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What is the optimization phase?
The optimization phase begins after the system goes live. The system is running in day-to-day operations, the core processes are up and running, and initial insights from live operation are available. It is precisely during this period that it becomes clear which project issues were intentionally set aside and where additional adjustments are needed.
The optimization phase is therefore not an unstructured follow-up, but rather a planned step to transform a system that has gone live into a robust solution that is easy to use in day-to-day operations.
These typically involve:
- Process stabilization
- Performance optimizations
- Data adjustments
- Refinement of interfaces
- Implementation of requirements that were not critical to the go-live
- Further functional enhancements
Why is the optimization phase important?
In migration projects, priorities are deliberately set before go-live. Not every requirement needs to be implemented in the first phase of deployment. What matters most is that the processes critical to operations function reliably. Issues of lesser relevance to the launch are therefore often postponed until after the system goes live.
In actual operation, a clearer picture then emerges:
- Which processes are already running smoothly?
- Where are there unnecessary manual steps?
- What data still needs to be updated or cleaned up?
- Where can response times or data transfers be improved?
- Which requirements were deliberately put on hold during the project?
It is precisely during this phase that it becomes clear, from a project perspective, that optimization is not merely a matter of technical refinements. Often, it involves process details, user guidance, turnaround times, and fine-tuning the integration between the ERP system, business units, and connected systems.
What topics are the focus of the optimization phase?
After the go-live, day-to-day operations reveal whether processes work just as well under real-world conditions as they did during testing and training. Some issues only become apparent when multiple employees are working in the system simultaneously, special cases occur more frequently, or handoffs between departments take place under time pressure.
Typical tasks include:
- Simplify process steps
- Reduce unnecessary loops
- Trace exceptions thoroughly
- Evaluate feedback from the business units
- Eliminate recurring causes of errors
Especially with new target processes, there is often a phase in which workflows are not fundamentally redesigned but are noticeably refined.
Another typical area of focus is data. Even if the production migration was successful, it is often only during live operation that it becomes clear where master data needs to be supplemented, cleaned up, or restructured.
This applies, for example, to:
- Incomplete master data
- Additional fields or attributes
- Consistent maintenance of new data structures
- Data that still needs to be adjusted for subsequent processes
This follow-up work is common in many projects, especially when new features or changes to process logic give rise to new data maintenance requirements.
Performance issues often only become clearly apparent after the system goes live. As long as a system is used only in project or test environments, real-world load conditions can be simulated only to a limited extent.
During the optimization phase, the following questions—among others—become relevant:
- Where do delays occur in heavily used processes?
- Which workflows can be sped up?
- Are there bottlenecks in data transfers or background processing?
- Which settings or technical adjustments improve usability?
Performance issues quickly become operationally significant, especially when multiple users access the same processes at the same time.
Even after going live, interfaces often enter a phase of fine-tuning. The technical connection is in place, but during actual operation, details come to light that did not surface with the same level of depth during testing.
Typical areas for optimization include:
- faster data transfers
- targeted error handling
- improved monitoring and control mechanisms
- more precise coordination of triggers and handoff points
Such adjustments are often minor in scope, but they have a noticeable effect on process stability and user acceptance.
In almost every project, there are features that are deliberately excluded from the initial go-live scope. The reason is usually not a lack of relevance, but rather a focus on ensuring a stable production launch.
Typical examples include:
- Convenience features
- additional analyses
- expanded quality or inspection processes
- process variants with less initial relevance
- organizational fine-tuning
The optimization phase provides the ideal framework for implementing these initiatives in an organized manner—without the pressure of the actual go-live.
What happens after the optimization phase?
After the optimization phase, many companies enter a structured phase of further development. At this point, the focus shifts from stabilization to continuous improvement, additional features, organizational developments, and the long-term expansion of the Infor landscape.
Depending on the setup, this step may transition into structured application management or an ongoing improvement process.
FAQs About the Optimization Phase
These are often requirements that were not critical to the launch—for example, convenience features, additional analyses, expanded testing processes, or organizational fine-tuning.
Because real-world load conditions, user numbers, and process volumes can only be replicated to a limited extent in project and test environments. It is only in day-to-day operations that it becomes clear where adjustments are needed.
Yes. Even after a successful migration, it often becomes apparent that data needs to be supplemented, cleaned up, or further standardized in terms of maintenance.
Hypercare is designed to provide immediate stabilization after go-live and focuses on rapid support during live operations. The optimization phase goes a step further and implements targeted improvements to processes, data, performance, and features.
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