Your leadership team shouldn\u2019t wait until the monthly Excel report to know how the business is performing. We build real-time executive dashboards that show revenue, margin, inventory, delivery performance, and production throughput \u2014 updated automatically from your operational systems. 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.
Revenue by product, customer, channel, and region with gross margin analysis. See where you\u2019re making money and where you\u2019re leaking it \u2014 updated daily or in near-real-time.
Inventory turns, days on hand, stockout rates, and excess stock by location and product category. Drill from summary to SKU-level detail without switching systems.
OEE, throughput, cycle times, scrap rates, and work order status across production lines and shifts. Identify bottlenecks and capacity issues as they develop, not after the fact.
On-time delivery rates, order-to-ship cycle times, backorder aging, and carrier performance. Customer-facing metrics that directly impact retention and satisfaction.
AR aging, DSO, AP aging, cash flow, and budget-to-actual comparisons. Financial health visible alongside operational metrics for a complete picture.
Dashboards accessible on phone, tablet, and desktop. Embed key metrics in the tools your team already uses \u2014 email digests, Teams/Slack notifications, and TV displays on the shop floor.
Align leadership on the metrics that matter, how they\u2019re calculated, and what targets look like. Define once so every dashboard uses the same definitions.
Map each KPI to its source data, identify gaps, and define the data pipeline requirements. If the data doesn\u2019t exist yet, we address the collection gap.
Iterative dashboard development with stakeholder review at each round. Power BI, Tableau, or Looker \u2014 whichever fits your environment. Design for clarity, not complexity.
Deploy dashboards with scheduled refresh, alerting rules, and access controls. Train leadership and department heads on navigation, filtering, and interpretation.
Executive Dashboards for Chicago industrial equipment operations - configured around local workflows, data ownership, and implementation governance.
Executive Dashboards for Chicago food & beverage operations - configured around local workflows, data ownership, and implementation governance.
Executive Dashboards for Chicago chemicals operations - configured around local workflows, data ownership, and implementation governance.
Executive Dashboards for Chicago electronics operations - configured around local workflows, data ownership, and implementation governance.
Executive Dashboards for Chicago financial services operations - configured around local workflows, data ownership, and implementation governance.
Executive Dashboards for Chicago distribution & logistics operations - configured around local workflows, data ownership, and implementation governance.
Power BI for most manufacturers \u2014 best integration with Microsoft stack, lowest cost, and sufficient for 90% of use cases. Tableau for organizations with complex visualization needs. Looker for Google Cloud-centric environments.
Depends on the data source. Financial data typically refreshes daily. Inventory and order data every 15\u201360 minutes. Production data can be near-real-time with MES integration. We design the refresh schedule around your decision-making cadence.
Yes. Row-level security ensures each user sees only their relevant data \u2014 regional managers see their region, sales reps see their accounts, plant managers see their facility. One dashboard, many views.
We can build dashboards that connect directly to source systems for a quick start. But for sustainable, scalable analytics, we recommend building the data warehouse first. The dashboards are only as good as the data layer beneath them.
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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