ARTIFICIAL INTELLIGENCE


How Otodata uses AI to revolutionise lubricant logistics


Guy Lanrezac, General Manager, Otodata Europe


In an industry where precision, uptime, and supply continuity are paramount, Otodata is redefining lubricant distribution through the power of artificial intelligence. As the world’s largest tank monitoring solution provider, Otodata brings cutting-edge AI capabilities to lubricant suppliers, distributors, and end users, delivering smarter operations and measurable impact. With over three million monitors deployed worldwide, Otodata’s systems continuously feed live data into intelligent platforms, strengthening algorithms and enabling customers to move from reactive to predictive operations.


Predictive inventory management Lubricant distributors have long faced the challenge of balancing inventory against unpredictable demand. Overstocking ties up capital, while shortages risk costly downtime for industrial customers. Otodata’s AI-powered remote tank monitoring systems solve this dilemma by providing real-time visibility into lubricant levels, consumption patterns, and refill cycles.


By analyzing historical usage data alongside environmental and operational factors, Otodata’s algorithms can: • Predict critical lows and consumption spikes before they happen


• Trigger automated alerts for refills, preventing costly runouts


• Optimise purchasing decisions by forecasting demand more accurately


This predictive capability eliminates guesswork and manual tracking. Distributors can plan deliveries with confidence, reduce emergency dispatches, and ensure that customers never face unexpected shortages.


Smarter logistics and route optimisation Beyond inventory, logistics is where AI delivers transformative value. Lubricant distribution often involves complex fleets, compartmentalised trucks carrying multiple blends, and customers spread across diverse geographies. Otodata’s systems help streamline these operations by: • Identifying the most efficient delivery routes based on tank levels, geography, and urgency


• Reducing fuel costs and carbon emissions through optimised scheduling


• Minimising inventory holding costs by aligning supply with actual demand


LUBE MAGAZINE AR TIFICIAL INTELLIGENCE DECEMBER 2025 25


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