A global food manufacturer ran 73 plants across 12 countries on 5 continents. Every plant manager could explain their own numbers. But there were no benchmarks. Relative to what? A highly automated meat plant in one country and a manual line in another are fundamentally different operations. And so productivity conversations stalled out the same way each time. "Our plant is different."
Leadership believed there an opportunity to improve organizational structure and operational efficiency across the network. But proving it plant by plant, with all their nuances, was difficult.
Falconi started with centralized global analyses that put every plant into clusters, by automation level, product type, and process family.
Within each cluster we organized around tons produced per operational FTE as a critical metric. Because plants in each cluster were sufficiently similar, the benchmarks could be trusted. We then analyzed the plants across seven additional categories: personnel, equipment efficiency, labor distribution, task division, performance variability, and waste.
The benchmarks created hypotheses, which the plants would then test. Nothing counted as savings until the hypothesis was validated and signed off by the plant. In total, 749 validated hypotheses became 854 concrete actions.
The rollout came in five waves: 6 plants, then 12, then 27, 42, and finally all 73. Falconi ran the early waves directly and trained internal teams to execute the later ones, so by the final waves the company was running the methodology itself.
New hypotheses and best practices were shared across plants using an "idea book", surfacing things like manning and break reviews, shift redesigns, changeover improvements, line speed, and contract reviews.
There were so silver bullets, but the savings in aggregate were material. Standardizing cooking times on a single recipe, for example, where actual times ranged from 103 to 552 minutes against one median, saved $244K a year on its own.
In six months they surfaced $171.7M in validated opportunity, a 17% savings opportunity against baseline. Of that, $138.4M was signed off by the plants.
The cluster benchmarks allows the organization to make apples to apples comparisons between plants, allowing optimization conversations to become more productive.
Having the plants sign-off on hypotheses once they were validated (vs. trying to impose them from the top) helped the plants take ownership of the changes, which made them stick.
And by training internal teams to run the later waves, Falconi left behind a repeatable system the team to execute on its own.