Customer-Oriented Artificial Intelligence and Analytics in Logistics and Supply Chains
A Special Issue of Logistics (ISSN 2305-6290) belonging to the section "Artificial Intelligence, Logistics Analytics, and Automation".
Deadline for manuscript submissions: 15 February 2027 | Viewed by 1131
Editors
Interests: reverse supply chain; operations management; remanufacturing; circular economy
Special Issues, Collections and Topics in MDPI journals
Interests: multicriteria decision support; reverse logistics; negotiation and group decision making; circular economy
Special Issues, Collections and Topics in MDPI journals
Interests: smart logistics; smart manufacturing; sustainable operations; sustainable management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid growth in the availability of operational, transactional, and customer-related data has significantly accelerated the adoption of Artificial Intelligence (AI) and advanced analytics in logistics and supply chain management. While existing research has largely focused on efficiency-driven applications—such as cost reduction, routing, and inventory optimization—there is increasing recognition of the strategic role played by customer-oriented metrics, including satisfaction, loyalty, service quality, and perceived value.
This Special Issue aims to explore how AI-based methods, such as machine learning, deep learning, and intelligent decision support systems, can be integrated with customer-centric indicators to enhance planning, forecasting, and decision-making processes in logistics and supply chains. In particular, this issue welcomes contributions that bridge operational and business data with behavioral and relational metrics, such as customer experience measures, Net Promoter Score (NPS), and service-level perceptions.
This Special Issue seeks to bring together both theoretical and applied research, covering topics ranging from demand and sales forecasting to logistics optimization, digital transformation, and intelligent supply chain design. By combining AI-driven analytics with customer-oriented perspectives, this issue aims to complement the existing literature and provide a more holistic understanding of logistics and supply chain performance.
Dr. P. Carmona Marques
Dr. Wesley Douglas Silva
Dr. João Reis
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 250 words) can be sent to the Editorial Office for assessment.
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 double-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Logistics is an international peer-reviewed open access monthly 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 1500 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
- demand and sales forecasting using AI and machine learning
- integration of customer experience and loyalty metrics in logistics analytics
- intelligent decision support systems for logistics and supply chains
- AI-driven performance measurement and service quality management
- customer-oriented digital transformation in supply chains
- analytics for sustainable and resilient logistics systems
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