Business
Data Leadership and AI in Reverse Logistics
Robson Esteves examines KPI translation in management and the gradual rise of artificial intelligence in reverse manufacturing.
In a conversation with Bruno Gimenes on Marketing - The Podcast, Robson Esteves addresses the need for leaders who can translate metrics into actionable decisions. He also explains how artificial intelligence is beginning to drive efficiency in the manual processes of reverse manufacturing.
In a conversation with Bruno Gimenes on Marketing - The Podcast, Robson Esteves, president of ABREE, analyzed the role of data in corporate management and the initial steps of applying artificial intelligence to recycling. The exchange was part of an episode exploring sustainability, regulation, and reverse logistics, focusing specifically on business metrics interpretation and operational efficiency.
The complete conversation can be watched in the video above, showcasing practical examples discussed during the recording regarding the sector's operational bottlenecks.
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Translating data as a leadership function
In a market increasingly focused on accumulating information and reports, Esteves argues that collecting metrics is useless without the analytical capability to guide practical action. The core management challenge lies not merely in having access to dashboards or performance indicators, but in understanding the true meaning behind raw figures and turning them into operational course corrections.
For Esteves, clear interpretation is the primary responsibility of those leading teams and complex processes. "Data is important; KPIs and indicators are very important to know if we are performing well, where we really need to improve an operation or a cost. But knowing how to translate that, I believe, is the core business of leadership," he states. He emphasizes that leaders must simplify technical diagnostics so teams grasp the strategic direction, preventing data from turning into an empty goal.
Artificial intelligence in reverse manufacturing
Addressing the adoption of new technologies, Esteves observes that artificial intelligence already features mature use cases in traditional linear delivery logistics, particularly in route optimization and cost reduction. However, in reverse logistics and electronics disposal, the industry is still evaluating viable application models, given that many products discarded today were manufactured years ago without built-in traceability technologies.
The most promising advancement in AI occurs in the reverse manufacturing stage, where technology assists with sorting and component separation. "When I talk about reverse manufacturing, there is genuinely a manual aspect. You have to go in, disassemble, remove one part here, another there, to shred that waste as individually as possible," Esteves explains, citing the fundamental separation of metal, plastic, and glass to bring scale and efficiency to recycling.
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