The Vanishing Act
A food-waste refiner was losing thousands of litres of used cooking oil each month. Drivers arrived at restaurant bins only to find them empty, and growing financial losses threatened the company’s operations. An initial theory pointed to a rival competitor’s crews siphoning the grease, so our team coordinated surveillance on that competitor. After several nights without incident, it was clear they were not involved and we needed a new approach.
Applying Data Analysis
The client provided a record of roughly 2,000 theft incidents, each noting date, time, and location. By charting these events over calendar weeks, we observed a consistent pattern: most losses occurred in the early hours of Tuesday and Thursday mornings. Using that pattern, we scheduled targeted monitoring for the next predicted window rather than maintaining round‑the‑clock stakeouts.
Geographic Clues
A spreadsheet can only tell half the story, so we next converted each incident address into map coordinates and overlaid them on a regional grid. Almost instantly, a geographic “bald spot” emerged—a swath of restaurants that had never reported a single theft. This begged the question: why would the thieves avoid this area?
Using OSINT, we identified and overlaid every known cooking-oil refinery—competitors included—onto the same map.
One operation sat squarely in the bull’s-eye of that untouched zone suggesting its crews deliberately avoided this area to maintain distance, operate unchecked, and reduce the risk of detection.
Focused Surveillance and Resolution
On the night identified by our predictive analysis and in the area pinpointed by mapping, our surveillance team observed the competitor’s trucks returning to bins and collecting oil. The recorded footage provided definitive evidence which was ultimately used to file criminal charges.
Key Outcomes
This investigation demonstrates how structured data review can streamline an inquiry. By isolating the most likely times and locations for theft, we reduced surveillance hours by over 70 percent and pinpointed the responsible party in a single operation. The client saw immediate cost savings and regained confidence in their collection process.
Conclusion
When routine methods fail, data‑driven techniques can reveal trends and geographical patterns that guide efficient investigations. For businesses facing repeated losses, combining incident records with straightforward analytic tools offers a clear path from problem identification to successful resolution.
