Practical Machine Learning: How Data Can Optimize Inventory Forecasting
People excel at spotting patterns and making adjustments based on feedback, while computers excel at processing huge amounts of data quickly. Put those capabilities together and you have machine learning, a technique with the potential to help businesses dramatically improve their inventory planning.
While the terms “machine learning” and “artificial intelligence” are often used interchangeably, the main takeaway is that these advanced technologies offer better ways to solve complex planning problems. With interest and investment in AI growing, more organizations are recognizing the benefits of data science to improve inventory forecasting accuracy and optimize stock replenishment. A McKinsey report found that AI-enabled supply chain management can reduce forecasting errors by up to 50%, while helping businesses scale back inventory by up to 50% and slash lost sales by up to 65%.
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