Industries
Data and AI Solutions for Manufacturing
Connect machines, MES and ERP data, then apply AI to raise OEE, cut scrap and downtime, plan supply more accurately and meet product data and sustainability requirements.
Build the Data Foundation for Smart Manufacturing
Discrete and process manufacturers have more data than ever: PLCs, sensors, vision systems, MES, quality systems, ERP and supplier portals. But much of it is trapped in point-to-point integrations and proprietary formats, so improvement teams still collect numbers by hand. Architectures such as the unified namespace, built on ISA-95 hierarchies and MQTT, give every system a shared, real-time view of the plant. On that foundation, analytics and industrial AI can finally scale beyond one-off pilots.
Hanoi Technology helps manufacturers build that foundation and put it to work. We connect shop floor and enterprise data, deliver OEE and quality analytics, predictive maintenance and AI visual inspection, and support planning, traceability and product data requirements such as the EU Digital Product Passport. We work alongside your engineers and IT team so solutions fit real operations and can be replicated across plants.
The Challenge
The Data Challenges Facing Manufacturers
Factories are rich in data but poor in connected, contextualized information.
Point-to-Point OT Integrations
Machines, MES, SCADA and ERP are often wired together with custom connections that break when anything changes. Adding a new data consumer, such as an analytics tool, becomes a project in itself.
Pilot Purgatory for Industrial AI
Many plants have run AI pilots for predictive maintenance or inspection that never scaled. Without contextualized data, MLOps and operator buy-in, each use case is rebuilt from scratch.
Supply and Demand Volatility
Component shortages, shifting customer demand and long supplier lead times make planning hard. Planners rely on spreadsheets that cannot keep up with frequent changes.
Growing Product Data and Sustainability Requirements
Customers and regulations increasingly demand product carbon footprints, material declarations and traceability, such as the EU Ecodesign Regulation’s Digital Product Passport. This requires data from engineering, production and suppliers that is rarely connected.
Business Value
What Modern Data and AI Deliver for Manufacturers
Higher OEE
Real-time visibility into availability, performance and quality losses by line and shift. Teams target the biggest losses with facts instead of anecdotes.
Less Unplanned Downtime
Condition monitoring and predictive models give early warning of failures. Maintenance is scheduled around production plans.
Lower Scrap and Rework
AI visual inspection and process analytics catch defects earlier and reveal root causes. Quality improves without slowing lines.
More Reliable Planning
Demand forecasting, inventory optimization and scheduling tools respond quickly to change. Service levels improve while working capital falls.
End-to-End Traceability
Genealogy from raw material to finished product supports recalls, warranty analysis and customer audits. Investigations take hours rather than days.
Scalable Industrial AI
A shared data foundation and MLOps let proven use cases roll out across lines and plants. Each new deployment is faster and cheaper than the last.
Architecture First, Then AI
The manufacturers seeing durable returns from AI rarely started with the algorithm. They started by making plant data accessible, contextualized and trustworthy, so every new use case could plug into the same foundation. We help you build that foundation and deliver value on top of it in the same program.
Our Services
Our Manufacturing Solutions
Six solution areas from the machine to the supply chain.
Industrial Data Foundation and Unified Namespace
We design and implement IIoT architectures that connect PLCs, sensors, SCADA, MES and ERP through a unified namespace with ISA-95 context, and stream data to the cloud.
- UNS and ISA-95 data model design
- Edge connectivity with OPC UA and MQTT
- Cloud data platform integration
OEE and Production Analytics
We build real-time dashboards and analyses of availability, performance and quality losses, downtime reasons and line constraints.
- OEE and loss analytics
- Downtime reason capture
- Shift and line performance dashboards
Predictive Maintenance
We develop condition monitoring and failure prediction for critical equipment and connect insights to maintenance planning in your CMMS or EAM.
- Condition monitoring and anomaly detection
- Remaining useful life models
- CMMS and EAM integration
AI Quality and Visual Inspection
We build computer vision models for defect detection and classification, and process analytics that link quality results back to machine parameters.
- Vision model development and deployment at the edge
- Defect classification and analytics
- Root-cause analysis dashboards
Supply Chain Planning and Optimization
We improve demand forecasting, inventory policies, production scheduling and supplier risk analytics with machine learning and optimization.
- Demand forecasting models
- Inventory and safety stock optimization
- Production scheduling optimization
Product Data, Traceability and Sustainability
We connect engineering, production and supplier data for genealogy, product carbon footprints and Digital Product Passport readiness.
- Product genealogy data model
- Product carbon footprint calculation
- Digital Product Passport data readiness
Explore
Services for Manufacturing
- IoTConnect machines and sensors securely.Learn more
- Machine LearningPredictive maintenance and quality models.Learn more
- AI Planning and OptimizationScheduling, inventory and capacity optimization.Learn more
- Planning, Budgeting and ForecastingIntegrated operational and financial planning.Learn more
- Master Data ManagementConsistent materials, products and suppliers.Learn more
- Sustainability and ESGProduct footprints and emissions reporting.Learn more
Our Approach
How We Engage with Manufacturers
A plant-first approach that proves value on one line and scales across the network.
- 01
Plant Walk and Data Assessment
We spend time with operations, maintenance, quality and IT teams on the shop floor, mapping machines, systems, data availability and the biggest losses.
Deliverables: Loss and opportunity map · OT and IT system inventory
- 02
Lighthouse Use Case Selection
We choose a first use case on a specific line or asset with clear value, available data and engaged owners, and define how success will be measured.
Deliverables: Use case charter · Baseline KPIs
- 03
Connect and Contextualize
We connect the relevant equipment and systems, build the data model and establish secure edge-to-cloud pipelines that later use cases will reuse.
Deliverables: Connected data sources · Contextualized data model
- 04
Build, Validate and Embed
We deliver analytics or AI in sprints, validate results with engineers and operators and embed them in daily routines such as shift meetings and maintenance planning.
Deliverables: Working solution on the line · Operator training and work instructions
- 05
Scale Across Lines and Plants
We package the solution as a reusable template, roll it out to further lines and plants and monitor performance and adoption over time.
Deliverables: Rollout playbook · Multi-site monitoring
Use Cases
Manufacturing Use Cases
Practical examples across discrete and process manufacturing.
Real-Time OEE for a Packaging Line
Machine states and counts stream from PLCs into a dashboard that shows losses by cause. Supervisors address the top micro-stoppages each shift instead of reviewing weekly reports.
Spindle Failure Prediction in CNC Machining
Vibration and load data from machining centers predict spindle bearing wear. Replacements happen during planned downtime, avoiding scrap and missed deliveries.
Surface Defect Detection
Cameras on an extrusion or stamping line detect scratches, dents and coating defects in real time. Defective parts are diverted and defect patterns are linked to process settings.
Injection Molding Parameter Optimization
Process data and quality results reveal the parameter windows that produce good parts. Engineers set more robust recipes and reduce scrap during start-ups.
Demand-Driven Production Planning
Forecasts combine orders, history and customer signals to drive production plans and component purchasing. Planners spend time on exceptions rather than rebuilding spreadsheets.
Digital Product Passport Readiness
Material, supplier and production data is linked to each product batch. The manufacturer can supply customers and authorities with composition, origin and carbon footprint information on request.
Technology
Platforms and Standards We Work With in Manufacturing
We build on open industrial standards and leading cloud and analytics platforms.
- ISA-95
- OPC UA
- MQTT Sparkplug B
- Unified Namespace
- HiveMQ
- AVEVA PI System
- Siemens and Rockwell PLC integrations
- MES integrations
- SAP S/4HANA
- Azure IoT Operations
- AWS IoT SiteWise
- Databricks
- Snowflake
- Grafana
- Power BI
- NVIDIA Jetson
Platforms
Deliver Manufacturing on Your Preferred Platform
- Microsoft AzureModernize data, analytics and AI within your Azure tenant.Explore
- AWSBuild, migrate and run data and AI workloads on Amazon Web Services.Explore
- DatabricksUnified lakehouse for data engineering, analytics and AI.Explore
- SnowflakeElastic cloud data platform for analytics and data sharing.Explore
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
Success Story · Logistics
Route optimization and fleet visibility for a regional logistics company
A regional delivery company handling thousands of shipments a day was planning routes and dispatching by hand. We built dynamic route optimization, a fleet dashboard and a driver app.
Read the Full StoryResources
Featured Resources
Industry trends and practical guidance to help you get more value from data, AI and technology.
FSMA 204 After the Delay: Using Shipment Monitoring to Get Traceability-Ready by 2028
The FDA Food Traceability Rule deadline moved to July 2028, but the data work is large. Here is how to use shipment monitoring and event data to get ready without wasting the extra time.
CSRD After the Omnibus: What the Revised ESRS Mean for Reporting From 2027
The Omnibus and the simplified ESRS reshaped EU sustainability reporting in 2026. Here is who is in scope, what changed in the standards and how to adapt your data and process.
OpenTelemetry in 2026: Building a Vendor-Neutral Observability Strategy
OpenTelemetry has become the default way to collect telemetry. Here is what changed in 2026 and a practical roadmap for a vendor-neutral observability stack.
Turn Factory Data into Measurable Gains
Book a free consultation to discuss OEE, maintenance, quality or planning priorities with our manufacturing team. We reply to every enquiry within 24 hours.
Manufacturing FAQ
What is a unified namespace and do we need one?
A unified namespace is an architecture in which machines and systems publish their current state to a central, structured hub, usually an MQTT broker organized by ISA-95 levels such as enterprise, site, area and line. Any authorized system can subscribe to the data it needs. It replaces fragile point-to-point integrations. Not every plant needs a full UNS on day one; we often start with the data needed for a first use case and design it so the namespace can grow.
Can you connect older machines that lack modern interfaces?
Yes. For legacy equipment we use protocol gateways, PLC connectivity, retrofit sensors for vibration, temperature or current, and in some cases vision-based reading of panels. We assess each machine type and recommend the least invasive option. All connectivity is designed with your OT and security teams to respect network segmentation and avoid interfering with control systems.
How do you avoid industrial AI pilots that never scale?
We address the usual reasons pilots stall: data that is not contextualized, models that are not monitored and solutions that operators do not trust. We build pilots on a reusable data foundation, involve operators and engineers in design, integrate outputs into existing workflows and set up MLOps so models are versioned, monitored and retrained. Each pilot ends with a rollout plan, not just a slide deck.
How long does a predictive maintenance or OEE project take?
A connected OEE dashboard on a single line can often be delivered in six to ten weeks once equipment access is arranged. Predictive maintenance usually takes three to five months for a first validated model, depending on the availability of sensor data and failure history. We deliver in sprints with demos so plant teams see progress early.
Can you help us prepare for Digital Product Passport requirements?
Yes. The EU Ecodesign for Sustainable Products Regulation introduces Digital Product Passports, with product-group requirements being defined through delegated acts over the coming years, starting with categories such as batteries. We help you identify the data you will need, such as materials, substances, origin and carbon footprint, map where it lives in your systems and suppliers, and build data models and pipelines to provide it reliably.
Do you work with our MES and ERP vendors?
We integrate with common MES, quality, maintenance and ERP platforms through their APIs, databases and standard interfaces, and we can extend them where needed. We are vendor-neutral and focus on making data flow reliably between systems rather than replacing investments you have already made. Where gaps exist, we can build lightweight applications that fill them.