Your legacy system holds 10, 20, even 30 years of data -- customer records, order history, pricing tables, inventory transactions. We migrate all of it with format conversion, cleansing, deduplication, and validation. Every record accounted for. Chicagoland spans everything from Caterpillar's heavy equipment operations in the western suburbs to the I-55 food processing corridor where companies like Conagra and Ingredion run 24/7 batch lines under FDA scrutiny. Illinois Tool Works alone operates dozens of decentralized divisions across the metro, each with its own ERP instance and production methodology. The sheer diversity of Chicago's manufacturing base — chemicals in Joliet, electronics in Elk Grove, packaging in Aurora — means no single playbook fits.
Chicago's real manufacturing challenge isn't any one plant — it's that a typical mid-market firm here runs three or four ERPs across acquired divisions, and nobody owns the integration layer.
Analyze every table, field, and relationship in your legacy data. Identify quality issues, data volumes, format inconsistencies, and transformation requirements before migration begins.
Define field-by-field mappings between legacy and target systems. Handle format conversions, code translations, concatenations, and business rule transformations.
Fix quality issues during migration -- standardize addresses, deduplicate customers, resolve orphaned records, and fill missing required fields with governed defaults.
Automated record counts, checksum validation, and business rule verification. Every record is accounted for -- nothing lost, nothing corrupted.
Migrate historical data in advance, then incrementally sync changes during the transition period. Minimize cutover window and reduce go-live risk.
Full rollback capability at every stage. If validation fails, revert and fix without impacting the legacy system or losing data.
Analyze source data volumes, quality, formats, and relationships. Produce a data quality report with issues ranked by severity and migration impact.
Design field-by-field mappings with transformation rules. Review with business stakeholders to validate that mappings preserve business meaning.
Build extraction, transformation, and loading pipelines. Automated, repeatable, and version-controlled -- not manual copy-paste.
Run full-volume test migrations against a non-production target. Validate record counts, data accuracy, and business rule compliance.
Execute the production migration with real-time monitoring, validation checkpoints, and rollback readiness. Verify before declaring success.
Data Migration & Validation for Chicago industrial equipment operations - configured around local workflows, data ownership, and implementation governance.
Data Migration & Validation for Chicago food & beverage operations - configured around local workflows, data ownership, and implementation governance.
Data Migration & Validation for Chicago chemicals operations - configured around local workflows, data ownership, and implementation governance.
Data Migration & Validation for Chicago electronics operations - configured around local workflows, data ownership, and implementation governance.
Data Migration & Validation for Chicago financial services operations - configured around local workflows, data ownership, and implementation governance.
Data Migration & Validation for Chicago distribution & logistics operations - configured around local workflows, data ownership, and implementation governance.
We categorize issues by severity: critical (blocks migration), major (requires business decision), and minor (automated fix). Critical and major issues are resolved before migration. Minor issues are fixed in-flight with documented rules.
Yes. We have extensive experience extracting data from DB2/400 -- packed decimal fields, EBCDIC encoding, physical/logical files, and multi-member files. We handle the format conversions that trip up generic ETL tools.
We recommend keeping the legacy system read-only for 6-12 months post-migration as a reference. This gives you a safety net for data questions and edge cases that emerge after go-live.
We preserve audit trails, timestamps, and historical records as required by your compliance framework. Migration logs document every transformation applied, creating an auditable chain of custody for your data.
Most manufacturers are still running workflows that require a person to touch every exception, every order, every routing decision. AI agents eliminate that bottleneck — not by replacing your people, but by handling the work that was always below their pay grade.
Most manufacturers forecast demand with spreadsheets, gut feel, and last year's numbers adjusted by 5%. ML models trained on your actual order history, seasonality patterns, and market signals replace guesswork with predictions your planning team can act on.
Odoo Maintenance captures work orders, failure reasons, repair times, and equipment history. We build AI models on top of that data to identify failure patterns and recommend maintenance windows before breakdowns occur — no new hardware, no IoT infrastructure required.
Odoo Quality captures inspection results, non-conformances, scrap reasons, and lot traceability across every production order. We build AI models on top of that data to surface defect patterns, predict quality risk, and trigger alerts before scrap accumulates — no cameras, no hardware.
Most manufacturers price by cost-plus formula or by whatever the sales rep negotiated last time. AI pricing models factor in material costs, competitive positioning, customer segment, order size, inventory position, and market conditions — governed by business rules so every price stays within approved boundaries.
When an order hits your system, someone decides which warehouse ships it — usually based on habit, proximity, or whoever answered the phone. AI order routing makes that decision in real time, optimizing across inventory availability, shipping cost, delivery speed, and warehouse workload.
Manufacturers still process thousands of POs, invoices, RFQs, spec sheets, and BOLs manually — reading PDFs, retyping data into the ERP, and fixing the errors that come with it. Document intelligence extracts structured data from unstructured documents automatically, with validation rules that catch errors before they enter your systems.
Your dealers call or email to check stock before placing orders because they can't see what's available. We give them live ATP visibility across all your warehouses — available, allocated, in-transit, and expected replenishment dates — straight from your ERP and WMS.
We govern cloud migration in phases — every dependency mapped, every workload sequenced, every cutover window defined. Zero-downtime migration for manufacturers who can't afford an outage.
Most manufacturing AI projects die in the pilot phase. We deploy AI that integrates into your actual workflows -- demand forecasting, predictive maintenance, pricing optimization, and intelligent routing -- governed by operational data contracts.
Your demand planning process runs on last year\u2019s sales adjusted by a gut-feel percentage. ML models trained on your actual order history, seasonal patterns, and market signals produce forecasts that are measurably more accurate \u2014 and they improve automatically as more data accumulates.
Your legacy system holds critical data that modern applications need -- but it has no APIs, no webhooks, and no modern integration points. We build a REST/GraphQL API layer on top of your legacy system so new applications can access data without touching the core.
Metrotechs starts with the operating questions: which records are trusted, which workflows are manual, which systems own each decision, and where AI can safely improve throughput.
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