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Applied Sciences
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Advances in Smart Production & Logistics
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Advances in Smart Production & Logistics

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 11094

Special Issue Editors

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
Special Issues, Collections and Topics in MDPI journals
Dr. João Reis

E-Mail Website
Guest Editor
Industrial Engineering and Management, Faculty of Engineering, Lusofona University and EIGeS, Campo Grande, 1749-024 Lisbon, Portugal
Interests: smart logistics; smart manufacturing; sustainable operations; sustainable management
Special Issues, Collections and Topics in MDPI journals
Dr. Shai Rozenes

E-Mail Website
Guest Editor
Faculty of Industrial Engineering and Technology Management, HIT Holon Institute of Technology, 52 Golomb Street, POB 305, Holon 5810201, Israel
Interests: service systems; project management; operations management; system dynamics

Special Issue Information

Dear Colleagues,

The intensive digitalization of production and logistics processes in recent years has brought with it heavy integration of artificial intelligence (AI) technologies and smart methods. Both AI technologies and the smart methods utilize the huge amounts of data and information that are profusely available through various digitalization projects. Many aspects of modern production and logistics are being progressively automated, opening opportunities for experimentation with big data and AI applications across a variety of sectors.

The Special Issue on “Advances in Smart Production and Logistics” welcomes submissions of recent research work on both production and logistics areas for artificial intelligence, and smart and advanced methods. The call is open to a broad thematic range of papers across manufacturing, production and logistics businesses for overcoming challenges in automation and decision making. The paper topics cover the recent applications of big data, AI, smart robotics and cobots, smart diagnosis, prognosis and healing, smart uses of computer vision, speech recognition, natural language processing (NLP), and other smart methods. Papers should report novel advances and research trends aiming to offer to readers knowledge for extending the adoption of AI in production and logistics systems, and inspiring managerial decision making and engineering innovation in the field.

The list of topics includes but is not limited to:

  • Intelligent control and support systems for manufacturing decision making
  • Intelligent control and support systems for logistics and supply chain decisions
  • Collaborative robotics (cobots) and smart robotics
  • Big data analytics for manufacturing and logistics systems and processes
  • Smart logistics and intelligence-based optimization and planning methods
  • Big data for logistics optimization
  • Machine learning applications in manufacturing, logistics, and supply chains
  • Digital twin modeling and decision making in the Industry 4.0 era
  • Logistics 4.0 and Smart Supply Chain (LSSC)
  • Smart application of machine vision
  • Smart routing and intelligent logistic transportation systems (ITS))
  • Smart maintenance with predictive and prescriptive capabilities
  • Data-driven and model-based prognostics
  • Prognostics and health management in smart manufacturing systems
  • Artificial intelligence for manufacturing processes
  • Fusion of sensor information for manufacturing processes
  • Smart quality assurance and intelligent inspection
  • Virtualization and simulation techniques for manufacturing decision making
  • Self-configuration, self-diagnosis, and IoT methods for shop floors
  • Self-optimization models for scheduling for shop floor operations
  • Blockchain technology, smart contracts, and traceability in the supply chain
  • Autonomy, autonomous vehicles, and drones
  • Human-machine collaboration (HMC) and smart human-machine interface

Dr. Yuval Cohen
Dr. João Reis
Dr. Shai Rozenes
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 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 2400 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.

Published Papers (4 papers)

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Research

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18 pages, 1871 KiB  
Article
Data Mining and Augmented Reality: An Application to the Fashion Industry
by Virginia Fani, Sara Antomarioni, Romeo Bandinelli and Filippo Emanuele Ciarapica
Appl. Sci. 2023, 13(4), 2317; https://doi.org/10.3390/app13042317 - 10 Feb 2023
Cited by 4 | Viewed by 2149
Abstract
The wider implementation of Industry 4.0 technologies in several sectors is increasing the amount of data regularly collected by companies. Those unstructured data need to be quickly elaborated to make on-time decisions, and the information extracted needs to be clearly visualized to speed [...] Read more.
The wider implementation of Industry 4.0 technologies in several sectors is increasing the amount of data regularly collected by companies. Those unstructured data need to be quickly elaborated to make on-time decisions, and the information extracted needs to be clearly visualized to speed up operations. This is strongly perceived in the quality field, where effective management of the trade-off between increasing quality controls to intercept product defects and decreasing them to reduce the delivery time represents a competitive challenge. A framework to improve data analysis and visualization in quality management is proposed, and its applicability is demonstrated with a case study in the fashion industry. A questionnaire assesses its on-field usability. The main findings refer to overcoming the lack in the literature of a decision support framework based on the joint application of association rules mining and augmented reality. The successful implementation in a real scenario has a twofold aim: on the one hand, sample sizes are strategically revised according to the supplier performance per product category and material; on the other hand, the daily quality controls are speeded up through accurate suggestions about the most occurrent defect and location per product characteristics, integrated with extra tips only for trainees. Full article
(This article belongs to the Special Issue Advances in Smart Production & Logistics)
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13 pages, 1988 KiB  
Article
New Framework for Complex Assembly Digitalization and Traceability Using Bill of Assembly and Smart Contracts
by Yuval Cohen and Shai Rozenes
Appl. Sci. 2023, 13(3), 1884; https://doi.org/10.3390/app13031884 - 1 Feb 2023
Cited by 2 | Viewed by 1443
Abstract
This paper proposes a framework to automate the generation of traceable and protected documentation of complex assembly processes. The final assembly in aviation, automotive, and appliances industries is a rigorous process that has limited capabilities of full traceability associated with: (1) the parts [...] Read more.
This paper proposes a framework to automate the generation of traceable and protected documentation of complex assembly processes. The final assembly in aviation, automotive, and appliances industries is a rigorous process that has limited capabilities of full traceability associated with: (1) the parts installed, (2) their fabrication processes, and (3) the assembly work. This is also the case for each of its sub-assemblies. The thousands of parts forming a hierarchy of sub-assemblies that are dynamically accumulated to compose the final assembly make full traceability a challenging feat that is almost unsurmountable. Such full traceability along the entire supply chain requires considerable cost and effort since it must be based on documentation of most assembled parts, assembly tasks, and inspection tasks that compose the full assembled product. In addition, security measures are needed to prevent hostile hacking and unauthorized approach to the assembly documentation throughout the entire supply chain. The related documentation and repeated verifications require considerable effort and have many chances for human errors. So, automating these processes has great value. This article expounds a framework that harnesses blockchain and smart-contract technology to offer automated traceable and protected documentation of the assembly process. For this purpose, we expand the concept of a Bill-Of-Assembly (BOA) to incorporate data from the bill of materials (BOM), the associated assembly activities, the associated activities’ specification parameters and materials, and the associated assembly resources (machines and/or operators). The paper defines the operation of the BOA with blockchain and smart-contract technology, for attaining full traceability, safety, and security, for the entire assembled product. Future research could extend the proposed approach to facilitate the usage of the BOA data structure in constructing a digital twin of the entire simulated system. Full article
(This article belongs to the Special Issue Advances in Smart Production & Logistics)
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20 pages, 731 KiB  
Article
Industrial Performance Measurement Systems Coherence: A Comparative Analysis of Current Methodologies, Validation and Introduction to Key Activity Indicators
by Italo Cesidio Fantozzi, Sebastiano Di Luozzo and Massimiliano Maria Schiraldi
Appl. Sci. 2023, 13(1), 235; https://doi.org/10.3390/app13010235 - 24 Dec 2022
Cited by 1 | Viewed by 1784
Abstract
This paper discusses and integrates the concept of complexity in the industrial performance measurement and management systems (PMM) theory, providing a comprehensive overview of the different methodologies used within the decision systems research area. It also discusses the importance of introducing Key Activity [...] Read more.
This paper discusses and integrates the concept of complexity in the industrial performance measurement and management systems (PMM) theory, providing a comprehensive overview of the different methodologies used within the decision systems research area. It also discusses the importance of introducing Key Activity Indicators (KAI) within PMM, specifically related to the Operations and Supply Chain management research and industrial areas. Moreover, it provides validation of the methodology through a case study concerning the production environment of a multinational pharmaceutical company. The main research objective is to design appropriate industrial PMM systems with the aim of increasing the industrial efficiency and effectiveness of manufacturing and service organizations. An analysis of the central industrial performance measurement systems design methods is conducted, classifying them into macro-categories and conducting a comparative study. Based on the analysis of the different proposed methods, organisations will be able to choose the best one based on their needs to design effective decision systems. The research work allows organisations to evaluate, assess, and design effective industrial performance measurement systems. Moreover, the proposed methodology can be easily integrated within an Industry 4.0 context, and benefit from the digitalization environment to obtain continuous feedback on the effectiveness of the industrial PMM. Full article
(This article belongs to the Special Issue Advances in Smart Production & Logistics)
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Review

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24 pages, 9936 KiB  
Review
Industry 4.0 and Lean Six Sigma Integration: A Systematic Review of Barriers and Enablers
by Jaime Macias-Aguayo, Lizzi Garcia-Castro, Kleber F. Barcia, Duncan McFarlane and Jorge Abad-Moran
Appl. Sci. 2022, 12(22), 11321; https://doi.org/10.3390/app122211321 - 8 Nov 2022
Cited by 10 | Viewed by 4672
Abstract
In recent years, Industry 4.0 (I4.0) has been a recurrent theme in the literature on Lean Six Sigma (LSS), given the synergies that can arise from their combination. However, their joint implementation presents several challenges. In this article, a systematic literature review (SLR) [...] Read more.
In recent years, Industry 4.0 (I4.0) has been a recurrent theme in the literature on Lean Six Sigma (LSS), given the synergies that can arise from their combination. However, their joint implementation presents several challenges. In this article, a systematic literature review (SLR) of research on I4.0 and LSS integration was performed. This review involved five database platforms and included seventy-four articles providing state-of-the-art knowledge on the topic, focusing on the barriers to and enablers of integration. As a result, 20 integration barriers were identified, highlighting the high implementation cost, long learning curve, and technology incompatibility as the main barriers. Seventeen enablers were found to facilitate and guarantee implementation success, highlighting investment in IT infrastructure and employee training, stakeholder involvement, and top management support. In addition, the article discusses actions to facilitate I4.0 and LSS integration in practice, determined by connecting the identified enablers to their corresponding barriers. Finally, the SLR identifies several avenues for future research. Full article
(This article belongs to the Special Issue Advances in Smart Production & Logistics)
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