The Checklist
Align Test and Production Environments
Manage Generic Inquiries Carefully
Use Built-In Data Tools
Apply Filters Strategically
Test Updates and Upgrades
Monitor System Performance continuously
Optimize Data Reading
Improve Data Processing
Adopt a Continuous Optimization Process
An ERP go-live represents a major milestone, but for many IT managers, the real work begins after the go-live phase.
As users increase, data volumes grow, and processes become more complex. Any system is prone to slowdowns and inefficiencies without consistent, careful management.
Acumatica optimization focuses on refining how data is read, processed, tested, and maintained to ensure peak performance as the system scales. With a strong post-go-live strategy, enterprises can improve the user experience.
Ensure Test Environments Mirror Production
Testing new production in segregated environments identifies issues before they go live across the system. More testing is almost always better for long-term performance, when resources allow.
For best testing practices, use production backups or snapshots to perform tests. Match data volume as closely as possible in testing environments, and replicate user load scenarios for more accurate test results.
Validate Generic Inquiries Before Production Use
Generic Inquiries (GIs) are powerful tools in reporting and data analysis. GI design significantly impacts performance. Here are some GI guidelines for Acumatica optimization:
Avoid Creating New GIs Directly in Production
As much as possible, build and validate GIs in a test environment before they go live in the ERP production environment. Confirm the accuracy of returned data, query efficiency, and impact on system performance.
Define the Business Purpose Clearly
Before creating a GI, ensure clarity of understanding. See that the business issue is addressed and verify the exact data needed to solve it.
Unfocused, overly broad GIs create another issue that pulls unnecessary data and slows the system.
Use Built-in Tools for Data Discovery
Acumatica provides helpful tools that improve GI design, namely Inspect Element and DAC Schema Browser.
For improved Acumatica optimization, inspect Element helps to identify where specific data points reside within the system.
DAC Schema Browser shows where data is stored, which tables are involved, and how fields relate across tables.
Apply Filters Strategically
Filters reduce system load when carrying out queries and tasks. Filters help the system identify efficient connections and reduce data parsing for many processes.
Filter Within Joins
Whenever possible, apply filters directly with joins rather than after data is retrieved. This puts the heavy lifting on the database engine and reduces the volume of data processed by Acumatica.
Use Clear Data Ranges and Conditions
Specifications narrow data management before a task even begins. Date ranges, status conditions, and record limits can narrow data ranges at the beginning of a task, reducing resource strain and speeding up performance for Acumatica optimization.
Test Updates and Upgrades Before Going Live
Updates and upgrades are important for performance, but they can contain issues that harm workflows and/or efficiency.
Always apply updates and upgrades in a test environment first to ensure performance. Validate functionality within the test environment and identify any conflicts with customizations.
After thorough testing, take updates live to benefit from them.
Leverage Built-In Monitoring and SaaS Tools
After the ERP goes live, ongoing performance management benefits from system behavior visibility.
For SaaS and cloud environments, tools often include built-in system monitors, request profilers, and trace facilities.
For Microsoft SQL deployments, such as SQL Server Management Studio (SSMS), tools can help analyze queries and database performance.
They allow IT teams to find bottlenecks, inefficient queries, and processing delays in real time.
Optimize How Data Is Read
Inefficient data retrieval constitutes one of the most common sources of performance issues. Acumatica can streamline data retrieval with targeted optimizations.
Review Data View Select Delegates
Select () delegates impact data retrieval efficiency. Poorly designed delegates unintentionally pull large volumes of unnecessary data. Implicit joins, subselects, and overly broad queries frequently slow load times.
Solve this by eliminating unnecessary joins and avoiding subselects when possible. For further Acumatica optimization, limit result sets to only required records for best results.
Precise data retrievals lower system strain and improve performance across the board.
Improve Data Processing Efficiency
Related to but distinguishable from data retrieval, data processing faces its own challenges. Even after the best ERPs go live, they will run slowly under excessive processing routines.
Reuse and Recycle Objects
Where possible, reuse objects and logic rather than creating new instances. This reduces memory consumption and processing overhead.
Redesign Resource-Intensive Processes
Processing workflow structures dictated resource allocation. Too much complexity or inefficient structuring bog down computation.
To improve Acumatica optimization, large processes can break into smaller steps to reduce instance load. Optimizing save operations improves system responsiveness.
Creating a Continuous Optimization Mindset
Acumatica optimization is not a one-and-done. As organizations grow, add users, and expand processes, performance tuning needs to become an ongoing practice.
The optimization mindset schedules regular reviews of customizations. It performs GI audits and monitors system metrics. Most importantly, it finds the resources to test changes before deployment.
Maximizing Acumatica Support With Etticus Solutions
Acumatica offers power and performance to enterprise organizations as soon as it goes live. It can streamline workflows, manage massive data tables, run analytics, and consolidate efforts. It can transform how an organization operates, but it does require continued effort.
Etticus Solutions offers ongoing support. Long after we customize and deliver your Acumatica build, we can support the software with regular optimization efforts. Lean on our experience to keep Acumatica running strong and clean as your business grows and takes on new challenges.
Frequently Asked Questions
What is Acumatica optimization, and why is it important after ERP go live?
Optimization improves system performance and efficiency. As data volume and user activity increase, systems often start to run slower. Optimization uses best practices to streamline under-the-hood activities to mitigate performance issues.
What are the most common sources of performance issues in Accumatica?
Inefficient data queries, poorly designed customizations, resource-intensive processing routines, unoptimized GIs, and under-tested changes cause the majority of performance issues.
Why should test environments mirror production systems?
When test environments use realistic data volumes and configurations, performance is tested as similarly as possible to live use cases. This provides the best feedback in the test environment to identify and remedy issues before they go live on the platform.
What tools can help monitor Acumatica Performance?
Acumatica features built-in tools like the system monitor, request profiler, and trace facilities. Each provides insight into activity and bottlenecks. For database analysis, SQL Server Management Studio can identify issues. Each tool carries out important roles for Acumatica optimization.
How often should optimization reviews be performed?
Optimization is an ongoing process, beyond your ERP go-live date. Quarterly and semi-annual reviews are common for optimization, but part of the optimization process is testing changes before taking them live. This should be done routinely.
Can Acumatica performance improve without major system changes?
This will depend, but in many cases, yes. Workflow optimizations for queries, filters, logic, and GIs can all yield significant efficiency gains. Regular Acumatica optimization can generate noticeable performance improvements when issues dramatically increase data draw and processing.