Mid-large manufacturers running ERPNext have clean transactional data — Work Orders, Job Cards, BOMs, Quality Inspections, downtime logs — but ERPNext core has no notion of what will happen next. Production planning is capacity- and lead-time-driven, not risk-aware: the Production Plan solves for material availability, not for the workcenter that is three shifts away from a bearing failure. Maintenance lives in the Asset Maintenance schedule as fixed calendar intervals, and quality is a pass/fail Inspection after the fact. When a critical machine fails mid-run or a yield drift goes unnoticed for a week, that intelligence was sitting in your data the whole time — ERPNext just isn't built to model it. This is exactly where teams bolt on spreadsheets, Power BI exports, and tribal knowledge.

ECOSIRE builds a proper Frappe app (ecosire_ai_manufacturing) that installs into your existing bench and adds a predictive layer directly on top of your ERPNext manufacturing documents — no data migration, no parallel system. We model the app as first-class DocTypes: a Sensor Reading ingest DocType (tag, asset, metric, value, timestamp) fed via the Frappe REST API and whitelisted ingest methods, plus Failure Risk Score, RUL Estimate (remaining useful life), Maintenance Recommendation, and Schedule Recommendation DocTypes that reference the native Asset, Workstation, Work Order, and Job Card records. Role profiles and DocType-level permissions keep the maintenance team, planners, and plant managers scoped to what they should see, and the whole thing is Frappe Desk-native — list views, reports, dashboards, and workspaces your users already know.

Technically, the intelligence runs through scheduler events and doc events rather than a black box. hooks.py registers scheduler_events (hourly/daily) that pull recent telemetry and Work Order history, run the trained models, and write scored predictions back as records. Server scripts and doc-event hooks on Job Card and Quality Inspection submit trigger anomaly checks in real time — a yield or scrap value outside the learned envelope raises a flagged record and routes an alert. Client scripts surface the risk scores and RUL estimates inline on the Asset and Work Order forms, with color-coded indicators and one-click drill-down. Failure-risk scoring uses your historical downtime and maintenance records; schedule recommendations weigh predicted asset availability against the existing Production Plan and Job Card sequence so planners get a ranked "sequence this instead" suggestion they can accept or ignore. Models are trained on your own data during the build, retrained on a scheduler cadence, and every prediction is explainable back to the contributing signals — no unauditable scoring.

Because this is build-to-order, nothing is a generic download. After a scoping call we confirm which assets are instrumented, what telemetry you can expose (PLC/SCADA/IoT gateway, CSV drop, or REST push), your quality metrics, and your ERPNext version (v15 or v16). We build against that scope, validate the models on your historical data, and deliver an installable app plus the trained pipeline. Typical delivery is 2-4 weeks from confirmed scope depending on data readiness and the number of asset classes. You get the source, a git repo handover, UAT on a staging bench with a rollback plan, and a post-go-live support window — the app is yours to run, extend, or hand to another team.