AUTOMATE
Manufacturing environments are characterized by a continuous need to improve processes and operational efficiency. Quantitative data-driven approaches have a wide range of use in this field. Below is a list of some of the possible applications of Automate's Data Analytics skills within the manufacturing sector:
- Forecast of the market demand and necessary production
- Forecast of the consumption of raw materials and other non-renewable (e.g. oils, components) or renewable (e.g. energy) resources
- Predictive maintenance of machinery and automated systems
- Dynamic quality control, automatic defects recognition and quality classification
- Production planning and scheduling for flow-shop, job-shop, production cells and assembly line systems
- Sequencing of production orders on single or parallel machines
- Planning and scheduling of auxiliary activities (e.g. preventive maintenance, setups, intralogistics and shipping operations)
- Automated AGVs (Automated Guided Vehicles) and shuttles dispatching
- Dynamic allocation of storage spaces in in inter-operational buffers
- Machinery, resources, automated handling and storage systems dimensioning
- Process design and optimization of routes and operations
- Optimization, rostering and allocation of specialized personnel
AUTOMATE
Rely on our professional services in Data Driven.
Major decisions are always linked to high investment costs or the need to quickly choose between different alternatives, without the ability to easily retrace one's steps.
Two factors are essential for making appropriate choices: experience and information. In particular, when sufficient data is available, problems can be approached by following a quantitative approach, taking advantage of skills, models, and algorithms developed specifically for decision support.
With our expertise in simulation, mathematical optimization, and forecasting, we are able to help clients in their search for the best data-driven solutions.
>>Learn more about our offerings in Data Analytics!
English 
Italian