The adoption of Artificial Intelligence (AI) and quality in data management are transforming the logistics sector, but without an adequate approach, 80% of companies could fail in their implementation, warned Enrique Vázquez, general director of Recurso Confiable , during a panel focused on logistics control towers, organized by #SoyLogístico Association .
The event featured panelists such as Juan Arévalo, head of Elektra ‘s Digital Supply Network ; Enrique Schleske, director of Supply Chain at Henkel ; and Mauricio Salgado, director of Supply Chain Operations at Coppel , and discussed the importance of correctly capturing, processing and analyzing data to facilitate decision-making .
In this regard, the specialists highlighted how companies face the risk of making decisions with incomplete or outdated data.
“A recent example is the congestion at the ports on the West Coast of the United States, a problem that led many companies to redirect their cargo to the port of Long Beach, whose operating volume increased by 34% in August,” said Mauricio Salgado.
In this context, he pointed out that companies that do not have a robust data control tower could not anticipate these types of problems in time, resulting in delayed merchandise and significant economic losses.
Juan Arévalo said that AI is useful when properly integrated into information systems, but warned that its successful implementation depends on having reliable and complete data.
“Even though 80% of AI projects could fail, the secret lies in the company’s preparation to properly manage the information. Data is relevant if it is complete and integral,” Arévalo emphasized.
In the same context, Salgado stressed the urgency of having factual data to avoid incorrect decisions that could result in cost overruns or loss of merchandise.
“Products that arrive too late are sold at auction, which leads to losses in the millions,” said Salgado.
The key, according to the panelists, is to get the right data and do it at the right time , since for them, excessive analysis and the search for perfection in data generate operational paralysis, which is especially critical in a dynamic logistics environment.
For his part, Enrique Schleske also spoke about how some companies still operate with obsolete methods , such as excessive dependence on Excel, which limits their ability to handle large volumes of information.
“With thousands of codes and combinations in play every day, it is critical to have advanced data management systems that enable real-time decisions. Without them, organizations risk falling behind,” Schleske said.
He also pointed to a generational trend in which some employees, accustomed to manual processes, resist the use of new technological tools , which slows down the transition to more efficient logistics.
Additionally, Enrique Vázquez said that automation is another key trend that is shaping the future of logistics. He said that while automation can optimize operations, it also generates frustration in teams when systems do not achieve the expected perfection.
“To avoid this problem, leaders must manage expectations and prepare teams to work within a reasonable margin of error. Perfection in logistics is unattainable, companies must focus on continuously improving their efficiency and ability to react,” said Vázquez.
Experts agreed that talent development is essential to meet these new challenges.
“It is not enough to have the data and the technology. It is essential that staff are trained to take advantage of these tools. It is a journey that companies must undertake, and there will be frustrations along the way, but it is the only way to remain competitive in a market where mistakes can cost millions,” concluded Juan Arévalo.
The panel concluded by highlighting that the future of logistics depends on successful integration of artificial intelligence and efficient data management, supported by trained personnel.
Furthermore, companies that achieve this combination will be in a privileged position to face the challenges of the sector, while those that lag behind in technological evolution run the risk of failure.
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