Control de Calidad e Inspección

01

From a set of digital images (photos or videos), a machine learning model is created and trained that addresses a specific casuistry: identification of failures in the production process, supervision of the quality of a finish, alerts of failures in the assembly line, etc.

Solution

The solution covers the necessary infrastructure in Azure to support the storage of the dataset, the computation necessary for the execution of the machine learning algorithms and its integration with the SW / HW environment of the company.

INDUSTRIA

Maximizar la producción optimizando la asignación de productos a líneas, operadores y maquinaria. Maximizar el uso de materias primas optimizando los procesos de corte.

LOGÍSTICA

Minimizar el tiempo de caminata optimizando la ubicación del producto y diseñando rutas óptimas para recoger y dejar. Minimizar los costos optimizando qué fabricar (cantidad) en cada planta y qué enviar a cada almacén.

SALUD

Maximizar la ocupación y uso de quirófanos optimizando la planificación de intervenciones y la asignación de quirófanos y profesionales.

Implementation plan

Fase 0: Viability

Preliminary model development to clear uncertainties would help clients understand the benefits of its developement and would allow us to identify potential risks

4 weeks

Fase 1: project

Iterative Model evolution to achieve the accuracy needed for production launch and integration with the HW/SW components determined in the proyect scope.

2-6 weeks

Fase 2: Scale

Model Evolution through use cases and production launch with complete integration with de rest of HW/SW components that set the global context.

Variable