New Challenges of Innovation, Sustainability, Resilience in X.0 Era

A special issue of Applied System Innovation (ISSN 2571-5577). This special issue belongs to the section "Industrial and Manufacturing Engineering".

Deadline for manuscript submissions: 30 March 2025 | Viewed by 12492

Special Issue Editors


E-Mail Website
Guest Editor
Department of Chemical, Materials and Industrial Production Engineering (DICMAPI), University of Naples Federico II, 80125 Naples, Italy
Interests: safety; maintenance; resilience; Industry 4.0
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
MIS-LISTD Laboratory, Computer Science Department, ENSMR, Rabat, Morocco
Interests: complex industrial systems; modeling and simulation; risk analysis; SCM; Industry 4.0; systems engineering

Special Issue Information

Dear Colleagues,

Today, in the era of digitalization, companies are experiencing new challenges more than ever before. Innovation, sustainability and resilience are the pillars of successful and sophisticated digital transformation.

X.0 companies can no longer focus on a simple short-term vision by producing the same recipes but are embracing more creative changes in order to be able to produce innovations and create new personalized experiences.

X.0 is the digital reinvention of the industry, combining the efficiency of transformation with research. This transformation will be able to create innovation and make these companies more resilient toward new sustainable growth to create value.

This session aims to share the most recent contributions in this area. Researchers and professionals are invited to present their work in the following or related fields:

  • Resilience;
  • Innovation and/or digitalization;
  • Sustainability;
  • Smart industry;
  • Industry 4.0/Industry X.0;
  • Artificial intelligence (AI);
  • Modeling and simulation;
  • Lean manufacturing/supply chain management;
  • Safety and maintenance;
  • Railways and trains.

Dr. Mario Di Nardo
Dr. Maryam Gallab
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. Applied System Innovation 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 1400 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.

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (8 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Editorial

Jump to: Research

3 pages, 209 KiB  
Editorial
New Innovation, Sustainability, and Resilience Challenges in the X.0 Era
by Maryam Gallab and Mario Di Nardo
Appl. Syst. Innov. 2023, 6(2), 39; https://doi.org/10.3390/asi6020039 - 13 Mar 2023
Cited by 3 | Viewed by 1646
Abstract
Facing a constantly evolving industry and customers that are becoming more fastidious, companies are seeking to adapt their manufacturing methods to meet market demands [...] Full article
(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)

Research

Jump to: Editorial

17 pages, 7936 KiB  
Article
Industrial Environmental Impact Assessment Method Based on Detection of Complex Anomalies in Time Series
by Elena Safonova, Alla Kravets, Maxim Shcherbakov, Alexey Kizim, Mohammad Al-Gunaid and Alexander Echin
Appl. Syst. Innov. 2024, 7(5), 89; https://doi.org/10.3390/asi7050089 - 24 Sep 2024
Viewed by 334
Abstract
To minimize the environmental impact of energy enterprises, it is important to promptly identify cases of possible changes in the quality of wastewater generated at power plants, that is, cases of exceeding the maximum permissible concentrations of contamination in wastewater. The goal of [...] Read more.
To minimize the environmental impact of energy enterprises, it is important to promptly identify cases of possible changes in the quality of wastewater generated at power plants, that is, cases of exceeding the maximum permissible concentrations of contamination in wastewater. The goal of the method for detecting complex anomalies in multidimensional time series obtained from smart energy stations’ sensor channels is to improve the accuracy of detecting contamination levels in industrial wastewater. To achieve this goal, the following tasks were addressed: methods for detecting time series anomalies were analyzed, the method for detecting complex anomalies was developed, software implementation of the algorithm was carried out, and experiments were conducted. The developed method is recommended for use in a smart energy monitoring system. Full article
(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)
Show Figures

Figure 1

18 pages, 1015 KiB  
Article
Advancing BIM and Sustainability with Coopetition: Evidence from the Portuguese Stone Industry
by Agostinho da Silva and Antonio J. Marques Cardoso
Appl. Syst. Innov. 2024, 7(4), 70; https://doi.org/10.3390/asi7040070 - 19 Aug 2024
Viewed by 871
Abstract
The construction industry plays a crucial role in the global economy but faces persistent challenges such as inefficiency, high costs, and significant environmental impact. Building Information Modelling (BIM) has been proposed as a solution to enhance efficiency and sustainability through digital representations of [...] Read more.
The construction industry plays a crucial role in the global economy but faces persistent challenges such as inefficiency, high costs, and significant environmental impact. Building Information Modelling (BIM) has been proposed as a solution to enhance efficiency and sustainability through digital representations of construction projects. However, the full potential of BIM has yet to be realized. A contributing factor to this gap is that construction manufacturing companies, which produce upstream parts and products used downstream in construction, are often overlooked in discussions of BIM’s benefits. This study explores the potential of coopetition networks to help manufacturing companies better align with BIM dimensions. Coopetition networks, which integrate competitive and cooperative strategies, present a promising method to enhance the effectiveness of manufacturing companies. Focusing on the Portuguese Ornamental Stone industry, the study employs an experimental pilot network facilitated by the Industrial Internet of Things (IIoT) to assess the effects of competition on labour productivity, on-time delivery, and environmental performance among stone companies. The findings indicate that coopetition networks significantly improve alignment with BIM requirements, enhancing operational efficiency and sustainability. Despite being limited by a small sample size, this research offers valuable insights into the role of manufacturing companies in BIM-enhanced construction projects and the broader applicability of coopetition networks in advancing BIM objectives. These results highlight the potential of coopetition networks as a strategic approach to improving performance in the construction industry. Full article
(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)
Show Figures

Figure 1

17 pages, 1554 KiB  
Article
Coopetition with the Industrial IoT: A Service-Dominant Logic Approach
by Agostinho da Silva and Antonio J. Marques Cardoso
Appl. Syst. Innov. 2024, 7(3), 47; https://doi.org/10.3390/asi7030047 - 31 May 2024
Cited by 2 | Viewed by 788
Abstract
Abstract: This research addresses the critical gap in enabling effective coopetition networks through technological innovation with the development of Cockpit4.0+, an Industrial Internet of Things (IIoT) artefact tailored for small- and medium-sized enterprises (SMEs). By employing the principles of Service-Dominant Logic (S-D Logic) [...] Read more.
Abstract: This research addresses the critical gap in enabling effective coopetition networks through technological innovation with the development of Cockpit4.0+, an Industrial Internet of Things (IIoT) artefact tailored for small- and medium-sized enterprises (SMEs). By employing the principles of Service-Dominant Logic (S-D Logic) and leveraging the Design Science Research (DSR) methodology, Cockpit4.0+ represents a pioneering approach to incorporating the IIoT within ecosystems for value co-creation. This facilitates competition and cooperation among firms, enhancing the operational dynamics within SME networks. Evaluated by experts in the ornamental stone sector, a significant sector of the Portuguese economy, the system demonstrated a positive functional acceptance rate of 78.9%. An experimental test was conducted following the positive preliminary functional evaluation of Cockpit4.0+, especially among more digitally advanced companies. The findings revealed that the on-time delivery performance under current best practices (CB.Ps) was 67.1%. In contrast, implementing coopetition network practices (CN.Ps) increased on-time delivery to 77.5%. These positive evaluations of Cockpit4.0+ underscore the practical applicability of S-D Logic and provide fresh insights into the dynamics of coopetition, particularly beneficial for SMEs. Despite its promising results, the real-world efficacy of IIoT systems like Cockpit4.0+ requires further empirical studies to verify these findings. Future research should focus on examining the scalability of Cockpit4.0+ and its adaptability across various sectors and enhancing its cybersecurity measures to ensure its long-term success and broader adoption. Full article
(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)
Show Figures

Figure 1

16 pages, 14128 KiB  
Article
A Road Behavior Pattern-Detection Model in Querétaro City Streets by the Use of Shape Descriptors
by Antonio Trejo-Morales and Hugo Jimenez-Hernandez
Appl. Syst. Innov. 2024, 7(3), 44; https://doi.org/10.3390/asi7030044 - 27 May 2024
Viewed by 841
Abstract
In this research, a proposed model aims to automatically identify patterns of spatial and temporal behavior of moving objects in video sequences. The moving objects are analyzed and characterized based on their shape and observable attributes in displacement. To quantify the moving objects [...] Read more.
In this research, a proposed model aims to automatically identify patterns of spatial and temporal behavior of moving objects in video sequences. The moving objects are analyzed and characterized based on their shape and observable attributes in displacement. To quantify the moving objects over time and form a homogeneous database, a set of shape descriptors is introduced. Geometric measurements of shape, contrast, and connectedness are used to represent each moving object. The proposal uses Granger’s theory to find causal relationships from the history of each moving object stored in a database. The model is tested in two scenarios; the first is a public database, and the second scenario uses a proprietary database from a real scenario. The results show an average accuracy value of 78% in the detection of atypical behaviors in positive and negative dependence relationships. Full article
(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)
Show Figures

Figure 1

17 pages, 2686 KiB  
Article
Industry 4.0 and Smart Systems in Manufacturing: Guidelines for the Implementation of a Smart Statistical Process Control
by Lucas Schmidt Goecks, Anderson Felipe Habekost, Antonio Maria Coruzzolo and Miguel Afonso Sellitto
Appl. Syst. Innov. 2024, 7(2), 24; https://doi.org/10.3390/asi7020024 - 16 Mar 2024
Cited by 4 | Viewed by 3090
Abstract
Digital transformations in manufacturing systems confer advantages for enhancing competitiveness and ensuring the survival of companies by reducing operating costs, improving quality, and fostering innovation, falling within the overarching umbrella of Industry 4.0. This study aims to provide a framework for the integration [...] Read more.
Digital transformations in manufacturing systems confer advantages for enhancing competitiveness and ensuring the survival of companies by reducing operating costs, improving quality, and fostering innovation, falling within the overarching umbrella of Industry 4.0. This study aims to provide a framework for the integration of smart statistical digital systems into existing manufacturing control systems, exemplified with guidelines to transform an existent statistical process control into a smart statistical process control. Employing the design science research method, the research techniques include a literature review and interviews with experts who critically evaluated the proposed framework. The primary contribution lies in a set of general-purpose guidelines tailored to assist practitioners in manufacturing systems with the implementation of digital, smart technologies aligned with the principles of Industry 4.0. The resulting guidelines specifically target existing manufacturing plants seeking to adopt new technologies to maintain competitiveness. The main implication of the study is that practitioners can utilize the guidelines as a roadmap for the ongoing development and implementation of project management. Furthermore, the study paves the way for open innovation initiatives by breaking down the project into defined steps and encouraging individual or collective open contributions, which consolidates the practice of open innovation in manufacturing systems. Full article
(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)
Show Figures

Figure 1

15 pages, 6683 KiB  
Article
Assessment of Batteries’ Contribution for Optimal Self-Sufficiency in Large Building Complexes
by Emmanuel Karapidakis, Marios Nikologiannis, Marini Markaki, Ariadni Kikaki and Sofia Yfanti
Appl. Syst. Innov. 2023, 6(6), 107; https://doi.org/10.3390/asi6060107 - 14 Nov 2023
Cited by 1 | Viewed by 1638
Abstract
The EU has set ambitious targets to combat climate change. Incorporating renewable energy technologies to reduce greenhouse gas emissions is a critical aspect of achieving the European Union’s (EU) 2030 climate goals. Similarly to all member countries of the EU, Greece shares the [...] Read more.
The EU has set ambitious targets to combat climate change. Incorporating renewable energy technologies to reduce greenhouse gas emissions is a critical aspect of achieving the European Union’s (EU) 2030 climate goals. Similarly to all member countries of the EU, Greece shares the same climate goals. In order to achieve these goals, ensuring a consistent supply and the effective use of clean energy is pursued, as it has a significant impact on the sustainable development and growth of the country. As the Greek tourism sector is one of the most energy-consuming of the national economy and a major contributor to the country’s GDP, opportunities are presented for innovation and investment in sustainable practices. Such investments must focus on buildings and facilities, where the energy consumption is concentrated. One of the most popular holiday destinations in Greece is the island of Crete. Visitation patterns are seasonal, which means during the summer months, Crete is exceptionally popular and more demanding energy-wise. One of the highest energy-demanding types of tourism-based businesses is the hospitality industry. Energy demands in hotels are driven by factors such as heating, cooling, lighting, and hot water. Thus, such activities require thermal and electrical energy to function. Electrical energy is one of the most essential forms of energy for hotels, as it powers a wide range of critical systems and services throughout the establishment. Therefore, the hotels are highly susceptible to fluctuations in energy prices which can significantly impact the operational costs of hotels. This paper presents an analysis of the annual consumption for the year of 2022 of five hotels located in Crete. An algorithm is also implemented which strives to minimize the capital expenditure (CAPEX), while ensuring a sufficient percentage of self-sufficiency. Full article
(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)
Show Figures

Figure 1

16 pages, 1714 KiB  
Article
Manufacturing Innovation: A Heuristic Model of Innovation Processes for Industry 4.0
by Maria Stoettrup Schioenning Larsen, Astrid Heidemann Lassen and Casper Schou
Appl. Syst. Innov. 2023, 6(6), 98; https://doi.org/10.3390/asi6060098 - 25 Oct 2023
Cited by 1 | Viewed by 1908
Abstract
Despite the promising potential of Industry 4.0, the transition of the manufacturing industry is still very slow-paced. In this article, we argue that one reason for this development is the fact that existing foundational process models of manufacturing innovation are developed for steady-state [...] Read more.
Despite the promising potential of Industry 4.0, the transition of the manufacturing industry is still very slow-paced. In this article, we argue that one reason for this development is the fact that existing foundational process models of manufacturing innovation are developed for steady-state conditions, not considering the complexity and uncertainty related to Industry 4.0. This lack of models built for the characteristics of Industry 4.0 further translates into a lack of operational approaches and insights into engaging with Industry 4.0 in practice. Therefore, this article presents a case study of developing a comprehensive Industry 4.0 solution and identifies key characteristics of the emerging process design. Based on the case study findings, we propose a heuristic model of an innovation process for manufacturing innovation. The proposed model uses an iterative process that allows experimentation and exploration with manufacturing innovation. The iterative approach continuously enhances knowledge levels and incorporates this knowledge in the process to refine the design of the manufacturing innovation. Furthermore, the iterative process design supports partitioning the complexity of the manufacturing innovation into smaller parts, which are easier to grasp, thereby improving the conditions for the successful adoption of manufacturing innovations for Industry 4.0. Full article
(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)
Show Figures

Figure 1

Back to TopTop