Artificial Intelligence (AI) is increasingly emerging as a major driver of transformation in the supply chain sector. Improved forecasting, transport optimization, process automation — the promises are many. Yet this enthusiasm, often amplified by hype, calls for a critical perspective.
Where some envision a fully autonomous system, others — including Interlog Solutions — advocate for AI that supports, rather than replaces, human decision-making. While AI can greatly enhance performance, decisions must remain understandable, controlled, and responsible.
The Promises of AI for the Supply Chain
AI brings tangible opportunities to the supply chain. In demand forecasting, it helps anticipate the volumes to produce or procure, thereby reducing overstock and shortages. Predictive models rely on historical data, market trends, and even societal behaviors.
In transport, intelligent systems adjust delivery routes in real time, reduce empty mileage, and select optimal paths. This results in better punctuality, lower costs, and reduced CO₂ emissions.
Lastly, automation is gaining ground in logistics processes. From warehouse management to transport planning, AI streamlines operations and improves the overall efficiency of supply chains.
AI and Decision Support: Keeping Humans in the Loop
Despite these advances, AI models often remain opaque. Their operation relies on complex algorithms that are difficult to interpret. This “black box” becomes a real concern when dealing with critical logistics decisions.
At Interlog Solutions, the stance is clear: AI should inform decisions, not dictate them. The software solutions in development aim to provide smart alerts, contextual recommendations, and predictive analyses. But the final decision always lies with the user. This approach combines AI’s computational power with human analytical insight, ensuring decisions are both informed and grounded in real-world realities.
Responsiveness is therefore enhanced — but always with discernment. This hybrid model, blending technology with human responsibility, enables organizations to harness AI’s capabilities while maintaining an understandable operational logic.
Environmental Issues: The CO₂ Benefit in Perspective
The excitement around AI should not overshadow its environmental cost. The data centers and infrastructure required for AI consume significant energy and water resources. Their carbon footprint is substantial — especially when training and running complex models like those based on deep learning.
This creates a paradox. On one hand, AI promises to reduce supply chain emissions through better planning, optimized routing, fewer returns, and refined inventory management. On the other hand, AI itself generates considerable emissions — which are often invisible in companies’ carbon assessments.
This calls for a trade-off. For AI to truly support decarbonization, a comprehensive measurement approach is essential: evaluating both operational gains and the environmental footprint of the technologies used. Developing eco-designed, energy-efficient solutions by default is becoming a necessity — aligning logistical performance with climate responsibility.
AI has already begun transforming the supply chain. But the key to successful integration lies not only in computational power, but in how AI is designed, governed, and put at the service of its users. Embracing explainable, energy-efficient AI that supports human decision-making is the path Interlog Solutions champions — for a responsible digital transformation of the supply chain.