What leaders see
Reports disagree about current conditions.
Teams debate demand, workload, capacity, inventory, service, delivery, and margin instead of acting on them.
Manufacturing Services
Manufacturing · Data Analytics · Data Warehouse
Data Warehouse & Integration
One warehouse for all your data, not one spreadsheet per department. Manufacturing records are often divided across ERP, MES, WMS, QMS, CRM, portals, files, and spreadsheets. We build governed pipelines and reconciled models so operational analytics use defined ownership and consistent measures instead of manual departmental extracts.
01
The Problem
Manufacturing reports disagree because records, ownership, timing, and definitions are fragmented.
Analytics problems begin when a dashboard is built before records, definitions, timing, lineage, and accountability have been reconciled across the operation.
What is actually happening
Measures inherit unresolved record conflicts.
Sources, identifiers, definitions, refresh timing, adjustments, and ownership differ across systems and teams.
What gets worse
Faster reporting accelerates the wrong answer.
More dashboards spread inconsistent measures and make exception ownership harder to establish.
02
What Changes
What Data Warehouse & Integration includes.
Manufacturing records are often divided across ERP, MES, WMS, QMS, CRM, portals, files, and spreadsheets. We build governed pipelines and reconciled models so operational analytics use defined ownership and consistent measures instead of manual departmental extracts.
01
Cloud Data Warehouse
Deploy Snowflake, BigQuery, or Azure Synapse as your central analytics warehouse. Schema designed for complex operating data models -- orders, inventory, production, quality, and financials.
02
ERP Data Integration
Monitored extraction from current and legacy ERP systems using an integration pattern suited to the source. Transaction, master, and configuration data move on an agreed cadence with validation and recovery controls.
03
WMS & MES Integration
Warehouse transactions, production completions, quality records, and shop-floor data integrated alongside ERP data. The warehouse sees the full operational picture.
04
Automated ETL Pipelines
Scheduled or event-driven pipelines extract, transform, and load data from source systems with explicit quality checks, deduplication, standardization, monitoring, and exception handling.
05
Data Quality Layer
Validation rules, anomaly detection, and data quality scoring applied during ingestion. Bad data is flagged and quarantined -- not loaded into the warehouse to corrupt downstream reports.
06
Semantic Layer & Data Models
Business-friendly models define measures such as revenue, inventory, and on-time delivery with accountable owners. Approved dashboards and reports use those governed definitions and expose any intentional variation.
03
How It Fits Manufacturing
How Data Warehouse & Integration fits the manufacturing operation.
Data & AnalyticsWhich decision the view supports, which records establish it, and how disagreement or missing data becomes visible.
Governance dependencyMeasures need defined ownership, timing, lineage, reconciliation, and access before dashboards can become a basis for operating decisions.
Operating records to reconcile
demand and orders
products, services, and inventory
work and capacity
quality and customer service
delivery and financial results
What must be defined before engineering begins
- What the manufacturing business needs to change and why.
- Which systems, records, risks, and readiness gaps shape the work.
- What should be built, how it fits the architecture, and in what order.
Related Services and Planning
What to evaluate next.
Follow the dependencies behind this service instead of treating it as an isolated project.
01
Data Services
Connect this reporting need to source records, governance, integration, analytics, and adoption.
Explore next step02
Integration Engineering
Acquire and reconcile the operating records the decision requires.
Explore next step03
AI on Your Data
Evaluate when governed data can support practical AI as well as reporting.
Explore next step04
Start Your Transformation Roadmap
New material work begins in Launchpad so Metrotechs can validate the operating need, evidence, feasibility, architecture direction, and sequence before engineering begins.
Explore next stepStart With the Manufacturing Objective
Define the smallest sound response and delivery sequence.
Metrotechs determines what the operation actually requires before selecting technology. New material work begins in Launchpad so the evidence, feasibility, architecture direction, priorities, and sequence can be validated before engineering begins.
Metrotechs designs, builds, integrates, and supports the approved solution for the manufacturing operation.
A technical-service request does not bypass discovery. The proposed solution remains a working hypothesis until Metrotechs validates it in Launchpad.
04
Delivery sequence
How Metrotechs delivers Data Warehouse & Integration.
Manufacturing records are often divided across ERP, MES, WMS, QMS, CRM, portals, files, and spreadsheets. We build governed pipelines and reconciled models so operational.
01
Source System Inventory
Catalog the data sources in scope, document volumes, update frequencies, ownership, and access methods, and map the flows that need to converge in the warehouse.
02
Schema & Model Design
Design the warehouse schema and semantic models based on your analytics requirements. Define dimensions, facts, and business metric calculations with stakeholder sign-off.
03
Pipeline Development
Build ETL/ELT pipelines for each source system. Implement data quality checks, transformation logic, and incremental refresh strategies.
04
Validation & Go-Live
Validate warehouse data against source systems. Reconcile counts, totals, and key metrics. Go live when data accuracy meets defined thresholds.
05
Monitoring & Maintenance
Deploy pipeline monitoring, data freshness alerts, and quality dashboards. Ongoing maintenance as source systems change or new data sources are added.
05
FAQ
Questions that usually decide the scope.
Straight answers to what manufacturing leaders ask before committing budget to this work.
We choose the platform from your cloud environment, source systems, analytical workloads, security obligations, existing skills, cost model, and reporting tools. BigQuery, Snowflake, and other managed warehouses are options, not automatic defaults.