Adexa Genie© Predictive & Prescriptive

Risk-resilient plans using machine learning and AI technology

Even though AI technology has been with us for decades, only now can we apply them to business functions, thanks to faster processors and availability of memory for big data.

Using ML, a system can be trained to predict potential issues and make recommendations to prevent unwanted outcome or to enhance the desired results.

Self-Correcting uses Past data in order to find trends that are changing the actual model of the supply chain.

Self-Improving uses past and future (or planning) data to identify frequency of issues and how to improve supply chain policies.

Self-Optimizing means the ability of algorithms themselves to self-improve their performance and efficiency

Use of attributes as AI expert systems

Adexa attributes and Attribute Based Planning is used to learn business rules from the users and planners. The attributes form Boolean expressions that define such business rules.

Temporal value of data

We can categorize data by its temporal value: Past, Present or Future. Past data may come from transaction systems and can be used for diagnostic and predictive purposes as well as causal analysis.

Industries_Aerospace

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Supply Chain Predictive & Prescriptive Analytics

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