Computational Lean Theory
During my undergraduate studies, I was introduced to lean thinking and lean principles – approaches that businesses and operations use to eliminate waste from their procedures. While these principles, developed and popularized by Toyota, have been around for a significant time, they’ve remained primarily a mental model and practice method. Though intriguing and innovative, lean thinking isn’t a model in the practical sense; it’s merely a thought mechanism.
This raises an important question: How do companies and operations actually verify they are practicing lean principles? Is it simply through word of mouth? One could argue that merely using the word “lean” in company communications doesn’t necessarily mean an organization is truly implementing lean principles. The fundamental flaw lies in the absence of a concrete measurement framework for lean thinking and its principles.
In our universe governed by axioms, everything that occurs within this structure follows logical principles that can be measured and tracked. Given that lean thinking has been extensively used in business for decades, there should be a framework to measure and gauge lean theory. This is where I propose the concept of Computational Lean Theory.
I believe lean thinking, traditionally used in businesses and supply chains, can be extended to autonomous systems, game theory, and control theory. At its core, lean thinking is about maximizing value from every resource – essentially squeezing the most value out of every decision.
Consider autonomous systems: What if they could navigate their environment in a way that extracts maximum value from their resources, such as battery life, to achieve their goals? This would provide crucial context to these systems and enhance their intelligence. Just as humans act strategically to conserve energy, the same principles could be applied to machines.
This approach addresses limitations in current reinforcement learning systems, which focus on maximizing cumulative rewards. While this works well for operations like games where resources are somewhat abstract, real-world settings involve limited resources. Computational Lean Theory could bridge this gap.
Game theory enables strategic decision-making in various domains – games, business, and politics. However, the contexts in which these decisions operate are often abstract. How do we determine if a decision is truly optimal, and optimal for whom? While a strategy might be optimal in an abstract sense, the real world is far more dynamic, layered with dependent and contextual variables.
Computational Lean Theory can add crucial context to decisions, allowing us to gauge their value through decision spaces that map possible outcomes, contextual values that quantify impact, resource utilization metrics, and environmental impact assessments. This transformation from mental model to mathematical framework would enable organizations to verify whether they’re actually maximizing value rather than merely assuming it. The implications extend beyond business efficiency – this framework could help individuals make better decisions in their personal lives.
The cornerstone of this theory is the Lean Efficiency Ratio – a metric that quantifies the value of each decision relative to resource utilization. This ratio would consider resource input, value output, contextual factors, long-term sustainability, and system-wide impact. By implementing this ratio, organizations and individuals can measure the actual efficiency of their processes, compare different strategic approaches, optimize resource allocation, make data-driven decisions, and track improvement over time.
Through this mathematical lens, we can finally move beyond simply claiming to be “lean” and instead demonstrate, measure, and continuously improve our efficiency in meaningful and quantifiable ways. Lean thinking can finally be abstracted from the mind and into algorithms and computational procedures.
