Teaching Practice8 min read5 February 2026

Teaching Lean and Process Design Without a Factory Floor

Lean principles are hard to teach without a physical process to observe and improve. Here is how simulation gives operations students the experiential foundation they need.

Lean is fundamentally an observational discipline. Taiichi Ohno's original injunction — go to the gemba, observe what is actually happening — presupposes that there is a physical process to observe. University operations management programmes face an obvious problem: there is no factory floor. There is a classroom, a textbook, and perhaps a paper-folding exercise that has been used to demonstrate batch processing for twenty-five years. The question is whether that is enough to build genuine lean thinking, and the answer, increasingly, is no.

Why Paper-Folding and Static Exercises Have a Ceiling

The classic lean teaching exercises — paper airplane factories, lego production lines, the ball-passing game — are effective introductions to specific waste categories. Students experience overproduction, waiting, and unnecessary transport in a way that is memorable and fun. But they hit a ceiling quickly. They do not expose students to the systemic interactions between lean decisions: the way that reducing batch sizes affects WIP inventory, which affects cash flow, which affects the ability to invest in quality improvements, which affects defect rates, which affects rework costs. Those interdependencies are where lean thinking becomes genuinely strategic — and they require a dynamic, multi-variable environment to teach.

The Seven Wastes Need to Be Experienced Sequentially

Muda, mura, and muri — waste, unevenness, and overburden — are most effectively understood when students experience them in sequence, not as a taxonomy to memorise. A simulation that allows teams to make process design decisions across multiple turns, observing how those decisions interact with variable demand and resource constraints, gives students the sequential experience that transforms waste classification from vocabulary into diagnosis. The student who has watched their WIP inventory balloon after a poorly calibrated batch size decision understands overproduction differently from the student who has only read about it.

Active learning approaches in operations management, including simulation and role-play, produce measurably higher retention of lean concepts and significantly better transfer to novel problem contexts.

Piercy & Rich, International Journal of Operations & Production Management, 2022

Connecting Lean to Digital Operations

Modern lean practice is inseparable from digital process management. Industry 4.0 has transformed the gemba: real-time sensor data, digital twins, and AI-assisted process monitoring mean that the waste identification that Ohno did by walking the factory floor is now done through dashboards and predictive analytics. Operations management education needs to reflect that reality — not by teaching specific software tools, but by giving students the experience of using data to identify and eliminate process waste in a dynamic environment.

SPPIN Sim's Process and Operations Simulation

SPPIN Sim provides operations teams with a live process simulation environment where lean decisions — about batch sizes, quality inspection frequency, supplier lead times, and process flow sequencing — produce visible, quantified outcomes across turns. Students can observe the relationship between their process design choices and metrics like throughput, defect rates, and total operational cost in real time. The simulation includes a live KPI dashboard that functions as a digital gemba, giving teams the data-driven process visibility that modern operations roles actually require.

Assessment and Reflection in Lean Education

One of the persistent challenges in lean education is generating meaningful reflective assessment. Asking students to write an essay about lean principles after a paper-folding exercise produces generic responses. Asking them to analyse their own team's process decisions — using the logged data from a multi-turn simulation — produces genuinely specific, analytical reflection. The student who can trace a quality problem back to a batch size decision made three turns earlier, articulate why that decision seemed rational at the time, and describe what they would change is demonstrating exactly the kind of kaizen mindset that lean education should develop.

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