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Applied Sciences
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Deep Learning in Supply Chain and Logistics
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Deep Learning in Supply Chain and 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: 20 July 2024 | Viewed by 4758

Special Issue Editors

Dr. Ahmed Shaban

E-Mail Website
Guest Editor
1. Mechanical Engineering Department, Faculty of Engineering, Fayoum University, Fayoum, Egypt
2. Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, Al-Khoud, Muscat 123, Oman
Interests: industrial engineering; supply chain; operations management; healthcare systems; solid waste management systems; statistical quality control; hazard analysis methodologies; energy systems; energy scheduling; smart grid; operations research; simulation modeling; applications of artificial intelligence
Dr. Giulio Di Gravio

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Rome, Italy
Interests: supply chain management; industrial operations and management; compliance and risk management; resilience engineering; performance variability in complex systems; product-service system (PSS)
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francesco Costantino

E-Mail
Guest Editor
Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Rome, Italy
Interests: inventory management; supply chain management; logistics; resilience management; information management; risk management
Special Issues, Collections and Topics in MDPI journals
Dr. Riccardo Patriarca

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Rome, Italy
Interests: resilience management; resilience engineering; safety and risk management; socio-technical systems modelling; operations management; aviation; supply chain management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight the possible applications of deep learning in supply chains and logistics. A logistics system consists of a set of facilities that are linked together by a transportation system. Facilities, meanwhile, are sites where materials are processed (manufactured, stored, sorted, sold, or consumed) and include manufacturing and assembly plants, warehouses, distribution centers, transhipment nodes, transportation terminals, retailers, mail sorting centers, waste incinerators, recycling plants, landfills, etc. A supply chain is defined as a complex logistics system in which raw materials are converted into finished products and then distributed to the customers. It includes suppliers, manufacturers, warehouses, distribution centers and retailers. The successful management of logistics and supply chain systems requires making optimal decisions relating to the flow of information, products, and funds. Each decision should be made to improve the fulfilment of supply chain goals (efficiency, quality, responsiveness, etc.). Those decisions can be categorized into strategic, tactical and operational, with designation factoring in the frequency of each decision and the time frame during which a decision phase has an impact. Current supply chain and logistics systems have become more reliant on modern technologies, information, ERP systems, allowing them to collect a huge amount of data about the different integrated processes in supply chains. The collected data can be utilized to improve decision making. In this regard, deep learning can be employed to automating the different supply chain operations and can also support the decision-making processes in supply chains. Deep learning techniques can be employed to develop prediction models for future demand, lead time, raw materials requirements. It can also be applied to develop automated systems for quality inspections and control. Deep learning can be used for the smart management of inventories, in addition to production and transportation within supply chains, and can be further applied for dynamic and smart scheduling and routing of vehicles. There are many other facets that deep learning can be applied to in the fields of chain and logistics. This Special Issue aims to collect high-quality and original research papers that address the applications of deep learning in the following research areas:

  • Supply Chain 4.0
  • Logistics 4.0
  • Smart supply chain management
  • Supply chain modeling, simulation and optimization
  • Operations scheduling and optimization
  • Supply chain analytics
  • Demand planning and prediction
  • Inventory management
  • Revenue management and pricing
  • Automation of supply chain operations
  • Supply chain disruption management
  • Intelligent logistics and supply chain systems
  • Warehouse automation
  • Real-time fleet monitoring and management
  • Routing and delivery optimization
  • Supplier selection and performance tracking
  • Autonomous delivery vehicles
  • Blockchain for supply chain
  • Real-time product tracking and tracing
  • Autonomous mobile robotics
  • Automated quality inspection and control systems
  • Anomaly detection and fraud management
  • Energy supply chains
  • Solid waste logistics systems
  • Applications of neural networks
  • Fuzzy logic
  • Hybrid deep learning approaches

Dr. Ahmed Shaban
Dr. Giulio Di Gravio
Prof. Dr. Francesco Costantino
Dr. Riccardo Patriarca
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.

Keywords

  • supply chain
  • logistics
  • forecasting
  • inventory management
  • transportation management
  • decision support systems
  • deep learning
  • neural networks

Published Papers (2 papers)

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Research

17 pages, 1018 KiB  
Article
Applying Machine Learning in Retail Demand Prediction—A Comparison of Tree-Based Ensembles and Long Short-Term Memory-Based Deep Learning
by Mehran Nasseri, Taha Falatouri, Patrick Brandtner and Farzaneh Darbanian
Appl. Sci. 2023, 13(19), 11112; https://doi.org/10.3390/app131911112 - 09 Oct 2023
Cited by 2 | Viewed by 2810
Abstract
In the realm of retail supply chain management, accurate forecasting is paramount for informed decision making, as it directly impacts business operations and profitability. This study delves into the application of tree-based ensemble forecasting, specifically using extra tree Regressors (ETRs) and long short-term [...] Read more.
In the realm of retail supply chain management, accurate forecasting is paramount for informed decision making, as it directly impacts business operations and profitability. This study delves into the application of tree-based ensemble forecasting, specifically using extra tree Regressors (ETRs) and long short-term memory (LSTM) networks. Utilizing over six years of historical demand data from a prominent retail entity, the dataset encompasses daily demand metrics for more than 330 products, totaling 5.2 million records. Additionally, external variables, such as meteorological and COVID-19-related data, are integrated into the analysis. Our evaluation, spanning three perishable product categories, reveals that the ETR model outperforms LSTM in metrics including MAPE, MAE, RMSE, and R2. This disparity in performance is particularly pronounced for fresh meat products, whereas it is marginal for fruit products. These ETR results were evaluated alongside three other tree-based ensemble methods, namely XGBoost, Random Forest Regression (RFR), and Gradient Boosting Regression (GBR). The comparable performance across these four tree-based ensemble techniques serves to reinforce their comparative analysis with LSTM-based deep learning models. Our findings pave the way for future studies to assess the comparative efficacy of tree-based ensembles and deep learning techniques across varying forecasting horizons, such as short-, medium-, and long-term predictions. Full article
(This article belongs to the Special Issue Deep Learning in Supply Chain and Logistics)
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24 pages, 1472 KiB  
Article
Engineering Supply Chain Transportation Indexes through Big Data Analytics and Deep Learning
by Damianos P. Sakas, Nikolaos T. Giannakopoulos, Marina C. Terzi and Nikos Kanellos
Appl. Sci. 2023, 13(17), 9983; https://doi.org/10.3390/app13179983 - 04 Sep 2023
Cited by 2 | Viewed by 967
Abstract
Deep learning has experienced an increased demand for its capabilities to categorize and optimize operations and provide higher-accuracy information. For this purpose, the implication of deep learning procedures has been described as a vital tool for the optimization of supply chain firms’ transportation [...] Read more.
Deep learning has experienced an increased demand for its capabilities to categorize and optimize operations and provide higher-accuracy information. For this purpose, the implication of deep learning procedures has been described as a vital tool for the optimization of supply chain firms’ transportation operations, among others. Concerning the indexes of transportation operations of supply chain firms, it has been found that the contribution of big data analytics could be crucial to their optimization. Due to big data analytics’ variety and availability, supply chain firms should investigate their impact on their key transportation indexes in their effort to comprehend the variation of the referred indexes. The authors proceeded with the gathering of the required big data analytics from the most established supply chain firms’ websites, based on their (ROPA), revenue growth, and inventory turn values, and performed correlation and linear regression analyses to extract valuable insights for the next stages of the research. Then, these insights, in the form of statistical coefficients, were inserted into the development of a Hybrid Model (Agent-Based and System Dynamics modeling), with the application of the feedforward neural network (FNN) method for the estimation of specific agents’ behavioral analytical metrics, to produce accurate simulations of the selected key performance transportation indexes of supply chain firms. An increase in the number of website visitors to supply chain firms leads to a 60% enhancement of their key transportation performance indexes, mostly related to transportation expenditure. Moreover, it has been found that increased supply chain firms’ website visibility tends to decrease all of the selected transportation performance indexes (TPIs) by an average amount of 87.7%. The implications of the research outcomes highlight the role of increased website visibility and search engine ranking as a cost-efficient means for reducing specific transportation costs (Freight Expenditure, Inferred Rates, and Truckload Line Haul), thus achieving enhanced operational efficiency and transportation capacity. Full article
(This article belongs to the Special Issue Deep Learning in Supply Chain and Logistics)
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