Artificial intelligence for complex production planning

Standard planning rules reach their limits when constraints, objectives, and dependencies become highly complex.With AI‑supported optimization algorithms, MCP enhances Opcenter APS with learning, adaptive decision logics – for planning problems that cannot be efficiently solved using classical rules.This enables economically optimized planning results even in highly variant‑rich or heavily constrained production environments.

Discover planning potential

Artificial intelligence is particularly well suited for planning requirements where multiple complex influencing factors must be considered simultaneously.While classical planning rules represent clearly defined priorities, AI enables the analysis of large combinatorial spaces and identifies optimization potential that rule‑based approaches can only capture to a limited extent.MCP develops and implements AI‑based planning logic specifically in areas where economic benefits can no longer be fully realized using conventional methods.

Typical use cases include:

Optimal workforce allocation

AI algorithms consider qualifications, availability, shift models, and productivity differences to optimally assign employees to existing production plans.

Optimal allocation of production resources

When multiple alternative assignment options exist, AI identifies the most economically viable resource allocation while taking bottlenecks into account.

Setup and cleaning optimization

AI analyzes complex setup matrices and process dependencies to generate sequences that further reduce changeover effort and downtime.

Optimal utilization of silos and tanks

Volumes, content restrictions, cleaning cycles, and process dependencies are evaluated simultaneously to efficiently manage allocations.

Energy costs

In furnace or cooling processes, AI optimizes batch composition to reduce machine runtimes while simultaneously ensuring output and on‑time delivery performance.

Visualisierung OpcenterAPS KI

How AI supports your production planning

Artificial intelligence enhances Opcenter APS with algorithmic optimization methods that systematically analyze large combinatorial solution spaces.While classical planning rules apply predefined decision logics, AI evaluates a wide range of possible alternatives in parallel and identifies the configuration that best fulfills defined economic objectives.Within MCP, several AI‑based planning logics have already been developed and implemented in productive use. These are applied particularly in cases where:
  • constraints are highly interdependent
  • objectives are conflicting
  • variant diversity is high
  • resources can be used in multiple alternative ways
  • classical heuristics are not sufficient
AI does not replace the existing planning logic, but selectively enhances it with powerful optimization components.This transforms rule‑based detailed scheduling into an adaptive, learning optimization system.
AI‑supported planning enables:
  • higher utilization of critical bottleneck resources
  • additional reduction of setup and downtime
  • optimized workforce and resource allocation
  • better utilization of complex process structures
  • faster response to dynamic changes
  • economically more stable planning outcomes in complex scenarios
Back to Opcenter APS

Benefit from AI where conventional planning reaches its limits

In a structured potential analysis, we assess whether your production setup can benefit from AI‑supported optimization methods and identify the economic impact that can realistically be achieved.
You will receive a well‑founded assessment of complexity, potential use cases, and implementation effort.

Assess your optimization potential
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