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Applied Sciences
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Design and Management of Serviceability Products and Systems, Smart Industrial...
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Design and Management of Serviceability Products and Systems, Smart Industrial and Manufacturing Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Industrial Technologies".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 20047

Special Issue Editor

Prof. Dr. Paweł Sitek

E-Mail Website
Guest Editor
Department of Control and Management Systems, Kielce University of Technology, 25-314 Kielce, Poland
Interests: constraint programming; operation research; optimization; decision support; logistics; manufacturing; supply chain management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The need to increase efficiency and productivity as well as flexibility and reliability in production has led to a revolution in the field of manufacturing. The use of modern production methods and tools as well as IT and AI solutions has resulted in the emergence of smart manufacturing and, in general, a new industrial philosophy, conventionally called Industry 4.0. On the other hand, manufacturers, designers, and consumers pay more and more attention to serviceability/maintainability products and systems, environmental aspects, and social issues, as well as the production and distribution processes themselves. An important element of this revolution is also to redefine the function and meaning of the human factor.

For this Special Issue titled “Design and Management of Serviceability Products and Systems, Smart Industrial and Manufacturing”, we invite authors to submit articles that take up the discussion and present solutions in the field of design and development of products, systems, and production and distribution processes that take into account serviceability/maintainability. The proposed subject of this Special Issue also includes the use of smart technologies such as cloud computing, Internet of Things (IoT), cyberphysical systems (CPS), intelligent vision systems, etc. Including the human factor in the abovementioned areas in the submitted articles is also welcome.

Prof. Dr. Paweł Sitek
Guest Editor

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.

Keywords

  • Serviceability products and systems
  • Industry 4.0
  • Smart manufacturing and distribution systems
  • Smart technology and cyber physical systems (CPS)
  • Internet of Things (IoT)
  • Intelligent vision systems
  • AI-driven approach to design, servicing, and production processes
  • The human factor in design, servicing, and production processes

Published Papers (8 papers)

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Research

16 pages, 1694 KiB  
Article
Enhancing Antifragile Performance of Manufacturing Systems through Predictive Maintenance
by Oana Chenaru, Stefan Mocanu, Radu Dobrescu and Maximilian Nicolae
Appl. Sci. 2022, 12(23), 11958; https://0-doi-org.brum.beds.ac.uk/10.3390/app122311958 - 23 Nov 2022
Cited by 1 | Viewed by 1549
Abstract
Antifragility was introduced as a term no later than 10 years ago. As presented by Taleb, antifragility means that a system becomes more resilient and more robust with every harmful and/or stressful action it is confronted with. This paper is based on a [...] Read more.
Antifragility was introduced as a term no later than 10 years ago. As presented by Taleb, antifragility means that a system becomes more resilient and more robust with every harmful and/or stressful action it is confronted with. This paper is based on a study which aimed to use the concept of antifragility during the design stage of a self-improving system. This way, it is expected to obtain a fast adaptive system capable of functioning at optimal parameters even when it works under adverse conditions or faces unforeseen changes in the environment. Assuming that an antifragile system not only maintains its robust behavior when faced with stressful and harmful events but even benefits from them to optimize its performance, the paper offers a detailed description of the features that must be ensured when designing a self-improving antifragile manufacturing system. By ensuring the property of antifragility, complex manufacturing systems are much safer to exploit under uncertain conditions, which brings major benefits to the process management. Starting from consecrated solutions such as preventive maintenance (PvM) and predictive maintenance (PdM) and using techniques of artificial intelligence, we present the concept of antifragile maintenance (AfM). Full article
(This article belongs to the Special Issue Design and Management of Serviceability Products and Systems, Smart Industrial and Manufacturing Systems)
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27 pages, 2256 KiB  
Article
Two-Stage Adaptive Large Neighbourhood Search for Team Formation and Worker Assignment Problems in Cellular Manufacturing Systems
by Thanatat Pasupa and Sadami Suzuki
Appl. Sci. 2022, 12(16), 8323; https://0-doi-org.brum.beds.ac.uk/10.3390/app12168323 - 19 Aug 2022
Viewed by 1573
Abstract
We present a novel mathematical programming model to address a team-oriented worker assignment problem, called the team formation and worker assignment problem (TFWAP). The model establishes a multi-skilled team with high group cohesion to meet cell operational requirements. To this end, we developed [...] Read more.
We present a novel mathematical programming model to address a team-oriented worker assignment problem, called the team formation and worker assignment problem (TFWAP). The model establishes a multi-skilled team with high group cohesion to meet cell operational requirements. To this end, we developed a two-stage decision methodology based on an adaptive large neighbourhood search (ALNS) method as a solution approach. The first stage was a team formation problem that maximised workers’ skills. The second stage was a worker assignment problem that minimised the total inventory level and variations in the average cell worker’s idle time. The performance of the two-stage ALNS method was assessed on ten cell formation benchmarks selected from the literature. The computational results show that the two-stage ALNS method could provide a solution equivalent to the exact method based on the heuristic-based brute force search (HBBFS) for small instances in the team formation stage. Moreover, the two-stage ALNS method outperformed the non-dominated sorting genetic algorithm-II (NSGA-II)-based single-stage decision methodology on all ten cell formation benchmarks in the worker assignment stage. Finally, the two-way analysis of variance (ANOVA) test highlighted the impact of the cell-cohesion requirement on performance when forming a team in a cell. Full article
(This article belongs to the Special Issue Design and Management of Serviceability Products and Systems, Smart Industrial and Manufacturing Systems)
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31 pages, 13277 KiB  
Article
Design and Implementation of an HCPS-Based PCB Smart Factory System for Next-Generation Intelligent Manufacturing
by Jinyoub Kim, Dongjoon Seo, Jisang Moon, Juhee Kim, Hayul Kim and Jongpil Jeong
Appl. Sci. 2022, 12(15), 7645; https://0-doi-org.brum.beds.ac.uk/10.3390/app12157645 - 29 Jul 2022
Cited by 6 | Viewed by 2372
Abstract
The next-generation intelligent smart factory system that is proposed in this paper could improve product quality and realize flexible, efficient, and sustainable product manufacturing by comprehensively improving production and management innovation via its digital network and intelligent methods that reflect the characteristics of [...] Read more.
The next-generation intelligent smart factory system that is proposed in this paper could improve product quality and realize flexible, efficient, and sustainable product manufacturing by comprehensively improving production and management innovation via its digital network and intelligent methods that reflect the characteristics of its printed circuit board (PCB) manufacturing design and on-site implementation. Intelligent manufacturing systems are complex systems that are composed of humans, cyber systems, and physical systems and aim to achieve specific manufacturing goals at an optimized level. Advanced manufacturing technology and next-generation artificial intelligence (AI) are deeply integrated into next-generation intelligent manufacturing (NGIM). Currently, the majority of PCB manufacturers are firms that specialize in processing orders from leading semiconductor and related product manufacturers, such as Samsung Electronics, TSMC, Samsung Electro-Mechanics, and LG Electronics. These top companies have been responsible for all product innovation, intelligent services, and system integration, with PCB manufacturers primarily playing a role in intelligent production and system integration. In this study, the main implementation areas were divided into manufacturing execution system (MES) implementation (which could operate the system using system integration), data gathering, the Industrial Internet of Things (IIoT) for production line connection, AI and real-time monitoring, and system implementation that could visualize the collected data. Finally, the prospects of the design and on-site implementation of the next-generation intelligent smart factory system that detects and controls the occurrence of quality and facility abnormalities are presented, based on the implementation system. Full article
(This article belongs to the Special Issue Design and Management of Serviceability Products and Systems, Smart Industrial and Manufacturing Systems)
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21 pages, 4923 KiB  
Article
A New Approach to the Allocation of Multidimensional Resources in Production Processes
by Jarosław Wikarek and Paweł Sitek
Appl. Sci. 2022, 12(14), 6933; https://0-doi-org.brum.beds.ac.uk/10.3390/app12146933 - 08 Jul 2022
Cited by 3 | Viewed by 1093
Abstract
Modern technologies in the field of automation, robotics and IT have significantly changed the face of modern production systems. In particular, the use of AVG, PLC, mobile robots, RFID, IoT, etc. results in modern production processes being characterized by, among others, shortened production [...] Read more.
Modern technologies in the field of automation, robotics and IT have significantly changed the face of modern production systems. In particular, the use of AVG, PLC, mobile robots, RFID, IoT, etc. results in modern production processes being characterized by, among others, shortened production cycles and supply chains, reduced production costs, increased product quality and reliability, etc. Moreover, the application of these technologies requires a new definition and methods of using production resources. Most often these are resources that are characterized by many functionalities, the so-called multidimensional resources, which can be configured, remotely controlled, updated, etc., and their use in many cases enables the self-optimization and self-organization of the production system. The article presents the problem of allocation and control of multidimensional resources in production processes. The proprietary formal model of the problem is proposed, as well as how to use it in both proactive and reactive modes. A procedure for reducing the size of the modeled problem is also proposed, the use of which enables a two-fold reduction in the number of constraints and even a fifty-fold reduction in the number of decision variables of the proposed model. This results in an almost hundredfold reduction in computation time for the considered data instances. An original hybrid approach is used to implement the model, which enables the integration of mathematical programming (MP) and programming in constrained logic (CLP). Model data and parameters have been saved as facts. Full article
(This article belongs to the Special Issue Design and Management of Serviceability Products and Systems, Smart Industrial and Manufacturing Systems)
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17 pages, 1160 KiB  
Article
Reducing the Total Product Cost at the Product Design Stage
by Marcin Relich, Izabela Nielsen and Arkadiusz Gola
Appl. Sci. 2022, 12(4), 1921; https://0-doi-org.brum.beds.ac.uk/10.3390/app12041921 - 12 Feb 2022
Cited by 13 | Viewed by 3244
Abstract
Currently used decision support systems allow decision-makers to evaluate the product performance, including a net present value analysis, in order to enable them to make a decision regarding whether or not to carry out a new product development project. However, these solutions are [...] Read more.
Currently used decision support systems allow decision-makers to evaluate the product performance, including a net present value analysis, in order to enable them to make a decision regarding whether or not to carry out a new product development project. However, these solutions are inadequate to provide simulations for verifying a possibility of reducing the total product cost through changes in the product design phase. The proposed approach provides a framework for identifying possible variants of changes in product design that can reduce the cost related to the production and after-sales phase. This paper is concerned with using business analytics to cost estimation and simulation regarding changes in product design. The cost of a new product is estimated using analogical and parametric models that base on artificial neural networks. Relationships identified by computational intelligence are used to prepare cost estimation and simulations. A model of product development, production process, and admissible resources is described in terms of a constraint satisfaction problem that is effectively solved using constraint programming techniques. The proposed method enables the selection of a more appropriate technique to cost estimation, the identification of a set of possible changes in product design towards reducing the total product cost, and it is the framework for developing a decision support system. In this aspect, it outperforms current methods dedicated for evaluating the potential of a new product. Full article
(This article belongs to the Special Issue Design and Management of Serviceability Products and Systems, Smart Industrial and Manufacturing Systems)
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17 pages, 1977 KiB  
Article
Optimizing Inventory Replenishment for Seasonal Demand with Discrete Delivery Times
by Mohammed Alnahhal, Diane Ahrens and Bashir Salah
Appl. Sci. 2021, 11(23), 11210; https://0-doi-org.brum.beds.ac.uk/10.3390/app112311210 - 25 Nov 2021
Cited by 1 | Viewed by 2017
Abstract
This study investigates replenishment planning in the case of discrete delivery time, where demand is seasonal. The study is motivated by a case study of a soft drinks company in Germany, where data concerning demand are obtained for a whole year. The investigation [...] Read more.
This study investigates replenishment planning in the case of discrete delivery time, where demand is seasonal. The study is motivated by a case study of a soft drinks company in Germany, where data concerning demand are obtained for a whole year. The investigation focused on one type of apple juice that experiences a peak in demand during the summer. The lot-sizing problem reduces the ordering and the total inventory holding costs using a mixed-integer programming (MIP) model. Both the lot size and cycle time are variable over the planning horizon. To obtain results faster, a dynamic programming (DP) model was developed, and run using R software. The model was run every week to update the plan according to the current inventory size. The DP model was run on a personal computer 35 times to represent dynamic planning. The CPU time was only a few seconds. Results showed that initial planning is difficult to follow, especially after week 30, and the service level was only 92%. Dynamic planning reached a higher service level of 100%. This study is the first to investigate discrete delivery times, opening the door for further investigations in the future in other industries. Full article
(This article belongs to the Special Issue Design and Management of Serviceability Products and Systems, Smart Industrial and Manufacturing Systems)
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12 pages, 2378 KiB  
Article
Algorithmic Method for the Design of Sequential Circuits with the Use of Logic Elements
by Adam Szcześniak and Zbigniew Szcześniak
Appl. Sci. 2021, 11(23), 11100; https://0-doi-org.brum.beds.ac.uk/10.3390/app112311100 - 23 Nov 2021
Cited by 5 | Viewed by 1955
Abstract
This article presents issues related to the design of sequential control systems. The algorithmic design method of sequential control systems is discussed, which allows the design of a diagram of any sequential system. The algorithmic method uses the description in the form of [...] Read more.
This article presents issues related to the design of sequential control systems. The algorithmic design method of sequential control systems is discussed, which allows the design of a diagram of any sequential system. The algorithmic method uses the description in the form of a connection formula. The connection formula defines the order of actuations of driver elements, in this case actuators. The algorithmic method is used, among others, for systems with actuators cooperating with distributors controlled electrically on both sides. The process of creating a system graph has been characterized. The operation of the system has been shown graphically. On the basis of the created graph describing the functions of signal processing, a method for rapid programming of sequential electro-pneumatic systems with the use of logic elements has been provided. A separate dedicated timing unit has been used to perform memory functions. Its operation is based on successive states, in such a way that the next state deletes the previous one. Graph-based systems have been validated through simulation using Festo’s FluidSim computer-aided design software. Full article
(This article belongs to the Special Issue Design and Management of Serviceability Products and Systems, Smart Industrial and Manufacturing Systems)
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15 pages, 1018 KiB  
Article
Dynamic Lead-Time Forecasting Using Machine Learning in a Make-to-Order Supply Chain
by Mohammed Alnahhal, Diane Ahrens and Bashir Salah
Appl. Sci. 2021, 11(21), 10105; https://0-doi-org.brum.beds.ac.uk/10.3390/app112110105 - 28 Oct 2021
Cited by 8 | Viewed by 4913
Abstract
This paper investigates the dynamic forecasting of lead-time, which can be performed by a logistics company for optimizing temporal shipment consolidation. Shipment consolidation is usually utilized to reduce outbound shipments costs, but it can increase the lead time. Forecasting in this paper is [...] Read more.
This paper investigates the dynamic forecasting of lead-time, which can be performed by a logistics company for optimizing temporal shipment consolidation. Shipment consolidation is usually utilized to reduce outbound shipments costs, but it can increase the lead time. Forecasting in this paper is performed in a make-to-order supply chain using real data, where the logistics company does not know the internal production data of manufacturers. Forecasting was performed in several steps using machine-learning methods such as linear regression and logistic regression. The last step checks if the order will come in the next delivery week or not. Forecasting is evaluated after each shipment delivery to check the possibility of delaying the current arriving orders for a certain customer until the next week or making the delivery to the customer immediately. The results showed reasonable accuracy expressed in different ways, and one of them depends on a type I error with an average value of 0.07. This is the first paper that performs dynamic forecasting for the purpose of shipment temporal consolidation optimization in the consolidation center. Full article
(This article belongs to the Special Issue Design and Management of Serviceability Products and Systems, Smart Industrial and Manufacturing Systems)
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