Solutions | Wizata

Provide the most qualitative products, achieve more with the same input, handle downtime and malfunction

Wizata platform gives the complete toolbox to optimize many aspects of production processes. For all applications, Wizata platform supports the 3 main steps of the creation of solutions :

Building AI solutions to improve a production process
Deploying AI solutions and making a positive impact to the process
Replicating AI solutions to all similar assets and harvesting high-scale costs improvements

Discover the Wizata Platform

Quality control

Sustain a high quality production

Through machine learning, you can find patterns in the historical data that led to improved or decreased quality, thus giving possibility to know what to do to avoid quality issues or increase quality from satisfying to even better.

A well-trained machine learning model can recognize, even beforehand in some cases, situations where quality is likely to get off-track and actions that were taken to return to desired quality. Once deployed in real-time execution, AI solutions can anticipate quality issue and prescribe process changes to avoid or minimize quality issue.

Trained in a more efficiency-driven approach, a machine learning model can also detect the optimal settings of production process that that resulted in top-tier product quality output. In production, such a model can make the process converge and maintain to these optimal conditions.

With a better control of process quality, the risks of reprocess/scrapping are drastically reduced and associated with savings in process costs with better quality products and associated price to the market.

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Performance

Achieve more with the same input

As production processes are constantly evolving over time, with fluctuating performance over time but also through engineering expertise, machine learning can be used to identify, reproduce and consolidate process configurations that led to cutting-edge process performances.

Machine learning models can detect optimal performance conditions and recommend corrective actions to reach them.

As AI can interpret data associations on higher dimensions that human mind, it can also integrate multiple KPIs simultaneously and provide best balance between performance of production process and side-effects that have negative consequences to maximize overall revenues such as: balance between throughput and CO2 emissions, best ratio between speed of process and quality output, or improving the overall yield by using the same or reduced energy and material inputs.

Maintenance

Ensure your production lines reliability

Prior to accidental events, some precursor signs are always showing up in a various range of measures on machines. Some behaviors can be obvious and easily identified through operators. Other behaviors are more subtle and can’t be interpreted by human mind, as a catastrophic event can be brought by sequential and/or concomitant addition of minor events.

Machine learning can be used to detect and/or prevent behaviors associated to specific production process machinery health and condition.

AI can be trained to recognize every situation associated with nominal performance of production process and detect or anticipate drifts from this nominal performance. Operators can be notified automatically of a process drift and eventually with corrective actions to return to normality.
AI can be trained to estimate a remaining useful life of machines parts, thus maximizing parts usage and management, in addition to a reduction of unplanned maintenance stops and optimized maintenance scheduling.
AI can be trained to detect and diagnose specific process anomalies, and sequence of actions required to recover nominal operation of the process.

This non-exhaustive methodological list converges to a main impactful outcome: process downtime reduction, which is as – if not more – important as quality control in terms of ROI.

Artificial intelligence customer case

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