In the first part, we looked at why production planning is a problem without clear gradients - why small changes can have huge consequences, why classic optimization methods fail here, and how quickly a small delay on the store floor can turn into a chain reaction.
Today we look at how the PAILOT ENGINE solves this problem - with a modern, AI-based approach that relies on a digital twin and brings evolutionary optimization to its peak.
Why evolutionary optimization is (still) state of the art
The reality of production is dynamic: orders shift, machines break down, staff are sick - classic heuristics or static rules quickly reach their limits. Why? Because they only work well as long as the framework conditions do not change.
That is why the PAILOT ENGINE relies on a robust principle: "Survival of the Fittest" - Darwin's evolutionary model, implemented as a genetic algorithm (GA). What has proven itself in nature also works in production optimization: our algorithms generate many different production plans, let them compete against each other, combine good approaches and mutate them further - until only the best remain.
What sets us apart from traditional GAs? We have intelligently extended the basic model - for greater accuracy, faster learning and real industrial application.
Digital intelligence meets digital reality
The core of our solution lies in the coupling of two components:
1. a detailed digital twin of your production
Our simulation environment precisely maps your real production - including machine behavior, material flows, transport times, set-up processes, etc. This means that we not only know what was planned, but also how the plan actually behaves on the store floor. We answer questions such as:
→ What happens if I postpone order X by 2 hours?
→ What does this mean for the on-time delivery of other orders?
2. an evolutionary agent that "moves through production"
This agent actively tests various control approaches - for example:
→ What happens if I optimize after the shortest setup time?
→ What is the benefit of prioritizing certain orders?
→ How does delivery reliability change if I reduce buffer times?
Target values such as on-time delivery, throughput time or plant utilization serve as evaluation criteria. The agent continuously learns which strategies work best under which conditions
The advantages compared to rule-based planning
Heuristics - i.e. rule-based procedures - have their raison d'être. They are fast, efficient and deliver good results in known scenarios. However, as soon as something changes - be it a new product type, unplanned machine maintenance or seasonal fluctuations in orders - they lose their effectiveness. They are rigid.
The PAILOT ENGINE, on the other hand, is dynamic. It learns what works right now in the current situation - not just what has worked in the past.
The result: adaptive production planning at a new level
The PAILOT ENGINE is not a standard solution, but a learning system that adapts to your specific dynamic production environment. Whether you are looking for APS software, a solution for production optimization or simply practical tips for production planning - our engine delivers:
✅ Robust, adaptive detailed scheduling for the smart factory
✅ Optimization that adapts flexibly to every new situation
✅ Simulations that give you certainty before you actually intervene
✅ Production planning software that not only plans - but also thinks for you
Conclusion
In a world where production processes are becoming increasingly complex and dynamic, you don't need a rigid set of rules - you need an engine that thinks, learns and grows with you. This is exactly what the PAILOT ENGINE is: a combination of digital twin, intelligent simulation and evolutionary optimization - tailor-made for the production planning of today and tomorrow.
If you want to know what this could look like in your production - talk to us. We will be happy to show you how you can take your production planning to the next level with the PAILOT ENGINE.
