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Artificial Intelligence in Supply Chain Management: Promoting Enterprise Sustainability and Optimization

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: closed (27 February 2024) | Viewed by 929

Special Issue Editor

Leavey School of Business, Santa Clara University, Santa Clara, CA, USA
Interests: supply chain decisions; operations management; supply chain management

Special Issue Information

Dear Colleagues,

The use of computing technology to improve the efficiency of supply chains has been around at least since World War 2 when the Simplex algorithm was invented by Dantzig to solve linear programming problems in logistics. However, the advent of the internet placed the power of supply chains in the hands of end-consumers, who have grown to expect "anywhere, anytime" delivery of their chosen products. Increasingly, the ability to make this happen critically depends on the use of Artificial Intelligence (AI) not only to better understand consumer preferences but also to extract all misalignment and noise in the supply chain in order to achieve better unit economics while delivering what consumers seek. There is a large variety of AI applications in supply chains, ranging from robotics in warehousing and delivery, computer vision to reduce inventory inaccuracy, to the use of combinatorial optimization in real-time planning and execution. This Special Issue on the use of AI in supply chains has a broad agenda. We seek papers on a wide variety of applications using all rigorous methodologies. Applied case studies with real data and quantitative analysis are especially encouraged. Additionally, of special interest is a focus on the implementation challenges of AI in practical settings as well as applications that have a social mission. Papers will be evaluated on the basis of their impact on supply chain efficiency, customer satisfaction, sustainability and DEI (diversity, equity and inclusion).

Dr. Ram Bala
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

  • artificial intelligence
  • supply chain management
  • cost reduction
  • responsiveness
  • data science
  • analytics
  • sustainability
  • AI ethics

Published Papers (1 paper)

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Research

31 pages, 2309 KiB  
Article
Artificial Intelligence Approach to Predict Supply Chain Performance: Implications for Sustainability
by Syed Mithun Ali, Amanat Ur Rahman, Golam Kabir and Sanjoy Kumar Paul
Sustainability 2024, 16(6), 2373; https://doi.org/10.3390/su16062373 - 13 Mar 2024
Viewed by 523
Abstract
The performance of supply chains significantly impacts the success of businesses. In addressing this critical aspect, this article presents a methodology for analyzing and predicting key performance indicators (KPIs) within supply chains characterized by limited, imprecise, and uncertain data. Drawing upon an extensive [...] Read more.
The performance of supply chains significantly impacts the success of businesses. In addressing this critical aspect, this article presents a methodology for analyzing and predicting key performance indicators (KPIs) within supply chains characterized by limited, imprecise, and uncertain data. Drawing upon an extensive literature review, this study identifies 21 KPIs using the balanced scorecard (BSC) methodology as a performance measurement framework. While prior research has relied on the grey first-order one-variable GM (1,1) model to predict supply chain performance within constrained datasets, this study introduces an artificial intelligence approach, specifically a GM (1,1)-based artificial neural network (ANN) model, to enhance prediction precision. Unlike the traditional GM (1,1) model, the proposed approach evaluates performance based on the mean relative error (MRE). The results demonstrate a significant reduction in MRE levels, ranging from 77.09% to 0.23%, across various KPIs, leading to improved prediction accuracy. Notably, the grey neural network (GNN) model exhibits superior predictive accuracy compared to the GM (1,1) model. The findings of this study underscore the potential of the proposed artificial intelligence approach in facilitating informed decision-making by industrial managers, thereby fostering economic sustainability within enterprises across all operational tiers. Full article
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