What problems does artificial intelligence solve?
With artificial intelligence, you can achieve an optimal level of production in the available machines, also find logistics patterns and monitor the times in which the route is established for efficient management for industrial sector.
AI applications in industry
- Predict fraudulent transactions of suppliers and distributors, through assisted clusterization with artificial intelligence.
- Mitigate reprocessing with a model that predicts the probability of defective units, according to the characteristics of the machines and / or online products.
- Reduce production waste with a probabilistic model on information on raw materials, products and waste.
- According to the product distribution information, a model can be established that improves decisions on capacities and distribution channels, thus optimizing time and resources.
Business impact with artificial intelligence
- Improve lost times and half day / month production.
- Definition of usability of raw materials: Value of raw materials, value of distribution and quantity of resources used in suppliers and distribution.
- Inventory of the number of good, damaged, reprocessed units.
- Determination of the volume of waste and storage.
- Distribution time prediction.
Suggested AI and analytics roadmap
- Understanding the customer’s production needs
- Acquisition of the information available to meet the need.
- Data quality evaluation for project feasibility.
- Exploratory analysis to understand the production line.
- Visit of the production line (expand the context).
- Approach to candidate models and data structuring.
- Model training and hyperparameter search.
- Note of the model and socialization with the client.
- Industrialization of the model.
Solution: Prediction model for productive states in industrial machines
For the client, the production information of a machine was analyzed in order to improve its production times. We proceeded as follows:
Dataset description: General characteristics of the tables to work, number of fields and records, types of variables, levels, formats, etc.
Statistical description of variables: Through summary statistics and pie and dispersion graphs.
Statistical analysis focused on the problem, in this case: Production times vs. production and production times as an index of improvement.
- Pre-model analysis: Verification that all components, data, software, hardware are in order.
- Mathematical construction of the model.
- Model training for industrial sector.
- DEMO display of the model.