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2024, Universal Journal of Computer Sciences and Communications
Consumer behavior is evolving, demanding a wide range of products with fast shipping and reliable service. The automotive aftermarket industry, worth billions, requires efficient distribution systems to stay competitive. Manufacturers strive to balance growth with product and service excellence. Distributors and retailers face the challenge of maintaining competitive pricing while keeping inventory levels low. An adequate supply chain and accurate product data are crucial for product availability and reducing stock issues. This ultimately increases profits and customer satisfaction.
IAEME PUBLICATION
REVOLUTIONIZING ASYNCHRONOUS SHIPMENTS: INTEGRATING AI PREDICTIVE ANALYTICS IN AUTOMOTIVE SUPPLY CHAINS2022 •
This paper discusses using AI predictive analytics to enhance the automotive supply chain. It highlights the importance of improving the consumer market system to boost revenue without added costs. The industry standard is to forecast consumer demand for the next 4-6 weeks, but flexibility is necessary to meet sudden spikes in demand. Integrated systems like the JIT system developed by Nissan help minimize inventory costs. However, the automotive industry faces challenges such as low service levels due to delays in product delivery. Applying the principle of constraint can help address supply chain issues. The supply-demand gap is both an inventory positioning and market mediation problem. Successful demand prediction and precise product availability can solve the inventory positioning issue while transitioning from high-cost advertising to sales, and price rebates can improve market.
Journal of Strategic Innovation and Sustainability
Application of Artificial Intelligence in Automation of Supply Chain ManagementInternational Journal of Engineering Research and Technology (IJERT)
IJERT-Inventory Management using Machine Learning2020 •
https://www.ijert.org/inventory-management-using-machine-learning https://www.ijert.org/research/inventory-management-using-machine-learning-IJERTV9IS060661.pdf A major requirement for small/medium-sized businesses is Inventory Management since a lot of money and skilled labor has to be invested to do so. E-commerce giants use Machine Learning models to maintain their inventory based on demand for a particular item. Inventory Management can be extended as a service to small/medium sized businesses to improve their sales and predict the demand of various products. Demand forecasting is a crucial part of all businesses and brings up the following question: How much stock of an item should a company/business keep to meet the demands, i.e., what should the predicted demand of a product be? Among its many benefits, a predictive forecast is a key enabler for a better customer experience through the reduction of out-of-stock situations, and for lower costs due to better planned inventory and less write-off items. We discuss the challenges of building an Inventory system and discuss the design decisions.
In a global market that makes room for more competitors by the day, some companies are turning to AI and machine learning to try to gain an edge. Supply chain and inventory management is a domain that has missed some of the media limelight, but one where industry leaders have been hard at work developing new AI and machine learning technologies over the past decade. Many well-known companies are now using machine learning to optimize business processes in ways that might have been deemed science fiction 30 years ago, from customer service inquiries to planning for next month's shelf supply based on satellite data. Supply chain and inventory management is primed to embody the concept of smart automation over the next five to 10 years. In this paper, we have investigated the research made till date and proposed a way to improve the inventory management so that it can benefit the customer as well as organizations.
Xi'an Jianzhu Keji Daxue Xuebao/Journal of Xi'an University of Architecture & Technology
Artificial Intelligence: Pertinence in Supply Chain and Logistics ManagementArtificial Intelligence (AI) is the revolutionary invention of human intelligence. Artificial Intelligence is nothing but the duplication of human in which machines are programmed to rationally think and behave like humans developed for very many purposes including business decision making, problem-solving, business data analysis and interpretation and information management. The application of AI in business endeavours decides the competitive advantage, market leadership, robust operating efficiency of corporates and other business houses. Exploiting the application of AI in the manufacturing and distribution process enables the organisations to reach the pinnacle in their business graph. Businesses are operating in the international market which is highly multifaceted and challenging to serve the world as a sole market for their products, services and their products and without the integration of technology into their business processes, they cannot assure the sustainable growth. The management of the process of transforming the raw materials into the final product is called Supply Chain Management (SCM) and the effective movement and storage of goods, services and information are called Logistics Management (LM). This article analyses the applications of Artificial Intelligence in Supply Chain and Logistics Management (SC&LM)
International Journal for Research in Applied Science & Engineering Technology (IJRASET)
Challenges and Solutions in Integrating AI with Legacy Inventory Systems2023 •
This paper delves into the intricate process of integrating Artificial Intelligence (AI) into legacy inventory systems, a critical challenge in the realm of modern inventory management. It presents a comprehensive analysis, exploring the multifaceted barriers encountered in this integration, particularly in traditional industries. The study identifies and examines key technical, organizational, and financial challenges, offering a nuanced understanding of the complexities involved. Innovative solutions and strategies are proposed to address these challenges, drawing on a rich array of existing literature and real-world case studies. The paper highlights successful integrations of AI in various sectors, extracting valuable lessons and best practices. It contributes significantly to the existing body of knowledge by bridging theoretical research with practical applications, providing insights that are both profound and actionable. This research not only illuminates the path forward for traditional industries seeking to embrace AI in inventory management but also serves as a valuable resource for practitioners and researchers in the field. The findings and strategies outlined in this study offer a roadmap for successful AI integration, marking a pivotal step in the evolution of inventory management practices.
International Journal of Logistics Research and Applications
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