Special Issue "Smart Services: Artificial Intelligence in Service Systems"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 November 2021.

Special Issue Editors

Dr. Marlene Amorim
E-Mail Website1 Website2
Guest Editor
Research Unit on Governance, Competitiveness and Public Policies (GOVCOPP), Department of Economics, Management, Industrial Engineering and Tourism (DEGEIT) Universidade de Aveiro, 3810-193 Aveiro, Portugal
Interests: service operations; service quality; innovation; digitalization
Special Issues and Collections in MDPI journals
Dr. Yuval Cohen
E-Mail Website
Guest Editor
Department of Industrial Engineering, Afeka Tel-Aviv College of Engineering, Tel-Aviv 69988, Israel
Interests: smart intelligent systems; service science; Industry 4.0; human–machine interaction; artificial emotional intelligence; operations management.
Dr. João Reis
E-Mail Website
Guest Editor
Industrial Engineering and Management, Faculty of Engineering, Lusofona University, 1749-024 Campo Grande, Lisbon, Portugal
Interests: artificial intelligence; digitalization; industrial engineering; operations management; service operations

Special Issue Information

Dear Colleagues,

Service provision systems have been pioneers in the experimentation of human–computer interactions with frontline employees, and with customers. This led to the development of systems for the effective integration of providers’ resources, employees, technology, and the customers. A prominent example has been the early adoption of online and mobile service delivery channels. This adoption triggered innovation in the design of service experiences, and in extending access and service convenience at an unprecedented pace. Digital technologies therefore find a fertile ground in services, where information is a core input for service production systems that can be exchanged and modified by different users and service contexts. The recent developments and the adoption of advanced information technologies (e.g., Internet-of-Things (IoT), cyber-physical systems, cloud computing) and artificial intelligence (e.g., machine learning (ML), computer vision (CV), sound and speech recognition, natural language processing (NLP)) are triggering promising new avenues for service innovation and the emergence of smart service systems. The expanding links between the physical world and networked technologies, including networked sensors, creates a powerful and augmented space for the interactions and collaboration between service providers and customers for value creation.

In today’s competitive business environment, organizations are facing challenges in dealing with big-data issues and real-time decision-making for improved customer satisfaction. Another challenge is to exploit the confluence of several new promising technologies, such as emotion recognition by tone and facial analysis, natural language processing (NLP), speech recognition, gesture recognition, etc. This confluence was the motivation for Germany to lead a transformation toward the 4th Generation Industrial Revolution (Industry 4.0) based on cyber-physical-system-enabled manufacturing and service innovation. As more software and embedded intelligence are integrated in various services, advanced technologies can further interlace intelligent algorithms with computing capabilities. These technologies will then be used to for the benefit of managing the customer journey as well as customer satisfaction.

Artificial intelligence (AI) is increasingly adopted in service production systems, and is a major source of innovation. Many aspects of modern service consumption are being progressively automated, opening opportunities for experimentation with big data and AI applications across a variety of sectors, including personal and financial services, health care, communications, education, transportation, travel and accommodation, to cite only a few. The adoption of such technologies is blurring the frontiers between the physical, digital, and biological spheres, and creating calls for the development of research and knowledge that can support decision-making in the management of such hybrid service systems, where the roles and responsibilities of humans is being redefined.

The Special Issue on “Artificial Intelligence Trends and Applications in Service Systems” welcomes submissions of recent research work on this promising application area for artificial intelligence. The call is open to a broad thematic range of papers covering the recent applications of big data and AI across service businesses, covering managerial and customer challenges, technologies, service robotics, and research trends aiming at offering to readers knowledge for extending the adoption of AI in services, and inspiring managerial decision and innovation in the field.

Recommended topics include, but are not limited to, the following:

  • Harnessing artificial intelligence (AI) for smart service applications:
    • Natural language processing (NLP);
    • Machine learning (ML);
    • Case-based reasoning (CBR);
    • Human tracking technologies (e.g., gesture recognition, facial analysis, eye tracking, etc.);
  • Industrial experiments and case studies dealing with smart services and platforms;
  • Smart health services;
  • A review of a smart service technology;
  • Organizational transformation to smart service;
  • Cyber infrastructure, IoT, and big data for smart service;
  • Designing the smart service operations;
  • Smart service ontology;
  • Social aspects of smart service;
  • Service improvement wait reduction in smart service system;
  • Smart service tourism management;
  • Sustainable and green service provision;
  • Customer’s journey in the smart service environment;
  • Dynamic real-time capabilities in a smart service system;
  • Sustainable smart service operations;
  • Service robots pilot applications and experiences.

Dr. Marlene Amorim
Dr. Yuval Cohen
Dr. João Reis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • service systems
  • smart service
  • service digitization
  • customer interactions
  • artificial intelligence
  • emotion detection
  • servitization
  • service robots
  • digitalization

Published Papers (8 papers)

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Research

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Article
Anticipating Future Behavior of an Industrial Press Using LSTM Networks
Appl. Sci. 2021, 11(13), 6101; https://doi.org/10.3390/app11136101 - 30 Jun 2021
Viewed by 516
Abstract
Predictive maintenance is very important in industrial plants to support decisions aiming to maximize maintenance investments and equipment’s availability. This paper presents predictive models based on long short-term memory neural networks, applied to a dataset of sensor readings. The aim is to forecast [...] Read more.
Predictive maintenance is very important in industrial plants to support decisions aiming to maximize maintenance investments and equipment’s availability. This paper presents predictive models based on long short-term memory neural networks, applied to a dataset of sensor readings. The aim is to forecast future equipment statuses based on data from an industrial paper press. The datasets contain data from a three-year period. Data are pre-processed and the neural networks are optimized to minimize prediction errors. The results show that it is possible to predict future behavior up to one month in advance with reasonable confidence. Based on these results, it is possible to anticipate and optimize maintenance decisions, as well as continue research to improve the reliability of the model. Full article
(This article belongs to the Special Issue Smart Services: Artificial Intelligence in Service Systems)
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Article
SemDaServ: A Systematic Approach for Semantic Data Specification of AI-Based Smart Service Systems
Appl. Sci. 2021, 11(11), 5148; https://doi.org/10.3390/app11115148 - 01 Jun 2021
Viewed by 718
Abstract
To develop smart services to successfully operate as a component of smart service systems (SSS), they need qualitatively and quantitatively sufficient data. This is especially true when using statistical methods from the field of artificial intelligence (AI): training data quality directly determines the [...] Read more.
To develop smart services to successfully operate as a component of smart service systems (SSS), they need qualitatively and quantitatively sufficient data. This is especially true when using statistical methods from the field of artificial intelligence (AI): training data quality directly determines the quality of resulting AI models. However, AI model quality is only known when AI training can take place. Additionally, the creation of not yet available data sources (e.g., sensors) takes time. Therefore, systematic specification is needed alongside SSS development. Today, there is a lack of systematic support for specifying data relevant to smart services. This gap can be closed by realizing the systematic approach SemDaServ presented in this article. The research approach is based on Blessing’s Design Research Methodology (literature study, derivation of key factors, success criteria, solution functions, solution development, applicability evaluation). SemDaServ provides a three-step process and five accompanying artifacts. Using domain knowledge for data specification is critical and creates additional challenges. Therefore, the SemDaServ approach systematically captures and semantically formalizes domain knowledge in SysML-based models for information and data. The applicability evaluation in expert interviews and expert workshops has confirmed the suitability of SemDaServ for data specification in the context of SSS development. SemDaServ thus offers a systematic approach to specify the data requirements of smart services early on to aid development to continuous integration and continuous delivery scenarios. Full article
(This article belongs to the Special Issue Smart Services: Artificial Intelligence in Service Systems)
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Article
Augmented Reality Maintenance Assistant Using YOLOv5
Appl. Sci. 2021, 11(11), 4758; https://doi.org/10.3390/app11114758 - 22 May 2021
Cited by 2 | Viewed by 859
Abstract
Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for [...] Read more.
Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed. Full article
(This article belongs to the Special Issue Smart Services: Artificial Intelligence in Service Systems)
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Article
The Role of Industry 4.0 and BPMN in the Arise of Condition-Based and Predictive Maintenance: A Case Study in the Automotive Industry
Appl. Sci. 2021, 11(8), 3438; https://doi.org/10.3390/app11083438 - 12 Apr 2021
Cited by 2 | Viewed by 618
Abstract
This article addresses the evolution of Industry 4.0 (I4.0) in the automotive industry, exploring its contribution to a shift in the maintenance paradigm. To this end, we firstly present the concepts of predictive maintenance (PdM), condition-based maintenance (CBM), and their applications to increase [...] Read more.
This article addresses the evolution of Industry 4.0 (I4.0) in the automotive industry, exploring its contribution to a shift in the maintenance paradigm. To this end, we firstly present the concepts of predictive maintenance (PdM), condition-based maintenance (CBM), and their applications to increase awareness of why and how these concepts are revolutionizing the automotive industry. Then, we introduce the business process management (BPM) and business process model and notation (BPMN) methodologies, as well as their relationship with maintenance. Finally, we present the case study of the Renault Cacia, which is developing and implementing the concepts mentioned above. Full article
(This article belongs to the Special Issue Smart Services: Artificial Intelligence in Service Systems)
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Article
Increasing the Reliability of an Electrical Power System in a Big European Hospital through the Petri Nets and Fuzzy Inference System Mamdani Modelling
Appl. Sci. 2021, 11(6), 2604; https://doi.org/10.3390/app11062604 - 15 Mar 2021
Viewed by 657
Abstract
The big hospitals’ electricity supply system’s reliability is discussed in this article through Petri nets and Fuzzy Inference System (FIS). To simulate and analyse an electric power system, the FIS Mamdani in MATLAB is implemented. The advantage of FIS is that it uses [...] Read more.
The big hospitals’ electricity supply system’s reliability is discussed in this article through Petri nets and Fuzzy Inference System (FIS). To simulate and analyse an electric power system, the FIS Mamdani in MATLAB is implemented. The advantage of FIS is that it uses human experience to provide a faster solution than conventional techniques. The elements involved are the Main Electrical Power, the Generator sets, the Automatic Transfer Switches (ATS), and the Uninterrupted Power Supply (UPS), which are analysed to characterize the system behaviour. To evaluate the system and identified the lower reliability modules being proposed, a new reliable design model through the Petri Nets and Fuzzy Inference System approach. The resulting approach contributes to increasing the reliability of complex electrical systems, aiming to reduce their faults and increase their availability. Full article
(This article belongs to the Special Issue Smart Services: Artificial Intelligence in Service Systems)
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Article
Smart Care Using a DNN-Based Approach for Activities of Daily Living (ADL) Recognition
Appl. Sci. 2021, 11(1), 10; https://doi.org/10.3390/app11010010 - 22 Dec 2020
Cited by 1 | Viewed by 619
Abstract
Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural [...] Read more.
Health care for independently living elders is more important than ever. Automatic recognition of their Activities of Daily Living (ADL) is the first step to solving the health care issues faced by seniors in an efficient way. The paper describes a Deep Neural Network (DNN)-based recognition system aimed at facilitating smart care, which combines ADL recognition, image/video processing, movement calculation, and DNN. An algorithm is developed for processing skeletal data, filtering noise, and pattern recognition for identification of the 10 most common ADL including standing, bending, squatting, sitting, eating, hand holding, hand raising, sitting plus drinking, standing plus drinking, and falling. The evaluation results show that this DNN-based system is suitable method for dealing with ADL recognition with an accuracy rate of over 95%. The findings support the feasibility of this system that is efficient enough for both practical and academic applications. Full article
(This article belongs to the Special Issue Smart Services: Artificial Intelligence in Service Systems)
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Review

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Review
High-Tech Defense Industries: Developing Autonomous Intelligent Systems
Appl. Sci. 2021, 11(11), 4920; https://doi.org/10.3390/app11114920 - 27 May 2021
Viewed by 762
Abstract
After the Cold War, the defense industries found themselves at a crossroads. However, it seems that they are gaining new momentum, as new technologies such as robotics and artificial intelligence are enabling the development of autonomous, highly innovative and disruptive intelligent systems. Despite [...] Read more.
After the Cold War, the defense industries found themselves at a crossroads. However, it seems that they are gaining new momentum, as new technologies such as robotics and artificial intelligence are enabling the development of autonomous, highly innovative and disruptive intelligent systems. Despite this new impetus, there are still doubts about where to invest limited financial resources to boost high-tech defense industries. In order to shed some light on the topic, we decided to conduct a systematic literature review by using the PRISMA protocol and content analysis. The results indicate that autonomous intelligent systems are being developed by the defense industry and categorized into three different modes—fully autonomous operations, partially autonomous operations, and smart autonomous decision-making. In addition, it is also important to note that, at a strategic level of war, there is limited room for automation given the need for human intervention. However, at the tactical level of war, there is a high probability of growth in industrial defense, since, at this level, structured decisions and complex analytical-cognitive tasks are carried out. In the light of carrying out those decisions and tasks, robotics and artificial intelligence can make a contribution far superior to that of human beings. Full article
(This article belongs to the Special Issue Smart Services: Artificial Intelligence in Service Systems)
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Other

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Technical Note
Smart Project Management: Interactive Platform Using Natural Language Processing Technology
Appl. Sci. 2021, 11(4), 1597; https://doi.org/10.3390/app11041597 - 10 Feb 2021
Cited by 1 | Viewed by 643
Abstract
Technological developments have made the construction industry efficient. The aim of this research is to solve communication interaction problems to build a project management platform using the interactive concept of natural language processing technology. A comprehensive literature review and expert interviews associated with [...] Read more.
Technological developments have made the construction industry efficient. The aim of this research is to solve communication interaction problems to build a project management platform using the interactive concept of natural language processing technology. A comprehensive literature review and expert interviews associated with techniques dealing with natural languages suggests the proposed system containing the Progressive Scale Expansion Network (PSENet), Convolutional Recurrent Neural Network (CRNN), and Bi-directional Recurrent Neutral Networks Convolutional Recurrent Neural Network (BRNN-CNN) toolboxes to extract the key words for construction projects contracts. The results show that a fully automatic platform facilitating contract management is achieved. For academic domains, the Contract Keyword Detection (CKD) mechanism integrating PSENet, CRNN, and BRNN-CNN approaches to cope with real-time massive document flows is novel in the construction industry. For practice, the proposed approach brings significant reduction for manpower and human error, an alternative for settling down misunderstanding or disputes due to real-time and precise communication, and a solution for efficient documentary management. It connects all contract stakeholders proficiently. Full article
(This article belongs to the Special Issue Smart Services: Artificial Intelligence in Service Systems)
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