D08 · PLANT-WIDE · IMPROVEMENT DISCOVERY
AI-Enabled Process Improvement Discovery
Find and prioritise practical improvement opportunities from process evidence, not from isolated dashboard signals.
NOVYRON is software-first and vendor-neutral: this is configurable software, integration and lifecycle service, not process equipment. The existing PLC or DCS keeps interlocks and regulatory control, and every recommendation stays inside a site-approved operating envelope under human oversight.
THE INDUSTRIAL PROBLEM
Plants often hold years of historian, quality and maintenance data while improvement work stays workshop-driven, hard to quantify and disconnected from operating constraints. A local change then moves a bottleneck somewhere else, or buys throughput with a hidden quality and utility cost.
THE NOVYRON LAYER
NOVYRON combines process mining, loss-tree analytics, causal hypotheses and digital-twin experiments into an evidence-backed improvement portfolio. Recurring loss modes are identified, the addressable opportunity is sized with its assumptions visible, and the selected ideas become monitored implementation experiments.
TYPICAL DATA INPUTS
- Historian tags and event logs
- Production orders, recipes and material genealogy
- Quality, scrap, rework and hold records
- Downtime reasons and maintenance work orders
- Energy, water, steam and compressed-air meters
- Operator annotations and change records
MODELS AND DECISIONS
- Unsupervised operating-mode discovery
- Process mining of the actual workflow against the designed one
- Loss-tree clustering and Pareto analysis
- Causal-impact and matched-run analysis for evaluating a change
- Bottleneck and queue simulation
- Opportunity ranking by value, confidence, effort and operational risk
What each deployment reads, which models it runs, how it connects and how much authority it is given are designed for the site. None of it is pre-installed hardware, and none of it is a guaranteed autonomous function.
CORE CAPABILITIES
- CROSS-SYSTEM TIME & BATCH ALIGNMENT
- TRANSPARENT OPPORTUNITY ASSUMPTIONS
- BEFORE / AFTER EXPERIMENT DESIGN
- BENEFITS REGISTER WITH CONFIDENCE BANDS
- DEPENDENCY & BOTTLENECK MAP
- MONITORING THAT OUTLIVES THE PROJECT
HOW IT WORKS
- S1Agree the business boundary and the definition of every KPI in scope
- S2Build an event and process data model everyone is willing to trust
- S3Discover operating modes, losses, constraints and candidate causes
- S4Validate each hypothesis with the engineers and operators who run the line
- S5Simulate or pilot the highest-value changes under an agreed design
- S6Track realised performance and detect benefit erosion afterwards
INTEGRATION POINTS
- Historian, MES and ERP
- LIMS and quality systems
- CMMS and downtime systems
- Energy-management platforms
- Digital-twin and workflow tools
NOVYRON connects read-only and runs in shadow mode first. Any write-back is designed, tested and approved separately. Interfaces are chosen to fit the architecture already on site rather than forcing a new hardware stack.
MEASUREMENT EXAMPLES
These are measures for a baseline and target-setting workshop, agreed from your own data. None of them is a statement of a result NOVYRON has achieved.
- Overall equipment effectiveness components
- First-pass yield, scrap and rework
- Cycle time and schedule adherence
- Energy and water per good unit
- Unplanned downtime and mean time between events
- Validated annualised benefit and sustainment rate
TYPICAL APPLICATIONS
- BROWNFIELD LINE IMPROVEMENT
- CAPACITY-RELEASE PROGRAMMES
- QUALITY-LOSS REDUCTION
- UTILITY & SUSTAINABILITY INITIATIVES
- DIGITAL PORTFOLIO DESIGN
DEPLOYMENT PATH
- 01KPI and data-contract workshop
- 02Loss baseline and data model
- 03Opportunity discovery sprint
- 04Engineering validation and prioritisation
- 05Controlled experiments
- 06Benefits governance and sustainment monitoring
WHAT STAYS OUTSIDE
Statistical association is never presented as causation. Every recommendation states its assumptions, the confounders considered, its confidence and the operational approval a change would need before anyone acts on it.
SAFETY, QUALITY AND GOVERNANCE
- The deployment architecture can be designed to segregate customer data by tenant and deployment boundary, subject to the hosting, access-control and contractual terms agreed with you. Edge, on-premises, private-cloud and managed-cloud patterns are all evaluated against your constraints.
- Role-based access, encryption in transit and at rest, audit logging, model and version traceability and a secure update process are defined during solution design.
- Data provenance, retention and permitted model-training use are documented contractually. Customer data is not silently pooled into general models.
- Predictions carry quality flags and uncertainty where that is technically meaningful. Drift and out-of-distribution conditions trigger review rather than false precision.
Bring us the process boundary
Share the process, the signals you already collect, the control architecture, the constraints and the outcome you need. An engineer turns that into a scoped data assessment, a shadow pilot and a deployment plan.