Your organisation truly lives and breathes continuous improvement — small, frequent, locally driven changes that never stop.
Systems Dynamics Process Simulation and Optimisation with Machine Learning and Artificial Intelligence shows you what to improve, where, and by how much — within your available budget — to achieve your goals.
It helps you focus effort, test scenarios safely, and predict the performance uplift before making changes in the real world.
Without simulation and optimisation, particularly in larger project-style improvements, you’re often flying blind. Hoping you’re addressing ‘THE’ problem / inefficiency (not just 'a' problem / inefficiency), in the right place and to the right degree, … rather than knowing you are, and being able to prove it!
Is it the pinnacle of all improvement methods? Not quite, perhaps - but it can be an exceptionally smart place to start.
See the difference between the no-and-pro simulation and optimisation approaches within the PDCA cycle below (just click the diagram if you want to freeze and enlarge it).
When should you simulate and optimise before improvement? The answer largely depends on the approach your organisation takes to improvement and the culture surrounding that approach - whether it favours incremental or project-style change.
Your organisation truly lives and breathes continuous improvement — small, frequent, locally driven changes that never stop.
Teams continually tweak the process based on what’s visible right now, and improvements are made close to the work, often without formal modelling.
Bottlenecks shift as constraints are relieved — and the cycle of tweaking the process and observing simply repeats.
This “follow the bottleneck” approach can work brilliantly in organisations that accept a little trial and error. But in cultures that demand measurable, rapid results, its slower and less predictable payback can frustrate leadership and make ongoing funding harder to justify.
You’ll usually get most benefit from simulation and optimisation when improvement is structured as a project — with defined goals, budgets, and sponsors. In that case:
You (with the help of ML / AI) can model the process, explore “what-if” options, and identify - and demonstrate - the most effective levers for change.
Optimisation ensures effort and investment are targeted both precisely and correctly — the right magnitude of change, in the right place. It also provides a persuasive story for senior stakeholders: “Here’s what will happen if we invest here, not there.”
Simulation up front doesn’t slow you down — it prevents wasted motion later. The trade-off: a short delay for modelling and data gathering, repaid by fewer false starts and stronger delivery confidence.
Yes - several.
Many organisations run both continuous improvement and project-style efficiency initiatives in parallel. In that case, simulation and optimisation are most valuable for the larger, project-based improvements that need quantifiable, defendable results.
Some mature organisations know their processes so intimately that simulation may add little profound insight — though even they may often use it as a validation lens to confirm that intuition aligns with data.
A brief summary of when, and when not, to simulate and optimise using Machine Learning and Artificial Intelligence ...
1 Simulate and optimise when: you’re investing serious time or budget, have significant and exacting improvement challenges, seeking measurable impact, or needing stakeholder confidence before implementation.
2 Skip or simplify simulation when: you’re in a rapid, low-risk continuous improvement culture that thrives on direct experimentation.