Production planning is the backbone of every modern production facility. But anyone who has ever tried to coordinate complex processes on the store floor knows that it's a bit like dominoes - except that the tiles are constantly changing position. While many optimization problems can be solved with clear rules and measurable progress, production planning is more like a dynamic puzzle. Small changes can have massive, often unpredictable effects - that's what makes it so challenging.
A single part can influence the entire chain
A product goes through three successive production steps:
- Task A on machine 1
- Task B on machine 2
- Task C on machine 3
If a tool fails in task A, a domino effect occurs:
Machine downtime
Task B cannot begin because A is not completed. Machine 2 remains unused, capacity is lost.
Postponements
The delayed start of B collides with other jobs on machine 2. Backlogs occur, causing other processes to be disrupted.
Effects on downstream processes
Task C is also postponed. Machine 3 is blocked - not only for this task, but also for other, independent processes.
Disruptions in logistics
Just-in-time deliveries can no longer be met. This leads to rising storage costs, missed deadlines and, in extreme cases, contractual penalties.
A seemingly minor failure quickly develops into a system-wide planning problem.
Why classic optimization approaches reach their limits
In many technical areas, improvements can be achieved step by step - for example when adjusting controllers, where a clear optimization path and direct feedback are available. Production planning is fundamentally different:
Discreet decisions
An order is either scheduled or not. A machine is either available or blocked. Intermediate states do not exist.
Non-linear effects
The smallest changes can destabilize or - more rarely - improve the entire system.
Delayed feedback
Whether an adjustment was actually advantageous often only becomes apparent after the entire scenario has been fully calculated.
Continuous, step-by-step optimization is therefore hardly possible. Instead, there are leaps between potential solutions - with very different results.
Why simple rules are no longer enough
Traditional heuristics and manual planning approaches are based on empirical values and work primarily in stable environments. In reality, however, production environments are highly dynamic. New product variants, unplanned maintenance or delivery bottlenecks can quickly override established rules.
Conclusion
Production planning is one of the most challenging tasks in manufacturing, as it is not a continuous optimization problem. Rather, it is a complex, dynamic system with numerous dependencies in which even minor disruptions can have far-reaching consequences. Adaptive, intelligent systems that can react flexibly to changes are therefore required - the PAILOT ENGINE is presented in the second part.
➡️ Click here for part 2: How the PAILOT ENGINE solves the problem
