Artificial intelligence (AI), in its most basic concept, refers to the ability of a computational system to perform tasks commonly associated with humans, as well as developing capabilities characteristic of human intelligence such as learning, reasoning, or self-correction. Today, AI controls a significant portion of the elements present in a person’s daily life, such as movie and series recommendations, memories or albums formed in photo galleries, and the differentiated selection of products promoted to a market niche, among others.
In the business realm, this concept is equally relevant. In a survey conducted in early 2024 by BCG of over 1,400 executives across more than 50 markets, over 89% of respondents ranked AI in the top three priorities for tech areas. Moreover, 54% of them, already aware of its benefits, expect a cost savings of at least 10% reflected in the growth of operational efficiency and significant improvements in customer service. It is undeniable that AI has emerged as a disruptive technology that is here to stay, capable of optimizing and transforming operations, including all those involved in the supply chain.
Although significant benefits have been demonstrated in logistics operations that utilize AI, the vast majority of companies in LATAM have yet to make the leap towards transformation. The adoption of AI in logistics processes should not be optional; the transition is necessary, as it not only adds value to operations but is also the clearest tool for remaining competitive in the long term. Some applications and benefits of this powerful tool for supply chain processes include:
1. Intelligent Demand Planning
Generative artificial intelligence has the ability to create more accurate sales forecasts, allowing for optimal scheduling of orders to suppliers, with a direct impact on inventory levels. Additionally, a generative AI model can develop flexible inventory policies aligned with business objectives, such as optimizing costs from overstock or stockouts using just-in-time strategies.
Applying AI tools for demand management fosters lean practices that reduce waste and prevent inventory shortages. More accurate demand planning also optimizes operations and resources in the supply chain.
2. Inventory Management
Having optimized demand planning directly benefits inventory management; however, the impacts of AI in this area are not limited to this. By using AI tools/models, some processes that directly affect inventory decision-making can be automated, such as smarter control of physical inventory processes or cycle counts, automatic inventory counts for full pallet locations, and automatic reorderings based on stock levels and demand studies, among others.
All these actions enable greater control and reliability of inventory—capabilities that are undoubtedly high-impact for companies managing a large number of SKUs and markets.
3. Improved Customer Relationships and Fulfillment Levels
Chatbot models are implemented to automate basic interactions with customers, allowing them to request updates on order status, modify delivery timelines, and make inquiries, all managed and resolved by AI. These models enable quicker first contact with the customer and allow customer service areas to focus on resolving more specialized issues.
4. Warehouse Automation
Automated processes reduce human errors and are optimal in execution. Having key processes such as picking, packing, and pre-shipment controls managed by a robot or algorithm has multiple advantages; AI has played an important role in this application.
Some of the most notable capabilities/functions of automation feature an implemented AI model. For example, in picking processes, many automations can reduce travel distances by predictively detecting bottlenecks instead of reactively, among other functionalities. This is thanks to improvements implemented by AI models, which continue to be enhanced for a high impact on distribution center operations.
5. Distribution and Transportation Coordination
AI has made distribution and transportation strategies much more accurate and efficient. The adoption of AI models in transportation management systems facilitates decision-making as it considers factors such as traffic, route costs, ideal vehicles, all in real-time.
Route planning, live tracking of order status and dispatch, minimization of risks that could cause delays in order delivery, and delivery of orders through non-human-operated devices are just a few of the additional benefits AI has provided.
Considering the points above, while the impact of implementing AI in supply chains is positive, the companies that have invested in this technology are few. How can one identify when a company is ready to implement AI? The answer is that while every business has its own goals and corresponding strategic planning, if some of the challenges to overcome include: high costs, operational inefficiency, delays, and low-quality customer service, volatility and uncontrollable high-impact changes, then evaluating the implementation of tools that include artificial intelligence and add value, flexibility, and adaptability to processes is a very good decision.
The applications and benefits of AI in logistics are more than those mentioned here; this is a topic that can undoubtedly be explored in depth according to the desired focus. The applications are so diverse and have so many levels that they can be chosen based on requirements, expectations, and resource availability. The best way to start down the right path for implementing AI-inclusive tools is to identify the areas of the company where this adds the most value, exploring in detail the different solutions and their applicability in the processes.
References
Boute, R.N., Udenio, M. (2023). AI in Logistics and Supply Chain Management. In: Merkert, R., Hoberg, K. (eds) Global Logistics and Supply Chain Strategies for the 2020s.
Apotheker, J., Duranton, S & et al. (2024). From potential to profit with GenAI. www.bcg.com/publications/2024/from-potential-to-profit-with-genai
Gleen, R., Chowdhury, S. & et al. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research.
Zhuravlova, Y., (2024). AI in logistics: How does it truly transform the field?
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