Sabisu combines deep learning and statistical techniques to provide state of the art monitoring and automatically initiate workflows.
All KPIs and metrics are automated using Sabisu Pipelines, aggregating raw data from enterprise and process sources to ensure accuracy and ad hoc analysis at all levels; process, plant, site, project and enterprise.
Key metrics such as time-on-tools are continuously maintained for instant comparison, analysis and optimisation – they’re up to date, all the time.
Sabisu uses deep learning & statistical algorithms in both asset operation and project domains, maintaining reference models (‘digital twins’) which predict behaviour, provide early warning and detect trending risks.
Sabisu integrates process expertise, vendor, EPC and sub-contractor data into analytics and predictive models to improve accuracy.
Sabisu analytics emphasise real-time performance, automating normally manual metrics including variable/fixed costs, quality & maintenance.
Critical Asset Monitoring in Sabisu is the most accurate yet seen, combining statistical and machine / deep learning methods to avoid false positives and noise induced errors.
Asset models reflect performance & link to engineering, maintenance and vendor data to inform forecasts and performance comparisons.
Sentiment analysis of shift and maintenance logs detects repeated asset or process issues which have not been highlighted in other ways.
Field capture links directly to Sabisu to automatically update schedules, reports, analytics, Site/Asset Models and predictions.