literature, related papers (5), intelligent manufacturing in the context of industry 4.0: a review, industry 4.0, past, present and future of industry 4.0 - a systematic literature review and research agenda proposal, scanning the industry 4.0: a literature review on technologies for manufacturing systems, industry 4.0 and the current status as well as future prospects on logistics, trending questions (1).

Social factors in manufacturing within Industry 4.0 context involve robots, implanted technologies, learning machines, and smart city applications, impacting societal and economic progress.

IEEE Account

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

The fourth industrial revolution (Industry 4.0): technologies disruption on operations and supply chain management

International Journal of Operations & Production Management

ISSN : 0144-3577

Article publication date: 20 November 2019

Issue publication date: 20 November 2019

Koh, L. , Orzes, G. and Jia, F.(J). (2019), "The fourth industrial revolution (Industry 4.0): technologies disruption on operations and supply chain management", International Journal of Operations & Production Management , Vol. 39 No. 6/7/8, pp. 817-828. https://doi.org/10.1108/IJOPM-08-2019-788

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

1. The fourth industrial revolution (Industry 4.0): technologies disruption on operations and supply chain management

1.1 context.

During the last five years, journals in robotics, electronics, computer science and production engineering have devoted significant attention to Industry 4.0 and related subjects, including additive manufacturing/3D printing, intelligent manufacturing and big data ( Lee et al. , 2014 ; Xi et al. , 2015; Pfeiffer et al. , 2016 ; Mosterman and Zander, 2016 ; Chen and Zhang, 2015 ; Jia et al. , 2016 ). A systematic literature review on Industry 4.0 or on some of its specific technologies (e.g. additive manufacturing) is provided by Liao et al. (2017) , Strozzi et al. (2017) and Khorram Niaki and Nonino (2017) among others. Although prominent scholars have acknowledged the relevance of Industry 4.0 for management in general, as well as for Operations and Production Management (O&PM) specifically ( Brennan et al. , 2015 ; Fawcett and Waller, 2014 ; Holmström and Romme, 2012 ; Melnyk et al. , 2018 ), relatively little consideration has been given to these topics by mainstream O&PM journals, especially on Industry 4.0 technologies’ disruption on operations and supply chain management. A few prominent exceptions are represented by the recent attempts to shed lights on: the link between Industry 4.0 and lean manufacturing ( Buer et al. , 2018 ; Tortorella and Fettermann, 2018 ); the link between Internet of Things (IoT) and supply chain management ( Ben-Daya et al. , 2017 ); the impact of additive manufacturing on supply chain processes and performances ( Liu et al. , 2014 ; Oettmeier and Hofmann, 2016 ; Li et al. , 2017 ); and the short-term supply chain scheduling in smart factories ( Ivanov et al. , 2016 ). While in the past there were very few pilot Industry 4.0 projects, the number of applications has significantly increased, both in terms of demonstration and “real” factories hence give rise to more empirical studies. Demonstration factories include Factory 2050 at the University of Sheffield (UK), Demonstration Factory at Aachen University (Germany), TRUMPF Group Factory in Chicago (USA) and SmartFactoryKL in Kaiserslautern (Germany), whilst “real” factories are at Audi’s Ingolstadt factory (Core77, 2016), Arla Foods ( ARC, 2016 ), Siemens’ Amberg plant ( Siemens, 2016 ) and Bosch’s Feuerbach plant in Stuttgart ( Automotive World, 2016 ). A recent survey conducted by PwC on more than 2,000 companies from 26 countries showed an overall adoption rate of Industry 4.0 concepts (e.g. digitization and integration) of 33 percent, and forecasted that it will reach 72 percent by 2020 ( PwC, 2015 ). This growth will be further fostered by the funding and innovation plans launched by several countries leading this industrial revolution, e.g., Manufacturing USA in the USA, Industrie du Futur in France, Industrie 4.0 in Germany, Industria 4.0 in Italy, Made in China 2025, Made Smarter UK. It is argued that different industrial sectors have different pace of adopting Industry 4.0. for instance, the aerospace sector has sometimes been characterized as “too low volume for extensive automation” however Industry 4.0 principles have been investigated by several aerospace companies, technologies have been developed to improve productivity where the upfront cost of automation cannot be justified, one example of this is the aerospace parts manufacturer Meggitt PLC’s project, M4. Here, the fourth industrial revolution (Industry 4.0) refers to the “confluence of technologies ranging from a variety of digital technologies (e.g. 3D printing, IoT, advanced robotics) to new materials (e.g. bio or nano-based) to new processes (e.g. data driven production, Artificial Intelligence, synthetic biology)” ( OECD, 2016 ). These technologies have the potential to revolutionize operations and supply chain management ( Brennan et al. , 2015 ; Holmström et al. , 2016 ; Rüßmann et al. , 2015 ; Fawcett and Waller, 2014 ; Waller and Fawcett, 2013 ). Industry 4.0 is not merely about integrating technologies, but it is about the whole concept of how future customer demands, resources and data are shared, owned, used, regenerated, exploited, organized and recycled to make a product or deliver a service, faster, cheaper, more efficiently and more sustainably ( Spath, 2013 ). As such, Industry 4.0 requires a rethinking and shift in mindset of how products are manufactured and services are produced, distributed/supplied, sold and used in the supply chain; thus, it will drive significant structural theoretical evolution and revolution for operations and supply chain management. Whilst classical theories such as resource based view, institutional theory, chaos theory, systems theory, stakeholder theory, transaction economic cost theory, evolutionary theory to name a few may need reshaping, the issues of trust will become prominent in such a disruptive digital environment, driving major evolvement of technological singularity in the transformation process, where blockchain may play a central role with IoT and Artificial Intelligence (AI) ( Carter and Koh, 2018 ).

2. Introduction

So far, all the industrial revolutions that took place in the past two centuries is promoted by altering production mode enabled by a specific emerging technology at that time ( Liao et al. , 2017 ). The arrival of steam engine promoted the first industrial revolution; the application of electricity led to the second revolution, and the widespread use of information technology and electronics products support the third revolution ( Liao et al. , 2017 ). The recent popularization of the IoT and cyber-physical system (CPS) ( Khaitan and McCalley, 2014 ) has attracted the attention of both enterprise and academics. Leveraging those two emerging technologies is promising to enable the higher level of connection between information, products and people ( Ibarra et al. , 2018 ), thereby making contributions to the current production mode. This phenomenon is considered as the fourth industrial revolution, also known as industry 4.0, which is about to bring about an extensive range of innovation from a variety of digital technologies ( Lu, 2017 ), advanced materials ( Schumacher et al. , 2016 ), innovative products ( Pereira and Romero, 2017 ), to new manufacturing processes ( Wagner et al. , 2017 ).

Industry 4.0 is an emerging concept deriving from technological advancement and disruptive developments in the industrial sector worldwide in the past few years (Dallasega et al. , 2017). It defines a methodology applying emerging technologies to revolutionize the current production that transits from machine dominant manufacturing to digital manufacturing ( Oztemel and Gursev, 2018 ). Some consider it as the integration of technologies such as CPS, IoT, Big Dara and Cloud manufacturing ( Pereira and Romero, 2017 ). However, there is a discourse arguing that industry 4.0 is not only regarding integrating technologies but concerning the whole concept of how to acquire, share, use, organize data and resource to make the product/service deliver faster, cheaper, more effective and more sustainable ( Piccarozzi et al. , 2018 ).

As the interest in the Industry 4.0 research is growing rapidly, these studies do not limit their focus on industry 4.0 itself, but seek to find the relationship between industry 4.0 and other topics. For instance, Piccarozzi et al. (2018) try to link industry 4.0 with management studies; Dallasega et al. (2018) investigate industry 4.0 in the context of the supply chain. Müller et al. (2018) and Kamble et al. (2018) explore the relationship between industry 4.0 and sustainable development.

This position paper intends to summarize the major topics in the current research regarding Industry 4.0 and charts key thematic future research directions and paradigms. In the following section, the paradigms and principles of industry 4.0 are concluded. Five technologies that are widely discussed in the current research are identified and the outcomes of industry 4.0 are discussed at the end of this position paper.

3. Paradigms in industry 4.0

According to Weyer et al. (2015) , industry 4.0 can be subdivided into three paradigms: the smart product, the smart machine and the augmented operator. This conclusion of the major paradigm of industry 4.0 is also agreed by Longo et al. (2017) and Mrugalska and Wyrwicka (2017) . The first paradigm is the smart products, it refers to objects and machines that are equipped with sensors and microchips, controlled by software, and connected to the internet ( Lu, 2017 ; Kamble et al. , 2018 ). Smart products can store the operational data and requirements independently, and further, the product can inform the machine-related manufacturing information, for instance, when to produce, where to produce, or what parameter should be adopted to complete the product manufacturing. In this case, smart product shifts the role of the workpiece in a system from passive to an active part ( Loskyll et al. , 2012 ).

The second paradigm is the Smart Machine. It refers to a device equipped with machine-to-machine and/or cognitive computing technologies (i.e. AI and machine learning (ML)). Through leveraging these technologies, machines can reason, problem-solve, make decision ad eventually take action. Smart machine brought decentralized self-organization, thus replacing the previous traditional production hierarchy ( Mrugalska and Wyrwicka, 2017 ). In such innovative system, the use of open networks and semantic descriptions allow the communication among the autonomic components ( Oztemel and Gursev, 2018 ), while the local control intelligence communicate with other devices, production modules and products, thereby, contributing to the improvement of flexibility and modularity of the production line ( Pereira and Romero, 2017 ).

The third paradigm of industry 4.0 is the augmented operator. This concept emphasizes the technological support of the worker in the production system with higher flexibility and modularity ( Weyer et al. , 2015 ). Mrugalska and Wyrwicka (2017) state that augmented operator addresses the knowledge automation in the system, therefore making them the most flexible and adaptive part in the production system. Workers in such production system are likely to encounter with varieties of tasks including specification, monitoring and verification of production strategy. Meanwhile, they may have to annually intervene in the self-organized production system. Under the support of mobile, context-sensitive user interfaces and user-focused assistance system ( Gorecky et al. , 2014 ), such workers play the role of strategic decision-makers and flexible problem-solvers in the circumstance of increasing technical complexity ( Mrugalska and Wyrwicka, 2017 ).

4. Design principles in industry 4.0

Based on the three paradigms mentioned above, some researchers further conclude six principles that should be considered when designing the implementation of industry 4.0 ( Oztemel and Gursev, 2018 ). Those principles include interoperability, virtualization, decentralization, real-time capability, service orientation and modularity ( Lu, 2017 , Oztemel and Gursev, 2018 ). Kamble et al. (2018) conduct a systematic literature review to develop a framework of sustainable industry 4.0 and further justify the role of these principles on industry 4.0 implementation.

First, interoperability is the first principle for industry 4.0. Interoperability refers to the ability of two systems to communicate with and understand each other and use the functions of one another ( Hermann et al. , 2016 ; Lu, 2017 ). It addresses the capability of data exchanging and information and knowledge sharing among systems ( Lu, 2017 ). It is assumed that interoperability is the key advantages of industry 4.0 as it ensures the connection and communication among products, machines and humans ( Mrugalska and Wyrwicka, 2017 ) throughout the diversified autonomous procedure ( Lu, 2017 ).

Further, Lu (2017) proposes a framework of interoperability of industry 4.0 and concludes four levels of interoperability in industry 4.0, including operational, systematic, technical and semantic interoperability. The author gives specific explanations for each level of interoperability. Operational interoperability indicates the concepts, standards, languages and relationships within the system. Systematic interoperability describes the methodologies, standards and models; technical interoperability illustrates tools and platforms for technical development, and the semantic interoperability ensures the exchanged information is well understood among different groups.

Qin et al. (2016) confirmed that interoperability constructs a trusted environment in a manufacturing system, in which information is accurately and swiftly shared among partners ( Kamble et al. , 2018 ), therefore resulting in a cost-saving operation with higher productivity ( Lu, 2017 ).

Virtualization is used for process monitoring and machine-to-machine communication. It indicates that devices have the capability of monitoring the physical process. The sensor data is linked to virtual plant models and simulation models, thus constructing the virtual copy of physical objects ( Mrugalska and Wyrwicka, 2017 ). Meanwhile, each device can be virtualized and become a part of the plant model. The virtual model can simulate various scenarios based on the monitored data. Once the potential risks or failures are detected in the virtual models, operators are informed and they can take the pre-emptive action ( Kamble et al. , 2018 ), thus reducing the actual error rate and smoothing the inter-company operations ( Brettel et al. , 2014 ).

Third, decentralization denotes that companies, operation staff, and even devices are able to make independent decision rather than depending on the centralized decision-making, It can be achieved with the use of embedded computer, which provides the operation staff or devices the capability of individual control and independent decision-making ( Marques et al. , 2017 ). As the development of customization and product variety, the flexible production line is expected to be extensively adopted. Overall control of the production line is less advisable. However, the embedded control system can empower each device or the unit of the device to make independent decisions, thus making the decision-making efficient and offering more flexibility ( Kamble et al. , 2018 ).

Fourth, real-time capability refers to the immediacy of data collection and analysis, and the real-time of data transmission. Smart factory requires continuous real-time data monitoring and analyzing, to detect the errors timely and satisfy the new demand. The collection of real-time data relies on big data technology ( Kamble et al. , 2018 ). The huge amount of data regarding machines, equipment, and products are collected from factories, and data regarding customers are collected from multiple sources such as social media or outlets. The analysis of those real-time data may alter the ways of decision-making and pose an impact on the profitability of the companies implementing industry 4.0.

Fifth, service orientation required that devices are capable of satisfying the needs of users through the internet of service. As all the entities in the production system are interconnected, and therefore, the concept of the product will extend from the product itself to product-service ( Lasi et al. , 2014 ). Service orientation indicates that product should be considering the users’ practical needs, such as user-friendly or convenience for maintenance, at the very beginning of product design. Moreover, through service orientation, corporate can achieve flexibility and agility and thus to have a quick response to the market change ( Kamble et al. , 2018 ).

Sixth, modularity refers to the device or the components of a device is produced following standards. Therefore, they can be assembled, replaced and expanded as needed in the modular production system ( Qin et al. , 2016 ). In this case, modularity provides smart factories with the capability of adapting capacity at a lower cost to cope with seasonal fluctuation and changes in production needs ( Mrugalska and Wyrwicka, 2017 ).

5. Technologies in industry 4.0

Lu (2017) defines industry 4.0 as an integrated, adapted, optimized, service-oriented and interoperable manufacturing process in which algorithms, big data and high technologies are included. Technologies are considered as the very heart of industry 4.0 as the interconnection in the industry 4.0 is supported by the adoption of software, sensor, processor and communication technologies ( Bahrin et al. , 2016 ). Five technologies are frequently discussed in the literature: IoT, big data analytics, cloud, 3D printing and robotic systems ( Piccarozzi et al. , 2018 ; Kamble et al. 2018 ), where technologies such as AI, ML, digital twin and 5G are emerging.

Internet of Things (IoT)

The IoT is an emerging industrial ecosystem. It facilitates the combination of intelligent machines, advanced predictive analytics and machine-human collaboration, aiming at promoting productivity, efficiency and reliability ( Kamble et al. , 2018 ). In industry 4.0, IoT can support the smart factory. It can lead to the creation of virtual networks to support the smart factory ( Xu et al. , 2018 ); meanwhile, it provides the factory with the ability to collect real-time data and transmit the data swiftly ( Yang et al. , 2017 ). Therefore, it enables the remote operation of manufacturing activities and affects collaboration among stakeholders ( Yang et al. , 2017 ). IoT can benefit the integration and coordination of product and information flow ( Tao et al. , 2014 ), and enable the decentralization of decision-making, interconnected devised can perform automatic analytics and decision-making, thus improving the responsiveness to the environment change ( Wang et al. , 2014 ).

Big data analytics

Manufacturing companies have realized that data analytics capabilities are imperative for their competitive advantage in the era of digitization. Therefore, they devote themselves to improving skills for algorithms development and data interpretation ( Lee et al. , 2017 ). Big data analytics and technologies can promote data collection from multiple sources, and the ability of comprehensive data analysis and real-time decision making based on the data analysis results ( Bahrin et al. , 2016 ). It has been widely adopted in manufacturing to monitor the process. Also, big data is used for failure detection, thus supporting new capabilities such as predictive analytics ( Lee et al. , 2017 ). Data quality and qualified data analysis capabilities are key to achieve the desired outcomes of big data analytics ( Kamble et al. , 2018 ). Therefore, leveraging the intelligence in big data to improve agility will require new challenges, for example how to ensure the data consistency and confidentiality in a long and complex supply chain ( Kamble et al. , 2018 ).

Cloud computing is a computing technology. Cloud computing centers can store and compute a huge amount of data, therefore promoting the manufacturing and production and further bringing organizations higher performance and lower cost ( Mitra et al. , 2017 ). Cloud computing is supported by virtualization technology, as it provides cloud computing with resource pooling, resource sharing, dynamic allocation, flexible extension and other capabilities ( Xu et al. , 2018 ). Xu et al. (2018) also address the usefulness of cloud computing in facilitating efficient data exchange and sharing. Through cloud computing, data can be stored in either private cloud or public cloud servers, and thus cloud computing can promote complex decision-making ( Xu et al. , 2018 ).

Cloud-based manufacturing is key to the success Industry 4.0 implementation. It enables the modularization and service-orientation in the field of manufacturing ( Xu et al. , 2018 ), where system orchestration and sharing of service and components are essential considerations and are affected by modularization and service-orientation ( Xu et al. , 2018 ). Branger and Pang (2015) assumed that cloud manufacturing is expected to be the next paradigm in manufacturing in Industry 4.0.

3D printing

3D printing relies on additive manufacturing (as opposed to subtractive manufacturing). Final products in 3D printing are built up with successive layers of materials ( Oztemel and Gursev, 2018 ), thus avoiding the component assembly in the production process. Additive manufacturing techniques can make contributions to industry 4.0 in terms of offering organizations construction advantages, as it allows to produce small batches of customized products with complex and lightweight design ( Kamble et al. , 2018 ). Chen and Lin (2017) state that the exploitation of 3D technology can optimize smart manufacturing and lean manufacturing. However, there are technical challenges in the use of 3D printing, namely, limited accuracy and productivity, and limited available material ( Chen and Lin, 2017 ). Because of the technical challenges, additive manufacturing (3D printing) is still in the initial stage. However, once the challenges have been solved, it is expected to see wider adoption of this technology in Industry 4.0 ( Kamble et al. , 2018 ).

Robotic systems

However, robotics has been used for production in many manufacturing industries, the modern robotics systems are more flexible, autonomous and smart and are able to communicate and cooperate with one another and even have learning ability ( Kamble et al. , 2018 ), leading to the next generation of robotic systems, namely, cobot (collaborative robots). Pei et al. (2017) state that the modern robotics can perform well in most of the processes in the smart factory, for instance, Mueller et al. (2017) proposed that it is feasible to use programmable dual-arm robots to efficiently distribute and allocate materials in the assembly line. Therefore, the application of modern robots can provide the factory with cost advantages and a wide range of capabilities ( Pei et al. , 2017 ). To ensure the safe operation of the robotics system, a device named safety eye is equipped. Once the device has detected any disturbance in the operation, it will stop the robot and will not reactivate the robot before the operators remove the objects that disturb the operation ( Kamble et al. , 2018 ).

6. Outcomes of industry 4.0

Considering industry 4.0 can revolutionize the products and manufacturing system in terms of operation, product, design, production processes and services across the supply chain, it is expected that implementing industry 4.0 can positively impact the industry, markets and multiple participants (Dallasega et al. , 2017). Pereira and Romero (2017) conclude six areas on which industry 4.0 may exert influence. Those areas include: industry, products and service, business model and market, economy, work environment and skills development. Kamble et al. (2018) further link industry 4.0 with sustainable development and argued that industry 4.0 can generate sustainable outcomes in terms of environmental, social and economic.

Industry 4.0 has brought manufacturing industry new decentralized and digitalized production patterns, in which the production elements are highly autonomous, and therefore they can trigger actions and respond to the environment change independently ( Pereira and Romero, 2017 ). Industry 4.0 also promote the integration of products and processes, thus transforming the production pattern from mass production to mass customization ( Lu, 2017 ). Additionally, production processes and operations are significantly affected by the emergence of smart factories and emerging technologies, such as IoT, 3D printing and robotic systems. In this case, Industry 4.0 can improve the flexibility in operations and efficiency in resource allocation ( Pereira and Romero, 2017 ). Dallasega et al. (2018) state that Industry 4.0 will not only affect the productivity in the manufacturing industry but also influence the entire supply chain from product development and manufacturing process to the product distribution. Products and services are also affected by industry 4.0. The principle of modularisation makes the products modular and configurable, and as a result, products and services are more customized to satisfy specific customer needs ( Jazdi, 2014 ).

Industry 4.0 has brought a number of new disruptive technologies that have altered the approaches of delivering products or services, hence affecting the traditional business models and encouraging the new business models ( Pereira and Romero, 2017 ). For instance, system integration and complexity in industry 4.0 will result in the emergence of more complex and digital market models, in which the barriers between information and physical structure are reduced ( Ibarra et al. , 2018 ).

Industry 4.0 is transforming jobs and required skills, which have impacts on the working environment and skills development. With more robots and smart machines is involved in the daily operation, the physical and virtual world are fusing together, thus launching transformation in the working environment. For example, as human-machine interfere requires the communication among smart machines, smart products and employees, ergonomic issues should be considered in the future system should stress the workers and their importance in the system ( Pereira and Romero, 2017 ). For skills development, as in the context of industry 4.0, interdisciplinary thinking and qualified skills in the social and technical field are required. These new competencies should be included in the employee training and education ( Pereira and Romero, 2017 ), to make workers and managers well prepared for this new industrial paradigm.

Moreover, Kamble et al. (2018) state that Industry 4.0 can lead to sustainable development. With the support of cloud computing and big data analytics, organizations can achieve cost reduction and lean production, thus realising the economic sustainability; Employing technologies such as sensing, detection and tracing analysis can help to mitigate the problem of industrial waste disposal, which facilitates the environmental sustainability; technologies (risk maps or wearable technologies) for improving the safety of employees in hazardous work areas helps to ensure the process safety and promote the social sustainability.

7. Methodological approaches adopted by Industry 4.0 research

Industry 4.0 literature is characterized by a prevalence of conceptual papers. Piccarozzi et al. (2018) found for instance in their systematic review on Industry 4.0 in management studies 54 percent of conceptual papers, mainly literature reviews and developments of models/frameworks. As far as empirical papers are concerned, qualitative methods (mainly case studies) and quantitative methods (surveys) are almost equally adopted (25 vs 21 percent, respectively).

An agreed definition and operationalization of the Industry 4.0 construct is missing ( Culot et al. , 2018 ). While some authors have indeed sought to develop maturity models and readiness indexes, which identify incremental levels of Industry 4.0 implementation (for a review see Mittal et al. , 2018 ), Industry 4.0 literature still relies on different operationalizations of the concept. As an example, the bunch of technologies considered as Industry 4.0 varies significantly from one paper to the other. This poses serious limitations to theory building and research comparability.

Finally, Industry 4.0 papers belong to a wide set of disciplinary domains. Muhuri et al. (2019) identified in their bibliometric analysis of Industry 4.0 the top 10 subject areas in the Scopus database. At the first place there is Engineering (65 percent [1] ), followed by Computer Science (45 percent), Business, Management and Accounting (16 percent) and Decision Sciences (14 percent). While these disciplines were the most important ones also in the previous investigation conducted by Liao et al. (2017) , their relative importance has significantly changed (Engineering was at the second place after Computer Science; Business, Management and Accounting and Decision Sciences were significantly less frequent). Besides this wide set of disciplines involved, there is however a limited number of interdisciplinary papers.

8. Suggestions for future Industry 4.0 research – methodological approach

As we pointed out in this position paper, Industry 4.0 research so far is still characterized by a prevalence of conceptual papers in the operations and production field. However paradigms, design principles and technologies prevalent to industry 4.0 have been examined. Whilst this might be partially justified by the novelty of the topic and the consequent limited adoption by companies (the Industry 4.0 concept was indeed introduced at the Hannover Fair in 2011), the scientific research cannot overlook the contact with the industrial world. One of the main challenges for future Industry 4.0 research is therefore to carry out more empirical investigations as well as large-scale data analysis. For this reason, we decided not to accept any conceptual contribution in our special issue (even though we received some high-quality conceptual papers). Alongside the traditional empirical methods (i.e. case study and survey), other exploratory methodologies – such as Delphi studies or focus groups – could bring significant insights given the interdisciplinary and “futuristic” nature of the topic.

A further potential methodological limitation of current Industry 4.0 research is the absence of agreed definitions and operationalizations of the main constructs. Without these operationalizations, there is a risk that the significant relationships observed are just due to the specific definitions considered and are not reproducible in other studies. A second significant challenge for future Industry 4.0 research is therefore to define the main Industry 4.0 constructs (e.g. Industry 4.0 adoption, Industry 4.0 maturity, Industry 4.0 readiness) and empirically validate them. This challenge will not be easy since both the technological landscape and the application fields of Industry 4.0 are rapidly evolving. Researchers should however find a way to define a common set of constructs to support further theory building and theory testing efforts.

The issue pointed out above is particularly significant in quantitative research, which is usually based on closed-ended questions or secondary data (requiring a precise operationalization of the measured constructs). The almost equal representation of qualitative and quantitative research might in this sense signal a potential issue. We therefore think that qualitative theory building papers should be particularly welcome in this stage, to develop a set of constructs and relationships to be tested on larger samples in a later stage.

Finally, Industry 4.0 is a highly interdisciplinary topic, involving a wide set of knowledge domains (e.g. automatic controls, robotics, sensors, computer science, and management) and actors (e.g. researchers, companies, technology providers, policy makers, schools). The successful transition toward Industry 4.0 requires indeed a joint effort of the above-mentioned actors to create a successful ecosystem ( Xu et al. , 2018 ). Interdisciplinary research should therefore be significantly encouraged at all levels. First, Industry 4.0 researchers should for instance try to aim in their paper more at the policy makers and the managers. Research should indeed support the different authorities to take better decision to support the digital transformation. Second, authors from different disciplines or affiliations (universities, applied research centers, companies, technology providers, governments and regulatory bodies) should try to systematically integrate the different perspectives and point of views. Finally, the reviewing and editorial board of journals might also be broadened/hybridized by involving experts from the industrial and the policy making worlds.

9. Conclusion

The purpose of this position paper is to summarize the major topics of recent research on industry 4.0. First, three paradigms and six principles of industry 4.0 are identified, and five technologies that are frequently discussed in industry 4.0 are concluded. The outcomes and impacts of industry 4.0 are discussed at the end. In addition, the methodological approaches in industry 4.0 research has been discussed, and future research directions and paradigms of industry 4.0 methodological approach have been proposed.

Although industry 4.0 has been widely discussed from multiple perspectives, as technology advancement still takes place constantly, thus continuously shaping the industry and organizations, there are abundant research opportunities in this topic. Meanwhile, with the increasingly in-depth understanding of industry 4.0, there are more research potentials to combine industry 4.0 with other research fields, to further investigate the industry 4.0 with a wider scope.

The sum of percentages exceeds 100 percent since some papers are categorized by Scopus in more than one category.

ARC ( 2016 ), “ Arla foods reduces packing machine integration costs using PackML ”, available at: www.arcweb.com/Blog/Post/519/Arla-Foods-Reduces-Packing-Machine-Integration-Costs-Using-PackML

Automotive World ( 2016 ), “ Industry 4.0 and the rise of smart manufacturing ”, available at www.automotiveworld.com/analysis/industry-4-0-rise-smart-manufacturing/

Bahrin , M.A.K. , Othman , M.F. , Azli , N.N. and Talib , M.F. ( 2016 ), “ Industry 4.0: a review on industrial automation and robotic ”, Jurnal Teknologi , Vol. 78 Nos 6-13 , pp. 137 - 143 .

Ben-Daya , M. , Hassini , E. and Bahroun , Z. ( 2017 ), “ Internet of things and supply chain management: a literature review ”, International Journal of Production Research , doi: 10.1080/00207543.2017.1402140 .

Branger , J. and Pang , Z. ( 2015 ), “ From automated home to sustainable, healthy and manufacturing home: a new story enabled by the Internet-of-Things and Industry 4.0 ”, Journal of Management Analytics , Vol. 2 No. 4 , pp. 314 - 332 .

Brennan , L. , Ferdows , K. , Godsell , J. , Golini , R. , Keegan , R. , Kinkel , S. , Srai , S.J. and Taylor , M. ( 2015 ), “ Manufacturing in the world: where next? ”, International Journal of Operations & Production Management , Vol. 35 No. 9 , pp. 1253 - 1274 .

Brettel , M. , Friederichsen , N. , Keller , M. and Rosenberg , M. ( 2014 ), “ How virtualization, decentralization and network building change the manufacturing landscape: an Industry 4.0 perspective ”, International Journal of Mechanical, Industrial Science and Engineering , Vol. 8 No. 1 , pp. 37 - 44 .

Buer , S.V. , Strandhagen , J.O. and Chan , F.T. ( 2018 ), “ The link between Industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda ”, International Journal of Production Research , Vol. 56 No. 8 , pp. 2924 - 2940 .

Carter , C. and Koh , L. ( 2018 ), Blockchain Disruption on Transport: Are you Decentralised Yet? , Transport Systems Catapult and The University of Sheffield .

Chen , G. and Zhang , J. ( 2015 ), “ Study on training system and continuous improving mechanism for mechanical engineering ”, The Open Mechanical Engineering Journal , Vol. 9 No. 1 , pp. 7 - 14 .

Chen , T. and Lin , Y.C. ( 2017 ), “ Feasibility evaluation and optimization of a smart manufacturing system based on 3D printing: a review ”, International Journal of Intelligent Systems , Vol. 32 No. 4 , pp. 394 - 413 .

Culot , G. , Nassimbeni , G. , Orzes , G. and Sartor , M. ( 2018 ), “ Industry 4.0: Ambiguities and limits of current definitions ”, Decision Sciences for the New Global Economy, 9th Annual EDSI Conference Proceedings .

Dallasega , P. , Rauch , E. and Linder , C. ( 2018 ), “ Industry 4.0 as an enabler of proximity for construction supply chains: a systematic literature review ”, Computers in Industry , Vol. 99 , pp. 205 - 225 .

Fawcett , S.E. and Waller , M.A. ( 2014 ), “ Can we stay ahead of the obsolescence curve? On inflection points, proactive preemption, and the future of supply chain management ”, Journal of Business Logistics , Vol. 35 No. 1 , pp. 17 - 22 .

Gorecky , D. , Schmitt , M. , Loskyll , M. and Zühlke , D. ( 2014 ), “ Human-machine-interaction in the industry 4.0 era ”, 2014 12th IEEE International Conference on Industrial Informatics (INDIN), IEEE , pp. 289 - 294 .

Hermann , M. , Pentek , T. and Otto , B. ( 2016 ), “ Design principles for industrie 4.0 scenarios ”, 2016 49th Hawaii International Conference on System Sciences (HICSS), IEEE , pp. 3928 - 3937 .

Holmström , J. and Romme , A.G.L. ( 2012 ), “ Guest editorial: five steps towards exploring the future of operations management ”, Operations Management Research , Vol. 5 No. 1 , pp. 37 - 42 .

Holmström , J. , Holweg , M. , Khajavi , S.H. and Partanen , J. ( 2016 ), “ The direct digital manufacturing (r) evolution: definition of a research agenda ”, Operations Management Research , Vol. 9 No. 1 , pp. 1 - 10 .

Ibarra , D. , Ganzarain , J. and Igartua , J.I. ( 2018 ), “ Business model innovation through Industry 4.0: a review ”, Procedia Manufacturing , Vol. 22 , pp. 4 - 10 .

Ivanov , D. , Dolgui , A. , Sokolov , B. , Werner , F. and Ivanova , M. ( 2016 ), “ A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0 ”, International Journal of Production Research , Vol. 54 No. 2 , pp. 386 - 402 .

Jazdi , N. ( 2014 ), “ Cyber physical systems in the context of Industry 4.0 ”, 2014 IEEE International Conference on Automation, Quality and Testing, Robotics, IEEE , pp. 1 - 4 .

Jia , F. , Wang , X. , Mustafee , N. and Hao , L. ( 2016 ), “ Investigating the feasibility of supply chain-centric business models in 3D chocolate printing: a simulation study ”, Technological Forecasting and Social Change , Vol. 102 , pp. 202 - 213 .

Kamble , S.S. , Gunasekaran , A. and Gawankar , S.A. ( 2018 ), “ Sustainable Industry 4.0 framework: a systematic literature review identifying the current trends and future perspectives ”, Process Safety and Environmental Protection , Vol. 117 , pp. 408 - 425 .

Khaitan , S.K. and McCalley , J.D. ( 2014 ), “ Design techniques and applications of cyberphysical systems: a survey ”, IEEE Systems Journal , Vol. 9 No. 2 , pp. 350 - 365 .

Khorram Niaki , M. and Nonino , F. ( 2017 ), “ Additive manufacturing management: a review and future research agenda ”, International Journal of Production Research , Vol. 55 No. 5 , pp. 1419 - 1439 .

Lasi , H. , Fettke , P. , Kemper , H.G. , Feld , T. and Hoffmann , M. ( 2014 ), “ Industry 4.0 ”, Business & Information Systems Engineering , Vol. 6 No. 4 , pp. 239 - 242 .

Lee , J. , Kao , H.A. and Yang , S. ( 2014 ), “ Service innovation and smart analytics for industry 4.0 and big data environment ”, Procedia CIRP , Vol. 16 , pp. 3 - 8 .

Lee , J.Y. , Yoon , J.S. and Kim , B.H. ( 2017 ), “ A big data analytics platform for smart factories in small and medium-sized manufacturing enterprises: an empirical case study of a die casting factory ”, International Journal of Precision Engineering and Manufacturing , Vol. 18 No. 10 , pp. 1353 - 1361 .

Li , Y. , Jia , G. , Cheng , Y. and Hu , Y. ( 2017 ), “ Additive manufacturing technology in spare parts supply chain: a comparative study ”, International Journal of Production Research , Vol. 55 No. 5 , pp. 1498 - 1515 .

Liao , Y. , Deschamps , F. , Loures , E.D.F.R. and Ramos , L.F.P. ( 2017 ), “ Past, present and future of Industry 4.0-a systematic literature review and research agenda proposal ”, International Journal of Production Research , Vol. 55 No. 12 , pp. 3609 - 3629 .

Liu , P. , Huang , S.H. , Mokasdar , A. , Zhou , H. and Hou , L. ( 2014 ), “ The impact of additive manufacturing in the aircraft spare parts supply chain: supply chain operation reference (SCOR) model based analysis ”, Production Planning & Control , Vol. 25 Nos 13-14 , pp. 1169 - 1181 .

Longo , F. , Nicoletti , L. and Padovano , A. ( 2017 ), “ Smart operators in industry 4.0: a human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context ”, Computers & Industrial Engineering , Vol. 113 , pp. 144 - 159 .

Loskyll , M. , Heck , I. , Schlick , J. and Schwarz , M. ( 2012 ), “ Context-based orchestration for control of resource-efficient manufacturing processes ”, Future Internet , Vol. 4 No. 3 , pp. 737 - 761 .

Lu , Y. ( 2017 ), “ Industry 4.0: a survey on technologies, applications and open research issues ”, Journal of Industrial Information Integration , Vol. 6 , pp. 1 - 10 .

Marques , M. , Agostinho , C. , Zacharewicz , G. and Jardim-Gonçalves , R. ( 2017 ), “ Decentralized decision support for intelligent manufacturing in Industry 4.0 ”, Journal of Ambient Intelligence and Smart Environments , Vol. 9 No. 3 , pp. 299 - 313 .

Melnyk , S.A. , Flynn , B.B. and Awaysheh , A. ( 2018 ), “ The best of times and the worst of times: empirical operations and supply chain management research ”, International Journal of Production Research , Vol. 56 Nos 1-2 , pp. 164 - 192 .

Mitra , A. , Kundu , A. , Chattopadhyay , M. and Chattopadhyay , S. ( 2017 ), “ A cost-efficient one time password-based authentication in cloud environment using equal length cellular automata ”, Journal of Industrial Information Integration , Vol. 5 , pp. 17 - 25 .

Mittal , S. , Khan , M. , Romero , D. and Wuest , T. ( 2018 ), “ A critical review of smart manufacturing and Industry 4.0 maturity models: implications for small and medium-sized enterprises (SMEs) ”, Journal of Manufacturing Systems , Vol. 49 , pp. 194 - 214 .

Mosterman , P.J. and Zander , J. ( 2016 ), “ Industry 4.0 as a cyber-physical system study ”, Software & Systems Modeling , Vol. 15 No. 1 , pp. 17 - 29 .

Mrugalska , B. and Wyrwicka , M.K. ( 2017 ), “ Towards lean production in industry 4.0 ”, Procedia Engineering , Vol. 182 , pp. 466 - 473 .

Muhuri , P.K. , Shukla , A.K. and Abraham , A. ( 2019 ), “ Industry 4.0: a bibliometric analysis and detailed overview ”, Engineering Applications of Artificial Intelligence , Vol. 78 , pp. 218 - 235 .

Müller , J.M. , Kiel , D. and Voigt , K.I. ( 2018 ), “ What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability ”, Sustainability , Vol. 10 No. 1 , p. 247 .

OECD ( 2016 ), “ Enabling the next production revolution: the future of manufacturing and services – interim report ”, available at: www.oecd.org/mcm/documents/Enabling-the-next-production-revolution-the-future-of-manufacturing-and-services-interim-report.pdf

Oettmeier , K. and Hofmann , E. ( 2016 ), “ Impact of additive manufacturing technology adoption on supply chain management processes and components ”, Journal of Manufacturing Technology Management , Vol. 27 No. 7 , pp. 944 - 968 .

Oztemel , E. and Gursev , S. ( 2018 ), “ Literature review of Industry 4.0 and related technologies ”, Journal of Intelligent Manufacturing , pp. 1 - 56 .

Pei , F.Q. , Tong , Y.F. , He , F. and Li , D.B. ( 2017 ), “ Research on design of the smart factory for forging enterprise in the industry 4.0 environment ”, Mechanics , Vol. 23 No. 1 , pp. 146 - 152 .

Pereira , A.C. and Romero , F. ( 2017 ), “ A review of the meanings and the implications of the Industry 4.0 concept ”, Procedia Manufacturing , Vol. 13 , pp. 1206 - 1214 .

Pfeiffer , T. , Hellmers , J. , Schön , E.M. and Thomaschewski , J. ( 2016 ), “ Empowering User Interfaces for Industrie 4.0 ”, Proceedings of the IEEE , Vol. 104 No. 5 , pp. 986 - 996 .

Piccarozzi , M. , Aquilani , B. and Gatti , C. ( 2018 ), “ Industry 4.0 in management studies: a systematic literature review ”, Sustainability , Vol. 10 No. 10 , p. 3821 .

PwC ( 2015 ), “ Industry 4.0: building the digital enterprise ”, available at: www.pwc.com/gx/en/industries/industries-4.0/landing-page/industry-4.0-building-your-digital-enterprise-april-2016.pdf

Qin , J. , Liu , Y. and Grosvenor , R. ( 2016 ), “ A categorical framework of manufacturing for industry 4.0 and beyond ”, Procedia CIRP , Vol. 52 , pp. 173 - 178 .

Rüßmann , M. , Lorenz , M. , Gerbert , P. , Waldner , M. , Justus , J. , Engel , P. and Harnisch , M. ( 2015 ), Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries , Boston Consulting Group .

Schumacher , A. , Erol , S. and Sihn , W. ( 2016 ), “ A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises ”, Procedia CIRP , Vol. 52 , pp. 161 - 166 .

Siemens ( 2016 ), “ ‘Industrie 4.0’: seven facts to know about the future of manufacturing ”, available at: www.siemens.com/innovation/en/home/pictures-of-the-future/industry-and-automation/digtial-factory-trends-industrie-4-0.html

Spath , D. ( 2013 ), Produktionsarbeit der Zukunft – Industrie 4.0 , IOA , Stuttgart , available at: www.produktionsarbeit.de/content/dam/produktionsarbeit/de/documents/Fraunhofer-IAO-Studie_Produktionsarbeit_der_Zukunft-Industrie_4_0.pdf (accessed August 18, 2015 ).

Strozzi , F. , Colicchia , C. , Creazza , A. and Noè , C. ( 2017 ), “ Literature review on the ‘Smart Factory’ concept using bibliometric tools ”, International Journal of Production Research , Vol. 55 No. 22 , pp. 6572 - 6591 .

Tao , F. , Zuo , Y. , Da Xu , L. and Zhang , L. ( 2014 ), “ IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing ”, IEEE Transactions on Industrial Informatics , Vol. 10 No. 2 , pp. 1547 - 1557 .

Tortorella , G.L. and Fettermann , D. ( 2018 ), “ Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies ”, International Journal of Production Research , Vol. 56 No. 8 , pp. 2975 - 2987 .

Wagner , T. , Herrmann , C. and Thiede , S. ( 2017 ), “ Industry 4.0 impacts on lean production systems ”, Procedia CIRP , Vol. 63 , pp. 125 - 131 .

Waller , M.A. and Fawcett , S.E. ( 2013 ), “ Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management ”, Journal of Business Logistics , Vol. 34 No. 2 , pp. 77 - 84 .

Wang , C. , Bi , Z. and Da Xu , L. ( 2014 ), “ IoT and cloud computing in automation of assembly modeling systems ”, IEEE Transactions on Industrial Informatics , Vol. 10 No. 2 , pp. 1426 - 1434 .

Weyer , S. , Schmitt , M. , Ohmer , M. and Gorecky , D. ( 2015 ), “ Towards Industry 4.0-standardization as the crucial challenge for highly modular, multi-vendor production systems ”, Ifac-Papersonline , Vol. 48 No. 3 , pp. 579 - 584 .

Xu , L.D. , Xu , E.L. and Li , L. ( 2018 ), “ Industry 4.0: state of the art and future trends ”, International Journal of Production Research , Vol. 56 No. 8 , pp. 2941 - 2962 .

Yang , C. , Lan , S. , Shen , W. , Huang , G.Q. , Wang , X. and Lin , T. ( 2017 ), “ Towards product customization and personalization in IoT-enabled cloud manufacturing ”, Cluster Computing , Vol. 20 No. 2 , pp. 1717 - 1730 .

Further reading

Kagermann , H. ( 2015 ), “ Change through digitization – value creation in the age of Industry 4.0 ”, Management of Permanent Change , Springer Gabler , Wiesbaden , pp. 23 - 45 .

Tjahjono , B. , Esplugues , C. , Ares , E. and Pelaez , G. ( 2017 ), “ What does industry 4.0 mean to supply chain? ”, Procedia Manufacturing , Vol. 13 , pp. 1175 - 1182 .

Related articles

All feedback is valuable.

Please share your general feedback

Report an issue or find answers to frequently asked questions

Contact Customer Support

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Literature review of Industry 4.0 and related technologies

2018, Journal of Intelligent Manufacturing

Related Papers

Samrudhi Deshmukh

The manufacturing industry is a sector of our society which constantly faces challenges with the variation in demand and supply. Optimization in such a dynamic environment becomes difficult for any industry. Advanced approaches constantly attempt to balance the profit of the supplier as well as contentment level of the customer. Automation in the industry assures that mass production will maintain the supply proportional to the demand. In order to take the automation to the next level, Industry 4.0 allows for a more flexible production environment by introducing the concept of digitalization through a cyber-physical system or CPS. It also becomes an attempt to keep the manufacturing sector updated with the advances in technology of this 21st Cy. This article attempts to list out the concepts of this next generation approach towards a more sustainable model of the manufacturing industry. Moreover, the research will also investigate the advantages and limitations of such a concept in ...

International Journal for Research in Applied Science and Engineering Technology IJRASET

IJRASET Publication

The objective of this paper is to visualize and to show the direction of Industry 4.0 to develop smart factories in future. The application of new techniques and technologies which are based upon the Internet of things (IOT) ,block chain technology, cloud computing and cyber physical system have paved the path of significant improvement such as increase of automation, quality of production and reduced the time between the development of new products and its launch.

Ahmed Elkaseer

The arrival of Industry 4.0 has popularised the concept of smart interactions between humans and the physical world that could realise the synergistic integration of intelligent manufacturing assets. However, a systematic and cogent approach to the practical and profitable application of Industry 4.0 is still missing. This paper presents practical approaches to the application of Industry 4.0 to manufacture with the aim of strengthening its competitiveness and meeting the growing serious challenges that threaten its profitability and survival. Precision Additive Metal Manufacturing is utilised in this study for demonstration purposes. A conceptual framework combined with two practical modules available in the market, a "native-design" and "Plug and Play", is proposed. These approaches offer flexible prototypes with sequential procedures that ultimately would allow for easy employment of Industry 4.0, and will help remove technical barriers to the development of m...

BEATRICE SANTOS

The evolution of information and communication technologies and its introduction into production processes are transforming traditional industry, bringing it to a new level of organizational development. In order to avail the benefits of these technologies to strengthen competitiveness in the global market, a new paradigm is being discussed worldwide. Industry 4.0 or 4th Industrial Revolution are some of the terms used to describe the implementation of "smart" devices that can communicate autonomously along the value chain. In this approach, machines use self-optimization, self-configuration, and artificial intelligence to provide better quality goods and services. In this context, the overall goal of the study is to intensify the discussion and provide an overview of Industry 4.0 to show how combination of advanced technologies and internet can create new opportunities to overcome the current industrial challenges.

MOHD SUHAIB

Received: 25 January 2019 Abstract Accepted: 14 August 2019 Low cost manufacturing of quality products remains an essential part of present economy and technological advances made it possible. Advances and amalgamation of information technology bring the production systems at newer level. Industry 4.0, factory for future, smart factory, digital manufacturing, and industrial automation are the new buzz words of industry stalwarts and academicians. These new technological revolutions bound to change not only the complete manufacturing scenarios but many other sectors of the society. In this paper an attempt has been made to capture the essence of Industry 4.0 by redefining it in simple words, further its complex, disruptive nature and inevitability along with technologies backing it has been discussed. Its enabling role in manufacturing philosophies like Lean Manufacturing, and Flexible Manufacturing are also reported. At last the challenges its adoption and future research areas are ...

The Journal of Engineering and Exact Sciences

LIZANDRA VERGARA

The concept of Industry 4.0 emerged in Germany as a strategy for innovation and recovery of economic performance and has spread worldwide. The digital transformation proposed by Industry 4.0 is driven by intelligent manufacturing processes, digitalization, flexibility, integration of systems, and real-time analysis of big data generating intelligent processes and services oriented to customer needs. However, despite the opportunities produced by technological innovation, the fourth industrial revolution has established an environment of uncertainty in the labor market and business models reflected throughout the social sphere. Thus, this study aimed to identify the potential impacts and challenges of Industry 4.0 through a systematic literature review (SLR). Our findings pointed to seven potential impacts of Industry 4.0 on manufacturing: (i) environmental, (ii) competitive, (iii) economic, (iv) education, (v) labor market, (vi) business models, and (vii) social. Additionally, six p...

Renewable and Nonrenewable Energy

M.vijaya Lakshmi , Dr. Ramachandran Manickam , Sathiyaraj Chinnasamy , A PAVITHRA

Industry 4.0 Wireless Supported by connectivity and sensors Factories has machines. These sensors Display the entire production line and monitor and can make its own decisions are integrated into one system. Industry 4.0 Smart production Uses processes to produce essential waste products to meet the COVID-19 epidemic shortage. Patients need themselves Essential medical supplies to get on time, during this crisis Medical disposable items and Smart supply chain of equipment it provides Industry is 4.0 as a flexible product line Smart system used. Intelligence (I) by RTC, real-time information on Internet of Things (Yoda) and almost all other digital technologies for the entire production process. Any medical area Design and upgrade pre-designed Using software is done quickly and to print the required components Digital production such as 3D printing Uses technologies. Industrial 4.0 Technologies COVID-19Diagnose symptoms of, It is related to this disease Helps to avoid the chances of getting sick Can predict. Possible health problems and expected recovery Helps keep track of opportunities. Govt for 19 explosions Assistive Industry 4.0 Notable Technologies. Cross New Business Opportunities Is open. However, flexibility, Performance and product customization the requirements of Real to handle value creation Procedures no longer apply. Industrial 4.0, (Fourth Industrial Revolution) is a term coined in Germany; more important ideas were first published in 2011.It is the internet of future technologies Physical systems (CPS), Integration in the Internet and production systems as defined. Control computers in real time, Plan and predict Helps. In the field of technology, in the production of this concept causing revolution and related services have been discussed in research for many years. One of the basic principles for creating an industrial 4.0 environment is to connect organizations and Working value chain individually and Control each other arbitrarily.

Research Expression is an interdisciplinary biannual and bilingual research journal published by Government VYT Post Graduate Autonomous College

Dr. Ashok D . Gaur

Technology has changed the face of the industrial and manufacturing world. This is the age of the fourth industrial revolution, commonly known as Industry 4.0. It means the introduction of automation and data exchange in manufacturing technologies like Cyber-Physical Systems, Internet of Things, big data and analytics, augmented reality, additive manufacturing, simulation, horizontal and vertical system integration, autonomous robots as well as cloud computing. It links between man and machine by integrating and combining artificial intelligence in machines. The main aims of Industry 4.0 to initiate transformation in industrial manufacturing through digitalization and exploitation of the potentials of new technologies. In essence, Industry 4.0 is related to the creation of creates "smart factory". Amazon, Uber, Facebook, "smart factories" and 3D printing, are among modern pioneers of the new industrial revolution. But with a great opportunity, comes great challenges. The aim of this paper is to present and facilitate an understanding of Industry 4.0 concepts, its drivers, enablers, goals, and limitations.

Sarah El Hamdi

The industrial sector has historically been linked to the prosperity, development and evolution of nations, hence to the genesis of the philosophy of competitiveness, mainly at the manufacturing level. An environment is in perpetual adaptation to trends, and the best example is the change of industrial ideology, as was the case in the past. The global society is facing the same challenges. Customer demands are becoming more and more specific and personalized, which means that factories have to innovate in terms of production and management of mudas in an eco-environmental context closely followed by several global organizations. Information and communication technologies have become the joker to master in order to stay in a market that is global and very competitive. At the heart of this evolution is the emergence of the Industry 4.0 concept, which includes a key element that is intelligent production in factories of the future that can meet different obstacles. The purpose of the c...

Industry 4.0 - Current Status and Future Trends

Ercan Oztemel

Loading Preview

Sorry, preview is currently unavailable. You can download the paper by clicking the button above.

RELATED PAPERS

IOP Conference Series: Earth and Environmental Science

Shahryar Sorooshian

Belmont Press

Dr.adel aneiba

Annual Reviews in Control

Hervé Panetto

International Journal of Online Engineering (iJOE)

juan david diaz

IntechOpen Limited - London

Meisam Gordan

International Journal of Scientific Research in Science and Technology

International Journal of Scientific Research in Science and Technology IJSRST

Mobility and Vehicle Mechanics

edina karabegovic

Dimitrios Koulouriotis

THE IJES Editor

Zenodo (CERN European Organization for Nuclear Research)

RACH MERVEILLE

Journal of Intelligent Manufacturing

Ahmad Barari

IFAC-PapersOnLine

Václav Kaczmarczyk

IJAERS Journal

IRJET Journal , Dr. Surabhi shanker

IRJET Journal

Technical, Economic and Societal Effects of Manufacturing 4.0

karl-erik michelsen

GIS SCIENCE JOURNAL

shimol philip

Aparna Unnikrishnan

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Industry 4.0 Technologies and their Implications for Environmental Sustainability in the Manufacturing Industry

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, view options, recommendations, sustainable additive manufacturing in the context of industry 4.0: a literature review.

Increasing environmental concerns have prompted a worldwide emphasis on the implementation of novel or revitalised technologies that allow manufacturing companies to produce in an environmentally sustainable way while reducing resource, global ...

Industry 5.0: improving humanization and sustainability of Industry 4.0

The widespread digitization and dynamic development of the technologies of the fourth industrial revolution, leading to the dehumanization of industry, have increased the interest of the scientific community in aspects of industrial humanization, ...

Can Industry 5.0 be seen as a remedy for the problem of waste in industrial companies?

Digitization, computerization, robotization and automation are setting new directions for the development of industrial companies. Modern digital technologies are shaping the framework of a new industrial era which is characterized not only by ...

Information

Published in.

Elsevier Science Publishers B. V.

Netherlands

Publication History

Author tags.

• Industry 5.0
• Industry 4.0
• Digitalization
• Circular Economy
• Sustainability
• Green Logistics
• Industrial Logistics
• Research-article

Contributors

Other metrics, bibliometrics, article metrics.

• 0 Total Citations
• 0 Total Downloads
• Downloads (Last 12 months) 0
• Downloads (Last 6 weeks) 0

View options

Login options.

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Share this publication link.

Copying failed.

Share on social media

Affiliations, export citations.

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Author Biographies
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Industry 4.0-compliant occupational chronic obstructive pulmonary disease prevention: literature review and future directions.

1. Introduction

2.1. copd diagnosis, 2.2. copd indicators and monitoring.

• Activities of Daily Living (ADL): COPD is typically accompanied by decreased ADL [ 23 ], which makes ADL assessment one of the best COPD evaluation methods. Furthermore, it is vital for COPD patients to increase their ADLs [ 24 ]. A study implemented real-time activity classification by placing sensors on the forearm, thigh, and sternum [ 25 ]. Wearable technologies such as smart vests and t-shirts were developed to reduce the number of sensors required, and cloud-connected platforms were designed for remote monitoring and interactions [ 26 , 27 ].
• Volatile Organic Compounds (VOCs): VOCs, such as isoprene and hexadecane, are typical kinds of the COPD biomarkers in exhaled breath [ 28 , 29 ]. A portable spectrometer has been proposed for chemical analysis [ 30 ]. However, there are contrary opinions on using VOC profiles for COPD diagnosis. Research shows that VOC profiles could identify patients with COPD accurately [ 31 , 32 , 33 ], while it was also observed by some studies that VOC profiles cannot distinguish smokers, including former smokers, from COPD patients [ 34 , 35 ].
• Blood Lactate Level: The blood lactate level is another COPD biomarker [ 36 ]. It has been reported that people with COPD tend to have a higher blood lactate level than their healthy counterparts while doing the same activities at the same intensity [ 36 ]. As a result, lactic acid has been proposed and used as a biomarker of COPD severity [ 37 ]. Several novel approaches using flexible electronics were developed to measure the lactic acid through human tears, saliva, and sweat [ 38 , 39 , 40 ]. However, in tests, it was found that these electronics were not very comfortable to wear, and their practicality needs further discussion [ 41 ].
• Saliva: Dysphagia is regarded as one of the high-risk phenotypes for the prediction of COPD exacerbation by some studies [ 42 ]. Research has found, as a less invasive way to screen dysphagia, that a repetitive saliva swallowing test cut-off value of 5 could be a strong predictor of COPD exacerbation [ 43 ]. Compared to bio-samples like blood and sputum, saliva is relatively easy to use, especially for home monitoring. A novel biosensor called COPD saliva-based point-of-care monitor has been designed to enable patients to undergo testing at home and identify exacerbation in time [ 44 , 45 ].
• Respiration: As aforementioned, the main symptoms of COPD are related to the patient’s respiratory condition. This follows from the medical explanation of why breathing will change is that sternomastoid muscles, which are accessory muscles, are used during the exacerbation period of COPD [ 46 ]. Research has explored the consistency and accuracy of breathing sounds at various airflow levels and predetermined bodily sites in individuals with COPD [ 47 ]. A conclusion was drawn that the most reliable interval of air flow is 0.4–0.6 L/s, and this applies to respiratory sound parameters at all anatomic locations [ 47 ]. Moreover, it is recommended to be considered in computerized auscultation for future use [ 47 ]. On the other hand, research shows that the respiration characteristics of COPD differ from other dyspnea diseases [ 48 ]. Furthermore, a study found the potential of computerized analyses of respiratory sounds in respiratory status monitoring for people suffering from COPD, and this study achieved “75.8% exacerbations were early detected with an average of 5 ± 1.9 days in advance at [sic] medical attention” [ 49 ].
• Cough and Sputum Production: Chronic cough and sputum production are not only very common in subjects with COPD, but have also been suggested as being predictive of disease progression, exacerbations, and hospitalizations [ 50 ]. Research on using chronic cough or phlegm to predict or identify COPD risk severity achieved some success [ 51 , 52 ]. It has been pointed out that each of them is an independent and statistically significant predictor of COPD [ 51 ]. Experiments successfully identified a subgroup of participants at a high COPD risk, irrespective of smoking habits. The study contends that the occurrence of a chronic cough or phlegm serves as an early indicator of COPD in a significant number of patients, regardless of their smoking status [ 51 ]. Sumner et al. considered cough only in a study, and a comparison was made of 68 randomly selected current and ex-smokers with COPD and smokers without COPD and healthy nonsmokers, using a custom-built cough sound recording device over 24 h. An outcome of this study was that objective cough monitoring is viable. Moreover, it offers a great prospect that cough monitoring can provide timely feedback for COPD interventions and also allows for adapting the new strategy in development [ 52 ].

2.3. Environmental Risks

3. industry 4.0-compliant management, 3.1. iot and data analysis.

• Adherence to and staying current with the latest regulations or directives;
• Protection of workers by identifying workplace hazards and near misses;
• Monitoring of training activities of employees on important EHS policies and procedures;
• Improvement of employee exposure information.
• Remote Health Condition Monitoring: Applying IoT for medical purposes, such as using wearable sensors for wireless monitoring, is a trending topic. A design of the Internet of Medical Things (IoMT) for remote respiratory rate monitoring of COPD patients was proposed to improve doctor–patient communication [ 67 ]. It used message queue telemetry transport protocol and could send clinical alarms according to configurable thresholds. Additionally, a smart vest was designed for breathing monitoring during the rest period, with embedded capacitive sensors [ 67 ]. Similarly, to make the vest more comfortable to wear, inkjet-printed sensor technology was used [ 68 ]. It is stretchable and wearable, and the design achieved high measurement accuracy at different postures and among different patients. In the research of these two smart vests, the only parameter considered was the breath rate of the wearer, and the measurements were supposed to be taken during the rest period. For the same purpose of remote respiration monitoring, much more physiological parameters were covered in the design of a smart mask proposed by Tipparaju et al. [ 69 ]. In this work, principle component analysis (PCA) was applied to analyze the respiration pattern of each participant, but neither the pathology basis nor how to use it for a particular disease was considered [ 69 ].
• Working Environment Control: To the best knowledge of the authors, universal examples of Industry 4.0-compliant environmental COPD risk control do not exist, probably due to the large variety of COPD substances. However, research on occupational exposure management IoT systems has been reported [ 70 , 71 ]. A project aimed at sustainable health management presented an IoT-based indoor environment monitoring system tracking O3 concentrations near photocopy machines [ 70 ]. The developed sensing node contains a Bluetooth module and a semiconductor O3 sensor, apart from which, the developed IoT system also includes gateway nodes and processing nodes. It was claimed that the design can be expanded to cover larger areas and more pollutants such as hydrocarbons and different-size particles [ 70 ]. Similarly, Fathallah et al. conducted work on occupational exposure estimation and proposed a real-time occupational exposure monitoring model [ 71 ]. It successfully quantified indoor worker exposure to formaldehyde and CO2 in real time using multi-pollutant sensor nodes and an indoor positioning system [ 71 ].
• ML-Assisted Assessment and Prediction: The introduction of ML to assist diagnosis is a new trend. Zarrin and Wenger developed an Artificial Neural Network (ANN) model for pattern recognition for COPD diagnosis [ 72 ]. In this study, eight fundamental parameters were considered: the viscosity of saliva samples, the ambient temperature, patient smoking background, cytokine level, pathogen load, mucin combinations, gender, and age. Moreover, the output was set to four different kinds of disease statuses: healthy, low probability, high probability, and COPD-diseased. After comparing to the actual states, an accuracy rate of 112 out of 200 was achieved [ 72 ]. Attempts of COPD readmission prediction have also been made. COPD patients were required to use accelerometer-based wrist-worn wearable devices during daily living and readmission risks for 30 days, and were predicted based on their physical activity, including the activity index and regularity index, and quality of activity [ 73 ]. The results from 16 COPD patients showed a sensitivity of 63% and a positive prediction rate of 37.78%, which can be considered a significant improvement in comparison to other clinical assessments [ 73 ].

3.2. Industry 4.0-Compliant Prevention Based on Underpinning OHS and Medical Management Approaches

• Elimination: physical removal of the hazard;
• Substitution: replacement of the hazard;
• Engineering Controls: isolation of people from the hazard;
• Administrative Controls: change the way people work;
• Personal Protective Equipment (PPE): protects the worker.
• The redefinition of medicine as an informative science;
• The interconnected domains composing complex diseases;
• The emerging technologies allowing for different approaches to understand and access patient data;
• New and powerful analytical systems.

4. Discussion

4.1. identification of opportunities and challenges.

• Health Condition Detection: current research on how digital technologies such as IoT and artificial intelligence can specially support the treatment of occupational-related COPD, rather than OHS management, more related to protection against and prevention of COPD. Systematic reviews reported that digital health interventions (DHIs) for COPD show some uptake problems, like low compliance rates and lack of personalization [ 77 , 78 ]. Some remote monitoring systems also present restricted utilization to specific times during the day [ 77 ]. Moreover, measurements should also be more adjustable to the requirements of the target population [ 78 ].
• Protection: active protection is one of the new trends in OHS 4.0; digital technologies like smart PPE and WSNs can provide more sources and types of data to support further analysis. However, COPD risk factors found in workplaces usually vary. Therefore, monitoring systems with fixed alarm values of one or two substances are not effective enough. It should be noted that some exposure exceeding critical values could be easily avoided by combining environmental monitoring systems with primary real-time intervention control, such as connecting traditional protection equipment like LEV systems and environmental sensors to cyber–physical systems (CPSs), allowing for reducing the exposure level in the workplace and maintaining it under WEL in real time. Regarding personal protection, Adjiski et al. devised smart underground mining PPE by introducing sensors and wireless communication modules into safety wear [ 75 ].
• Assessment: the integration of traditional assessment methods and ML algorithms can improve accuracy and help optimizing management. COPD risk assessment in workplaces needs to be more personalized and dynamic. The lack of personalization of current approaches, which use the same standard for different workers at different ages and different jobs can result in misdiagnoses. With the idea of new conceptual OHS management, digital technologies such as data fusion (e.g., sound and temperature) and ML show high potential for assessment assistance and decision optimization. For example, without motion working state recognition, health condition monitoring could be meaningless. Moreover, unlike other industrial diseases, such as HAVS and MSDs, there are no “ergonomic tools” for COPD, nor well-developed analysis and assessment standards. In addition, it is hard to diagnose or predict COPD as it is a “chronic” disease, influenced by several factors, including various substances and lifestyle habits like exercise and smoking.

4.2. Future Trends and a Vision of Implementation

• Real-Time Monitoring: Continuous health condition monitoring is necessary to analyze the influences of COPD risk factors on workers. Motion monitoring is also needed for working state recognition, while real-time environmental monitoring helps identify the COPD risk factors and workers’ positions.
• Dynamic Exposure Assessment: Data fusion technology can combine the information acquired from the wearable sensors and environmental sensors, using it to establish what happened where, when, and to whom. Exposure assessments like exposure profiles and hot points should be carried out considering job types and layout.
• Effective and Targeted Intervention: Intervention must be performed in a personalized way. Instead of pre-setting values, ML algorithms can help producing effective and targeted intervention standards based on different health conditions and jobs. For example, intervention including alarm, LEV control, and smoke cessation suggestions could be delivered through CPS in the workplace. This way, the lung function prediction of each worker and COPD risk assessment of a workplace could be performed without long-time observations.

5. Conclusions

• Heath condition detection methods from industrial perspective are needed for the purpose of occupational protection. Moreover, for different target populations, measurements should be adjusted for a better uptake.
• Traditional hazard assessments rely on manual periodic checks, which are both time-consuming and expensive, and lead to less accurate results. Sensor-based hazard monitoring is supposed to deal with a wide range of hazards. Dynamic WELs, i.e., exposure thresholds varying with time, should be calculated and derived to drive active protection or real-time intervention control introduced by CPS.
• COPD is a chronic disease with complex causes and varies from person to person. Compared to other diseases, it is difficult to convert current COPD diagnosis criteria into computer algorithms. A personalized diagnosis taking an individual’s physical states and circumstances into account is vital for accurate conclusion in decision making.

Author Contributions

Institutional review board statement, informed consent statement, conflicts of interest.

• Global Initiative for Chronic Obstructive Lung Disease. Global Strategy for Prevention, Diagnosis and Management of COPD. Available online: https://goldcopd.org/archived-reports/ (accessed on 2 July 2023).
• HSE. COPD Causes-Occupations and Substances. Available online: https://www.hse.gov.uk/copd/causes.htm (accessed on 2 July 2023).
• Adisesh, A.; Waters-Banker, C. Causes, diagnosis, and progression of COPD following workplace exposure to vapours, gases, dust and fumes. Methods 2021 , 2 , Q6. [ Google Scholar ]
• Soumagne, T.; Caillaud, D.; Degano, B.; Dalphin, J.C. Similarities and differences between occupational COPD and COPD after smoking {BPCO professionnelles et BPCO post-tabagique: Similarités et différences}. Rev. Mal. Respir. 2017 , 34 , 607–617. [ Google Scholar ] [ CrossRef ]
• Bala, S.; Tabaku, A. Chronic obstructive pulmonary disease in iron-steel and ferrochrome industry workers. Cent. Eur. J. Public. Health 2010 , 18 , 93–98. [ Google Scholar ] [ CrossRef ]
• Dement, J.M.; Welch, L.; Ringen, K.; Bingham, E.; Quinn, P. Airways obstruction among older construction and trade workers at Department of Energy nuclear sites. Am. J. Ind. Med. 2010 , 53 , 224–240. [ Google Scholar ] [ CrossRef ]
• Melville, A.M.; Pless-Mulloli, T.; Afolabi, O.A.; Stenton, S.C. COPD prevalence and its association with occupational exposures in a general population. Eur. Respir. J. 2010 , 36 , 488–493. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hutchings, S.; Rushton, L.; Sadhra, S.; Fishwick, D. Estimating the Burden of Occupational Chronic Obstructive Disease due to occupation in Great Britain. Occup. Environ. Med. 2017 , 74 , A114. [ Google Scholar ] [ CrossRef ]
• Department of Health (UK). Consultation on a Strategy for Services for Chronic Obstructive Pulmonary Disease (COPD) in England. Available online: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/213840/dh_113279.pdf (accessed on 4 July 2023).
• Forum of International Respiratory Societies (FIRS). The Global Impact of Respiratory Disease-Second Edition. Eur. Resp. Soci. 2017 . Available online: https://static.physoc.org/app/uploads/2019/04/22192917/The_Global_Impact_of_Respiratory_Disease.pdf (accessed on 30 May 2021).
• Guarascio, A.J.; Ray, S.M.; Finch, C.K.; Self, T.H. The clinical and economic burden of chronic obstructive pulmonary disease in the USA. Clinicoecon. Outcomes Res. 2013 , 5 , 235–245. [ Google Scholar ] [ CrossRef ]
• Fletcher, M.J.; Upton, J.; Taylor-Fishwick, J.; Buist, S.A.; Jenkins, C.; Hutton, J.; Barnes, N.; Van Der Molen, T.V.; Walsh, J.W.; Jones, P.; et al. COPD uncovered: An international survey on the impact of chronic obstructive pulmonary disease [COPD] on a working age population. BMC Public Health 2011 , 11 , 612. [ Google Scholar ] [ CrossRef ]
• Foo, J.; Landis, S.H.; Maskell, J.; Oh, Y.M.; van der Molen, T.; Han, M.K.; Mannino, D.M.; Ichinose, M.; Punekar, Y. Continuing to Confront COPD International Patient Survey: Economic Impact of COPD in 12 Countries. PLoS ONE 2016 , 11 , e0152618. [ Google Scholar ] [ CrossRef ]
• HSE. Work-related Chronic Obstructive Pulmonary Disease (COPD) Statistics in Great Britain 2023. Available online: https://www.hse.gov.uk/statistics/assets/docs/copd.pdf (accessed on 22 November 2023).
• Golse, N.; Joly, F.; Combari, P.; Lewin, M.; Nicolas, Q.; Audebert, C.; Samuel, D.; Allard, M.A.; Cunha, A.S.; Castaing, D.; et al. Predicting the risk of post-hepatectomy portal hypertension using a digital twin: A clinical proof of concept. J. Hepatol. 2021 , 74 , 661–669. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Low, J.X.; Wei, Y.; Chow, J.; Ali, I.F. ActSen-AI-Enabled Real-Time IoT-Based Ergonomic Risk Assessment System. In Proceedings of the 2019 IEEE International Congress on Internet of Things (ICIOT), Milan, Italy, 8–13 July 2019; pp. 76–78. [ Google Scholar ]
• Aiello, G.; Giallanza, A.; Giovino, I. Safety optimized shift-scheduling system based on wireless vibration monitoring for mechanical harvesting operations. Chem. Eng. Trans. 2017 , 58 , 349–354. [ Google Scholar ] [ CrossRef ]
• NIH. What Is COPD? Available online: https://www.nhlbi.nih.gov/health/copd (accessed on 24 February 2024).
• NHS. Chronic Obstructive Pulmonary Disease (COPD)-Symptoms. Available online: https://www.nhs.uk/conditions/chronic-obstructive-pulmonary-disease-copd/symptoms/ (accessed on 20 September 2022).
• NHS. Spirometry. Available online: https://www.nhs.uk/conditions/spirometry/ (accessed on 8 June 2022).
• Mahler, D.A.; Wells, C.K. Evaluation of clinical methods for rating dyspnea. Chest 1988 , 93 , 580–586. [ Google Scholar ] [ CrossRef ]
• Jones, P.W.; Harding, G.; Berry, P.; Wiklund, I.; Chen, W.H.; Leidy, N.K. Development and first validation of the COPD Assessment Test. Eur. Respir. J. 2019 , 34 , 648–654. [ Google Scholar ] [ CrossRef ]
• Peruzza, S.; Sergi, G.; Vianello, A.; Pisent, C.; Tiozzo, F.; Manzan, A.; Coin, A.; Inelmen, E.M.; Enzi, G. Chronic obstructive pulmonary disease (COPD) in elderly subjects: Impact on functional status and quality of life. Respir. Med. 2013 , 97 , 612–617. [ Google Scholar ] [ CrossRef ]
• Monjazebi, F.; Dalvandi, A.; Ebadi, A.; Khankeh, H.R.; Rahgozar, M.; Richter, J. Functional status assessment of COPD based on ability to perform daily living activities: A systematic review of paper and pencil instruments. Glob. J. Health Sci. 2015 , 8 , 210–223. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chen, B.R.; Patel, S.; Toffola, L.D.; Bonato, P. Long-term monitoring of COPD using wearable sensors. In Proceedings of the 2nd Conference on Wireless Health, San Diego, CA, USA, 10–13 October 2011; pp. 1–2. [ Google Scholar ] [ CrossRef ]
• Bellos, C.C.; Papadopoulos, A.; Rosso, R.; Fotiadis, D.I. Identification of COPD patients’ health status using an intelligent system in the CHRONIOUS wearable platform. IEEE J. Biomed. Health Inform. 2014 , 18 , 731–738. [ Google Scholar ] [ CrossRef ]
• Chouvarda, I.; Philip, N.Y.; Natsiavas, P.; Kilintzis, V.; Sobnath, D.; Kayyali, R.; Henriques, J.; Paiva, R.P.; Raptopoulos, A.; Chetelat, O.; et al. WELCOME—Innovative integrated care platform using wearable sensing and smart cloud computing for COPD patients with comorbidities. In Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2014; pp. 3180–3183. [ Google Scholar ]
• Van Berkel, J.J.B.N.; Dallinga, J.W.; Möller, G.M.; Godschalk, R.W.L.; Moonen, E.J.; Wouters, E.F.M.; Van Schooten, F.J. A profile of volatile organic compounds in breath discriminates COPD patients from controls. Respir. Med. 2010 , 104 , 557–563. [ Google Scholar ] [ CrossRef ]
• Ratiu, I.A.; Ligor, T.; Bocos-Bintintan, V.; Mayhew, C.A.; Buszewski, B. Volatile organic compounds in exhaled breath as fingerprints of lung cancer, asthma and COPD. J. Clin. Med. 2021 , 10 , 32. [ Google Scholar ] [ CrossRef ]
• Hendricks, P.I.; Dalgleish, J.K.; Shelley, J.T.; Kirleis, M.A.; McNicholas, M.T.; Li, L.; Chen, T.C.; Chen, C.H.; Duncan, J.S.; Boudreau, F.; et al. Autonomous in situ analysis and real-time chemical detection using a backpack miniature mass spectrometer: Concept, instrumentation development, and performance. Anal. Chem. 2014 , 86 , 2900–2908. [ Google Scholar ] [ CrossRef ]
• Basanta, M.; Jarvis, R.M.; Xu, Y.; Blackburn, G.; Tal-Singer, R.; Woodcock, A.; Singh, D.; Goodacre, R.; Thomas, C.P.; Fowler, S.J. Non-invasive metabolomic analysis of breath using differential mobility spectrometry in patients with chronic obstructive pulmonary disease and healthy smokers. Analyst 2010 , 135 , 315–320. [ Google Scholar ] [ CrossRef ]
• Hauschild, A.C.; Baumbach, J.I.; Baumbach, J. Integrated statistical learning of metabolic ion mobility spectrometry profiles for pulmonary disease identification. Genet. Mol. Res. 2012 , 11 , 2733–2744. [ Google Scholar ] [ CrossRef ]
• Phillips, C.O.; Syed, Y.; Parthaláin, N.M.; Zwiggelaar, R.; Claypole, T.C.; Lewis, K.E. Machine learning methods on exhaled volatile organic compounds for distinguishing COPD patients from healthy controls. J. Breath. Res. 2012 , 6 , 036003. [ Google Scholar ] [ CrossRef ]
• Cristescu, S.M.; Gietema, H.A.; Blanchet, L.; Kruitwagen, C.L.J.J.; Munnik, P.; Van Klaveren, R.J.; Lammers, J.W.J.; Buydens, L.; Harren, F.J.M.; Zanen, P. Screening for emphysema via exhaled volatile organic compounds. J. Breath. Res. 2011 , 5 , 046009. [ Google Scholar ] [ CrossRef ]
• Fens, N.; Zwinderman, A.H.; Schee, M.P.; Nijs, S.B.; Dijkers, E.; Roldaan, A.C.; Cheung, D.; Bel, E.H.; Sterk, P.J. Exhaled breath profiling enables discrimination of chronic obstructive pulmonary disease and asthma. Am. J. Respir. Crit. Care Med. 2009 , 180 , 1076–1082. [ Google Scholar ] [ CrossRef ]
• Engelen, M.P.; Casaburi, R.; Rucker, R.; Carithers, E. Contribution of the respiratory muscles to the lactic acidosis of heavy exercise in COPD. Chest 1995 , 108 , 1246–1251. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Maekura, R.; Hiraga, T.; Miki, K.; Kitada, S.; Yosimura, K.; Miki, M.; Tateishi, Y. Difference in the physiological response to exercise in patients with distinct severity of COPD pathology. Respir. Care 2014 , 59 , 252–262. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Thomsen, M.; Ingebrigtsen, T.S.; Marott, J.L.; Dahl, M.; Lange, P.; Vestbo, J.; Nordestgaard, B.G. Inflammatory biomarkers and exacerbations in chronic obstructive pulmonary disease. JAMA 2013 , 309 , 2353–2361. [ Google Scholar ] [ CrossRef ]
• Kim, J.; Valdés-Ramírez, G.; Bandodkar, A.J.; Jia, W.; Martinez, A.G.; Ramírez, J.; Mercier, P.; Wang, J. Non-invasive mouthguard biosensor for continuous salivary monitoring of metabolites. Analyst 2014 , 139 , 1632–1636. [ Google Scholar ] [ CrossRef ]
• Bergeron, M.F. Heat cramps: Fluid and electrolyte challenges during tennis in the heat. J. Sci. Med. Sport. 2003 , 6 , 19–27. [ Google Scholar ] [ CrossRef ]
• Implantable & Wearable Medical Devices for Chronic Obstructive Pulmonary Disease. Available online: https://www.nihr.ac.uk/documents/implantable-and-wearable-medical-devices-for-chronic-obstructive-pulmonary-disease/11943 (accessed on 3 June 2022).
• Steidl, E.; Ribeiro, C.S.; Gonçalves, B.F.; Fernandes, N.; Antunes, V.; Mancopes, R. Relationship between dysphagia and exacerbations in chronic obstructive pulmonary disease: A literature review. Int. Arch. Otorhinolaryngol. 2015 , 19 , 74–79. [ Google Scholar ] [ CrossRef ]
• Yoshimatsu, Y.; Tobino, K.; Sueyasu, T.; Nishizawa, S.; Ko, Y.; Yasuda, M.; Ide, H.; Tsuruno, K.; Miyajima, H. Repetitive saliva swallowing test predicts COPD exacerbation. Int. J. Chron. Obstruct Pulmon Dis. 2019 , 14 , 2777–2785. [ Google Scholar ] [ CrossRef ]
• Patel, N.; Belcher, J.; Thorpe, G.; Forsyth, N.R.; Spiteri, M.A. Measurement of C-reactive protein, procalcitonin and neutrophil elastase in saliva of COPD patients and healthy controls: Correlation to self-reported wellbeing parameters. Respir. Res. 2015 , 16 , 62. [ Google Scholar ] [ CrossRef ]
• Patel, N.; Jones, P.; Adamson, V.; Spiteri, M.; Kinmond, K. Chronic Obstructive Pulmonary Disease Patients’ Experiences of an Enhanced Self-Management Model of Care. Qual. Health Res. 2016 , 26 , 568–577. [ Google Scholar ] [ CrossRef ]
• Higginson, R. Respiratory assessment in critically ill patients: Airway and breathing. Br. J. Nurs. 2013 , 18 , 456–461. [ Google Scholar ] [ CrossRef ]
• Jácome, C.; Marques, A. Computerized respiratory sounds are a reliable marker in subjects with COPD. Respir. Care 2015 , 60 , 1264–1275. [ Google Scholar ] [ CrossRef ]
• Faisal, A.; Alghamdi, B.J.; Ciavaglia, C.E.; Elbehairy, A.F.; Webb, K.A.; Ora, J.; Neder, J.A.; O’Donnell, D.E. Common mechanisms of dyspnea in chronic interstitial and obstructive lung disorders. Am. J. Respir. Crit. Care Med. 2016 , 193 , 299–309. [ Google Scholar ] [ CrossRef ]
• Fernandez-Granero, M.A.; Sanchez-Morillo, D.; Leon-Jimenez, A. Computerized analysis of telemonitored respiratory sounds for predicting acute exacerbations of COPD. Sensors 2015 , 15 , 26978–26996. [ Google Scholar ] [ CrossRef ]
• Miravitlles, M. Cough and sputum production as risk factors for poor outcomes in patients with COPD. Respir. Med. 2011 , 105 , 1118–1128. [ Google Scholar ] [ CrossRef ]
• Di Marco, R.; Accordini, S.; Cerveri, I.; Corsico, A.; Antó, J.M.; Kunzli, N.; Janson, C.; Sunyer, J.; Jarvis, D.; Chinn, S.; et al. Incidence of chronic obstructive pulmonary disease in a cohort of young adults according to the presence of chronic cough and phlegm. Am. J. Respir. Crit. Care Med. 2007 , 175 , 32–39. [ Google Scholar ] [ CrossRef ]
• Sumner, H.; Woodcock, A.; Kolsum, U.; Dockry, R.; Lazaar, A.L.; Singh, D.; Vestbo, J.; Smith, J.A. Predictors of objective cough frequency in chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 2013 , 187 , 943–949. [ Google Scholar ] [ CrossRef ]
• Zuo, J.; Rameezdeen, R.; Hagger, M.; Zhou, Z.; Ding, Z. Dust pollution control on construction sites: Awareness and self-responsibility of managers. J. Clean. Prod. 2017 , 166 , 312–320. [ Google Scholar ] [ CrossRef ]
• Kurth, L.; Doney, B.; Weinmann, S. Occupational exposures and chronic obstructive pulmonary disease (COPD): Comparison of a COPD-specific job exposure matrix and expert-evaluated occupational exposures. Occup. Environ. Med. 2017 , 74 , 290–293. [ Google Scholar ] [ CrossRef ]
• Fishwick, D.; Naylor, S. COPD and the workplace. Is it really possible to detect early cases? Occup. Med. 2007 , 57 , 82–84. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Saraei, M.; Heydarbeygi, E.; Mehrdad, R.; Rouryaghoub, G. Quality of spirometry tests in periodic examination of workers. Int. J. Occup. Hyg. 2018 , 10 , 75–79. [ Google Scholar ]
• Wang, B.H.; Cong, S.; Bao, H.L.; Feng, Y.J.; Fan, J.; Wang, N.; Fang, L.W.; Wang, L.H. Analysis on occupational exposure to dust and harmful gas and corresponding protection in adults aged 40 years and older in China, 2014. Zhonghua Liu Xing Bing Xue Za Zhi 2018 , 39 , 563–568. [ Google Scholar ] [ CrossRef ]
• Li, C.Z.; Zhao, Y.; Xu, X. Investigation of dust exposure and control practices in the construction industry: Implications for cleaner production. J. Clean. Prod. 2019 , 227 , 810–824. [ Google Scholar ] [ CrossRef ]
• Lebecki, K.; Małachowski, M.; Sołtysiak, T. Continuous dust monitoring in headings in underground coal mines. J. Sustain. Min. 2016 , 15 , 125–132. [ Google Scholar ] [ CrossRef ]
• Podgorski, D.; Majchrzycka, K.; Dąbrowska, A.; Gralewicz, G.; Okrasa, M. Towards a conceptual framework of OSH risk management in smart working environments based on smart PPE, ambient intelligence and the Internet of Things technologies. Int. J. Occup. Saf. Ergon. 2017 , 23 , 1–20. [ Google Scholar ] [ CrossRef ]
• Babrak, L.M.; Menetski, J.; Rebhan, M.; Nisato, G.; Zinggeler, M.; Brasier, N.; Baerenfaller, K.; Brenzikofer, T.; Baltzer, L.; Vogler, C.; et al. Traditional and digital biomarkers: Two worlds apart? Digit. Biomark. 2019 , 3 , 92–102. [ Google Scholar ] [ CrossRef ]
• Connected Health: How Digital Technology Is Transforming Health and Social Care. Available online: https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/life-sciences-health-care/deloitte-uk-connected-health.pdf (accessed on 4 September 2023).
• Wearables in United Kingdom Market Overview 2023–2027. Available online: https://www.reportlinker.com/market-report/Consumer-Electronics/513215/Wearables (accessed on 1 August 2023).
• Wearable Technology Market Size, Share & Trends Analysis Report By Product (Head & Eyewear, Wristwear), by Application (Consumer Electronics, Healthcare), by Region (Asia Pacific, Europe), and Segment Forecasts, 2023–2030. Available online: https://www.grandviewresearch.com/industry-analysis/wearable-technology-market (accessed on 4 May 2024).
• What is EHS Software?|Intelex. Available online: https://www.intelex.com/ehs/ehs-software/ (accessed on 19 September 2023).
• What is EHS Software? The Complete Guide to EHS Software. Available online: http://www.perillon.com/what-is-ehs-software (accessed on 19 September 2023).
• Naranjo-Hernández, D.; Talaminos-Barroso, A.; Reina-Tosina, J.; Roa, L.M.; Barbarov-Rostan, G.; Cejudo-Ramos, P.; Márquez-Martín, E.; Ortega-Ruiz, F. Smart vest for respiratory rate monitoring of COPD patients based on non-contact capacitive sensing. Sensors 2018 , 18 , 2144. [ Google Scholar ] [ CrossRef ]
• Al-Halhouli, A.; Al-Ghussain, L.; Khallouf, O.; Rabadi, A.; Alawadi, J.; Liu, H.; Oweidat, K.A.; Chen, F.; Zheng, D. Clinical Evaluation of Respiratory Rate Measurements on COPD (Male) Patients Using Wearable Inkjet-Printed Sensor. Sensors 2021 , 21 , 468. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tipparaju, V.V.; Wang, D.; Yu, J.; Chen, F.; Tsow, F.; Forzani, E.; Tao, N.; Xian, X. Respiration pattern recognition by wearable mask device. Biosens. Bioelectron. 2020 , 169 , 112590. [ Google Scholar ] [ CrossRef ]
• Firdhous, M.F.M.; Sudantha, B.H.; Karunaratne, P.M. IoT enabled proactive indoor air quality monitoring system for sustainable health management. In Proceedings of the 2nd International Conference on Computing and Communications Technologies (ICCCT), Chennai, India, 23–24 February 2017; pp. 216–221. [ Google Scholar ] [ CrossRef ]
• Fathallah, H.; Lecuire, V.; Rondeau, E.; Calvé, S.L. Development of an IoT-based system for real time occupational exposure monitoring. In Proceedings of the The Tenth International Conference on Systems and Networks Communications, ICSNC 2015, Barcelone, Spain, 15–20 November 2015. [ Google Scholar ]
• Zarrin, P.S.; Wenger, C. Pattern recognition for COPD diagnostics using an artificial neural network and its potential integration on hardware-based neuromorphic platforms. In Proceedings of the Artificial Neural Networks and Machine Learning–ICANN 2019: Workshop and Special Sessions: 28th International Conference on Artificial Neural Networks, Munich, Germany, 17–19 September 2019; pp. 284–288. [ Google Scholar ]
• Lin, W.Y.; Verma, V.K.; Lee, M.Y.; Lin, H.C.; Lai, C.S. Prediction of 30-Day Readmission for COPD Patients Using Accelerometer-Based Activity Monitoring. Sensors 2019 , 20 , 217. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hierarchy of Controls|NIOSH|CDC. Available online: https://www.cdc.gov/niosh/topics/hierarchy/ (accessed on 13 January 2022).
• Adjiski, V.; Despodov, Z.; Mirakovski, D.; Serafimovski, D. System architecture to bring smart personal protective equipment wearables and sensors to transform safety at work in the underground mining industry. Rud.-Geološko-Naft. Zb. 2019 , 34 , 37–44. [ Google Scholar ] [ CrossRef ]
• Hood, L.; Friend, S.H. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat. Rev. Clin. Oncol. 2011 , 8 , 184–187. [ Google Scholar ] [ CrossRef ]
• O’Connor, S.; Hanlon, P.; O’Donnell, C.A.; Garcia, S.; Glanville, J.; Mair, F.S. Understanding factors affecting patient and public engagement and recruitment to digital health interventions: A systematic review of qualitative studies. BMC Med. Inform. Decis. Mak. 2016 , 16 , 120. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Cruz, J.; Brooks, D.; Marques, A. Home telemonitoring in COPD: A systematic review of methodologies and patients’ adherence. Int. J. Med. Inform. 2014 , 83 , 249–263. [ Google Scholar ] [ CrossRef ]
• Barnes, P.J.; Burney, P.G.; Silverman, E.K.; Celli, B.R.; Vestbo, J.; Wedzicha, J.A.; Wouters, E.F. Chronic obstructive pulmonary disease. Nat. Rev. Dis. Primers 2015 , 1 , 15076. [ Google Scholar ] [ CrossRef ]
• Nnaji, C.; Awolusi, I.; Park, J.W.; Albert, A. Wearable Sensing Devices: Towards the Development of a Personalized System for Construction Safety and Health Risk Mitigation. Sensors 2021 , 21 , 682. [ Google Scholar ] [ CrossRef ]
• Gimhae, G.N. Six human factors to acceptability of wearable computers. Int. J. Multimedia Ubiquitous Eng. 2013 , 8 , 103–114. [ Google Scholar ]
• Yaacoub, J.A.; Salman, O.; Noura, H.N.; Kaaniche, N.; Chehab, A.; Malli, M. Cyber-physical systems security: Limitations, issues and future trends. Microprocess. Microsyst. 2020 , 77 , 103201. [ Google Scholar ] [ CrossRef ]
• Kalirai, K.K. The Effects of Chronic Obstructive Pulmonary Disease on Work Related Outcomes. University of Birmingham, 2016. Available online: https://etheses.bham.ac.uk/id/eprint/6846/ (accessed on 19 November 2022).
• Shanmuganathan, V.; Suresh, A. LSTM-Markov based efficient anomaly detection algorithm for IoT environment. Appl. Soft Comput. 2023 , 136 , 110054. [ Google Scholar ] [ CrossRef ]
• Chouvarda, I.; Kilintzis, V.; Haris, K.; Kaimakamis, V.; Perantoni, E.; Maglaveras, N.; Mendes, L.; Lucio, C.; Teixeira, C.; Henriques, J.; et al. Combining pervasive technologies and Cloud Computing for COPD and comorbidities management. In Proceedings of the 2014 4th International Conference on Wireless Mobile Communication and Healthcare-Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH), Athens, Greece, 3–5 November 2014; pp. 352–356. [ Google Scholar ]
• Chung, H.; Jeong, C.; Luhach, A.K.; Nam, Y.; Lee, J. Remote pulmonary function test monitoring in Cloud platform via smartphone built-in microphone. Evol. Bioinform. Online 2019 , 15 , 1176934319888904. [ Google Scholar ] [ CrossRef ]
• Tavakol, E.; Azari, M.; Zendehdel, R.; Salehpour, S.; Khodakrim, S.; Nikoo, S.; Saranjam, B. Risk Evaluation of Construction Workers’ Exposure to Silica Dust and the Possible Lung Function Impairments. Tanaffos 2017 , 16 , 295–303. [ Google Scholar ]
• Liu, H.; Allen, J.; Zheng, D.; Chen, F. Recent development of respiratory rate measurement technologies. Physiol. Meas. 2017 , 40 , 07TR01. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Martinez, F.J.; Han, M.K.; Allinson, J.P.; Barr, R.G.; Boucher, R.C.; Calverley, P.M.; Celli, B.R.; Christenson, S.A.; Crystal, R.G.; Fagerås, M.; et al. At the root: Defining and halting progression of early chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 2018 , 197 , 1540–1551. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Drugman, T.; Urbain, J.; Bauwens, N.; Chessini, R.; Valderrama, C.; Lebecque, P.; Dutoit, T. Objective study of sensor relevance for automatic cough detection. IEEE J. Biomed. Health Inf. 2013 , 17 , 699–707. [ Google Scholar ] [ CrossRef ]
• Chang, Z.; Jia, K.; Han, T.; Wei, Y.M. Towards more reliable photovoltaic energy conversion systems: A weakly-supervised learning perspective on anomaly detection. Energy Convers. Manag. 2024 , 316 , 118845. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Jiang, Z.; Bakker, O.J.; Bartolo, P.J. Industry 4.0-Compliant Occupational Chronic Obstructive Pulmonary Disease Prevention: Literature Review and Future Directions. Sensors 2024 , 24 , 5734. https://doi.org/10.3390/s24175734

Jiang Z, Bakker OJ, Bartolo PJ. Industry 4.0-Compliant Occupational Chronic Obstructive Pulmonary Disease Prevention: Literature Review and Future Directions. Sensors . 2024; 24(17):5734. https://doi.org/10.3390/s24175734

Jiang, Zhihao, Otto Jan Bakker, and Paulo JDS Bartolo. 2024. "Industry 4.0-Compliant Occupational Chronic Obstructive Pulmonary Disease Prevention: Literature Review and Future Directions" Sensors 24, no. 17: 5734. https://doi.org/10.3390/s24175734

Article Metrics

Further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

Challenges in coffee fermentation technologies: bibliometric analysis and critical review

• Review Article
• Open access
• Published: 02 September 2024

Cite this article

You have full access to this open access article

• Valeria Hurtado Cortés   nAff1 ,
• Andrés Felipe Bahamón Monje   ORCID: orcid.org/0000-0002-2620-148X 1 ,
• Jaime Daniel Bustos Vanegas   nAff1 &
• Nelson Gutiérrez Guzmán   nAff1  

Advancements in coffee processing technologies have led to improved efficiency in field operations, but challenges still exist in their practical implementation. Various alternatives and solutions have been proposed to enhance processing efficiency and address issues related to safety, standardization, and quality improvement in coffee production. A literature review using SciMAT and ScientoPy software highlighted advancements in fermentation tanks and the emergence of novel fermentation methodologies. However, these innovations lack sufficient scientific evidence. Researchers are now focusing on systematic approaches, such as controlled fermentations and evaluating the influence of microorganisms and process conditions on sensory attributes and coffee composition. Brazil is the leader in coffee bean fermentation research, but the number of published papers in the field has recently decreased. Despite this, efforts continue to improve process control and optimize product quality. The study emphasizes the need for further innovation in coffee fermentation technologies to increase efficiency, sustainability, and profitability while minimizing environmental impact. Implementing these advancements promises a more sustainable and quality-driven future for the coffee industry.

Similar content being viewed by others

Relationship Between Coffee Processing and Fermentation

Influence of pre-and post-harvest factors on the organoleptic and physicochemical quality of coffee: a short review

How Technologies Are Working in the Coffee Sector

Avoid common mistakes on your manuscript.

Introduction

Coffee is a crucial agricultural product and a popular beverage globally. Its growing popularity has led to the need for improved processes to enhance cup quality. The coffee fruit, also known as almond or green coffee, is composed of five layers that protect the endosperm. These layers, known as pulp, mucilage, parchment, and epidermis, form the endosperm and are subjected to roasting to form the flavor and aroma of the coffee drink.

The processing of coffee fruit involves stages to preserve the quality of the almond, which is then subjected to roasting. The types of processing include dry, semi-dry, and wet (de Melo Pereira et al. 2015 ). Dry processing involves pulping, fermentation, washing, drying, threshing, and roasting. Semi-dry processing involves mechanical removal of the exocarp and part of the mucilage, while wet processing involves drying and threshing.

Green coffee, obtained after processing, varies according to agro-climatological characteristics, species, variety, processing type, and post-harvest operations. Control of these operations is essential to preserve grain quality and maintain the consistency of its sensory profile.

Coffee fermentation is a metabolic process that converts sugars into energy and compounds through the action of enzymes in mucilage (Silva et al. 2013 ). This process involves the pulped coffee mass being kept in closed containers for 12 to 72 h to remove the mesocarp (mucilage) attached to the parchment. The mucilage, composed of simple sugars and a pectic substrate, is degraded into alcohols and organic acids by microorganisms, such as yeasts and bacteria (Correa et al. 2014 ; Pereira et al. 2016b ).

Coffee fermentation is a metabolic process that converts sugars into energy and compounds through the action of enzymes in mucilage. This process involves the pulped coffee mass being kept in closed containers for 12 to 72 h to remove the mesocarp (mucilage) attached to the parchment. The mucilage, composed of simple sugars and a pectic substrate, is degraded into alcohols and organic acids by microorganisms, such as yeasts and bacteria (Bressani et al. 2021b ).

Metabolites diffuse through parchment and endosperm, potentially causing exosmosis during fermentation until chemical potential equilibrium is achieved. The decrease in pH causes mucilage degradation, which can be removed by washing with water. Controlled fermentation can produce beverages with special aromas and flavors, such as sweet, citrus, and fruity.

Mucilage removal can be performed using mechanical or enzymatic methods, such as mechanical abrasion in ELMU-type mucilage removal machines and enzymes like Ultrazym 100, Irgazim 100, Benefax, and Cofepec. Recent research has revealed that the microbial degradation process during fermentation leads to physical and chemical changes in almonds, impacting the sensory characteristics of the resulting drink (da Mota et al. 2020 ; Elhalis et al. 2021 ).

Factors affecting reaction rates during coffee fermentation include temperature, water availability, fermentation time, and fruit maturity index. Devices designed for the operation must allow control and measurement of these factors to standardize the process.

This article shows the advances achieved over time with the technologies and methods used in the fermentation of coffee, taking into account the improvement in the processes of safety, standardization and quality of coffee. A bibliographic analysis of research focused on technologies and representative authors with related publications, among other aspects, was carried out. All with the purpose of observing the progress in the fermentation process with the objective of continuing researching and looking for solutions to obtain efficient and quality processing.

Search methodology

A bibliometric analysis was conducted on coffee bean fermentation publications to identify trends, emerging research directions, and relationships with fermenters and process variables, using Scopus database and ScientoPy and SciMAT software.

Databases, keywords and search criteria

This study analyzed data from the Scopus database on coffee, bean, and fermentation from 2001 to 2022, focusing on top countries, document types, and institutions, and downloaded in .CSV format for ScientoPy and SciMAT software.

Analysis using ScientoPy software

The downloaded data from Scopus were then loaded and processed with ScientoPy and the pre-processing results are presented in Table  1 . The journals and keywords categories were analyzed with the software to know the most relevant issues, total documents published by journal and keywords and evolution of them in the period from 2001 to 2022.

Analysis using SciMAT software

The data was preprocessed in modules “Knowledge base” and “Group sets” to remove duplicates and related keywords. The analysis was set to three periods (2001–2017, 2018–2020, and 2021–2022) and parameters were selected in “Analysis” to create evolution maps, strategic diagrams, and clusters.

Search results

Trends in publications over time.

The search for 231 documents from 2001 to 2022 revealed a stable trend in published documents. Between 2012 and 2022, there was a decrease in publications (Fig.  1 A and B). The top five countries were Brazil, followed by Indonesia, China, South Korea, and Colombia (Fig.  1 C). The majority of documents were articles, with a small percentage of reviews. The Universidade Federal de Lavras leads the list with 26 publications on the search topic, followed by Brazilian Universidade Federal do Parana (Fig.  1 -D). Indonesian institutions Hasanuddin University and Universitas Sylah Kuala also appear in the top 10. Nestlé S.A. ranks sixth in the top ten.

Documents published in the search topic (Graphs extracted from Scopus online database). ( A ) Documents published by year ( B ) Documents published by type ( C ) Documents published by country ( D ) Documents published by institution

There is no clear trend in the number of publications in the top 10 journals over time. Food Research International and IOP Conference Series: Earth and Environmental Science had the largest publications in 2020 and 2021 respectively. The evolution of the top 10 keywords is showed in Fig.  2 . This graph corroborates the main terms used and shows an increase over time of this words. After “Coffee”, which is a general term, “Fermentation” term has the highest number of documents published in the period analyzed, but according to the percentage of documents published in the last year 2021–2022 graph, “coffee fermentation” had the highest value (42.1%) in comparison to the other terms, which indicates that in the last year, this topic has had a higher relative growth.

Keywords in research related to the search topic ( A ) Cloud diagram of the top 1000 words ( B ) Evolution graph of the top 10 words

Although in general the interest in the fermentation process of coffee remains constantly growing, the topics addressed are diverse, which can be evidenced in the variety of key terms that the search throws up. Figure  2 -A shows the cloud of words specifically related to the fermentation process, such as types of fermentation, process control, variables involved, and fermenters. In relation to the latter, only two specific terms about it appear in the cloud, “bioreactors” and “bioreactor”; and some terms related with variables, control process or devices such as “controlled fermentation”, “temperature distribution”, ohmic technology”, “ohmic heating”.

Besides, some differences are obtained with the processing of data in SciMAT (Table  2 ), since this tool allows groups conformations in similar words and documents not related with the topic, however, words like “Fermentation” and “Coffee” remains as main terms, which is expected since these are general terms. Respect to the journals, the results are similar with some exceptions.

Topic evolution map and strategic diagram and cluster’s network

Figure  3 shows the graphs generated by SciMAT from the analysis of the data associated with the search string. According to the evolution map (Figs.  3 -1), for period 2001–2017 it can be observed nine main terms: “bacteria”, “coffee aroma”, “metabolomics”, “arabica”, “fungal fermentations”, “beverages”, “microorganisms”, “types of fermentation”, and “coffee”. In the period 2018–2020, the number of relevant terms increase to eleven, and only “microorganisms”, “coffee” and “type of fermentation” terms remain. New terms are included such as “classification”, “genetics”, “bacillus”, “yeast”, “seeds”, “sensory analysis” and “volatiles”. With respect to period 2020–2022, “bioreactors and process variables” term appears for the first time and there are new terms, for instance, analytical techniques as “spectrometry” and “chromatography”.

( 1 ) Evolution map of the relevant terms regarding the search topic in the documents reported from 2001 to 2022, and relevant relationships on evolution map. The color lines represent the main associations found, and ( 2 ) Strategic diagram associated with topic of interest for period ( A ) 2001–2017, ( B ) 2018–2020, ( C ) 2021–2022. ( 3 ) Cluster of terms associated with topic of interest for period ( A ) 2001–2017, ( B ) 2018–2020, ( C ) 2021–2022

With respect to the relevant associations shown on the evolution map (Figs.  3 -1), it is possible to observe terms associated with the study of microorganisms (relationships highlighted in red), such as bacteria, especially of the genus Bacillus, fungal fermentation, mainly related to yeasts, and lactic acid fermentation. These terms, in turn, present associations with parameters such as sensory analysis and volatiles. The latter is related to “bioreactor and process variables” in the last period. The term “bioreactor and process variables” is backward associated with “coffee”, a general term, and “types of fermentation”, which, in turn, is associated with “coffee aroma” (relationships highlighted in blue). These associations are anticipated due to the disparate processing methodologies, where the conditions of the process, the utilization of starter cultures, the type of microorganisms employed, and the generation of organic acids that contribute to alterations in the volatile and aroma profiles of roasted coffee are implicated (da Mota et al. 2020 ). Additionally the control of process variable offered by bioreactors has recently shown to contribute to the production of coffee with higher sensory quality and reproducibility (de Carvalho Neto et al. 2018 ).

The strategic diagrams and cluster networks for each period are presented in Fig.  3 (2) and (3). The volume of the spheres is proportional to the number of published documents associated with each theme. The upper-right quadrant is motor-themes, with terms in the upper-left quadrant being highly development and isolated themes. The lower-left quadrant is emerging or declining themes, while the lower-right quadrant is transversal and general. In the first period, “beverages”, “metabolomics”, and “fungal fermentation” are motor themes, while “types of fermentation” is an emergent theme. In the second period, “types of fermentation” remains an emergent theme, with “yeast” added to this category. In the third period, “coffee” is less frequent, with more specific topics such as “bacillus”, “genetics”, and “classification” as motor themes (Elhalis et al. 2021 ). For the period 2020–2022, “volatiles” and “metabolomics” are motor themes, related to research on volatiles and metobolites generated in process fermentation (Elhalis et al. 2021 ; Prakash et al. 2022 ). “Types of fermentation” and “coffee” are tranversal themes, while “lactic acid fermentation” is an emergent theme. “Bioreactors and process variables” is an isolated theme for this period.

Figure  3 (3) displays clusters of terms related to bioreactors and process variables control. The volume of spheres is proportional to the number of documents corresponding to each keyword, and the thickness of the link between two spheres is proportional to the equivalence index between these words. For the period 2001–2017, “bioreactor and process variables” is slightly related to “microorganisms”, “drying process”, “plant seed”, and “mucilage”. For the second period, “volatile” term shows a slight relationship with “bioreactor and process variables”, which in turn is associated with “coffee beans”. The main associations with “bioreactor and process variables” may be with “microorganisms” and “volatile”.

As regards the period 2021–2022 (Figs.  3 - (3) -C), strong bonds are observed between “Bioreactor and process variables”, “Food supply” and “Agriculture”, since the fermentation process is usually on-farm process, although efforts have been made to the process control (Martinez et al. 2017 ; de Carvalho Neto et al. 2018 ). Slight associations are seen with more specific terms such as “Liquid media”, “Enzymes”, “UV-VIS-Spectrophotometry” and “Biochemical analysis”. These terms are directly related to the fermentation process and its monitoring, whereby those relationships are expected.

Coffee fermentation methodologies

Scientific publications related to coffee fermentation devices were searched in the Web of Science, Scopus, and Science Direct databases. Published patents, as well as devices developed by different companies in the sector, were also included in the review. The evolution and characteristics of the devices and their impact on the quality and sensory profile of the coffee were tabulated and summarized in tables.

Table  3 presents various methods for controlling coffee fermentation, focusing on temperature, processing time, and microorganism addition. These parameters affect the grain’s physical-chemical composition and sensory profile. Fermentation times range from 12 h to several days, with low temperatures slowing microbial kinetics and requiring several days for pH to reach 3.8. Mass transfer between mucilage and grain layers occurs mainly through diffusion, leading to more complex profiles in long-time fermentation.

Evolution of technologies for coffee fermentation

Coffee fermentation devices are containers that allow the product volume to be maintained under homogeneous conditions during the process. Fermentation can be done dry or submerged, with the latter ensuring that all grains are in contact with an equal volume of oxygen. Most producers follow this method for a more homogeneous fermentation. Initially, pulped coffee was fermented in vat-type tanks made of wood, cement, or brick, which were plastered or enameled with cement or covered with baked clay veneers. The floor was built with a slope of 6 to 8% and a width-to-height ratio of 1:1.5 to facilitate leachate drainage and product removal. The final processing time was determined based on producer experience, such as the hole and touch test, which is subjective and prone to errors.

Producers have noticed that fermentation under certain conditions can result in heterogeneous coffee products with sour and fermented flavors due to the difficulty in controlling process variables. To improve sanitary conditions, Cenicafé and other manufacturers have developed high-density polyethylene vat-type tanks, which are lightweight, easy to handle, and clean. Cenicafé in Colombia has successfully maintained a stabilized process with less washing water consumption using Ecomill ® technology in stainless steel and high-density polyethylene. These systems incorporate cylindrical fermentation tanks with inverted cone-shaped bases and 60° horizontal inclination, allowing coffee to flow out by gravity.

Widyotomo, S., and Yusi, Y. ( 2013 ) evaluated the fermentation of cherry coffee in a horizontal type fermenter with electrical resistance and agitation (Fig.  4 -4-A). Working at 50% capacity (20 kg/batch), temperatures between 20 and 40 °C and times between 6 and 18 h, the authors defined optimal operating conditions at 25 °C and 12 h of processing. In an attempt to remove the mucilage using low temperatures to reduce the consumption of washing water, Bressani et al. ( 2020 ) evaluated a cold fermenter prototype. Temperatures close to 2 °C managed to denature the structure of the mucilage and then it was removed mechanically (Fig.  4 -4-B). In Brazil, some private companies have developed commercial prototypes for fermentation control. The Palinialves company developed a rotating cylinder (Fig.  4 -4-C) with a galvanized sheet, steel or stainless steel structure with internal blades and rpm control for a homogeneous mix. The system is completely sealed and has a relief valve for pressure control and temperature sensors. Its maximum capacity is 10,000 L. The Campotech company with the support of Embrapa and the Instituto Federal do Sul de Minas, developed a device for the controlled fermentation of 1,250 L of cherry or pulped coffee. The device, in the form of a vertical cylinder and conical base, has a helical agitation and temperature control systems for heating or cooling the coffee mass (Fig.  4 -4-D).

( 1 ) Vat-type tank for fermentation of pulped coffee. ( 2 ) High-density polyethylene vat-type tanks for coffee fermentation. ( A ) Cenicafé ( B ) Rotoplast ® . ( 3 ) Fermentation tank in Ecomill ® Technology. ( A ) 1,000 to 1,500 kg load capacity. ( B ) 2,000 to 6,000 kg load capacity. ( 4 ) Closed systems for coffee fermentation. ( A ) Widyotomo, S., & Yusi, Y. 2013 , ( B ) Correa et al. 2014 , ( C ) Palinialves, ( D ) CampoTech – Jacu Digital

Final remarks

Colombia, with over a century of coffee production experience, has limited knowledge in developing innovative fermentation prototypes. The fermentation process for washed coffees was once considered unimportant, focusing only on removing mucilage to reduce drying time. This neglect of safety and quality has led to issues with materials like concrete, majolica, wood, and cement. Technological advances in the last decade have led to the use of safe materials like high-density polyethylene and stainless steel in fermenters. Prototype fermenters or bioreactors with variable control systems and mechanical agitation have been developed in countries like Peru, Brazil, Chile, Spain, Indonesia, and Colombia. However, these high-cost technologies remain inaccessible to most producers. The industry has developed solutions such as helical-type central agitators and rotating drums, both with high energy consumption. A strategy is being evaluated for mixing through the recirculation of leachate, which requires less energy than the entire coffee mass. However, the impact of this methodology on the process quality has not been scientifically evaluated (Widyotomo and Yusianto 2013 ).

Conclusions

A bibliometric analysis of the literature on coffee fermentation indicates a growing interest and progress in research and development of technologies to improve sensory quality, safety, efficiency, and sustainability. Improvements in fermentation tanks have been identified in terms of materials, designs, and the incorporation of accessories such as digital sensors. Innovations in fermentation methodologies and a more scientific approach by researchers in this field have also been observed.

Moreover, the analysis indicates that issues related to coffee bean fermentation are undergoing constant evolution, with Brazil emerging as a leading contributor in this field. Despite a decline in the number of published papers over the past three years, research is focused on the design of controlled fermentations and the evaluation of the influence of microorganisms and process conditions on the sensory quality and composition of coffee. Nevertheless, it is observed that prototypes designed to regulate process variables, such as agitation and temperature, are costly and may be inaccessible to small-scale producers.

Collectively, these findings indicate that the integration of innovative technologies, enhanced methodologies, and a rigorous scientific approach is transforming the coffee industry towards enhanced efficiency, safety, and sustainability, with the potential to benefit both producers and consumers globally.

Data availability

Not applicable.

Code availability

Batista da Mota MC, Batista NN, Dias DR, Schwan RF (2022) Impact of microbial self-induced anaerobiosis fermentation (SIAF) on coffee quality. Food Biosci 47. https://doi.org/10.1016/j.fbio.2022.101640

Bressani APP, Martinez SJ, Evangelista SR et al (2018) Characteristics of fermented coffee inoculated with yeast starter cultures using different inoculation methods. LWT - Food Sci Technol 92:212–219. https://doi.org/10.1016/j.lwt.2018.02.029

Article   CAS   Google Scholar  

Bressani APP, Martinez SJ, Sarmento ABI et al (2020) Organic acids produced during fermentation and sensory perception in specialty coffee using yeast starter culture. Food Res Int 128:108773. https://doi.org/10.1016/j.foodres.2019.108773

Article   CAS   PubMed   Google Scholar  

Bressani APP, Batista NN, Ferreira G et al (2021a) Characterization of bioactive, chemical, and sensory compounds from fermented coffees with different yeasts species. Food Res Int 150. https://doi.org/10.1016/j.foodres.2021.110755

Bressani APP, Martinez SJ, Sarmento ABI et al (2021b) Influence of yeast inoculation on the quality of fermented coffee (Coffea arabica var. Mundo Novo) processed by natural and pulped natural processes. Int J Food Microbiol 343. https://doi.org/10.1016/j.ijfoodmicro.2021.109107

Brioschi D Junior, Carvalho Guarçoni R, de Cássia Soares da Silva M et al (2021) Microbial fermentation affects sensorial, chemical, and microbial profile of coffee under carbonic maceration. Food Chem 342:128296. https://doi.org/10.1016/j.foodchem.2020.128296

Cassimiro DM, de Batista J, Fonseca NN HC, et al (2022) Coinoculation of lactic acid bacteria and yeasts increases the quality of wet fermented Arabica coffee. Int J Food Microbiol 369. https://doi.org/10.1016/j.ijfoodmicro.2022.109627

Correa EC, Jiménez-Ariza T, Díaz-Barcos V et al (2014) Advanced characterisation of a coffee fermenting tank by multi-distributed wireless sensors: spatial interpolation and phase space graphs. Food Bioproc Tech 7:3166–3174. https://doi.org/10.1007/s11947-014-1328-4

da Mota MCB, Batista NN, Rabelo MHS et al (2020) Influence of fermentation conditions on the sensorial quality of coffee inoculated with yeast. Food Res Int 136. https://doi.org/10.1016/j.foodres.2020.109482

de Carvalho Neto DP, de Melo Pereira GV, Finco AMO et al (2018) Efficient coffee beans mucilage layer removal using lactic acid fermentation in a stirred-tank bioreactor: kinetic, metabolic and sensorial studies. Food Biosci 26:80–87. https://doi.org/10.1016/j.fbio.2018.10.005

De Carvalho Neto DP, De Vinícius G, Finco AMO et al (2020) Microbiological, physicochemical and sensory studies of coffee beans fermentation conducted in a yeast bioreactor model. Food Biotechnol 34:172–192. https://doi.org/10.1080/08905436.2020.1746666

de Melo Pereira GV, Soccol VT, Pandey A et al (2014) Isolation, selection and evaluation of yeasts for use in fermentation of coffee beans by the wet process. Int J Food Microbiol 188:60–66. https://doi.org/10.1016/j.ijfoodmicro.2014.07.008

de Melo Pereira GV, Neto E, Soccol VT et al (2015) Conducting starter culture-controlled fermentations of coffee beans during on-farm wet processing: growth, metabolic analyses and sensorial effects. Food Res Int 75:348–356. https://doi.org/10.1016/j.foodres.2015.06.027

Elhalis H, Cox J, Frank D, Zhao J (2021) The role of wet fermentation in enhancing coffee flavor, aroma and sensory quality. Eur Food Res Technol 247:485–498. https://doi.org/10.1007/s00217-020-03641-6

Evangelista SR, da Cruz Pedrozo Miguel MG, de Souza Cordeiro C et al (2014) Inoculation of starter cultures in a semi-dry coffee (Coffea arabica) fermentation process. Food Microbiol 44:87–95. https://doi.org/10.1016/j.fm.2014.05.013

Evangelista SR, Miguel MG, da Silva CP CF, et al (2015) Microbiological diversity associated with the spontaneous wet method of coffee fermentation. Int J Food Microbiol 210:102–112. https://doi.org/10.1016/j.ijfoodmicro.2015.06.008

Fioresi DB, Pereira LL, Catarina da Silva Oliveira E et al (2021) Mid infrared spectroscopy for comparative analysis of fermented arabica and robusta coffee. Food Control 121. https://doi.org/10.1016/j.foodcont.2020.107625

Martinez SJ, Bressani APP, da Miguel MG CP, et al (2017) Different inoculation methods for semi-dry processed coffee using yeasts as starter cultures. Food Res Int 102:333–340. https://doi.org/10.1016/j.foodres.2017.09.096

Martins PMM, Ribeiro LS, Miguel MG da CP, et al (2019) Production of coffee (Coffea arabica) inoculated with yeasts: impact on quality. J Sci Food Agric 99:5638–5645. https://doi.org/10.1002/jsfa.9820

Martins PMM, Batista NN, da Miguel MG CP, et al (2020) Coffee growing altitude influences the microbiota, chemical compounds and the quality of fermented coffees. Food Res Int 129:108872. https://doi.org/10.1016/j.foodres.2019.108872

Partida-Sedas JG, Muñoz Ferreiro MN, Vázquez-Odériz ML et al (2019) Influence of the postharvest processing of the Garnica coffee variety on the sensory characteristics and overall acceptance of the beverage. J Sens Stud 34:1–12. https://doi.org/10.1111/joss.12502

Article   Google Scholar  

Pereira GV, de Carvalho Neto M, Medeiros DP ABP, et al (2016a) Potential of lactic acid bacteria to improve the fermentation and quality of coffee during on-farm processing. Int J Food Sci Technol 51:1689–1695. https://doi.org/10.1111/ijfs.13142

Pereira GVM, Soccol VT, Soccol CR (2016b) Current state of research on cocoa and coffee fermentations. Curr Opin Food Sci 7:50–57. https://doi.org/10.1016/j.cofs.2015.11.001

Prakash I, R SS, P SH, et al (2022) Metabolomics and volatile fingerprint of yeast fermented robusta coffee: a value added coffee. Lwt 154:112717. https://doi.org/10.1016/j.lwt.2021.112717

Pregolini VB, de Melo Pereira GV, da Silva Vale A et al (2021) Influence of environmental microbiota on the activity and metabolism of starter cultures used in coffee beans fermentation. Fermentation 7. https://doi.org/10.3390/fermentation7040278

Ribeiro LS, Ribeiro DE, Evangelista SR et al (2017) Controlled fermentation of semi-dry coffee (Coffea arabica) using starter cultures: a sensory perspective. LWT - Food Sci Technol 82:32–38. https://doi.org/10.1016/j.lwt.2017.04.008

Samaniego Rodriguez MA (2019) Evaluación de maceración carbónica y adición de levaduras (Saccharomyces cerevisiae) durante el lavado de café Geisha (Coffea arabica). Escuela Agrícola Panamericana 41

Silva CF, Vilela DM, de Souza Cordeiro C et al (2013) Evaluation of a potential starter culture for enhance quality of coffee fermentation. World J Microbiol Biotechnol 29:235–247. https://doi.org/10.1007/s11274-012-1175-2

Sulaiman I, Hasni D (2022) Microorganism growth profiles during fermentation of Gayo Arabica wine coffee. IOP Conf Ser Earth Environ Sci 951. https://doi.org/10.1088/1755-1315/951/1/012076

Widyotomo S, Yusianto D (2013) Optimizing of Arabica coffee bean fermentation process using a controlled fermentor. Pelita Perkebunan (a coffee and cocoa. Res Journal) 29:53–68. https://doi.org/10.22302/ICCRI.JUR.PELITAPERKEBUNAN.V29I1.191

Download references

Project financed by the Ministry of Science and Technology of Colombia (Minciencias). Project of the General System of Royalties BPIN No. 2020000100460.

Open Access funding provided by Colombia Consortium

Author information

Valeria Hurtado Cortés, Jaime Daniel Bustos Vanegas & Nelson Gutiérrez Guzmán

Present address: Facultad de Ingeniería, Grupo de Investigación Agroindustria USCO, Universidad Surcolombiana, Centro Surcolombiano de Investigación en Café – CESURCAFÉ, Avenida Pastrana Borrero Carrera 1a, Neiva, 410001, Huila, Colombia

Authors and Affiliations

Facultad de Ingeniería, Grupo de Investigación Agroindustria USCO, Universidad Surcolombiana, Centro Surcolombiano de Investigación en Café – CESURCAFÉ, Avenida Pastrana Borrero Carrera 1a, Neiva, 410001, Huila, Colombia

Andrés Felipe Bahamón Monje

You can also search for this author in PubMed   Google Scholar

Contributions

The writing and formatting of the article was done by Valeria Hurtado, Jaime Bustos and Andrés Bahamon, the revision and acceptance was reviewed by Jaime Bustos and Nelson Gutierrez.

Corresponding author

Correspondence to Andrés Felipe Bahamón Monje .

Ethics declarations

Ethics approval, consent to participate, consent for publication, competing interests.

The authors declare that we have no conflicts of interest or competing interests that could influence my objectivity or the impartiality of the work.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cortés, V.H., Bahamón Monje, A.F., Bustos Vanegas, J.D. et al. Challenges in coffee fermentation technologies: bibliometric analysis and critical review. J Food Sci Technol (2024). https://doi.org/10.1007/s13197-024-06054-5

Download citation

Revised : 06 July 2024

Accepted : 05 August 2024

Published : 02 September 2024

DOI : https://doi.org/10.1007/s13197-024-06054-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Postharvest
• Sustainability
• Process control
• Find a journal
• Publish with us
• Track your research