An AI use case is a set of actions to reach a business goal utilizing artificial intelligence

Emergencies can be detected beforehand and can be alerted to near hospitals, ambulances, driver's family members, etc. This use case focuses on the person's facial emotions to detect the same medical emergency.

There could be a spike in the demand for a product or a service due to some external factors. An ML model tracks these factors by means of old data and avoids surplus or deficit during these spikes.

After defining the right ontology, NLP models will scrap documents to find the wished information. e.g., Query is "Injector temperature for polipropilene parts". The model scraps millions of documents and outputs all information similar to the query.

Chatbots and conversational AI can be connected to well-knitted quality knowledge database to automatically communicate issues among stakeholders.

With old quality issues and their solutions, it is possible to train a model. For future quality issues, the model will output past solutions with a high level of coincidence (accuracy).

With NLP and ML techniques and a proper ontology, similarities in text can be detected and therefore hidden knowledge extracted (e.g., 70 of the last 300 complaints about price, pointed to a subscription issue).

For those parts that present similarities project after project (e.g., fatigue test on a door handle ), an ML model can be created that, with dimensional and finite elemnts data from old parts, can automatically propose the correct finite element distribution for a new part with the current dimensional restrictions as input.

With input data such as parts and machines availability, customer deadlines, and test requirements (e.g., length), an ML model is created that outputs a validation planing in real-time.

"Electricity can be priced differently depending on many variables such as location, type of car, cost of electricity at a certain time in the day, type of customer, etc., An AI system can fairly and dynamically adequate the price adapted to each circumstance. Additionally, device operations can be controlled based on pricing signals.

Technology demonstration of real-time pricing service impacts upon sending signals to connected devices to control their operation based on pricing signals and/or grid conditions"

Based on charging station data utilization such as time of the day, electricity purchased, the price paid, postcode of charger, type of charger, etc., an ML model can be trained to predict how chargers will be next used.

"By means of machine learning techniques, Volt Var can be optimized based on real-time circumstances such as electricity needs, DER distribution, smart meters, weather, etc. Demonstrate enhanced algorithms to leverage Volt Var Optimization (VVO) for grid management services. Potential use cases include distribution capacitors to reduce transmission congestion and leveraging distributed smart inverters (further improve grid stability)"

"Based on DER utilisation historical data, an ML model can be created to leverage DERMS thus facilitating enhanced visibility and control over DERs and other grid assets. Leverage DERMS to facilitate enhanced visibility/control over DERs & other grid assets; incorporate additional technologies into DERMS and increase DER coordination through aggregation for optimized dispatch, including DR & EV Integration, VVO functionality, direct resource aggregation, and load cycling. The project may also explore dispatch DERs for restoration switching use cases leveraging the estimated time of restoration forecasts"

Batteries connected to the grid are aggregated so that, in moments of need, electricity is injected back into the grid, and in moments of abundance, electricty is stored in the batteries.

Applying machine learning algorithms (anomaly detection) in real-time data flows (quality indicators such as ratio machine parameters/scraped parts) allows the reaction to reduce quality costs in real-time (such as scrapped parts).

With NLP and ML techniques, future quality issues can be automatically classified based on past classified quality issues.

Through anomaly detection, variables from equipment are measured, unusual patterns detected, and the maintenance operator is alerted. E.g., rotos, haters, electric devices, pumps.

Using changeover historical data, it is possible to improve the efficiency of equipment changeover. Using scrap data and additional sources such as machine parameter, operator, weather, etc., it is possible to reduce scrap by discovering patterns in the data.

Using the data from past settings, ML can determine the best set of parameters to be used for a new part.

Artificial intelligence monitors collect information, controls, evaluates and manages energy consumption in factories. It controls energy usage and reduces it during peak hours, identifies and signals problems, and detects equipment failures before they occur.

With NLP and computer audition techniques, it is possible to connect customer questions with the right answer and output human-like audio.

Car users activate thousands of features while being in the car. By means of AI, these activities can be tracked and a profile created so that the car features adapt to the user for a unique experience.

With anomaly detection, suspicious events are identified through the sensors installed in each system and communicated to the driver/service supplier.

There are spikes in need of vehicles in determined locations depending on several variables like weather, time, ocurring events, etc. A model is trained which predicts the demand for cars considering all aforementioned variables.

An AI system gathers information from fleet usage and needs, recommending which user should use which vehicle, and adapting the car features to each user every time.

Sensors located throughout the car allow machine learning (anomaly detection) to detect deviation in the car components ensuring preventive maintenance.

Having a live update in the traffic flow could result, by means of ML, in predicting the best rerouting of vehicles. This can also help making space for the emergency vehicles such as fire brigets, ambulance, police vehicles.

A car ride could be priced differently depending on many variables such as location, type of car, price of electricity, type of customer, etc., An AI system can fairly and dynamically adequate the price adapted to each circumstance.

After defining the right ontology, NLP models will scrap documents to find the wished information. e.g., Query is "screw with diameter bigger than 2mm". The model scraps millions of documents and outputs all information similar to the query.

By means of NLP, customer feedback is gathered. All desgin-related topics are clustered and visualized by the development team, which can act accordingly after.

For those parts that present similaritries project after project (e.g., part fixtures for testing or production), an ML model can be created that, with dimensional data from old parts, can automatically generate a new part with the current dimensional restrictions as input.

ML permits to detect data anomalies (outliers) in real-time and with high precision in comparison to manual or statistical anomaly detection.