The traditional approach to supply chain management attempts to forecast future demand for resources based on historical data. Supply chain managers then add a safety stock to these levels to prevent stockouts and delays in production. These safety levels can be anywhere from weeks of extra supply to twice normal demand depending on the variation in needs for the product. This inventory level supports the overall production plan including stock levels in individual locations and transportation plans to meet manufacturing needs. Moving and holding extra inventory is a significant expense for manufacturers who are constantly looking for ways to improve profitably.
AI based supply chain optimization can utilize a variety of factors including historical data, environmental data and recent trends to predict optimal resource needs at each stage of production.
AI models can also be used to find anomalous behavior in current resource utilization and pinpoint areas for further investigation by supply chain managers. In retail situations, AI models can determine desirable inventory levels by making tradeoffs between inventory level versus expected sales. AI models can also be used to update resource plans, reroute inventory where it is needed, and streamline resource requirements to reduce downtime, reduce costs, increase production speed and increase profits from manufacturing operations.
The mission at H2O.ai is to democratize AI for all so that more people across industries can use the power of AI to solve business and social challenges. Leading global industrial such as Stanley Black and Decker have partnered with H2O.ai to deliver the next generation of industrial manufacturing solutions powered by H2O Driverless AI. H2O Driverless AI is an award-winning platform for automatic machine learning that empowers data science teams to scale machine learning efforts by dramatically increasing the speed to develop highly accurate predictive models. Driverless AI includes innovative features of particular interest to manufacturers including machine learning interpretability (MLI), reason codes for individual predictions, and automatic time series modeling.
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Dr. Robert Coop
Artificial Intelligence and Machine Learning Manager, Stanley Black & Decker
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