DELMIA Quintiq Performance Management




For an effective performance-oriented design, we consider both non-functional requirements (NFRs) and projected business, data, and end user growths, to future proof the DELMIA Quintiq implementation. Some best practices to consider are:

There is no one-size-fits-all approach for performance-oriented design. Decisions are based on the size of the puzzle, constraints imposed by the software or environment and the customer’s technical requirements.


The solutions Ab Ovo develops for our customers are, in many cases, mission critical, therefore designing a performance-oriented system is vital to the success of our projects. This also includes designing effective load & performance tests to help determine performance issues prior to go-live.

– Number of datasets: Use one or more datasets based on the size of the planning puzzle to optimize simplicity, performance, and scalability.

– Specialized datasets: Deploy separate datasets for integration, master data and optimization to help improve performance.

– DELMIA Quintiq Dataset Store (DSS): Help eliminate performance issues by using DSS technology to store datasets on the file system.

– Background processing: Configure integration and optimizer transactions to run in the background via Reactive Quill.

– Physical or virtual environment: Use virtual machines for scalability and fast deployment or physical machines to enhance performance.


Designing an effective load & performance test (LPT) is critical to identify performance issues. In some cases, the design process is relatively trivial. As the scope, size, user base and mission criticality of the system increase, the LPT design becomes more challenging. Some best practices to consider are:

– Test timeline: We work with the customer to identify what a peak hour in a planner’s day looks like use that as a basis for the test design.

– Realistic design: We consider factors that simulate the reality of the planner’s world to create a realistic test design.

– Smart scoping: Our aim is to simulate peak, realistic end user and transaction loads in an LPT.

– Realistic data: We aim to execute tests on Production data volumes using artificial identifiers or names.

– Test scenarios: In most cases, we simulate a single test scenario in an LPT that represents maximum loads and potential user actions.

– Usage profiles: Planners typically have specific usage profiles, which should be considered while designing a test.

– Architecture: To ensure results represent of actual performance, tests are performed on Production-like hardware and Quintiq architecture.


Ab Ovo offers a tailored approach that first focuses on the customer’s real-world use cases and performs a performance scan to determine where bottlenecks exist.

To complement this, we have created a tool that automates the parsing and visualization of relevant performance and system statistics from Quintiq log files. 

Using existing file parsing tools to analyze Quintiq logs or manually inspecting them can become tedious and time consuming, especially if a large volume of logs is involved or if the aim is to determine performance trends over a long period.

A product that automates that parsing, collection and visualization of interesting data from Quintiq log files will make performance analysis a lot more efficient than it is for us at present. We also see great value in this for customers, as it will enable them to quickly uncover performance issues / performance trends and react to them before they become a showstopper for business.

Ab Ovo is a Platinum DELMIA Quintiq partner with
120+ specialized employees with a global customer footprint.


We are not limited by the below list. Let us know if you want to get in touch and have a chat what our team can do for your business to create value.


Improved transaction times by 10x for a major rail transport provider in Western Europe

Truck Transportation

Reduced BI report load times by 50% for a big European retailer chain


Improved capacity calculation times within scenario datasets by 70% for a major Canadian freight railway company

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