Unified Manufacturing Data Architecture
The UMDA implementation journey
A proven 15-step path across five phases, from a data vision to scaled, AI-driven operations. It works with the systems you already own, with no rip-and-replace.
Click a phase to jump to its steps. The roadmap tracks where you are as you scroll.
Strategic foundation
Establish clear objectives, understand your current data landscape, and identify high-value opportunities for transformation.
Define vision & KPIs
Align leadership around measurable objectives like OEE, quality, or energy reduction, connecting data initiatives to business outcomes.
Key output — vision statement & 3–5 headline KPIs
Inventory data landscape
Catalog existing sources (MES, SCADA, ERP, IoT) and document data quality, ownership, and integration points.
Key output — complete data inventory register
Prioritize use cases
Score opportunities by impact and complexity, then select 2–3 pilots that deliver quick wins while proving the approach.
Key output — ranked use-case backlog
Data architecture design
Create standardized models that ensure consistency across the ecosystem while enabling seamless integration between domains.
Establish CDM framework
Design Common Data Models per domain (Production, Quality, Assets, Supply Chain, Energy), applying ISA-88/95 for alignment.
Key output — CDM design docs & data dictionaries
Design cross-domain harmonization
Define how data flows between domains, map relationships like lot-to-batch, and establish shared reference datasets.
Key output — harmonization specification docs
Core infrastructure
Deploy the technical foundation for real-time streaming, contextualized storage, edge computing, and enterprise integration.
Deploy Unified Namespace
Implement real-time distribution over MQTT or OPC UA, creating a single source of truth for operational data.
Key output — UNS online & streaming events
Build Unified Data Layer
Stand up the central platform for storage, contextualization, and processing across real-time and historical analytics.
Key output — UDL live with sample data
Set up Edge Intelligence Hub
Deploy edge computing for real-time analytics, enabling immediate insight and action on the production floor.
Key output — EIH running at first site
Integrate enterprise systems
Connect ERP, PLM, CMMS, and Quality systems to the UDL for bi-directional data flows across the enterprise.
Key output — bi-directional data flows
Governance & intelligence
Establish governance, enable advanced analytics, and create feedback loops for continuous improvement and model optimization.
Implement governance & MDM
Define steward roles, set security policies, and create a Master Data Management hub for critical reference data.
Key output — governance policy & MDM hub
Enable analytics & AI platform
Provision model-development environments, create feature stores, and establish MLOps for sustainable AI deployment.
Key output — AI workspace ready
Create Feedback Data Layer
Capture AI predictions, human feedback, and outcomes to enable continuous model improvement and learning.
Key output — FDL linked to UDL
Execution & scale
Prove value through pilots, expand across the enterprise, and establish practices for continuous optimization.
Execute pilot AI projects
Launch selected use cases, measure KPI impact, and document lessons learned before broader deployment.
Key output — pilot deployed & lessons learned
Scale across sites & domains
Roll out the framework to additional plants and expand CDM coverage to new domains based on pilot success.
Key output — adoption roadmap updated
Sustain & optimize
Establish DevOps/MLOps routines, review KPIs quarterly, and maintain a continuous improvement cycle.
Key output — quarterly KPI review deck
Ready to transform your manufacturing data?
The UMDA journey delivers value at every step while building toward a fully integrated, AI-enabled enterprise.