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Automate external data discovery and enrichment for more accurate predictive models.
When you think about risks to your supply chain, what springs to mind? An earthquake? A supplier going bust? A global pandemic? Extreme events like these clearly pose huge dangers to supply networks - but you’re far more likely to be caught out by something entirely mundane.
A delivery of business-critical materials delayed by a day or two, just as you're running low, for example. A cyberattack on a shipping company used by one of your suppliers. An equipment failure in an office somewhere way back in the chain that sets off a butterfly effect of disruption, spiraling costs, and missed orders.
In our deeply interconnected, globalized world, it’s harder and harder to track the weak spots in your supply chain. Or how a minor problem can spiral into a much bigger issue as it progresses along the chain.
That’s why we created this handy guide to understanding, modeling, and mitigating supply chain risk using supply chain risk analytics.
In it, you’ll discover:
Automate external data discovery and enrichment for more accurate predictive models.
It's time to take feature generation - a subset of feature engineering - from an art to a science by opening up additional data sources to achieve breakthroughs in predictive models.
This complete guide breaks down data acquisition into six steps, including data provider due diligence and data provider tests to uplift your model's accuracy.
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