Applied System Innovation

Editorial

Jump to: Research

3 pages, 209 KiB

Open AccessEditorial

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 6 | Viewed by 2031

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

20 pages, 3225 KiB

Open AccessArticle

Interdepartmental Optimization in Steel Manufacturing: An Artificial Intelligence Approach for Enhancing Decision-Making and Quality Control

by José M. Bernárdez, Jonathan Boo, José I. Díaz and Roberto Medina

Appl. Syst. Innov. 2025, 8(3), 63; https://doi.org/10.3390/asi8030063 - 4 May 2025

Viewed by 251

Abstract

Recent advances in artificial intelligence have intensified efforts to improve quality management in steel manufacturing. In this paper, we present the development and results of a system that aims to learn from the decisions made by experts to anticipate the problems that affect [...] Read more.

Recent advances in artificial intelligence have intensified efforts to improve quality management in steel manufacturing. In this paper, we present the development and results of a system that aims to learn from the decisions made by experts to anticipate the problems that affect the final quality of the product in the steel rolling process. The system integrates a series of modules, including event filtering, automatic expert knowledge extraction, and decision-making neural networks, developed in a phased approach. The experimental results, using a three-year historical dataset, suggest that our system can anticipate quality issues with an accuracy of approximately 80%, enabling proactive defect prevention and a reduction in production losses. This approach demonstrates the potential for industrial AI applications for predictive quality assurance, highlighting the technical foundations and potential for industrial applications. Full article

(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)

► Show Figures

Figure 1

35 pages, 5077 KiB

Open AccessArticle

Data Management Maturity Model—Process Dimensions and Capabilities to Leverage Data-Driven Organizations Towards Industry 5.0

by Lara Pörtner, Andreas Riel, Benedikt Schmidt, Marcel Leclaire and Robert Möske

Appl. Syst. Innov. 2025, 8(2), 41; https://doi.org/10.3390/asi8020041 - 21 Mar 2025

Viewed by 899

Abstract

Data-driven organizations aim to control business decisions based on data. However, despite significant investments in digitalization, studies show that many organizations continue to face challenges in fully realizing the benefits of data. Existing maturity models for digital transformation, data management, and data-driven organizations [...] Read more.

Data-driven organizations aim to control business decisions based on data. However, despite significant investments in digitalization, studies show that many organizations continue to face challenges in fully realizing the benefits of data. Existing maturity models for digital transformation, data management, and data-driven organizations lack a comprehensive, industry-agnostic, and practically validated approach to addressing industry challenges. This work introduces a refined data management maturity model developed using De Bruin’s maturity model assessment methodology. The model aims to incorporate all key elements of a data-driven organization, emphasizing the interdependencies required to evaluate maturity levels and provide targeted recommendations for addressing data-related challenges during the transition to a data-driven organization. An initial validation with 31 industry experts confirmed the model’s feasibility and practical applicability. As the next step, we plan to validate the model further by deploying the full questionnaire and deriving the maturity of each process dimension, along with its weighting, through assessments with industry partners from various sectors, including automotive, aviation, consumer goods/manufacturing, pharma, and media. Preliminary findings also underscored the importance of a deeper focus on the organization dimension, particularly in the context of Industry 5.0. Future research will refine the model through iterative development phases to address this critical area. Full article

(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)

► Show Figures

Figure 1

25 pages, 4205 KiB

Open AccessArticle

A Real-Time Human–Machine–Logistics Collaborative Scheduling Method Considering Workers’ Learning and Forgetting Effects

by Wenchao Yang, Sen Li, Guofu Luo, Hao Li and Xiaoyu Wen

Appl. Syst. Innov. 2025, 8(2), 40; https://doi.org/10.3390/asi8020040 - 18 Mar 2025

Viewed by 521

Abstract

In the era of Industry 5.0, human-centric manufacturing necessitates deep integration between workers and intelligent workshop scheduling systems. However, the inherent variability in worker efficiency due to learning and forgetting effects poses challenges to human–machine–logistics collaboration, thereby complicating multi-resource scheduling in smart workshops. [...] Read more.

In the era of Industry 5.0, human-centric manufacturing necessitates deep integration between workers and intelligent workshop scheduling systems. However, the inherent variability in worker efficiency due to learning and forgetting effects poses challenges to human–machine–logistics collaboration, thereby complicating multi-resource scheduling in smart workshops. To address these challenges, this study proposes a real-time task-driven human–machine–logistics collaborative framework designed to enhance multi-resource coordination in smart workshops. First, the framework incorporates a learning-forgetting model to dynamically assess worker efficiency, enabling real-time adjustments to human–machine–logistics resource states. Second, a task-driven self-organizing approach is introduced, allowing human, machine, and logistics resources to form adaptive groups based on task requirements. Third, a task slack-based matching method is developed to facilitate real-time, adaptive allocation of tasks to resource groups. Finally, the proposed method is validated through an engineering case study, demonstrating its effectiveness across different order scales. Experimental results indicate that, on average, completion time is reduced by no less than 10%, energy consumption decreases by at least 8%, and delay time is reduced by over 70%. These findings confirm the effectiveness and adaptability of the proposed method in highly dynamic, multi-resource production environments. Full article

(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)

► Show Figures

Figure 1

23 pages, 7518 KiB

Open AccessArticle

Viable and Sustainable Model for Adoption of New Technologies in Industry 4.0 and 5.0: Case Study on Pellet Manufacturing

by Pavel Solano García, Ana Gabriela Ramírez-Gutiérrez, Oswaldo Morales Matamoros and Ana Lilia Coria Páez

Appl. Syst. Innov. 2025, 8(1), 14; https://doi.org/10.3390/asi8010014 - 17 Jan 2025

Cited by 1 | Viewed by 784

Abstract

This manuscript presents the development and testing of a novel model designed to help organizations, particularly small and medium-sized enterprises (SMEs), address the challenges of integrating new technologies within the frameworks of Industry 4.0 and 5.0. The proposed model is a metamodel that [...] Read more.

This manuscript presents the development and testing of a novel model designed to help organizations, particularly small and medium-sized enterprises (SMEs), address the challenges of integrating new technologies within the frameworks of Industry 4.0 and 5.0. The proposed model is a metamodel that evaluates organizational and contextual vulnerabilities concerning both existing technologies and potential external technologies under consideration for adoption. It synthesizes three foundational frameworks: the Viable System Model (VSM), the principles of viable and sustainable systems, and the Technology, Organization, and Environment (TOE) Model. The findings demonstrate the practical applicability of this model in an SME context, showcasing its ability to facilitate the gradual and sustainable adoption of new technologies. By aligning business needs with technological solutions and leveraging insights from computer science and organizational cybernetics, the model adapts to varying levels of technological adoption, integrating organizational dynamics and business evolution to support the implementation of emerging technologies. Full article

(This article belongs to the Special Issue New Challenges of Innovation, Sustainability, Resilience in X.0 Era)

► Show Figures

Figure 1

19 pages, 1076 KiB

Open AccessArticle

Green Spare Parts Evaluation for Hybrid Warehousing and On-Demand Manufacturing

by Idriss El-Thalji

Appl. Syst. Innov. 2025, 8(1), 8; https://doi.org/10.3390/asi8010008 - 3 Jan 2025

Viewed by 1191

Abstract

Additive manufacturing and digital warehouses are transforming the way industries manage and maintain their spare parts inventory. Considering digital warehouses and on-demand manufacturing for spare parts during the project phase is a strategic decision that involves trade-offs depending on the operational needs and [...] Read more.

Additive manufacturing and digital warehouses are transforming the way industries manage and maintain their spare parts inventory. Considering digital warehouses and on-demand manufacturing for spare parts during the project phase is a strategic decision that involves trade-offs depending on the operational needs and pricing structure. This paper aims to explore the spare part evaluation process considering both physical and digital warehouse inventories. A case asset is purposefully selected and four spare part management concepts are studied using a simulation modeling approach. The results highlight that the relevant digital warehouse scenario, used in this case, managed to completely reduce all emissions related to global spare parts supply; however, this was at the expense of reducing availability by 15.1%. However, the hybrid warehouse scenario managed to increase availability by 11.5% while completely reducing all emissions related to global spare parts supply. Depending on the demand rate, the digital warehousing may not be sufficient alone to keep the production availability at the highest levels; however, it is effective in reducing the stock amount, simplifying the inventory management, and making the supply process more green and resilient. A generic estimation model for spare parts engineers is provided to determine the optimal specifications of their spare parts supply and inventory while considering digital warehouses and on-demand manufacturing. 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, 7936 KiB

Open AccessArticle

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 1282

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

Open AccessArticle

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 1828

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

Open AccessArticle

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 4 | Viewed by 1613

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

Open AccessArticle

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 1245

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

Open AccessArticle

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 11 | Viewed by 6660

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

Open AccessArticle

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 3 | Viewed by 1904

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

Open AccessArticle

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 3 | Viewed by 2548

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

Journal Menu

Journal Browser

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

Share This Special Issue

Special Issue Editors

Special Issue Information

Benefits of Publishing in a Special Issue

Published Papers (13 papers)

Editorial

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI