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Using Machine Learning Tools to Improve Supply Chain Performance

#artificialintelligence

In simple terms, that "most important role" is the cycle of observation followed by critical thinking followed by action. It's important to bear in mind that the proper goal of Machine Learning (ML) is not abdication of human responsibility for decision-making. Rather, it's improving our individual and collective ability to make better decisions by leveraging increased speed, accuracy and absence of bias. Our context here is supply chain planning and execution, but there is no reason to limit the scope of Machine Learning. When it comes to designing and creating technology solutions for supply chain analytics and business intelligence, this is not a throw-away idea buried in a long-forgotten PowerPoint presentation.


Precisely Enhances Confident Decision-Making with Dynamic Weather Context

#artificialintelligence

Precisely, the global leader in data integrity, announced the availability of Precisely Dynamic Weather, a new offering that provides historical, real-time, and forecasted weather data at a hyper-local level for greater context in decision-making. The data service enhances Precisely's ability to provide customers with maximum accuracy, consistency, and context in data, which are the essential elements of data integrity. Dynamic Weather delivers weather information at specific locations by revealing patterns related to rainfall, hail, wind, temperature and more for a variety of use cases across industry sectors. While most weather services allow users to visualize areas on a regional level, Dynamic Weather has the ability to drill down to the minute at a radius of just half a mile. This enables users to see how extreme weather conditions -- like hail, wind, rain, or snow -- impact specific properties or routes.


A Guide to Decision Trees for Machine Learning and Data Science

#artificialintelligence

Decision Trees are a class of very powerful Machine Learning model cable of achieving high accuracy in many tasks while being highly interpretable. What makes decision trees special in the realm of ML models is really their clarity of information representation. The "knowledge" learned by a decision tree through training is directly formulated into a hierarchical structure. This structure holds and displays the knowledge in such a way that it can easily be understood, even by non-experts. You've probably used a decision tree before to make a decision in your own life.


A Guide to Decision Trees for Machine Learning and Data Science

#artificialintelligence

Decision Trees are a class of very powerful Machine Learning model cable of achieving high accuracy in many tasks while being highly interpretable. What makes decision trees special in the realm of ML models is really their clarity of information representation. The "knowledge" learned by a decision tree through training is directly formulated into a hierarchical structure. This structure holds and displays the knowledge in such a way that it can easily be understood, even by non-experts. You've probably used a decision tree before to make a decision in your own life.