From discrete to process manufacturing, Babadook unifies MRP, quality, supply chain, and shop floor data on one platform — with real-time OEE, predictive maintenance, and zero-defect quality management.
Zero-defect quality management
Every part, every batch, every operator — traceable. SPC charts update in real time. When a dimension drifts, the system flags the machine, the operator, and the affected parts before the next inspection cycle.
ISO 9001, IATF 16949, and AS9100 quality standards built in. FMEA, control plans, and corrective actions — all on the same system as production data. When a customer audit asks for the quality history of a specific batch, the answer is one query, not a week of report compilation.
After implementation
With real-time SPC
With real-time visibility
Real-time costing vs month-end
Most factories run on a gap between what the MES knows and what the ERP knows. The MES knows the machine is down. The ERP knows the order is late. Nobody knows why — because the data lives in two systems that communicate through overnight batch jobs and manual reconciliation.
Industry 4.0 was supposed to solve this. IoT sensors, digital twins, and predictive analytics were supposed to give real-time visibility. Instead, they added another layer of data that nobody can join to the ERP.
Babadook unifies the manufacturing stack on one platform — from machine telemetry to financial close — with one transaction boundary.
Material requirements planning against live inventory, live capacity, and live demand. Available-to-promise and capable-to-promise checks run before a sales rep commits a delivery date. When stock is short, the system says so before the customer hears a date that cannot be kept.
Work orders carry BOM, routing, quality checks, and cost accumulation in one object. When a work order closes, costs post to finance and inventory updates in the same transaction. OEE, yield, and scrap feed BI without a batch delay.
IoT sensors, digital twins, and predictive analytics only deliver value when they are joined to production data, quality data, and financial data. Babadook's platform makes the connection native — not a project.
From machine telemetry to financial close — one platform, one transaction boundary.
Six manufacturing KPIs — all measured in real time, all from one system, all without batch delays.
See how products, modules and evidence compose around this industry's next operational question.
Manufacturers need product catalogs, documentation sites, dealer portals, and marketing campaigns. DXP handles all of it on one platform with multi-language support and native analytics.
Build product pages visually. Manage technical documentation in the Content Lake. Localize for global markets. Track dealer portal engagement. Automate lead nurturing for B2B sales.
Maturity: This section maps specified edge, vision, buffering, and VLA capabilities to industrial workflows. Production-line and physical-action use remain subject to site, hardware, latency, and safety acceptance.
Manufacturing operates at the edge — factory floors, production lines, warehouses, and field deployments where latency, reliability, and memory constraints are non-negotiable. Python-based inference engines with 2GB memory footprints and interpreter overhead cannot serve these environments. Prometheus ships as a 50MB binary that fits in 4GB of RAM and starts in under 2 seconds cold.
Quality inspection vision models running at sub-50ms latency. Predictive maintenance on Jetson edge devices with offline buffering and automatic sync when connectivity returns. VLA robots at sub-200ms planning with safety-bounded physical action. Model compression down to 10MB for extreme edge deployment. The same inference engine that runs on a 10,000-GPU cluster runs on a factory-floor edge device — same binary, same API, same governance.
Manufacturing runs software on the factory floor — PLCs, edge devices, industrial gateways. Source control, CI/CD, and container registry must run on-premises with intermittent connectivity. When a production line is down, the latest firmware must be deployable without internet access.
Haephestus runs edge-constrained on factory floor infrastructure with bounded cache and offline bundle import. Source control, registry, and CI/CD on the same network as your production equipment. When a machine needs a firmware update, the artifact is already on the local registry — no cloud round trip.
See how Haephestus combines with the products, operational owners and evidence required by this industry.
A work order reaches a line whose tooling, calibration and material state no longer match the plan. MAXACOR catches the mismatch before the run, follows the accepted correction and returns evidence of what the line actually produced.
Compare the requested work with current equipment, tooling, material and safety state.
Connect every run to the machine, software, operator, lot and quality result that shaped it.
Prepare production, inventory, quality and maintenance updates for the owning systems to accept.
A planner can see that the fixture, calibration or material on the line no longer matches the work order before the shift commits to an impossible run.
Manufacturing data is inherently multi-modal — PLM data is relational and 3D, BOMs are graphs, quality records are documents, sensor data is time-series, and supplier networks are graphs with geographic components. Today, this data lives in PLM, ERP, MES, QMS, and historian systems that cannot answer cross-modality questions without ETL delay.
GEOM-db converges MES transactions, supply-chain graphs, equipment sensor time-series, and CAD/B-rep geometry in one engine. When a defect appears, the design rationale, the BOM graph, the production telemetry, and the quality records are one query away — not a cross-system investigation.
From CAD model through production to quality and recall — multi-modal data on one engine.
CAD models — glTF, STL, STEP, IGES, IFC — are ingested as native manifold objects, not blobs in a file server. Geometry is queryable — measure distances, compute cross-sections, find intersections. The BOM is a graph. The quality records are relational. All in one engine, all joinable in one query.
A revised component can affect an assembly, its geometric fit, a maintenance procedure and the assets already in service. GEOM-db brings graph dependencies, exact geometry, topology and versioned records into the same investigation without treating every connected object as equally affected.
Engineers can inspect which relationship found the candidate and which geometric or topological test established the result. Long calculations can checkpoint and resume after interruption with the source generation still attached.
Manufacturing operations involve equipment, processes, materials, and people — all with states that change over time. When a machine fails, the impact propagates through the production line, the supply chain, and the delivery commitments. A dashboard shows the failure. It does not simulate the impact, recommend a response, or verify the recovery.
Lugh-E models the factory as objects — machines with capabilities, processes with states, and materials with locations. When a machine fails, the simulation engine shows the impact on production, the supply chain, and customer commitments. The recommendation engine suggests recovery actions. The governed action lifecycle ensures the response is authorized before execution.
A process begins drifting while downstream equipment continues to consume the output. Lugh-E connects machine observations, process dependencies and the current production picture, helping the plant team inspect the work and material that may be affected rather than reacting to one alarm in isolation.
Teams can compare a controlled slowdown, an alternate sequence or a maintenance intervention against current evidence. Production systems, quality owners and protected industrial controllers keep their own authority. Recovery is assessed from what the owners and independent observations establish, not from the disappearance of the alert.
Discrete, process, or mixed-mode — XRP handles BOM management, routing, work orders, contract manufacturing, costing, and capacity planning on one system.
Quality inspections, nonconformance reports, and corrective actions connect directly to the work orders that produced them. Real-time OEE, yield, and scrap data feed finance and planning without a batch delay. When a work order closes, costs post and inventory updates in the same transaction.
From design handoff through production to delivery — every stage on one ledger.
MRP runs against real inventory, real capacity, and real demand. When a plan is infeasible, XRP tells you which resource, which constraint, and which order is the bottleneck. What-if scenarios let you test demand changes before committing.
XRP runs on the plant network — self-hosted or air-gapped where ITAR, CMMC, or customer contracts require it, with ruggedized edge terminals on lines that lose connectivity. Machine integration arrives through OPC UA, Modbus, and MQTT adapters feeding sensor and counter data into work-order operations. Existing SAP, Oracle, or legacy MES connections migrate through proof-gated profiles while the floor keeps running.
First-article inspection, PPAP documentation, lot genealogy, and calibration records carry evidence envelopes from the operations that produced them. Quality holds block shipment by system enforcement, not policy memos. When an auditor or customer asks for device history, the work order, its lots, its operators, and its inspection results assemble from sealed events — FDA, AS9100, and IATF regimes handled as governed extensions of the same model.
Manufacturing detail lives on dedicated pages: Manufacturing for BOMs, routings, and orders, MRP Planning for material plans, Shop Floor for execution, Planning for feasible schedules, and Migration for leaving legacy MES behind.
A machine is free, but its tooling is unavailable and the next operation needs a different skill. XRP checks material, routing, labour, capacity and quality conditions around the work order before treating the slot as usable production time.
When the accepted sequence changes, planners can follow the effect into purchasing, floor execution, delivery and job cost. MRP, production scheduling and manufacturing execution work from the same business context instead of leaving finance to reconstruct the shift after it ends.
Industrial workloads combine ordinary services, edge systems, accelerators, control boundaries, intermittent networks, and strict evidence obligations. Phosphor keeps deterministic, hard-real-time, kernel-adjacent, and general distributed profiles distinct while sharing semantics, tooling, verification, and provenance.
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