Predictive analytics for demand forecasting: A deep learning-based decision support system

Punia, Sushil; Shankar, Sonali

doi:10.1016/j.knosys.2022.109956

Cited by 29 publications

(10 citation statements)

References 43 publications

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“…One of the key applications of machine learning in supply chain management is demand forecasting. By analyzing historical sales data, market trends, and external factors, machine learning algorithms can generate more accurate demand forecasts, enabling businesses to optimize inventory levels, minimize stockouts, and reduce excess inventory carrying costs (Punia and Shankar, 2022;Apeh et al, 2023). Machine learning algorithms can also be applied to predictive maintenance, quality control, and supply chain risk management.…”

Section: Ai-driven Technologies In Supply Chain Managementmentioning

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: Ai-driven Technologies In Supply Chain Managementmentioning

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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“…Numerous multivariate time-series techniques, including ARIMAX, have also been used. The specifics of these techniques can be found in Hyndman & Athansopoulos [8].…”

Section: Literature Surveymentioning

confidence: 99%

An Efficient Novel Approach on Machine Learning Paradigmsfor Food Delivery Company through Demand Forecastıng in societal community

Yerragudipadu,

Gurram,

Sri Rayapudi

et al. 2023

E3S Web Conf.

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A food delivery business must be able to accurately forecast demand on a daily and weekly basis since it deals with a lot of perishable raw components. A warehouse that keeps too much inventory runs the danger of wasting items, whereas a warehouse that maintains too little inventory runs the risk of running out of stock, which might lead consumers to switch to your competitors. Planning for purchasing is essential because most raw materials are perishable and delivered on a weekly basis. For this issue to be resolved, demand forecasting is crucial. With the aid of historical data-driven predictive research, demand forecasting determines and forecasts future consumer demand for a good or service. By predicting future sales and revenues, demand forecasting assists the organisation in making more educated supply decisions. Regression methods like linear regression, decision trees, and Xgboost are used to overcome this issue.

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“…En un estudio del año 2021, se utilizó un modelo de aprendizaje profundo para analizar datos de ventas y predecir las tendencias de ventas futuras, lo que permitió una toma de decisiones más informada (Cortés, 2020); además, un estudio del año 2020 encontró que la utilización de la inteligencia artificial en la toma de decisiones empresariales mejoró la precisión de las decisiones (Cuzzocrea, 2011). En comparación con los estudios anteriores, la inteligencia artificial ha permitido una toma de decisiones más informada y precisa en la creación y modelamiento de bases de datos (Punia & Shankar, 2022).…”

Section: Toma De Decisionesunclassified

El papel de la Inteligencia Artificial como herramienta de apoyo en la generación y gestión de Bases de Datos.

Freire Lescano,

Lozada Torres,

Pico Pico

et al. 2023

dilemas

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El desarrollo de bases de datos representa un proceso complejo que involucra la participación de expertos en cada etapa del procedimiento. La inteligencia artificial (IA) ha surgido como una herramienta que puede brindar apoyo en este proceso al automatizar numerosas tareas necesarias para la creación de bases de datos de calidad superior. En este estudio, se examinan las técnicas empleadas en la creación de bases de datos, y se analiza el papel de la IA en este contexto; además, se abordan las ventajas y limitaciones asociadas a dichos enfoques, y se presentan ejemplos concretos de su aplicación en la práctica. La investigación continua es necesaria para aprovechar al máximo el potencial de la IA en la creación de bases de datos.

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Predictive analytics for demand forecasting: A deep learning-based decision support system

Cited by 29 publications

References 43 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

An Efficient Novel Approach on Machine Learning Paradigmsfor Food Delivery Company through Demand Forecastıng in societal community

El papel de la Inteligencia Artificial como herramienta de apoyo en la generación y gestión de Bases de Datos.

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