Connected business operations. Flexible deployment.
Learn how to structure parallel running phases in ERP migrations to mitigate data integrity risks, validate business processes, and ensure a smooth cutover without disrupting operations.
Planning for parallel running periods in ERP migration projects involves operating both the legacy and new ERP systems simultaneously for a defined duration to validate data integrity, business process accuracy, and system performance before fully decommissioning the old system. This approach is critical for mitigating the high risks associated with enterprise resource planning transitions, including data loss, financial discrepancies, and operational downtime. The primary goal is to ensure that the new system can handle real-world workloads and produce accurate results without disrupting ongoing business operations. Key risks to control during this phase include data synchronization errors, user confusion due to dual-entry requirements, and the complexity of reconciling outputs from two different systems. A well-structured parallel run requires clear decision criteria, robust data reconciliation mechanisms, and a defined timeline that balances thorough validation with project cost and resource constraints. It is not a simple 'copy and paste' of operations but a controlled experiment where discrepancies are identified, analyzed, and resolved before the final cutover.
Before initiating the parallel run, stakeholders must define the specific scope of operations that will be duplicated. Not all business processes may need to run in parallel; typically, critical financial modules, inventory management, and order processing are prioritized. The objective is to verify that the new ERP system produces the same financial statements, inventory levels, and operational metrics as the legacy system under identical conditions. This requires establishing a baseline of expected outcomes from the legacy system. For example, if the legacy system generates a monthly profit and loss statement, the new system must generate an identical statement for the same period. Defining these objectives clearly helps in setting measurable success criteria. It also helps in identifying which departments and user groups will be involved in the dual-entry process, allowing for targeted training and support. The scope should be documented in a formal parallel running plan that includes timelines, responsibilities, and escalation paths for issues that arise during the period.
Critical business processes are those that have a direct impact on revenue, compliance, or operational continuity. These typically include accounts payable, accounts receivable, general ledger, inventory valuation, and sales order processing. Identifying these processes requires input from finance, operations, and IT leaders. Each process should be mapped to specific data points that need to be reconciled. For instance, in accounts payable, the total liability balance, individual vendor balances, and payment status must match between the two systems. This mapping ensures that the reconciliation process is focused and efficient. It also helps in identifying any customizations or unique business rules in the legacy system that need to be replicated or adapted in the new system. By focusing on critical processes, organizations can manage the complexity of the parallel run and ensure that the most important aspects of the business are validated first.
Success criteria for the parallel run should be quantitative and objective. Common metrics include the percentage of data records that match exactly between the two systems, the number of critical defects found, and the time taken to resolve discrepancies. For financial data, a tolerance threshold may be established, such as a variance of less than 0.1% in total balances. For operational data, such as inventory counts, a higher tolerance may be acceptable depending on the nature of the goods. These criteria should be agreed upon by all stakeholders before the parallel run begins. They serve as the basis for the go/no-go decision at the end of the period. If the success criteria are not met, the parallel run may need to be extended, or additional remediation work may be required. Clear success criteria prevent subjective judgments and ensure that the decision to proceed with cutover is based on data rather than opinion.
Data synchronization is the technical backbone of a successful parallel run. The goal is to ensure that both systems have the same data at the same time, allowing for meaningful comparison. This can be achieved through real-time integration, batch processing, or manual data entry. Real-time integration is the most accurate but also the most complex and expensive. It requires robust middleware and API connections between the two systems. Batch processing is less complex but introduces a time lag, which can complicate reconciliation. Manual data entry is the simplest but also the most error-prone and labor-intensive. The choice of synchronization method depends on the volume of data, the frequency of transactions, and the available budget and technical resources. Regardless of the method, a robust reconciliation process is essential. This involves comparing data from both systems on a regular basis, such as daily or weekly, and investigating any discrepancies. The reconciliation process should be automated where possible to reduce manual effort and improve accuracy.
Automating data reconciliation is a best practice for large-scale ERP migrations. Manual reconciliation is time-consuming and prone to human error, especially when dealing with thousands of transactions. Automated tools can compare data fields between the two systems and flag discrepancies for review. These tools can be built using custom scripts or purchased as part of an ERP migration suite. The key is to define the rules for comparison clearly. For example, should the comparison be based on exact matches or within a tolerance range? Should it include all fields or only key fields? The output of the automated reconciliation should be a report that highlights discrepancies, categorized by severity. This report should be reviewed by a team of data analysts and business users who can investigate the root cause of each discrepancy. Automating this process allows for more frequent reconciliation, which helps in identifying and resolving issues early in the parallel run.
Data conflicts are inevitable during a parallel run. They can arise from differences in data entry practices, system logic, or timing issues. For example, a transaction may be recorded in the legacy system but not yet in the new system due to a delay in synchronization. Or, the two systems may calculate a tax amount differently due to different tax tables. Handling these conflicts requires a clear protocol. The first step is to identify the root cause of the discrepancy. Is it a data entry error, a system configuration issue, or a fundamental difference in business logic? Once the root cause is identified, a decision must be made on how to resolve it. In some cases, the data in one system may need to be corrected. In other cases, the configuration of one system may need to be adjusted. All resolutions should be documented to ensure that the same issue does not recur. A log of all discrepancies and their resolutions should be maintained as part of the project documentation.
Parallel running places a significant burden on end users, who are required to enter data into both systems. This can lead to fatigue, errors, and resistance to the new system. To manage this, it is essential to define clear roles and responsibilities for each user. Some users may be responsible for data entry in both systems, while others may focus on reconciliation and validation. It is important to provide adequate training and support to users during this period. Training should cover not only how to use the new system but also how to handle discrepancies and report issues. Support should be readily available, with a dedicated help desk or support team that can respond quickly to user queries. Communication is also critical. Regular updates should be provided to users on the progress of the parallel run, any issues that have been identified, and any changes to the process. This helps in maintaining user engagement and reducing anxiety.
Effective training is crucial for the success of the parallel run. Users need to understand why they are entering data into two systems and how their actions impact the validation process. Training should be practical and hands-on, using real-world scenarios that reflect the users' daily tasks. It should also cover common errors and how to avoid them. Support should be proactive, with help desk staff monitoring user activity and reaching out to users who are struggling. This can be done through regular check-ins or by analyzing user activity logs. The goal is to ensure that users feel supported and confident in their ability to perform their tasks in both systems. This not only improves the accuracy of the data but also helps in building user acceptance of the new system.
Issues will arise during the parallel run, and it is important to have a clear escalation path for resolving them. The escalation path should define who is responsible for resolving different types of issues. For example, data entry errors may be resolved by the user or a data analyst, while system configuration issues may require the involvement of an ERP consultant or developer. The escalation path should also define the timeframes for resolving issues. Critical issues that impact the accuracy of the data or the operation of the system should be resolved within a few hours, while less critical issues may be resolved within a few days. The escalation path should be communicated to all stakeholders and included in the parallel running plan. This ensures that issues are resolved quickly and efficiently, minimizing the impact on the project timeline.
The technical infrastructure for the parallel run must be robust and reliable. This includes the hardware, software, and network connections required to run both systems simultaneously. The new ERP system should be deployed in a production-like environment that mirrors the production environment of the legacy system. This ensures that the performance and behavior of the new system are representative of how it will perform in production. The environment should be isolated from the production environment to prevent any unintended impact on live operations. Data synchronization between the two systems should be tested thoroughly before the parallel run begins. This includes testing the integration points, data mapping, and error handling. Any issues found during testing should be resolved before the parallel run starts. The technical infrastructure should also be monitored closely during the parallel run to identify any performance issues or failures.
Performance and stability are critical during the parallel run. The new ERP system must be able to handle the same volume of transactions as the legacy system without degradation in performance. This requires load testing and stress testing before the parallel run begins. Load testing simulates normal usage, while stress testing simulates peak usage. The results of these tests should be analyzed to identify any bottlenecks or performance issues. Any issues found should be resolved before the parallel run starts. During the parallel run, performance should be monitored continuously. Metrics such as response time, throughput, and error rates should be tracked and compared to the baseline established during testing. If performance degrades during the parallel run, it may indicate a problem with the new system or the integration between the two systems. This should be investigated and resolved immediately.
Backup and recovery procedures are essential for protecting data during the parallel run. Both the legacy and new systems should be backed up regularly, and the backups should be tested to ensure that they can be restored successfully. The backup strategy should include full backups, incremental backups, and transaction log backups. The frequency of backups should be determined based on the volume of data and the acceptable level of data loss. In the event of a data loss or system failure, the backup and recovery procedures should allow for a quick and efficient restoration of the data. This minimizes the impact on the parallel run and the overall project timeline. The backup and recovery procedures should be documented and tested regularly to ensure that they are effective.
Monitoring and reporting are essential for tracking the progress of the parallel run and identifying issues early. A dashboard should be created that displays key metrics such as the number of transactions processed, the number of discrepancies found, and the status of issue resolution. This dashboard should be updated in real-time or at regular intervals, such as daily. The dashboard should be accessible to all stakeholders, including project managers, IT staff, and business users. Regular reports should be generated that summarize the progress of the parallel run, the issues that have been identified, and the actions that have been taken to resolve them. These reports should be shared with stakeholders on a regular basis, such as weekly. The reports should be clear and concise, highlighting the key findings and any risks to the project. This helps in maintaining transparency and ensuring that all stakeholders are aligned on the status of the project.
Key performance indicators (KPIs) should be defined to measure the success of the parallel run. Common KPIs include the data accuracy rate, the number of critical defects, the average time to resolve issues, and the user satisfaction score. The data accuracy rate measures the percentage of data records that match exactly between the two systems. The number of critical defects measures the number of issues that have a significant impact on the accuracy of the data or the operation of the system. The average time to resolve issues measures the efficiency of the issue resolution process. The user satisfaction score measures the level of satisfaction of the users with the parallel run process. These KPIs should be tracked over time and compared to the success criteria defined earlier. If the KPIs are not meeting the success criteria, corrective actions should be taken to improve the process.
Regular communication with stakeholders is essential for maintaining alignment and managing expectations. Stakeholders should be kept informed of the progress of the parallel run, any issues that have been identified, and any changes to the plan. This can be done through regular meetings, email updates, or a project portal. The communication should be clear and concise, highlighting the key findings and any risks to the project. It is also important to listen to stakeholder feedback and address any concerns that they may have. This helps in building trust and ensuring that all stakeholders are committed to the success of the project. Regular communication also helps in identifying any potential issues early, allowing for proactive action to be taken.
The decision to proceed with cutover should be based on objective criteria, not subjective opinions. The criteria should be defined at the beginning of the parallel run and agreed upon by all stakeholders. Common criteria include meeting the data accuracy rate, resolving all critical defects, and achieving user acceptance. The decision should be made by a cross-functional team that includes representatives from IT, finance, operations, and project management. The team should review the KPIs and the issue log to determine if the criteria have been met. If the criteria have not been met, the parallel run should be extended, or additional remediation work should be performed. The decision to proceed with cutover should be documented and communicated to all stakeholders. This ensures that the decision is transparent and that all stakeholders are aligned on the next steps.
The go/no-go decision process should be formal and structured. It should include a review of the KPIs, the issue log, and the user feedback. The process should also include a risk assessment to identify any remaining risks to the cutover. The decision should be made by a designated decision maker, such as the project sponsor or the CIO. The decision maker should have the authority to make the final call on whether to proceed with cutover. The decision should be documented and communicated to all stakeholders. If the decision is to proceed with cutover, a detailed cutover plan should be developed. If the decision is not to proceed, a remediation plan should be developed to address the issues that have been identified.
A rollback plan is essential for mitigating the risks of cutover. The rollback plan should define the steps that will be taken to revert to the legacy system if the new system fails to meet the success criteria after cutover. The plan should include the criteria for triggering a rollback, the steps for reverting to the legacy system, and the communication plan for notifying stakeholders. The rollback plan should be tested before cutover to ensure that it is effective. Testing the rollback plan helps in identifying any gaps or issues in the plan and allows for them to be addressed before cutover. Having a tested rollback plan provides peace of mind and reduces the risk of a failed cutover.
After the parallel run is complete and the decision to proceed with cutover has been made, the focus shifts to stabilization and decommissioning. Stabilization involves monitoring the new system closely to identify and resolve any issues that arise. This includes monitoring performance, data accuracy, and user satisfaction. Any issues found should be resolved quickly to ensure that the new system is stable and reliable. Decommissioning involves shutting down the legacy system and archiving its data. This should be done carefully to ensure that no data is lost and that the legacy system is properly secured. The decommissioning process should be documented and approved by all stakeholders. It should also include a plan for handling any residual data or reports that may be needed from the legacy system.
Post-cutover monitoring is essential for ensuring that the new system is performing as expected. This includes monitoring key performance indicators such as response time, throughput, and error rates. It also includes monitoring data accuracy and user satisfaction. Any issues found should be investigated and resolved quickly. The monitoring should continue for a defined period, such as 30 to 90 days, to ensure that the system is stable and reliable. The results of the monitoring should be reported to stakeholders regularly. This helps in maintaining transparency and ensuring that any issues are addressed promptly.
Decommissioning the legacy system should be done carefully to avoid any data loss or security risks. The first step is to archive the data from the legacy system. This ensures that the data is preserved for future reference. The second step is to shut down the legacy system. This should be done in a controlled manner to avoid any disruption to operations. The third step is to secure the legacy system. This includes removing access to the system and destroying any sensitive data. The decommissioning process should be documented and approved by all stakeholders. It should also include a plan for handling any residual data or reports that may be needed from the legacy system.
Common questions on this topic. Talk to our team for anything specific to your business.
The duration varies based on complexity, typically ranging from 1 to 3 months. It depends on the volume of data, the number of business processes involved, and the frequency of transactions. A longer period allows for more thorough validation but increases costs and user burden.
Data conflicts are handled by identifying the root cause, such as data entry errors or system logic differences. A protocol is established to resolve conflicts, which may involve correcting data in one system or adjusting configuration. All resolutions are documented to prevent recurrence.
While not mandatory for all migrations, parallel running is highly recommended for complex migrations involving significant data volumes or critical business processes. It provides a safety net to validate the new system before full cutover, reducing the risk of failure.
A short parallel running phase may not provide enough time to identify and resolve all issues, leading to data integrity problems, financial discrepancies, and operational disruptions after cutover. It may also result in user fatigue and resistance to the new system.
Success is measured using predefined KPIs such as data accuracy rate, number of critical defects, average time to resolve issues, and user satisfaction score. These KPIs are compared to success criteria defined at the beginning of the parallel run.
Yes, data synchronization and reconciliation can be automated using middleware, APIs, and custom scripts. Automation reduces manual effort, improves accuracy, and allows for more frequent reconciliation, which helps in identifying and resolving issues early.
Discover how manufacturing ERP unifies production, finance, and supply chain data to eliminate silos, standardize reporting, and drive operational visibility for executive decision-making.
A practical guide for CIOs and ERP consultants on identifying, resolving, and preventing duplicate records during ERP migration and data consolidation to ensure data integrity and operational continuity.
Discover how professional services firms use ERP to standardize project operations, improve financial control, and achieve forecast precision. Learn the architecture, integration, and governance required for scalable growth.
Discuss your workflows, modules and deployment options with our team.
Back to resources