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Production and Industrial Service Management in the Industry 4.0 Era: Digital Techniques in Operations

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 17616
Please contact the Guest Editor or the Section Managing Editor at ( [email protected]) for any queries.

Special Issue Editors

Prof. Dr. Thomas Friedli

E-Mail Website
Guest Editor
Institute of Technology Management, University of St. Gallen, Dufourstrasse 40a, 9000 St. Gallen, Switzerland
Interests: digitalization; operational excellence; smart services; global production networks
Dr. Lukas Budde

E-Mail Website
Guest Editor
Affiliation: Institute of Technology Management, University of St. Gallen, Dufourstrasse 40a, 9000 St. Gallen, Switzerland
Interests: smart manufacturing; digitalization; digital platforms; complexity management

Special Issue Information

Industry 4.0 and digitalization in general have received increasing attention in designing and managing manufacturing operations. These topics vary from digital transformations, digital manufacturing use cases, and platform approaches to new business models. The usage of digital technologies facilitates operational innovation by creating new and transforming existing organizations and processes to increase a firm’s competitiveness. Digital transformation allows the exploitation of digital technologies to enhance internal processes and the reinvention of existing products, services, or business models to meet changing business and market requirements. The emergence of digital platforms in manufacturing operations include different players along the value chain network, which place new demands on organizations in terms of their choices of vertical and horizontal scope and the management of their boundaries and collaborations.

In contribution to these discussions, this Special Issue sheds light on the impacts of digital technologies, such as cloud computing, digital twins, and AI, on organizational performance.

Prof. Dr. Thomas Friedli
Dr. Lukas Budde
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 submissions that pass pre-check are 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. Sensors 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 2600 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

  • Digital technologies
  • Cloud computing
  • Digital twins
  • Artificial intelligence
  • Global manufacturing networks
  • Digital platforms.

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Published Papers (3 papers)

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Research

25 pages, 430 KiB  
Article
Performance-Cost Trade-Off in Auto-Scaling Mechanisms for Cloud Computing
by Iure Fé, Rubens Matos, Jamilson Dantas, Carlos Melo, Tuan Anh Nguyen, Dugki Min, Eunmi Choi, Francisco Airton Silva and Paulo Romero Martins Maciel
Sensors 2022, 22(3), 1221; https://doi.org/10.3390/s22031221 - 5 Feb 2022
Cited by 6 | Viewed by 4189
Abstract
Cloud computing has been widely adopted over the years by practitioners and companies with a variety of requirements. With a strong economic appeal, cloud computing makes possible the idea of computing as a utility, in which computing resources can be consumed and paid [...] Read more.
Cloud computing has been widely adopted over the years by practitioners and companies with a variety of requirements. With a strong economic appeal, cloud computing makes possible the idea of computing as a utility, in which computing resources can be consumed and paid for with the same convenience as electricity. One of the main characteristics of cloud as a service is elasticity supported by auto-scaling capabilities. The auto-scaling cloud mechanism allows adjusting resources to meet multiple demands dynamically. The elasticity service is best represented in critical web trading and transaction systems that must satisfy a certain service level agreement (SLA), such as maximum response time limits for different types of inbound requests. Nevertheless, existing cloud infrastructures maintained by different cloud enterprises often offer different cloud service costs for equivalent SLAs upon several factors. The factors might be contract types, VM types, auto-scaling configuration parameters, and incoming workload demand. Identifying a combination of parameters that results in SLA compliance directly in the system is often sophisticated, while the manual analysis is prone to errors due to the huge number of possibilities. This paper proposes the modeling of auto-scaling mechanisms in a typical cloud infrastructure using a stochastic Petri net (SPN) and the employment of a well-established adaptive search metaheuristic (GRASP) to discover critical trade-offs between performance and cost in cloud services.The proposed SPN models enable cloud designers to estimate the metrics of cloud services in accordance with each required SLA such as the best configuration, cost, system response time, and throughput.The auto-scaling SPN model was extensively validated with 95% confidence against a real test-bed scenario with 18.000 samples. A case-study of cloud services was used to investigate the viability of this method and to evaluate the adoptability of the proposed auto-scaling model in practice. On the other hand, the proposed optimization algorithm enables the identification of economic system configuration and parameterization to satisfy required SLA and budget constraints. The adoption of the metaheuristic GRASP approach and the modeling of auto-scaling mechanisms in this work can help search for the optimized-quality solution and operational management for cloud services in practice. Full article
(This article belongs to the Special Issue Production and Industrial Service Management in the Industry 4.0 Era: Digital Techniques in Operations)
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21 pages, 841 KiB  
Article
Knowledge Graph Based Hard Drive Failure Prediction
by Tek Raj Chhetri, Anelia Kurteva, Jubril Gbolahan Adigun and Anna Fensel
Sensors 2022, 22(3), 985; https://doi.org/10.3390/s22030985 - 27 Jan 2022
Cited by 20 | Viewed by 5080
Abstract
The hard drive is one of the important components of a computing system, and its failure can lead to both system failure and data loss. Therefore, the reliability of a hard drive is very important. Realising this importance, a number of studies have [...] Read more.
The hard drive is one of the important components of a computing system, and its failure can lead to both system failure and data loss. Therefore, the reliability of a hard drive is very important. Realising this importance, a number of studies have been conducted and many are still ongoing to improve hard drive failure prediction. Most of those studies rely solely on machine learning, and a few others on semantic technology. The studies based on machine learning, despite promising results, lack context-awareness such as how failures are related or what other factors, such as humidity, influence the failure of hard drives. Semantic technology, on the other hand, by means of ontologies and knowledge graphs (KGs), is able to provide the context-awareness that machine learning-based studies lack. However, the studies based on semantic technology lack the advantages of machine learning, such as the ability to learn a pattern and make predictions based on learned patterns. Therefore, in this paper, leveraging the benefits of both machine learning (ML) and semantic technology, we present our study, knowledge graph-based hard drive failure prediction. The experimental results demonstrate that our proposed method achieves higher accuracy in comparison to the current state of the art. Full article
(This article belongs to the Special Issue Production and Industrial Service Management in the Industry 4.0 Era: Digital Techniques in Operations)
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16 pages, 3050 KiB  
Article
A Digital Twin Approach for the Improvement of an Autonomous Mobile Robots (AMR’s) Operating Environment—A Case Study
by Paweł Stączek, Jakub Pizoń, Wojciech Danilczuk and Arkadiusz Gola
Sensors 2021, 21(23), 7830; https://doi.org/10.3390/s21237830 - 25 Nov 2021
Cited by 50 | Viewed by 7314
Abstract
The contemporary market creates a demand for continuous improvement of production, service, and management processes. Increasingly advanced IT technologies help designers to meet this demand, as they allow them to abandon classic design and design-testing methods in favor of techniques that do not [...] Read more.
The contemporary market creates a demand for continuous improvement of production, service, and management processes. Increasingly advanced IT technologies help designers to meet this demand, as they allow them to abandon classic design and design-testing methods in favor of techniques that do not require the use of real-life systems and thus significantly reduce the costs and time of implementing new solutions. This is particularly important when re-engineering production and logistics processes in existing production companies, where physical testing is often infeasible as it would require suspension of production for the testing period. In this article, we showed how the Digital Twin technology can be used to test the operating environment of an autonomous mobile robot (AMR). In particular, the concept of the Digital Twin was used to assess the correctness of the design assumptions adopted for the early phase of the implementation of an AMR vehicle in a company’s production hall. This was done by testing and improving the case of a selected intralogistics task in a potentially “problematic” part of the shop floor with narrow communication routes. Three test scenarios were analyzed. The results confirmed that the use of digital twins could accelerate the implementation of automated intralogistics systems and reduce its costs. Full article
(This article belongs to the Special Issue Production and Industrial Service Management in the Industry 4.0 Era: Digital Techniques in Operations)
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