I’ve hesitated to write this blog because I’m not a Six Sigma Expert. Heck, I’m not even a lean practitioner. At Minitab we believe in “following the best ideas” and maybe this Statistical CFO is on to something. So, apologies in advance if you find this insulting – or flat out wrong – but I think DMAIC is missing a critical letter: S for Simulation. And if continuous improvement is all about challenging the status quo, isn’t it odd that we’ve been following the same 5 letters since the 1980s?
Don’t Get Me Wrong, DMAIC is Great, I Just Think It’s Incomplete
If you’re reading this, you’re probably familiar with DMAIC. Regardless, to make my point let’s have a quick review. DMAIC stands for Define, Measure, Analyze, Improve and Control.
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I would argue that simulation plays a role throughout the DMAIC process. If you’re mapping out the status quo in the Measure phase, you may even want to simulate your current process. For the purposes of this blog, I’ll focus on simulation as part of the bridge from analysis to improvement.
During the Analyze phase, teams develop hypotheses about causal relationships between inputs and outputs (often referred to as Xs and Ys). They use statistical analysis and data to validate the hypotheses and assumptions they’ve made so far.
During the improvement phase, a team works to design a new process, which typically involves some solutions mapping and often uses Designs of Experiments (DOE). DOEs allow teams to systematically test different combinations and use optimization techniques to find the ideal solution.
Why Add Simulation?
Traditional DMAIC assumes that after analysis, you move directly into improvement testing. But that often means piloting changes in real operations, which can be expensive, disruptive, and risky.
By adding a Simulation phase, you can:
- Model uncertainty and quantify risks before changes are implemented.
- Experiment virtually with different solutions to find the optimal design.
- Save cost and time by avoiding trial-and-error in live systems.
- Build confidence with stakeholders by showing data-driven forecasts of potential outcomes.
Simulation in Action: Monte Carlo (Non-Manufacturing)
Imagine a finance team wants to improve invoice processing times. Baseline data shows invoices take anywhere from 2 to 10 days, and the customer requirement is 95% on-time delivery (within 7 days). Using Minitab Workspace’s Monte Carlo Simulation, you can run 10,000 simulated invoice cycles with current variation. The results show only 72% on-time delivery today.
Before implementing improvements (like automation of approvals, standardized templates) that reduce variation, you run those scenarios in simulation. Instead of piloting blindly, the team can make a data-driven case for an improvement.
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Simulation in Action: Discrete Events (Non-Manufacturing)
A hospital is redesigning its emergency department. Patient flow depends on arrival patterns, triage, room availability, and staff scheduling. Running pilots in the real emergency room is impractical and unsafe.
With Simul8, the hospital maps patient arrival and flow through the system. They test scenarios: adding triage nurses, changing shift overlaps, or redesigning patient routing.
Simulation shows which changes reduce wait times and increase throughput without overspending on resources. This virtual experimentation allows leaders to make informed, safe, and cost-effective decisions.
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Simulation in Action: Monte Carlo (Manufacturing)
Imagine an injection molding operation looking to improve yield. Scrap rates vary widely across shifts, tied to cycle time, cooling, and material lot variability.
By running Monte Carlo Simulation with Minitab, the operation models yield against cycle time distributions and cooling time distributions. The simulation shows that reducing cycle time by 10% increases defect risk >30%.
Instead of chasing shorter cycle times, simulation enables the plant to hold the cycle times stable and optimize cooling design.
Simulation in Action: Discrete Events (Manufacturing)
Imagine a company wants to rearrange a packaging line to accommodate a new product. Rather than investing in a new layout, use Simul8 discrete event simulation to evaluates alternative layouts, accounting for operator walking distance, machine cycle times, and conveyor capacity.
Simulation may show the optimal layout before committing to construction.
Are You Ready to Add Simulation to Your DMAIC Process?
Process improvement today requires more than good analysis — it requires predictive foresight. By incorporating Simulation into DMAIC, organizations can move faster, reduce risk, and achieve more reliable results.
Isn’t it time we update our vocabulary? DMAIC served us well, but the future belongs to DMASIC.