From warehouses to last-mile delivery: AI’s role in transforming supply chain management

AI technologies such as machine learning (ML), natural language processing (NLP), and generative AI are revolutionizing supply chains by streamlining operations, reducing inefficiencies, and optimizing decision-making. These tools enhance automation in customer relationship management (CRM), inventory tracking, and logistics planning. AI-driven demand forecasting models leverage historical data, market trends, and external variables to predict future demand with greater accuracy, reducing stock shortages and overproduction.

The evolution of artificial intelligence (AI) has reshaped various industries, and supply chain management (SCM) is no exception. With the emergence of Industry 4.0, AI-driven solutions are enhancing supply chain efficiency, resilience, and strategic decision-making.

A recent study titled AI Applications in Supply Chain Management: A Survey, authored by Adamos Daios, Nikolaos Kladovasilakis, Athanasios Kelemis, and Ioannis Kostavelis, and published in Applied Sciences (2025), offers a comprehensive analysis of AI's transformative impact on key SCM processes. The study explores AI’s role in inventory management, procurement, demand forecasting, transportation, and risk management, highlighting both the opportunities and challenges associated with AI adoption in modern supply chains.

AI’s role in enhancing supply chain efficiency

AI technologies such as machine learning (ML), natural language processing (NLP), and generative AI are revolutionizing supply chains by streamlining operations, reducing inefficiencies, and optimizing decision-making. These tools enhance automation in customer relationship management (CRM), inventory tracking, and logistics planning. AI-driven demand forecasting models leverage historical data, market trends, and external variables to predict future demand with greater accuracy, reducing stock shortages and overproduction. Moreover, AI-powered chatbots and virtual assistants in CRM systems improve customer interactions and service efficiency by handling inquiries and processing orders in real-time.

In transportation and logistics, AI optimizes route planning by analyzing real-time traffic, weather conditions, and fuel efficiency, ensuring cost-effective and timely deliveries. Autonomous vehicles and drone-based deliveries, powered by AI, are gaining traction, further enhancing last-mile logistics efficiency. The study emphasizes that AI's ability to process vast amounts of data and generate predictive insights significantly improves supply chain responsiveness and agility.

Overcoming challenges and barriers to AI adoption

Despite the benefits, the study identifies several challenges hindering AI adoption in SCM. Implementation complexity, high initial investment costs, and the need for digital maturity within organizations pose significant barriers. Many businesses struggle with integrating AI into existing supply chain networks due to legacy infrastructure and a lack of skilled personnel. Ethical considerations, such as data privacy concerns and algorithmic biases, also impact AI deployment in SCM.

Additionally, supply chain resilience depends on AI-driven risk management solutions that can identify potential disruptions and mitigate risks proactively. AI-based predictive analytics enable companies to assess vulnerabilities, monitor supplier performance, and optimize contingency planning. However, resistance to AI adoption among decision-makers, concerns over job displacement, and the need for transparency in AI-driven decisions remain critical challenges for widespread implementation.

Future trends: Human-centric AI and sustainable SCM

The study highlights emerging trends in AI adoption for SCM, including a shift towards human-centric AI, which enhances collaboration between humans and intelligent systems. AI-powered digital twins—virtual simulations of supply chain networks—are becoming integral to optimizing operations and improving decision-making. The integration of AI with blockchain technology is also gaining attention for enhancing transparency and traceability in supply chains, reducing fraud, and improving compliance with sustainability goals.

Sustainability is another key focus area, with AI-driven solutions being leveraged to optimize resource allocation, minimize waste, and improve carbon footprint tracking. Companies are increasingly adopting AI for responsible sourcing and ethical supply chain practices, ensuring regulatory compliance while promoting environmental sustainability. The future of AI in SCM lies in developing adaptable, secure, and ethically responsible AI systems that foster efficiency while upholding social and environmental responsibility.

Conclusion: Embracing AI for a smarter supply chain

AI is undeniably shaping the future of supply chain management by enhancing efficiency, mitigating risks, and driving innovation. As businesses navigate the complexities of global supply networks, AI-driven solutions will play a pivotal role in ensuring operational resilience and agility. The study underscores the importance of strategic AI adoption, digital transformation, and continuous investment in AI research to unlock the full potential of AI in SCM.

While challenges persist, the benefits of AI in supply chain management far outweigh the drawbacks, making it a necessity for companies aiming to remain competitive in the rapidly evolving digital landscape. With the right balance of technology, human expertise, and ethical considerations, AI-powered supply chains will define the next era of industry evolution, fostering sustainability, resilience, and intelligent decision-making.