Demand forecasting based machine learning algorithms on customer information: an applied approach

Zohdi, Maryam; Rafiee, Majid; Kayvanfar, Vahid; Salamiraad, Amirhossein

doi:10.1007/s41870-022-00875-3

Cited by 26 publications

(5 citation statements)

References 56 publications

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“…We compared the results achieved by the proposed method in this context with those obtained by assuming a uniform distribution and estimating its mean and variance based on historical data. We call this approach Historic Estimation Method (HEM) [25], additionally we compared with the best method of [26], in this case using Extreme Learning Machines (ELM).…”

Section: Resultsmentioning

confidence: 99%

Fog Computing and Industry 4.0 for Newsvendor Inventory Model Using Attention Mechanism and Gated Recurrent Unit

Gonzalez,

Avelar Sosa,

Bravo

et al. 2024

Logistics

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Background: Efficient inventory management is critical for sustainability in supply chains. However, maintaining adequate inventory levels becomes challenging in the face of unpredictable demand patterns. Furthermore, the need to disseminate demand-related information throughout a company often relies on cloud services. However, this method sometimes encounters issues such as limited bandwidth and increased latency. Methods: To address these challenges, our study introduces a system that incorporates a machine learning algorithm to address inventory-related uncertainties arising from demand fluctuations. Our approach involves the use of an attention mechanism for accurate demand prediction. We combine it with the Newsvendor model to determine optimal inventory levels. The system is integrated with fog computing to facilitate the rapid dissemination of information throughout the company. Results: In experiments, we compare the proposed system with the conventional demand estimation approach based on historical data and observe that the proposed system consistently outperformed the conventional approach. Conclusions: This research introduces an inventory management system based on a novel deep learning architecture that integrates the attention mechanism with cloud computing to address the Newsvendor problem. Experiments demonstrate the better accuracy of this system in comparison to existing methods. More studies should be conducted to explore its applicability to other demand modeling scenarios.

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Section: Resultsmentioning

confidence: 99%

Fog Computing and Industry 4.0 for Newsvendor Inventory Model Using Attention Mechanism and Gated Recurrent Unit

Gonzalez,

Avelar Sosa,

Bravo

et al. 2024

Logistics

View full text Add to dashboard Cite

show abstract

“…Recent developments in IoT technology have enabled real-time tracking of goods, with research indicating substantial improvements in inventory accuracy and logistics efficiency. Emerging research is focusing on how AI can optimize supply chain sustainability by improving resource efficiency and reducing waste (Carbonneau, Laframboise, & Vahidov, 2008;Feizabadi, 2022;Seyedan & Mafakheri, 2020;Zohdi, Rafiee, Kayvanfar, & Salamiraad, 2022). The integration of AI into SCM represents a transformative shift towards more intelligent, responsive, and efficient supply chains.…”

Section: Ai-driven Scm Optimizationmentioning

confidence: 99%

Enhancing manufacturing productivity: A review of AI-Driven supply chain management optimization and ERP systems integration

Olubunmi Adeolu Adenekan,

Nko Okina Solomon,

Peter Simpa

et al. 2024

Int. j. manag. entrep. res

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This abstract delves into the realm of manufacturing productivity enhancement through the review of AI-driven supply chain management (SCM) optimization and Enterprise Resource Planning (ERP) systems integration. As industries strive for operational excellence, the convergence of artificial intelligence (AI) and supply chain management emerges as a transformative force in driving efficiency, agility, and competitiveness. Through a comprehensive analysis, this abstract examines the synergistic relationship between AI-driven SCM optimization and the integration of ERP systems, elucidating their collective impact on manufacturing productivity. AI-driven SCM optimization encompasses a spectrum of technologies and methodologies, including predictive analytics, machine learning, and autonomous decision-making systems, aimed at optimizing various facets of the supply chain, from demand forecasting and inventory management to production planning and logistics optimization. By harnessing the power of AI, manufacturers can enhance forecasting accuracy, reduce lead times, optimize inventory levels, and mitigate supply chain disruptions, thereby improving overall productivity and customer satisfaction. Integration of ERP systems plays a complementary role in manufacturing productivity enhancement by providing a centralized platform for data management, process automation, and cross-functional collaboration. Through seamless integration with AI-driven SCM optimization tools, ERP systems enable real-time data exchange, actionable insights, and end-to-end visibility across the supply chain, facilitating informed decision-making and agile response to dynamic market conditions. Drawing insights from case studies and industry examples, this abstract highlights best practices, challenges, and emerging trends in AI-driven SCM optimization and ERP systems integration. Strategies for successful implementation, including organizational readiness assessment, change management, and stakeholder engagement, are discussed to guide manufacturers in unlocking the full potential of these transformative technologies. In conclusion, the convergence of AI-driven SCM optimization and ERP systems integration offers a compelling pathway for enhancing manufacturing productivity, driving operational excellence, and sustaining competitive advantage in the digital era.. Keywords: Artificial Intelligence, Supply Chain Management, Enterprise Resource Planning, Manufacturing Productivity, AI Integration, Predictive Analytics.

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“…AI-powered demand forecasting models leverage advanced algorithms and machine learning techniques to analyze historical sales data, market trends, and external factors, such as economic indicators or weather patterns (Franki et al, 2023;Oriekhoe et al, 2024). By identifying patterns and correlations in large datasets, AI algorithms can generate more accurate demand forecasts, reducing forecasting errors and improving inventory planning (Zohdi et al, 2022). These models can adapt to changing business conditions and incorporate new data in real-time, enabling businesses to respond quickly to fluctuations in demand and market dynamics.…”

Section: Applications Of Ai In Supply Chain Optimizationmentioning

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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Demand forecasting based machine learning algorithms on customer information: an applied approach

Cited by 26 publications

References 56 publications

Fog Computing and Industry 4.0 for Newsvendor Inventory Model Using Attention Mechanism and Gated Recurrent Unit

Fog Computing and Industry 4.0 for Newsvendor Inventory Model Using Attention Mechanism and Gated Recurrent Unit

Enhancing manufacturing productivity: A review of AI-Driven supply chain management optimization and ERP systems integration

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

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