Connected business operations. Flexible deployment.
Learn how to structure parallel run periods to validate data integrity, process accuracy, and system stability before fully decommissioning your legacy ERP.
A parallel run period is a critical phase in ERP migration where both the legacy and new ERP systems operate simultaneously for a defined duration. This approach allows organizations to validate data accuracy, process integrity, and system stability before fully decommissioning the old system. The primary goal is to ensure that the new ERP produces identical or acceptable results compared to the legacy system under real-world conditions. Key risks to control during this phase include data discrepancies, process bottlenecks, user confusion, and integration failures. Without a structured parallel run, organizations risk migrating incomplete or inaccurate data, leading to financial reporting errors and operational disruptions. The duration and scope of the parallel run depend on the complexity of the business processes, the volume of data, and the criticality of the ERP modules involved. A well-managed parallel run provides measurable confidence in the new system's readiness for full production use.
Determining the scope and duration of the parallel run requires a detailed analysis of business processes and data flows. Not all modules need to run in parallel for the same duration. For example, financial modules may require a longer parallel run to validate month-end closing processes, while inventory modules may need a shorter period to confirm transactional accuracy. The duration should be long enough to cover at least one full business cycle, such as a monthly or quarterly closing period, to capture recurring processes and seasonal variations. Responsible roles include the ERP project manager, finance leaders, operations managers, and IT directors. Dependencies include the availability of staging environments, data synchronization tools, and user training completion. Measurable checks include defining success criteria for data reconciliation, such as a 99.9% match rate for critical financial data. Decision criteria for extending or shortening the parallel run should be based on the number and severity of discrepancies found during reconciliation.
Success criteria for the parallel run must be defined before the period begins. These criteria should include specific metrics for data accuracy, process completion, and system performance. For data accuracy, define acceptable variance thresholds for different data types, such as financial transactions, inventory levels, and customer records. For process completion, ensure that all critical business processes, such as order-to-cash and procure-to-pay, are executed successfully in the new system. For system performance, monitor response times, error rates, and resource utilization to ensure the new system can handle production workloads. These criteria should be documented in the cutover plan and reviewed by all stakeholders. Clear success criteria provide an objective basis for deciding when to end the parallel run and proceed with decommissioning the legacy system.
Data reconciliation is the core activity during the parallel run period. It involves comparing data between the legacy and new ERP systems to identify discrepancies. This process should be automated wherever possible using reconciliation tools that can compare large volumes of data quickly and accurately. Reconciliation should cover all critical data entities, including financial accounts, inventory items, customer and vendor records, and open transactions. Discrepancies should be categorized by severity, with critical discrepancies requiring immediate resolution. A discrepancy log should be maintained to track the root cause, resolution status, and impact of each issue. Responsible roles include data migration specialists, finance analysts, and IT support teams. Dependencies include access to both systems, reconciliation tools, and clear data mapping documentation. Measurable checks include the number of discrepancies found, the time taken to resolve them, and the trend in discrepancy rates over time. A decreasing trend in discrepancies indicates that the new system is stabilizing and approaching production readiness.
Manual reconciliation is time-consuming and error-prone, especially for large datasets. Automating reconciliation tasks using scripts or specialized tools can significantly improve efficiency and accuracy. These tools can compare data fields, calculate variances, and generate reports highlighting discrepancies. Automation also allows for continuous monitoring during the parallel run, enabling teams to identify and address issues in real time. When selecting reconciliation tools, consider their ability to handle complex data structures, support multiple data sources, and integrate with the ERP systems. Ensure that the tools are configured to match the specific data mapping and transformation rules used during migration. Automated reconciliation provides a consistent and repeatable process for validating data accuracy, reducing the risk of human error and improving the overall reliability of the parallel run.
During the parallel run, users must operate both the legacy and new ERP systems, which can lead to confusion and errors. Clear roles and responsibilities are essential to ensure that users know which system to use for specific tasks. For example, users may be required to enter transactions in both systems or use the new system for certain processes while relying on the legacy system for others. Training and communication are critical to prevent user errors and ensure consistent data entry. Responsible roles include the change management team, ERP administrators, and department heads. Dependencies include completed user training, clear communication plans, and access to support resources. Measurable checks include user error rates, support ticket volumes, and user feedback on system usability. A well-managed user experience during the parallel run reduces the risk of data discrepancies and improves user adoption of the new system.
The new ERP system must be monitored for performance and stability during the parallel run. This includes tracking response times, error rates, resource utilization, and system availability. Performance issues can indicate underlying problems that may affect production readiness. Monitoring should be continuous, with alerts configured for critical thresholds. Responsible roles include IT operations teams, system administrators, and performance engineers. Dependencies include monitoring tools, logging systems, and clear escalation procedures. Measurable checks include average response times, error rates, and system uptime. A stable and performant system during the parallel run provides confidence that it can handle production workloads. Performance regressions should be investigated and resolved before ending the parallel run.
Discrepancies and defects found during the parallel run must be managed systematically. A defect triage process should be established to prioritize issues based on their impact on business operations and data accuracy. Critical defects, such as those affecting financial reporting or inventory accuracy, should be resolved immediately. Non-critical defects can be addressed after the parallel run if they do not impact core business processes. A defect log should track the status of each issue, including the root cause, resolution steps, and verification of the fix. Responsible roles include the ERP project manager, development team, and quality assurance team. Dependencies include access to development environments, clear defect management processes, and stakeholder communication. Measurable checks include the number of defects resolved, the time taken to resolve them, and the trend in defect rates. A decreasing trend in defects indicates that the new system is stabilizing and approaching production readiness.
Integrations and interfaces between the new ERP system and other applications must be validated during the parallel run. This includes testing data flows, API calls, and event-driven processes to ensure that data is transmitted accurately and in a timely manner. Integration failures can lead to data inconsistencies and operational disruptions. Responsible roles include integration specialists, IT architects, and application owners. Dependencies include access to integration environments, clear data contracts, and monitoring tools. Measurable checks include the success rate of integration transactions, the time taken to process them, and the number of errors encountered. A well-validated integration layer ensures that the new ERP system can operate seamlessly within the broader IT ecosystem.
Financial reporting is a critical aspect of ERP validation. During the parallel run, financial statements generated by the new ERP system must be compared with those from the legacy system. This includes balance sheets, income statements, and cash flow statements. Discrepancies in financial reporting can have significant implications for compliance and decision-making. Responsible roles include finance leaders, accountants, and ERP analysts. Dependencies include access to financial data, reporting tools, and clear accounting policies. Measurable checks include the variance in financial figures, the time taken to reconcile them, and the trend in variances over time. Accurate financial reporting is a key indicator of the new ERP system's readiness for production use.
The decision to end the parallel run should be based on predefined success criteria and the results of reconciliation and validation activities. If all critical discrepancies have been resolved, system performance is stable, and user adoption is satisfactory, the parallel run can be concluded. However, if significant issues remain, the parallel run should be extended until they are resolved. The decision should be made by a cross-functional team, including the ERP project manager, finance leaders, IT directors, and operations managers. Dependencies include the completion of all validation activities, the resolution of critical defects, and stakeholder approval. Measurable checks include the achievement of success criteria, the trend in discrepancy rates, and user feedback. A well-timed end to the parallel run ensures that the new ERP system is fully validated and ready for production use.
After the parallel run, the legacy ERP system should be decommissioned in a controlled manner. This includes archiving data, revoking user access, and shutting down system components. A decommissioning plan should be developed to ensure that no critical data or processes are lost. Responsible roles include IT operations teams, data management specialists, and project managers. Dependencies include data archiving tools, access control systems, and clear communication plans. Measurable checks include the completion of data archiving, the revocation of user access, and the shutdown of system components. A smooth decommissioning process ensures that the organization can fully transition to the new ERP system without operational disruptions.
| Phase | Key Activities | Responsible Roles | Success Criteria |
|---|---|---|---|
| Planning | Define scope, duration, and success criteria | ERP Project Manager, Finance Leaders | Documented plan with clear metrics |
| Execution | Run both systems, reconcile data, monitor performance | IT Teams, Finance Analysts, Users | 99.9% data match rate, stable performance |
| Validation | Resolve discrepancies, validate integrations and reporting | Development Team, QA Team, Integration Specialists | All critical defects resolved, integrations validated |
| Decommissioning | Archive data, revoke access, shut down legacy system | IT Operations, Data Management | Legacy system fully decommissioned |
Common questions on this topic. Talk to our team for anything specific to your business.
The duration varies based on complexity, but it typically covers at least one full business cycle, such as a monthly or quarterly closing period, to capture recurring processes and seasonal variations.
Key risks include data discrepancies, user confusion, increased operational costs, and potential integration failures. These risks can be mitigated through clear roles, automated reconciliation, and continuous monitoring.
Data reconciliation involves comparing data fields between the two systems using automated tools. Discrepancies are logged, categorized by severity, and resolved systematically.
It is safe to stop the parallel run when all critical discrepancies have been resolved, system performance is stable, and user adoption is satisfactory, as defined by predefined success criteria.
Key metrics include data match rates, discrepancy resolution times, system response times, error rates, and user error rates. These metrics provide objective measures of system readiness.
Discrepancies are logged, categorized by severity, and prioritized for resolution. Critical discrepancies are addressed immediately, while non-critical ones may be resolved after the parallel run.
A practical guide for finance leaders and IT directors to validate data accuracy after ERP migration. Includes audit checklists, reconciliation steps, and risk mitigation strategies to ensure financial integrity.
Learn how to design a distribution ERP architecture that scales with your business. This guide covers system-of-record decisions, integration boundaries, and process standardization to prevent operational fragmentation.
Learn how to build a manufacturing ERP visibility framework to identify and resolve production bottlenecks across multiple plants. This guide covers data architecture, process standardization, and integration strategies for improved operational control.
Discuss your workflows, modules and deployment options with our team.
Back to resources