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Sustainable and Agile Manufacturing in the Era of Industry 4.0: Application of Simulation, Optimization, Lean, and Emerging Digital Technologies

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 44028

Special Issue Editor

Assoc. Prof. Dr. Masood Fathi

E-Mail Website
Guest Editor
Department of Production & Automation Engineering, School of Engineering Science, University of Skövde, P.O.Box 408, SE-541 28, Skövde, Sweden
Interests: operations management; operations research; production and logistics optimization; heuristic and metaheuristic algorithms; mathematical modeling; simulation-based optimization; production planning and scheduling; supply chain management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the Industry 4.0 era, the manufacturing industry is continuously challenged by the market to improve product quality, increase production efficiency, ensure safety, and enhance sustainability while maintaining agility in manufacturing products and satisfying customer demand. Lack of agility or manufacturing inflexibility can result in time-consuming product changes that slow time-to-market and limit the company’s ability to compete. Digital technologies such as smart wearables, or augmented/virtual/mixed reality are undoubtedly the driving force behind manufacturing agility through making the right information accessible at the right time. Moreover, informed decision making as well as smart production planning and scheduling drive manufacturing agility in the Industry 4.0 context. An intelligent production planning, scheduling, and control system have an incredibly important role in collecting, processing, and inferring the industrial data for making informed and right decisions instantaneously to deal with environmental uncertainties, and to further enable and support manufacturing agility. The efficient, flexible, and reliable logistics system is also an indispensable part of sustainable and agile manufacturing. Optimization methods that can provide flexible optimal configuration and operations allow manufacturers to change from one product variant to another quickly or even switch to a new product within a short time, conditions that are strongly needed in the era of Industry 4.0. Simulation and digital twins also play a significant role in directing the manufacturing industry toward sustainability through the realization of the virtual factory concept where technologies and changes can be tested at low cost and in a virtual environment before implementation. The lean manufacturing philosophy and its improvement tools and concepts should also not be forgotten as the cornerstone of sustainable production.

The main aim of this Special Issue is to publish a collection of high-quality research papers on sustainable and agile manufacturing, including both review and original research articles. 

Assoc. Prof. Dr. Masood Fathi
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. Sustainability 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

  • Manufacturing digitalization
  • Sustainable production
  • Smart logistics
  • Agile and intelligent manufacturing
  • Optimization
  • Augmented/virtual/mixed reality
  • Lean manufacturing
  • Simulation and digital twin
  • Industry 4.0 technologies
  • Production planning and scheduling

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Published Papers (6 papers)

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Research

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21 pages, 1221 KiB  
Article
Reframing Demand Forecasting: A Two-Fold Approach for Lumpy and Intermittent Demand
by Jože Martin Rožanec, Blaž Fortuna and Dunja Mladenić
Sustainability 2022, 14(15), 9295; https://doi.org/10.3390/su14159295 - 29 Jul 2022
Cited by 12 | Viewed by 5463
Abstract
Demand forecasting is a crucial component of demand management. While shortening the forecasting horizon allows for more recent data and less uncertainty, this frequently means lower data aggregation levels and a more significant data sparsity. Furthermore, sparse demand data usually result in lumpy [...] Read more.
Demand forecasting is a crucial component of demand management. While shortening the forecasting horizon allows for more recent data and less uncertainty, this frequently means lower data aggregation levels and a more significant data sparsity. Furthermore, sparse demand data usually result in lumpy or intermittent demand patterns with irregular demand intervals. The usual statistical and machine learning models fail to provide good forecasts in such scenarios. Our research confirms that competitive demand forecasts can be obtained through two models: predicting the demand occurrence and estimating the demand size. We analyze the usage of local and global machine learning models for both cases and compare the results against baseline methods. Finally, we propose a novel evaluation criterion for the performance of lumpy and intermittent demand forecasting models. Our research shows that global classification models are the best choice when predicting demand event occurrence. We achieved the best results using the simple exponential smoothing forecast to predict demand sizes. We tested our approach on real-world data made up of 516 time series corresponding to the daily demand, over three years, of a European original automotive equipment manufacturer. Full article
(This article belongs to the Special Issue Sustainable and Agile Manufacturing in the Era of Industry 4.0: Application of Simulation, Optimization, Lean, and Emerging Digital Technologies)
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17 pages, 702 KiB  
Article
Selection of a Wheat Harvester according to Qualitative and Quantitative Criteria
by Hadi Lalghorbani and Ali Jahan
Sustainability 2022, 14(3), 1313; https://doi.org/10.3390/su14031313 - 24 Jan 2022
Cited by 1 | Viewed by 2479
Abstract
With the development of technology and the expansion of agricultural machinery diversity, the need for an appropriate group decision-making system has arisen. The increasing number of criteria and alternatives complicates the decision-making process. Moreover, the uncertainty in the data leads to more complexity [...] Read more.
With the development of technology and the expansion of agricultural machinery diversity, the need for an appropriate group decision-making system has arisen. The increasing number of criteria and alternatives complicates the decision-making process. Moreover, the uncertainty in the data leads to more complexity in the decision. To select a wheat combine, multiple quantitative criteria were considered, such as the grain tank, rated horsepower, speed draining, and cleaning, along with qualitative criteria, including the level of harvest or harvest losses, fuel consumption, comfort and safety, the ability to harvest wet and lying, and price. To rank seven alternatives through the MULTIMOORA (multi-objective optimization on the basis of ratio analysis) method, a group decision making model applied for qualitative criteria and the Simos method was used for weighting as a subsidiary of mental groups. The performance of the hybrid model was confirmed by experts in agricultural machinery. The consensus model, when used in the process of group decision making, reduces the conflict level of decision-makers regarding criteria, alternatives and the decision matrix. The results of this research will be beneficial for industrial agriculture, especially wheat combine buyers. The proposed explainable consensus model can be used to construct decision support systems and can be applied to various decision-making problems owing to operability and easiness. Full article
(This article belongs to the Special Issue Sustainable and Agile Manufacturing in the Era of Industry 4.0: Application of Simulation, Optimization, Lean, and Emerging Digital Technologies)
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23 pages, 2407 KiB  
Article
Supply Chain Optimization Considering Sustainability Aspects
by Mohammad Ali Beheshtinia, Parisa Feizollahy and Masood Fathi
Sustainability 2021, 13(21), 11873; https://doi.org/10.3390/su132111873 - 27 Oct 2021
Cited by 9 | Viewed by 4125
Abstract
Supply chain optimization concerns the improvement of the performance and efficiency of the manufacturing and distribution supply chain by making the best use of resources. In the context of supply chain optimization, scheduling has always been a challenging task for experts, especially when [...] Read more.
Supply chain optimization concerns the improvement of the performance and efficiency of the manufacturing and distribution supply chain by making the best use of resources. In the context of supply chain optimization, scheduling has always been a challenging task for experts, especially when considering a distributed manufacturing system (DMS). The present study aims to tackle the supply chain scheduling problem in a DMS while considering two essential sustainability aspects, namely environmental and economic. The economic aspect is addressed by optimizing the total delivery time of order, transportation cost, and production cost while optimizing environmental pollution and the quality of products contribute to the environmental aspect. To cope with the problem, it is mathematically formulated as a mixed-integer linear programming (MILP) model. Due to the complexity of the problem, an improved genetic algorithm (GA) named GA-TOPKOR is proposed. The algorithm is a combination of GA and TOPKOR, which is one of the multi-criteria decision-making techniques. To assess the efficiency of GA-TOPKOR, it is applied to a real-life case study and a set of test problems. The solutions obtained by the algorithm are compared against the traditional GA and the optimum solutions obtained from the MILP model. The results of comparisons collectively show the efficiency of the GA-TOPKOR. Analysis of results also revealed that using the TOPKOR technique in the selection operator of GA significantly improves its performance. Full article
(This article belongs to the Special Issue Sustainable and Agile Manufacturing in the Era of Industry 4.0: Application of Simulation, Optimization, Lean, and Emerging Digital Technologies)
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22 pages, 3452 KiB  
Article
Implementation of POLCA Integrated QRM Framework for Optimized Production Performance—A Case Study
by Wanzhu Wang, Qazi Salman Khalid, Muhammad Abas, Hao Li, Shakir Azim, Abdur Rehman Babar, Waqas Saleem and Razaullah Khan
Sustainability 2021, 13(6), 3452; https://doi.org/10.3390/su13063452 - 20 Mar 2021
Cited by 5 | Viewed by 4302
Abstract
Quick response manufacturing (QRM) is a relatively new concept that enfolds all the preceding approaches, namely, just in time (JIT), flexible manufacturing, agile manufacturing, and lean production. QRM is compatible with existing materials requirement planning (MRP) systems and can be implemented efficiently. The [...] Read more.
Quick response manufacturing (QRM) is a relatively new concept that enfolds all the preceding approaches, namely, just in time (JIT), flexible manufacturing, agile manufacturing, and lean production. QRM is compatible with existing materials requirement planning (MRP) systems and can be implemented efficiently. The ideas from QRM have been highly influential in custom-made engineer-to-order and make-to-order (ETO/MTO) high-mix and low-volume production environments. This study investigates the effectiveness of the POLCA (paired cell overlapping loops of cards) integrated QRM framework for reducing lead time. The POLCA integrated QRM approach was implemented in a precise product manufacturing industry. The industry was facing high penalties due to improper planning and uncontrolled lead times. The implementation of QRM with the POLCA framework indicated optimized production scheduling and significant improvement in lead time and work in process (WIP). After implementing the new manufacturing strategy, the performance parameters showed significant improvement in terms of reducing the percentage loss of profit. Full article
(This article belongs to the Special Issue Sustainable and Agile Manufacturing in the Era of Industry 4.0: Application of Simulation, Optimization, Lean, and Emerging Digital Technologies)
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Review

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21 pages, 1078 KiB  
Review
The Application of Industry 4.0 Technological Constituents for Sustainable Manufacturing: A Content-Centric Review
by Tan Ching Ng, Sie Yee Lau, Morteza Ghobakhloo, Masood Fathi and Meng Suan Liang
Sustainability 2022, 14(7), 4327; https://doi.org/10.3390/su14074327 - 6 Apr 2022
Cited by 47 | Viewed by 10859
Abstract
Industry 4.0 has been associated with the rise of disruptive intelligence and information technologies. These cutting-edge technologies have the potential to increase productivity while simultaneously having a significant impact on social and environmental sustainability. As a result, manufacturers must evaluate the role of [...] Read more.
Industry 4.0 has been associated with the rise of disruptive intelligence and information technologies. These cutting-edge technologies have the potential to increase productivity while simultaneously having a significant impact on social and environmental sustainability. As a result, manufacturers must evaluate the role of these innovative technologies in sustainable development, as these technologies have the potential to address prevalent sustainability issues. A content-centric study of the implementation of these Industry 4.0 cutting-edge technologies in sustainable manufacturing is currently absent. A systematic literature study was conducted to explain the potential contribution of these novel technologies to the economic, social, and environmental dimensions of manufacturing industries. This study describes how these cutting-edge technologies are used in sustainable manufacturing. The findings of this study are particularly beneficial to practitioners who seek to apply one or more digital technologies to sustainable development. Full article
(This article belongs to the Special Issue Sustainable and Agile Manufacturing in the Era of Industry 4.0: Application of Simulation, Optimization, Lean, and Emerging Digital Technologies)
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20 pages, 1201 KiB  
Review
Applications of Blockchain Technology in Sustainable Manufacturing and Supply Chain Management: A Systematic Review
by Ahmad A. A. Khanfar, Mohammad Iranmanesh, Morteza Ghobakhloo, Madugoda Gunaratnege Senali and Masood Fathi
Sustainability 2021, 13(14), 7870; https://doi.org/10.3390/su13147870 - 14 Jul 2021
Cited by 128 | Viewed by 15395
Abstract
Developing sustainable products and processes is essential for the survival of manufacturers in the current competitive market and the industry 4.0 era. The activities of manufacturers and their supply chain partners should be aligned with sustainable development goals. Manufacturers have faced many barriers [...] Read more.
Developing sustainable products and processes is essential for the survival of manufacturers in the current competitive market and the industry 4.0 era. The activities of manufacturers and their supply chain partners should be aligned with sustainable development goals. Manufacturers have faced many barriers and challenges in implementing sustainable practices along the entire supply chain due to globalisation, outsourcing, and offshoring. Blockchain technology has the potential to address the challenges of sustainability. This study aims to explain the applications of blockchain technology to sustainable manufacturing. We conducted a systematic literature review and explained the potential contributions of blockchain technology to the economic, environmental, and social performances of manufacturers and their supply chains. The findings of the study extend our understanding of the blockchain applications in sustainable manufacturing and sustainable supply chains. Furthermore, the study explains how blockchain can influence the sustainable performance of manufacturers by creating transparency, traceability, real-time information sharing, and security of the data capabilities. Full article
(This article belongs to the Special Issue Sustainable and Agile Manufacturing in the Era of Industry 4.0: Application of Simulation, Optimization, Lean, and Emerging Digital Technologies)
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