MEILLER Aufzugtüren GmbH specializes in the development and production of high-quality elevator door solutions for passenger and freight elevators. In pre-production at the Trumpf laser, it is determined every day whether delivery dates are met and how efficiently material is used. This is exactly where planning complexity becomes visible. Sequencing, setup and plate layout (nesting) are directly interconnected. Even a small change can noticeably shift the daily schedule.
MEILLER aims not only to manage this complexity, but to use it strategically to create an operational advantage. The goal is a process that remains stable, is easy to understand and continues to function reliably even when short-term changes occur. To achieve this, DeepSynergy.AI brings the relevant planning logics together in an automated detailed scheduling approach. All requirements are considered consistently. The result is a robust production plan that can be updated quickly when changes occur and truly holds up in day-to-day operations.
– Production Manager, MEILLER –
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A due date is a fixed delivery or completion date that serves as an anchor for prioritization. In practice, prioritization becomes difficult when related orders are not clearly linked in the system or carry different due dates. This creates a distorted view of priorities. A consistent due-date logic ensures that related orders are evaluated together. This makes sequencing more stable and easier for the team to understand.
In laser cutting, parts are cut from metal sheets. How parts are combined on a sheet has a direct impact on material efficiency. If residual areas remain unused, scrap increases. Intelligent planning therefore considers not only the sequence, but also meaningful combinations of parts. This makes it possible to utilize sheets more efficiently without losing sight of due-date requirements.
Setup includes the preparatory steps required before an order can be processed. Changes, for example between materials or process parameters, create additional effort. This effort directly influences sequencing. Intelligent planning reduces setup effort by grouping changes where it makes sense while still respecting due-date requirements. This stabilizes the daily schedule and reduces short-term replanning.
MEILLER’s expectation is a structured and transparent planning approach. Due dates should be managed reliably, material should be used efficiently, and coordination across production should become more stable.
This is where the collaboration with MEILLER begins. Based on the initial situation, the key trade-offs and MEILLER’s expectations, we start with a joint workshop. In this workshop, we capture the relevant planning logics, dependencies and priorities.
These planning logics are then documented in a structured way and discussed with MEILLER. This creates a shared understanding of what is being planned, which logics guide decisions and which objectives take priority. It increases the traceability of decisions and creates a solid foundation for implementation.
On this basis, we configure the DeepSynergy.AI Planning Engine. The documented logic becomes the foundation for the software and the first production plan.
At MEILLER, DeepSynergy.AI maps detailed scheduling and control at the Trumpf laser as a consistent planning logic. The solution consists of five building blocks that jointly manage sequencing, setup logic and sheet layout.
Orders are prioritized so that due dates are reliably taken into account. Dependencies between orders are systematically considered as well. This creates a sequence that remains workable in day-to-day operations.
Setup activities and changeovers directly influence both effort and sequence. The setup logic consistently reflects these influences, including relevant special cases. This makes sequencing more realistic and more stable.
In laser cutting, the way parts are combined on sheets determines material efficiency. The logic treats sheet layout as an integrated part of detailed scheduling. As a result, sequencing and material decisions are considered together.
Where it makes sense, orders are broken down into smaller quantities and combined. This enables more flexible planning without losing sight of due-date requirements. In this way, planning can respond better to real operational constraints.
New or changed orders do not trigger manual replanning, but a consistent update of the plan. The logic remains the same; only the input parameters change. This allows the plan to be regenerated quickly.
In day-to-day operations, it is not the model that matters, but the impact. At MEILLER, planning becomes traceable and repeatable. Decisions are based on a consistent logic rather than situational individual decisions. Changes can be integrated cleanly into the process without planning losing stability.
Clarity on the impact of priorities
It becomes transparent how different priorities affect due dates, material utilization and the overall planning process.
Less coordination effort
Planning and production work on a shared basis. Coordination becomes more targeted and operational effort decreases.
More stable process despite disruptions
Short-term changes and operational disruptions lead to less instability. The process remains manageable and can be adjusted in a structured way.
Planning becomes scalable
Recurring decisions are handled consistently. Know-how is systematically captured and is less dependent on individual people.
Material efficiency and on-time delivery combined
Multiple objectives are considered simultaneously instead of being optimized in isolation.
Better coordination in production
Downstream processes are supplied more reliably. Process fluctuations decrease and collaboration in production becomes more stable.
Many production orders consist of multiple quantities or line items that must be completed together by a specific date. In traditional systems, however, planning is often still done at order level. This creates a disadvantage: when orders are treated only as closed units, potential in sheet layout remains unused.
A key lever is therefore planning at piece level. Individual parts of an order can be combined with parts from other orders so that sheets are utilized more effectively and scrap is reduced. The critical point is that the common due date of the higher-level order must still be met consistently.
Piece-level logic instead of rigid order logic
DeepSynergy.AI breaks production orders down into plannable units. This makes it possible to distribute parts across multiple sheets and combine them with parts from other orders at the same time.
Orders become plannable at piece level.
Parts from different orders can be nested together.
Due dates remain consistent across the overall order.
At MEILLER, a single order may include several identical components required for a door or assembly by a defined due date. If this order were planned only as a whole, the parts would often be grouped on one sheet or on a few directly consecutive sheets. As a result, free space in the nesting layout often remains unused, even though it could be meaningfully filled with suitable parts from other orders.
In detailed scheduling, these orders are therefore broken down to piece level. Individual components of one order can be distributed across different sheets and combined with parts from other orders during nesting. This results in a significantly more efficient sheet layout: residual areas are used better, scrap is reduced, and at the same time it remains ensured that all required quantities of the order are produced in time for the due date.
Better utilization of available sheet area
Less scrap through intelligent nesting
On-time delivery despite cross-order combination
1. Potential analysis
Together, we assess the current planning situation at MEILLER: Where are the bottlenecks, challenges and trade-offs? This creates a clear picture of the potential within planning and where DeepSynergy.AI can create value.
2. Parallel planning
In joint workshops, we analyze the existing planning process and make planners’ knowledge structured and usable. Requirements, dependencies, priorities and planning logics are identified together and translated into a robust planning foundation. On this basis, an initial production plan is created with DeepSynergy.AI and reviewed alongside the existing planning approach.
3. Validation
In the final step, the results are evaluated in parallel operation. To do this, we compare key KPIs of the current planning approach with the results generated by DeepSynergy.AI. This creates a solid basis for transparently assessing impact, robustness and the next steps.
Whether in production, logistics or other planning challenges
– DeepSynergy.AI is your partner for complex solutions
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