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Editorials

New advancement in information technologies for industry 4.0

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Pages 402-405 | Received 24 Jul 2019, Accepted 30 Jul 2019, Published online: 13 Mar 2020

Based on the rapidly developed information technologies such as Cyber-Physical Systems, Internet of Things, Cloud Computing and Big Data Analytics, at present, the concept of Industry 4.0 has become a reality in numerous concrete scenarios and applications. In those areas, one of the primary aims lies in building the distributed, collaborative and automated design & manufacturing workflow for the smart factory, manufacturing, and logistics. By following the design principles that first proposed by Hermann et al. in their article titled ‘Design Principles for Industrie 4.0 Scenarios’ in 2016, both industrial and academia are working hard to tackle the four associated challenges: 1) how to better enable the ubiquitous smart equipment to connect and communicate with each other (the Interconnection Principle); 2) how to better analyse and leverage the collected immense amounts of data and information (the Information Transparency Principle); 3) how to better support human beings to make decisions and conduct difficult tasks (the Technical Assistance Principle) and 4) how to better take advantage of decentralized architecture for decision making (the Decentralized Decisions Principle).

While the previous EIS special issue on ‘Industry 4.0 and Big Data Innovation’ more focused on the research progresses of Big Data technology in Industry 4.0, this special issue takes a further step for providing the readers with the latest advances in information technologies for industry 4.0, by considering above fundamental motivations. Based on the submissions in ‘the 5th International Conference on Enterprise Systems (ES 2017)’ and an open call for papers procedure, we selected 8 representative research articles for publication after rigorous peer-review processes. Towards those above-mentioned challenges, these 8 articles propose promising solutions and excellent literature reviews for supporting industry 4.0 to better change the traditional manufacturing. Here, we provide an integrative perspective of this special issue by summarizing each contribution contained therein.

Drones, namely unmanned aerial vehicles (UAVs), are a highly efficient and low-cost means of transport in recent years. In the scenario of e-Commerce, delivery packages using drones will deem play an essential role. However, delivery with drones has some limitations in the actual operation. To overcome this challenge, Li et al. in the invited paper (Continuum approximation models for joint delivery systems using trucks and drones) focused on exploring the economics related to the joint delivery system using trucks and drones. Transportation distances and costs are approximated as simple functions using continuum approximation (CA) methods, which can keep critical issues and trade-offs in focus. The main contribution is to develop cost models using the derived methods and gain a greater understanding of delivery activities by focusing on the trade-offs between major components, which are valuable for decision-makers: choose economical delivery mode based on customer density, partition service region into optimal sub-regions, and obtain an optimal delivery ratio between trucks and drones.

The key to discovering potential opportunity information in cross-organisation business processes (COBPs) is to identify the primary roles and actors, i.e., how to obtain their associations according to the interactive behaviours within the complex social networks. The information of roles in COBPs is commonly considered important and explicitly related to activities contained in COBPs. In the work by Tan et al. (Method towards discovering potential opportunity information during cross-organisational business processes using role identification analysis within complex social network), the authors defined the role as a configurable resource model integrating the capabilities and knowledge required to the qualified actors. Furthermore, they introduced two networks named as role-based interactive behaviour network and handover of work social network to investigate the information on roles. They also discussed how to build the complex social network mapped on roles from COBPs and proposed an approach to obtaining the potential opportunity information by combining with the significance of roles and actors.

As the aging society is coming and there are increasing senior citizens in need of the physiological and psychological care, it is necessary to put forward innovative technologies to provide high-quality and affordable health care services to the aging population. For solving the challenges, the automation and information technologies are introduced into healthcare to break this dilemma, and the associated issues become particularly prominent in the context of Industry 4.0. In the invited paper by Cui et al. (Development of smart nursing homes using systems engineering methodologies in industry 4.0), the authors proposed an iterative and life-cycle development process based on the Vee model in Systems Engineering. Some tools, like the house of the quality matrix, are introduced to continuously identify, allocate, and refine requirements from older residents, engineers, and other stakeholders. The development model proposes highlights the synchronization development of a cyber-physical system with a smart nursing home. Furthermore, agent-based simulation is implemented to illustrate the feasibility and applicability of their work.

Energy consumption and industrial intelligence are essential in both industrial and household environments. Current state-of-the-art research is in the development of information systems and networked components to reduce energy consumption and to cycle/recovery waste electronics, and on the use of Internet technologies to change human’s conservation awareness and behaviours. In the work by Ji et al. (Designing a smart information system: the influence of feedback on energy conservation persuasion), the authors explored whether a smart information system can motivate users to conserve energy in the home environment. Three kinds of feedback information based on user decisions were discussed: emotion feedback with a happy mood, average feedback of a comparison group, and ranking feedback within a comparison group. An experiment validated that all the feedbacks had a positive influence on users’ energy conservation performance. Users’ positive attitude toward the social group that they lived in and compared with would increase users’ environmentally friendly behaviours. Finally, several suggestions on the design of energy feedback are provided.

In recent years, the development of smart grid helps to reduce the gap and enhance the connection between industry 4.0 and the energy area. The smart distribution network is an advanced architecture in which the components such as power storage, power supply, and power quality improvement could be organised in a much intelligent way. Lin et al. in the invited paper (A voted based random forests algorithm for smart grid distribution network faults prediction) focused on fault prediction in the smart distribution network. A modified version of voted random forest algorithm (VRF) was proposed for enhancing the predicting accuracy of the faults. The authors changed the decision process by redesigning the voting algorithm by introducing multiple SVM models for voting model training. Based on the trained models, a simple NSGA algorithm is applied to find the best voting model. Results show that the new algorithm could improve the accuracy and recall rate of the fault prediction, especially for the recall rate of the negative samples.

In the design and implementation of some industry 4.0 systems, one of the basal problems in computer vision is object detection, which aims to detect objects and enclose them by a tight bounding box from an image, in which the visual memory plays an essential role for the human’s visual system to detect objects. The features of an object stored in the visual memory have much lower dimensions than the features contained within an image. In the work by Dai et al. (Object detection based on visual memory: a feature learning and feature imagination process), the authors simulated the visual memory as a feature learning and feature imagination (FLFI) process to build an object detection algorithm. The method is constructed by a bottom-up feature learning and a top-down feature imagination. The proposed object detection method was tested using publicly available benchmark data sets, and the result indicates that it is fast and more robust.

As the health care costs are going up tremendously with the current world population, biosensors networks will provide the platform for patient real-time and remote health monitoring. In the invited paper by Raju et al. (LSPR detection of extracellular vesicles using a silver-PDMS nano-composite platform suitable for sensor networks), a novel method for the detection of exosomes in body fluids and cell cultures have been developed. The method is an optical method, based on LSPR of silver nanoparticles. The method was extended to a microfluidic environment, amenable to be integrated into a sensor network. However, for clinical applications, their isolation, detection, and quantification methods in bio-fluids are challenging. Herein, the authors presented a simple label-free technique to capture and detect EVs by using a synthetic peptide, called Vn96. To quantify EVs, an LSPR detection technique was used. This work is an attempt to adopt a biosensing method to the future requirements of Industry 4.0.

The vision of factories-of-the-future is motivating many industrial companies to modernise their existing portfolio of systems and services to maintain market share and improve business agility. For long-lived industrial systems, it is challenging to adapt legacy assets to a service-oriented stream in cloud computing and Internet-of-things contexts. For this reason, many research studies have proposed techniques and methodologies to migrate legacy industrial functions and systems at different hierarchy levels of automation control. In the work by Hongyu Pei Breivold (Towards factories of the future: migration of industrial legacy automation systems in the cloud computing and Internet-of-things context), the author described a comprehensive overview of existing studies in the techniques, experiences, and methodologies used for migrating legacy industrial systems in the cloud computing and Internet-of-things context, as well as industry practices to achieve the vision of smart factories. Better understanding of the challenges encountered in legacy migration processes will help researchers and practitioners in their further efforts.

Although in this special issue significant efforts have been made from the perspective of different design principles of industry 4.0, we should note that many other exciting areas such as its relationship with Fog/Edge Computing and its Cybersecurity issues are also worthy of being explored in future. Before the end of this editorial, we would like to thank the anonymous reviewers for their great efforts in reviewing the submitted manuscripts, without them this special issue would not have been published with such high quality. We would like to thank the editor-in-chief office of Enterprise Information Systems for their supportive guidance during the whole process in the organization of this special issue.

Additional information

Funding

This work was supported by the Deakin University [ASL 2019].

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