Advanced analytics and AI thrive on data, especially high-quality, accurate and well-organized data.
In this article, I delve into the core challenges surrounding data quality and propose solutions that can enable supply chain leaders to leverage the full potential of advanced analytics.Data quality is the bedrock upon which advanced analytics and AI solutions stand. Yet, an unsettling lack of trust in data quality plagues the decision-making process for supply chain leaders and executives.
For instance, consider a multinational electronics manufacturer relying on an advanced SaaS inventory optimization tool. By integrating data from suppliers, production lines and distribution centers, this tool analyzes real-time data to optimize inventory across manufacturing and the supply chain, which can result in improved on-time delivery rates, customer satisfaction and excess inventory costs.
Elevate data quality through refining master data, optimizing ERP order policies and enhancing accuracy in-demand and inventory data. Utilize data quality tools and software to automate the process of data cleansing, validation and enrichment. These tools can help identify and rectify inconsistencies, redundancies and inaccuracies in the data.
4. Regularly monitor data quality metrics and analytics performance for continuous monitoring and improvement.
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