Improving data quality in manufacturing
Often there is a serious challenge in relation to how businesses process and manage customer data in their ERP system. This typically involves maintaining mandatory fields, validating new vendors and customers, maintaining master data, identifying mismatches, finding and fixing incorrect vendor and customer data and creating new vendor profiles. These are costly and time-consuming processes for manufacturers, and the risk is that they are unable to deliver products as fast as possible.
Poorly managed master data can lead to significant financial losses. Machine learning technology can address this by finding patterns in the master data and propose new rules based on these patterns. Data stewards can review and accept these rules which can then be automatically integrated into your master data process. Data can be validated quicker, thus enabling new vendors and clients to be added with minimum delay.
When it comes to the automatic data validation, wrong supplier or payment details will be prevented which can potentially save money and make the invoicing and billing process run with minimum flaws, resulting in better cashflow and with improved cashflow forecasts.
In the second part of this blog, we’ll explain how you can benefit from advanced analytics to improve supply chain planning even amid global uncertainty.