Unleashing capacity at Heineken México with systems thinking from MIT

It’s no secret that a manufacturer’s ability to maintain and ideally increase production capability is the basis for long-run competitive success. But discovering a way to significantly increase production without buying a single piece of new equipment — that may strike you as a bit more surprising. 

Global beer manufacturer Heineken is the second-largest brewer in the world. Founded in 1864, the company owns over 160 breweries in more than 70 countries and sells more than 8.5 million barrels of its beer brands in the United States alone. In addition to its sustained earnings, the company has demonstrated significant social and environmental responsibility, making it a globally admired brand. Now, thanks to a pair of MIT Sloan Executive Education alumni, the the firm has applied data-driven developments and AI augmentation to its operations, helping it solve a considerable production bottleneck that unleashed hidden capacity in the form of millions of cases of beer at its plant in México.

Little’s Law, big payoffs

Federico Crespo, CEO of fast-growing industrial tech company Valiot.io, and Miguel Aguilera, supply chain digital transformation and innovation manager at Heineken México, first met at the MIT Sloan Executive Education program Implementing Industry 4.0: Leading Change in Manufacturing and Operations. During this short course led by John Carrier, senior lecturer in the System Dynamics Group at MIT Sloan, Crespo and Aguilera acquired the tools they needed to spark a significant improvement process at Mexico’s largest brewery.

Ultimately, they would use Valiot’s AI-powered technology to optimize the scheduling process in the presence of unpredictable events, drastically increasing the brewery throughput and improving worker experience. But it all started with a proper diagnosis of the problem using Little’s Law.

Often referred to as the First Law of Operations, Little’s Law is named for John D.C. Little, a professor post tenure at MIT Sloan and an MIT Institute Professor Emeritus. Little proved that the three most important properties of

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