A literature review of the current applications of machine learning and their practical implications

Chagas, Beatriz Nery Rodrigues; Viana, Julio; Reinhold, Olaf; Lobato, Fábio Manoel França; Jacob, Antônio F. L.; Alt, Rainer

doi:10.3233/web-200429

Cited by 14 publications

(5 citation statements)

References 130 publications

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“…AI technologies enable businesses to adapt to changing market conditions, customer preferences, and demand patterns more quickly and effectively. Machine learning algorithms can analyze market data, customer feedback, and sales trends to identify emerging opportunities, optimize product assortments, and tailor marketing strategies to meet evolving customer needs (Chagas et al, 2020;Farayola et al, 2023). For example, in the retail industry, AI-powered demand forecasting models can analyze sales data, promotional activities, and external factors to predict future demand more accurately, enabling businesses to optimize inventory levels, minimize stockouts, and improve customer satisfaction.…”

Section: Impact Of Ai On Supply Chain Resiliencementioning

confidence: 99%

Leveraging Artificial Intelligence for Enhanced Supply Chain Optimization: A Comprehensive Review of Current Practices and Future Potentials

Olorunyomi Stephen Joel,

Adedoyin Tolulope Oyewole,

Olusegun Gbenga Odunaiya

et al. 2024

Int. j. manag. entrep. res

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The integration of artificial intelligence (AI) technologies into supply chain management has emerged as a crucial avenue for enhancing efficiency, agility, and responsiveness in modern business operations. This comprehensive review synthesizes current practices and future potentials of leveraging AI for supply chain optimization. Beginning with an overview of traditional supply chain management challenges, the review elucidates how AI solutions address these complexities by enabling predictive analytics, real-time visibility, and intelligent decision-making. The review delves into the diverse applications of AI across different stages of the supply chain, including demand forecasting, inventory management, logistics optimization, and supplier relationship management. Examples of AI-driven technologies such as machine learning, natural language processing, and robotic process automation are analyzed for their role in revolutionizing supply chain operations. Furthermore, the review highlights the transformative impact of AI on supply chain resilience, emphasizing its ability to mitigate disruptions, adapt to dynamic market conditions, and optimize resource allocation. The review also addresses critical considerations such as data privacy, ethical implications, and organizational readiness for AI adoption within supply chain contexts. Lastly, the review discusses future research directions and potential advancements in AI-enabled supply chain management, envisioning intelligent autonomous supply chains characterized by self-learning systems, collaborative ecosystems, and enhanced sustainability practices. In conclusion, this review underscores the pivotal role of AI in driving continuous innovation and competitive advantage within supply chain networks, while also emphasizing the importance of strategic planning and responsible implementation to harness its full potential. Keywords: AI, Supply Chain, Optimization, Practices, Review.

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Section: Impact Of Ai On Supply Chain Resiliencementioning

confidence: 99%

Leveraging Artificial Intelligence for Enhanced Supply Chain Optimization: A Comprehensive Review of Current Practices and Future Potentials

Olorunyomi Stephen Joel,

Adedoyin Tolulope Oyewole,

Olusegun Gbenga Odunaiya

et al. 2024

Int. j. manag. entrep. res

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“…The literature emphasizes the prevalent use of predictive analytics, machine learning, and artificial intelligence (AI) in CRM practices in the USA (Chagas et al,2020). Predictive analytics, driven by advanced algorithms, allows organizations to forecast customer behavior and preferences.…”

Section: Literature Reviewmentioning

confidence: 99%

Business analytics in CRM: A comparative review of practices in the USA and Africa

Rhoda Adura Adeleye,

Ndubuisi Leonard Ndubuisi,

Onyeka Franca Asuzu

et al. 2024

World J. Adv. Res. Rev.

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In the dynamic landscape of global business, Customer Relationship Management (CRM) has emerged as a crucial strategy for organizations aiming to enhance customer satisfaction and loyalty. This study delves into the realm of Business Analytics (BA) within CRM, examining the diverse practices employed in the USA and Africa. The purpose is to identify similarities, differences, and potential areas for improvement, with the overarching goal of fostering more effective CRM strategies. The American business landscape, characterized by advanced technological infrastructure and matured data analytics capabilities, provides a fertile ground for sophisticated CRM practices. A comprehensive analysis of leading companies in the USA reveals a strong reliance on predictive analytics, machine learning, and artificial intelligence to glean actionable insights from vast datasets. Such approaches empower organizations to personalize customer experiences, optimize marketing strategies, and drive overall business growth. In contrast, the African business environment exhibits unique challenges and opportunities. While certain regions may face infrastructural constraints, innovative CRM practices tailored to local contexts are emerging. This study sheds light on the utilization of mobile technology, social media analytics, and localized data collection methods as crucial tools in the African CRM landscape. These adaptive strategies not only address challenges but also present opportunities for leapfrogging traditional CRM models. The comparative review uncovers key areas of convergence and divergence between the two regions, highlighting the need for a nuanced approach to global CRM strategies. By synthesizing insights from the USA and Africa, organizations can develop a more holistic understanding of effective CRM practices. This research contributes valuable insights to the ongoing discourse on global business analytics, offering a roadmap for organizations to navigate the intricacies of CRM implementation in diverse and dynamic markets.

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“…Esse tipo de sistema pressupõe que um computador pode tomar decisões com base em experiências e na solução de problemas anteriores (Monard & Baranauskas, 2005). Inúmeras pesquisas têm evidenciado o papel da ML para resolução de problemas como por exemplo a sofisticação nas análises sensoriais (Fuentes et al, 2021;Qannari, 2017); a previsão de demanda no setor de energia (Antonopoulos et al, 2020) e no varejo (Fildes et al, 2019); as melhorias no processo de CRM (Chagas et al, 2020) e na segmentação de consumidores (Ahani et al, 2019;Farrokhi et al, 2021;Kachamas et al, 2019); o uso para o aperfeiçoamento das técnicas de marketing digital (Saura, 2021); dentre inúmeras outras. Todas compartilham a percepção da importância da ML para melhoria na compreensão dos dados, buscando aperfeiçoar a performance da organização a partir de decisões mais assertivas.…”

Section: Clusterização De Consumidores Via Machine Learningunclassified

“…Um cluster é entendido como um grupo significativo de objetos que possuem características comuns, e são úteis para explorar dados, identificar padrões e fazer análises, oferecendo uma forma de compreender e extrair informações relevantes de grandes conjuntos de dados (Dogan, Ayçin & Bulut, 2018). A segmentação auxilia as empresas a alocarem seus recursos de marketing de maneira eficaz, possibilitando que grupos específicos de cliente recebam a devida atenção, subsidiando assim a fidelização, por meio da construção de estratégias de longo prazo para a gestão de relacionamentos com os clientes (CRM) (Anitha & Patil, 2019;Chagas et al, 2020).…”

Section: Clusterização De Consumidores Via Machine Learningunclassified

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Segmentação via Machine Learning: Proposta De Clusterização De Consumidores Do E-Commerce De Uma Empresa Multinacional Do Varejo Esportivo

Falqueto¹,

Cezar

2022

HOLOS

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O objetivo desse artigo é apresentar uma proposta de segmentação da base de consumidores do e-commerce de uma empresa multinacional do varejo esportivo, a partir da clusterização de dados via Machine Learning. Para isso, foi realizado um estudo quantitativo com dados de 526.686 clientes do e-commerce de uma empresa multinacional que atua no Brasil nesse setor. Os dados foram analisados pela análise de cluster, utilizando a metodologia proposta por Jain, Murty e Flynn (1999). A partir da segmentação atual, limitada ao valor gasto pelo cliente, a nova proposta de segmentação, construída a partir do algoritmo K-means considerou novas variáveis como o número de pedidos nos doze meses anteriores, e seus respectivos tempos de inatividade. O uso desse algoritmo via Machine Learning se mostrou satisfatório, visto que foi possível obter três segmentos válidos que se diferenciavam da segmentação adotada atualmente.

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A literature review of the current applications of machine learning and their practical implications

Cited by 14 publications

References 130 publications

Leveraging Artificial Intelligence for Enhanced Supply Chain Optimization: A Comprehensive Review of Current Practices and Future Potentials

Leveraging Artificial Intelligence for Enhanced Supply Chain Optimization: A Comprehensive Review of Current Practices and Future Potentials

Business analytics in CRM: A comparative review of practices in the USA and Africa

Segmentação via Machine Learning: Proposta De Clusterização De Consumidores Do E-Commerce De Uma Empresa Multinacional Do Varejo Esportivo

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