was done through expensive , timeconsuming and ultimately inaccurate data cleansing , followed by traditional use of spreadsheet technologies or BI tools .
“ Today , AI can glean insights and provide perspectives from vast arrays of unstructured data , or at the very least , perform analyses that were previously done through complex pivot tables and bespoke spreadsheet models .
“ For instance , during a typical strategic sourcing event where multiple suppliers provide commercial and qualitative responses , AI can now instantly combine all responses , draw conclusions , highlight anomalies , run scenarios and provide actionable insights . This shift from manual data handling to AI-driven analysis saves time and enhances decision-making accuracy .”
Improving supplier risk assessment and management with data analytics Data analytics significantly improves supplier risk assessment and management by monitoring key metrics such as financial health , delivery performance , sustainability and legal compliance . Often , supply chains are at the mercy of suppliers , both from a compliance standpoint and in providing the items needed to operate efficiently .
“ Analytics can often be a crystal ball , giving teams the chance to be proactive rather than reactive .”
Olivier echoes that analytics can identify patterns indicating potential disruptions to the supply chain . He adds : “ This allows procurement teams to proactively mitigate risk and ensure that high-quality products
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