You're faced with complex issues in ERP. How can you harness data analysis to find solutions?

Perform a thorough data cleansing operation to correct errors, remove duplicates, and fill in missing information, ensuring that your data is reliable before analysis.

When facing complex ERP issues, data analysis can be a powerful tool for finding solutions. Start with a data audit to assess data quality, consistency, and relevance across systems. Identify discrepancies, redundancies, and gaps by analyzing transactional, master, and metadata. By examining historical trends, operational bottlenecks, and performance metrics, key problem areas become clearer. Leveraging tools like data visualization and predictive analytics enables you to forecast potential risks and align ERP functionalities with business goals, improving decision-making and system efficiency.

ERP data audit is continuous. Daily monitoring is needed to ensure that the integration of ERP modules is correctly updating automatic transactions generated by source transactions. Month and year end controls ensure the accuracy, consistency, completeness and timeliness of ERP data for reliable reporting.

Data audits in ERP systems help identify and correct data quality issues, ensuring data integrity and accuracy. They enhance system performance by spotting bottlenecks and configuration errors. Audits also support compliance with regulatory standards and improve decision-making by providing reliable data. Regular audits lead to optimized ERP systems and better business outcomes by verifying the operations performed and the processes followed for recording data.

This topic is a very tricky one and a very easy one to respond. Complex issues are … complex … meaning they have relevant size and difficulty. Data is not an easy subject … data analysis neither … in fact it is very complex and one of the kind issue type of problem. So, going back to the question, use all the knowledge and intelligence available. Out of the box ideas are usually the best ones in these cases. Contract someone from the outside with a fresh and a clear view; someone competent, very competent.

Identifying patterns in ERP data is crucial for resolving complex issues and optimizing system performance. By analyzing transaction flows, user behavior, and system interactions, you can uncover recurring trends, inefficiencies, or anomalies. Pattern recognition helps highlight bottlenecks, such as delays in processing or data inconsistencies, and reveals opportunities for automation and process improvements. Advanced techniques like machine learning can also be applied to predict future outcomes and enhance decision-making, allowing businesses to adapt and refine their ERP strategies proactively.

Combine data from different business functions (e.g., sales, finance, supply chain) to gain a comprehensive view of performance and uncover interdependencies that may contribute to inefficiencies.

ERP analytics enhanced with machine learning and generative AI provides descriptive and predictive insights. Clustering for market segmentation, association of products likely to be bought together, classification of credit risks, demand forecasting and sentiment analysis are various analytical methods for improved decision-making.

Recognizing patterns in ERP systems helps address complex issues by uncovering hidden trends and anomalies. This facilitates root cause analysis, predictive maintenance, and process optimization. Patterns provide critical insights for informed decision-making and highlight data inconsistencies, enhancing data quality. Utilizing pattern recognition results in more efficient and dependable ERP systems.

Root analysis is essential for diagnosing the underlying causes of ERP system issues. By systematically investigating data anomalies, process failures, and integration challenges, it uncovers the core problems rather than just addressing symptoms. Techniques like the 5 Whys, Pareto analysis, and fishbone diagrams help pinpoint contributing factors, whether it's data corruption, workflow misalignment, or software incompatibility. Identifying root causes allows for targeted solutions, preventing recurring issues and improving overall ERP functionality, system reliability, and user satisfaction.

Root cause analysis (RCA) helps resolve complex ERP issues by identifying the underlying factors contributing to problems, rather than just addressing symptoms. This approach enables targeted and effective solutions, preventing recurrence and improving system reliability. RCA also enhances decision-making by providing a clear understanding of issues, leading to optimized processes and better overall performance. Methods such as the 5 Whys and Fishbone Diagram can be instrumental in systematically uncovering root causes.

Gather feedback from end-users involved in the processes related to the patterns. Their insights can provide practical perspectives on where and why issues are occurring.

In my experience , data analysis has always helped the way developers and functionals approach the problem . For example - if you have a problem with how data is getting updated to your table via a custom program . ( Let's say something is missing ) . Then you can access the database table first and learn how and when the data is missed. What are the key points that occur whenever data is missed ? Is it a new type of data which might have been missed at the time of designing the program ? Once you have such type of data , send it to your developer, it can save many hours of debugging and can lead to faster resolution

Drilling down and across key performance indicators visualized as graphs and charts provides insights into more details including the source transactions. The root cause of below-expectations sales performance can be traced to the orders which are not fulfilled as promised due to manufacturing bottlenecks.

Solution modeling in ERP systems is essential for visualizing and analyzing business processes to identify inefficiencies and design optimized workflows. For example, using Microsoft Dynamics 365 to enhance inventory management can significantly improve visibility and control. By leveraging predictive modeling, organizations can simulate various scenarios and test solutions in a virtual environment, reducing risks and ensuring informed decision-making. This approach not only aligns ERP systems with business goals but also ensures they are effective, scalable, and capable of delivering better business outcomes.

Results are monitored with performance metrics like manufacturing throughput, inventory turnover, customer churn, revenue and operating margin analysed for trends and patterns which are graphically visualized in dashboards and narrated with recommendations for specific roles in storytelling. This enterprise performance monitoring supports decision-making for continuous business process improvement.

The ERP strategy guides the selection, implementation and operations of the best-fit ERP solution and evolves with the changing business goals, objectives and strategy in the dynamic business environment. Analytical insights help to identify business processes which need to be continuously improved for the enterprise to remain competitive and grow sustainably.