Applications of Machine Learning Techniques in Supply Chain Optimization

Makkar, Sandhya; Devi, G. Naga Rama; Solanki, Vijender Kumar

doi:10.1007/978-981-13-8461-5_98

Cited by 27 publications

(8 citation statements)

References 4 publications

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“…The epitome of this implementation can be attributed to the ability of ML algorithms to automate quality examination in the wake of assembling equivalent patterns from a multitude of datasets (Wang et al, 2019). All things being considered, ML is also capable of identifying the most important driving factors to determine the supply chain network's success and improv the overall LSCM performance (Makkar et al, 2020). For example, ML-based algorithms can quickly identify consumer trends, thereby paving the way for a more efficient response to changes in the supply chain and improving customer experience (Adebola and Onyekwelu, 2019).…”

Section: Introductionmentioning

confidence: 99%

A systematic review of machine learning in logistics and supply chain management: current trends and future directions

Akbari

Anh

2021

BIJ

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PurposeThis paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current literature, contemporary concepts, data and gaps and suggesting potential topics for future research.Design/methodology/approachA systematic/structured literature review in the subject discipline and a bibliometric analysis were organized. Information regarding industry involvement, geographic location, research design and methods, data analysis techniques, university, affiliation, publishers, authors, year of publications is documented. A wide collection of eight databases from 1994 to 2019 were explored using the keywords “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract. A total of 110 articles were found, and information on a chain of variables was gathered.FindingsOver the last few decades, the application of emerging technologies has attracted significant interest all around the world. Analysis of the collected data shows that only nine literature reviews have been published in this area. Further, key findings show that 53.8 per cent of publications were closely clustered on transportation and manufacturing industries and 54.7 per cent were centred on mathematical models and simulations. Neural network is applied in 22 papers as their exclusive algorithms. Finally, the main focuses of the current literature are on prediction and optimization, where detection is contributed by only seven articles.Research limitations/implicationsThis review is limited to examining only academic sources available from Scopus, Elsevier, Web of Science, Emerald, JSTOR, SAGE, Springer, Taylor and Francis and Wiley which contain the words “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract.Originality/valueThis paper provides a systematic insight into research trends in ML in both logistics and the supply chain.

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Section: Introductionmentioning

confidence: 99%

A systematic review of machine learning in logistics and supply chain management: current trends and future directions

Akbari

Anh

2021

BIJ

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“…Vendor selection is an essential process in SCM. Decision Trees, Gaussian Processes Classifier, Vendor selection, Generalised Regression neural network (GRNN), Gradient Boosting, RL, and SVM are commonly used algorithms to optimise this process (13)(14)(15)(16).…”

Section: Usage Of Algorithms Per Supply Chain Functionmentioning

confidence: 99%

Exploring, categorisation and usage of artificial intelligence algorithms for Supply Chain Management

Hangl,

Krause,

Behrens

2023

12-Th International Conference of Management Sustainability – Security – Quality Book of Proceedings

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This paper explores the categorisation and usage of Artificial intelligence Algorithms (AIA) in Supply Chain Management (SCM) and discusses the potential benefits and challenges of implementing AI in SCM. The study reviews the literature on the application of AI in SCM. It provides a comprehensive list of commonly used algorithms in different SCM applications, such as vendor selection, production planning, resource allocation, quality control, predictive maintenance, visibility, demand forecasting, inventory optimisation, transportation optimisation, and customer segmentation. The paper highlights the versatility and potential for the cross-functional use of many AI algorithms. It identifies challenges and limitations, including data quality and availability, privacy and security concerns, and a lack of understanding and trust in AI systems. The study concludes that AI has the potential to significantly impact SCM by increasing efficiency, accuracy, and flexibility while reducing costs. However, further research is needed to develop best practices for adopting and implementing AI in SCM and to address ethical implications related to AI in SCM.

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“…Analysis of real-life data reveals that integrating 'different forecasting models that include time series algorithms, support vector regression model, and deep learning method' improved demand forecasting accuracy [46]. Broadly, scholars recommend AI-based techniques for anticipating demand [49].…”

Section: Predicting Future Demandmentioning

confidence: 99%

Artificial Intelligence, Social Media and Supply Chain Management: The Way Forward

Khatua

et al. 2021

Electronics

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Supply chain management (SCM) is a complex network of multiple entities ranging from business partners to end consumers. These stakeholders frequently use social media platforms, such as Twitter and Facebook, to voice their opinions and concerns. AI-based applications, such as sentiment analysis, allow us to extract relevant information from these deliberations. We argue that the context-specific application of AI, compared to generic approaches, is more efficient in retrieving meaningful insights from social media data for SCM. We present a conceptual overview of prevalent techniques and available resources for information extraction. Subsequently, we have identified specific areas of SCM where context-aware sentiment analysis can enhance the overall efficiency.

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Applications of Machine Learning Techniques in Supply Chain Optimization

Cited by 27 publications

References 4 publications

A systematic review of machine learning in logistics and supply chain management: current trends and future directions

A systematic review of machine learning in logistics and supply chain management: current trends and future directions

Exploring, categorisation and usage of artificial intelligence algorithms for Supply Chain Management

Artificial Intelligence, Social Media and Supply Chain Management: The Way Forward

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