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APPLICAZIONE NEL MONDO REALE

IA per l'ottimizzazione del processo

Affina i processi operativi complessi, migliora la qualità e la coerenza del prodotto e riduci il lavoro manuale con le nostre soluzioni di intelligenza artificiale. Identifica le anomalie più rapidamente e risolvile con maggiore successo, indipendentemente dal fatto che si verifichino durante la fermentazione, la pulizia in loco o mentre un tritacarne è in funzione in una vasca di filtrazione.

A visual representation of AI for process optimization and anomaly detection with a focus on a graph and a person.

Challenge

Even small deviations in process control systems can have a serious impact on quality for breweries and beverage manufacturers. Yet to discover these early on, manufacturers need to analyze vast amounts of data.

Solution

AI is the perfect solution to optimize complex data. It makes connections in the process control systems and can identify deviations from the desired norm. It then notifies process managers via analyses, alarms or visualizations.

Identify anomalies — before they become problems

In brewing and beverage manufacturing, forewarned is forearmed. AI frees up process managers to tackle other tasks while it assesses the many factors involved in operational processes. Our solutions can:

  • Optimize both legacy and new systems
  • Support decision making and corrective action
  • Rectify anomalies before problems develop
  • Prevent unnecessary downtime
  • Achieve a consistent and high level of quality
  • Promote innovation and portfolio expansion
Un grafico che mostra una fabbrica con un focus sull'ottimizzazione dei processi utilizzando la tecnologia IA.

Versatile

Our AI solution can be used equally in existing or new systems that use BRAUMAT or SISTAR. Older systems still have a lot of potential for optimization.

Preventive

AI can identify anomalies at an early stage, so process managers can take action and prevent unnecessary downtimes caused by faults.

Efficient

AI can decrease busy work for your skilled workers. Rather than manually monitoring sensors, they can spend time on more important tasks. 

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