Are you about to start your ERP enabled business transformation journey or are in the middle of it? Are you experiencing challenges with inaccurate inventory or product costing data conversion results during your business transformation journey? If so, you’re not alone. Accurate master data conversion is crucial for a successful Enterprise Resource Planning (ERP) application implementation, as it drives financial postings that directly impact external and internal reporting.
Some of the known implications of inaccurate data conversion include a lack of timely reliable data for decision-making, the need for manual effort to adjust postings, month-end reporting challenges, and skewed manufacturing variance analysis. Inaccurate data will also raise red flags from an audit, quality, and compliance perspective.
Supply chain, manufacturing, and customer processes can be negatively impacted e.g. customer profitability reports rely on accuracy of standard cost information or Bill of Material, Router data is needed for Material Requirement Planning and financial planning simulations in manufacturing companies.
During business transformation programs, users and stakeholders often point out these red flags as it becomes impossible to GoLive with inaccurate information. If not planned and executed well, unsuccessful master data conversion can cause significant program delays and incremental costs, thereby causing a negative sentiment with the stakeholders and end user community.
Therefore, it’s essential to mitigate these challenges and achieve accuracy by following best practices for data conversion and the cutover process. This includes confirming that your standard cost rollup and inventory valuation match those of your legacy system and conducting relevant checks to confirm data alignment once conversion is complete. As a best practice, relevant checks and balances should be implemented throughout the program lifecycle.
Please feel free to message me if you would like to learn more about tips for confirming the accuracy of your inventory valuation/product costing data, data validation rules, and measures for accuracy. Don’t let bad data cost you time, money, and decision-making capabilities.
Additionally, what are your suggestions or ideas for using machine learning and artificial intelligence for data conversion/ERP implementations? Comment below with challenges, mitigations or ideas that you would like to share.
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