Artificial intelligence has shown potential in transforming supply chain management and logistics. The emerging innovations have illustrated the promising capabilities of AI systems in business operations. As AI revolutionizes businesses, it helps in data analysis, demand predictions, optimization of logistics and transportation routes, and identification of inefficiencies in businesses. As a result, supply chain businesses benefit from reduced operational costs, accurate demand predictions, and improved customer interaction.
Over the years, technology has transformed the world into a digital future. Apart from cloud computing and blockchain, Artificial intelligence has emerged as a prominent technology. It has the capability of imitating human intelligence and communicating with machines. In supply chain management, AI helps address problems with higher accuracy, faster, and on a larger scale. Although some organizations face challenges in adopting an AI system, AI has supported several supply chain organizations and provided benefits to their implementations. The research aims to highlight ways in which AI applications have supported operations in the supply chain.
In supply chain logistics, AI has influenced the monitoring of the freight-forwarding process on a large scale and predicted their shipping requirements. It can be utilized in analyzing data, optimizing transportation routes and logistics, making demand predictions, and investigating inefficiencies present in the supply chain. According to Mohsen (2023), the emergence of fuzzy logic and expert systems introduced deep learning applications, big data development, and different graphic processing units. The monitoring system present in AI can track the status, condition, and location of goods in transit. Also, by analyzing previous data, AI can help make future predictions on demand, helping to make informed decisions that meet customers’ needs.
Additionally, AI applications can be integrated into multiple supply chain processes like quality control, order intake, supplier selection, and inventory management. AI algorithms can automate order processes by classifying inventory based on urgency and streamlining delivery cycles. Also, AI has sensors and detectors that effectively identify product deviations or anomalies (Cannas et al., 2023). This helps maintain quality standards and consumers’ needs. In inventory management, through AI predictions, managers can understand when to reduce excess inventory, mitigate stock-outs, and optimize stock levels.
Integration of AI customer interfaces in supply chain business has influenced customer satisfaction. The presence of virtual assistants and chatbots helps in providing fast responses. The chatbots automate client interaction, estimating delivery time and order status and addressing grievances or inquiries. Such AI platforms improve customer experience, service quality, and customer retention. Also, AI communication models can enhance sales and marketing strategies based on customer demographics, regions, or products consumed as predicted from market trends and consumer data.
Artificial techniques have improved efficiency in supply chains. According to Belhadi et al. (2024), the development of self-driving systems has positively simulated the logistic performance and transportation functions in supply chains. As the AI frameworks support decision-making in warehouse distribution, they also enhance adaptative capabilities by making complex systems reconfigurable and organized. In addition, the self-learning algorithm in AI enables firms to develop capabilities for processing information that helps address issues in uncertain environments.
In supply chain management, AI has improved accuracy in demand forecasting. AI algorithms can analyze market trends and patterns, including factors like pandemics and product features that affect product demand. In the past, several supply chain industries mainly relied on historical data and statistical techniques like Autoregressive integrated moving averages and ARIMA to make predictions. Such methods are prone to errors and are time-consuming. AI algorithms for demand forecasting have been a game-changer in logistics. The machine learning algorithm present in AI provides demand forecasts based on real-time data using information from online reviews, demographics, social media, and weather. For emerging products with no historical evidence, AI can cluster data from products with similar features and lifecycles. Such integrations make AI predictions more accurate in analyzing market trends and patterns.
Currently, several supply chain organizations operate in a highly competitive market where disruptions like financial crises or supplier loss can highly affect a business’s operational performance. According to Zamani et al. (2023), apart from data analytics, AI can improve resilience for businesses in the supply chain in case of disruption. “ AI can develop backup plans in the event of disruptions such as traffic jams or severe weather. With the ability to analyze vast amounts of diverse, real-time data—including traffic, weather, vehicle specifications, and fuel costs—the AI can devise the most efficient transportation routes” (Richey et al., 2023, p.533). AI systems make data streams actionable and useful in mitigating risks and addressing the challenges of misinformation. In addition, AI can influence strategies that predict the probability of risk occurring and its impact on the business (Zamani et al., 2023). Thereby helping implement relevant decisions to mitigate such negative effects.
The study articulates insights on the impact of growing AI in the field of supply chain. AI has positively affected inventory management, sales prediction, order processing, distribution, demand forecasting, and transportation. By automating such processes, AI has shown capabilities to improve operational performance and satisfy customers’ needs.
Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. Annals of Operations Research, 333(2), 627-652.
Cannas, V. G., Ciano, M. P., Saltalamacchia, M., & Secchi, R. (2023). Artificial intelligence in supply chain and operations management: a multiple case study research. International Journal of Production Research, 1-28.
Mohsen, B. M. (2023). Impact of Artificial Intelligence on Supply Chain Management Performance. Journal of Service Science and Management, 16(1), 44-58.
Richey Jr, R. G., Chowdhury, S., Davis‐Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532-549.
Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2023). Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review. Annals of Operations Research, 327(2), 605-632.