Data Analytics · Production

AI-Ready Production Analytics connected to the data your AI solutions need.

Q: Do we have to replace existing systems first?

Usually no. We first determine whether the current systems can expose trustworthy records through governed integrations. Replacement only enters the plan when the current stack blocks the AI-connected outcome.

Q: What has to be ready before delivery starts?

The important prerequisites are source-system ownership, usable data, permissions, exception rules, integration paths, and a clear operating owner for the workflow after launch.

Q: Do we need an MES to use production analytics?

No. We can build analytics from ERP work order data, PLC signals, operator input tablets, or a combination. An MES provides the richest data, but useful production analytics can be built from whatever data sources you have today. In an AI-first scope, this defines the records, permissions, and workflow rules so AI, dashboards, forecasting, and reporting workflows answer from the same operational truth.

Q: How do you handle manual production processes?

For manual operations without machine data, we deploy operator input stations -- tablets or terminals at work centers where operators log starts, stops, counts, and scrap. Simple input, structured data. In an AI-first scope, this defines the records, permissions, and workflow rules so AI, dashboards, forecasting, and reporting workflows answer from the same operational truth.

Metrotechs treats production Analytics as an AI-connected data foundation: define the operating outcome, connect the records and permissions, then deliver the workflow only when the data can be trusted.

Start with Launchpad View assessments

AI-connected operating problem

The data foundation only works when the records behind it can support practical AI.

The problem is rarely that the model cannot generate an answer. The real problem is that the data, permissions, exception rules, and action boundaries are not governed well enough for AI to affect production work.

What leaders see

Promising pilots that do not change daily work.

Teams test tools, get useful output, and still copy results into spreadsheets, tickets, emails, or ERP screens by hand.

What is actually happening

The AI is disconnected from the operating record.

Source data, permissions, business rules, exception handling, and audit trails are not clean enough for the system to take action.

What gets worse

Automation scales uncertainty.

Bad inputs move faster, decisions become harder to trace, and teams lose confidence before AI becomes operationally useful.

Scope

What this service has to produce.

The work is organized as modules because AI-connected delivery scope should be visible before the build starts.

AI outcome map

Define the data decision, user experience, or workflow decision this service must improve before choosing tools or implementation scope.

Trusted operating data readiness

Map the records AI and connected workflows need to trust, including ERP records, orders, inventory, customers, suppliers, production, fulfillment, finance, service, quality, and reporting data.

System connection and permissions

Connect the relevant systems - ERP, data warehouses, databases, spreadsheets, BI tools, APIs, cloud storage, and operational applications - with access rules, integration boundaries, auditability, and exception handling.

Real-Time OEE Monitoring

Availability, performance, and quality tracked in real time for every production line and work center. OEE calculated automatically from machine data, MES, or operator input -- not end-of-shift paperwork. In an AI-first scope, this defines the records, permissions, and workflow rules so AI, dashboards, forecasting, and reporting workflows answer from the same operational truth.

Cycle Time Analysis

Actual cycle times measured against standard times by machine, product, and operator. Identify variation patterns, slow-running jobs, and setup time opportunities. In an AI-first scope, this defines the records, permissions, and workflow rules so AI, dashboards, forecasting, and reporting workflows answer from the same operational truth.

Scrap & Rework Tracking

Scrap rates by product, machine, shift, and defect type. Connect scrap events to upstream process parameters to identify root causes, not just symptoms. In an AI-first scope, this defines the records, permissions, and workflow rules so AI, dashboards, forecasting, and reporting workflows answer from the same operational truth.

Architecture

The service has to fit the operating layer it touches.

Intelligence layerWhich decisions can be automated, which need review, and which should stay human-owned.

Governance dependencyThe agent needs governed inputs, clear action boundaries, and audit logging before it can touch production workflows.

Data the model must trust

ERP history

exception queues

pricing rules

quality records

fulfillment events

What we check before delivery

Which system owns the record of truth.
Where manual work or reconciliation enters the workflow.
Which integrations, rules, or data cleanup have to come first.

Next step

Start with the operating problem, then sequence the build.

Metrotechs maps the record, traces the workflow, identifies the leakage, and turns the scope into a practical plan for AI, data, ERP-connected records, cloud, integrations, reporting, governance, and automation.

Start with Launchpad View assessments

Built around real records, workflows, governance, and production handoffs.

Scoped to what can be connected, owned, and operated after launch.

Delivery sequence

How the work moves from diagnosis to production.

Metrotechs treats production Analytics as an AI-connected data foundation: define the operating outcome, connect the records and permissions, then deliver the workflow only when.

Name the AI-connected outcome

Start with the decision, answer, recommendation, route, forecast, portal, or workflow the business wants to improve.

Map trusted operating data

Trace the source systems, owners, fields, permissions, manual handoffs, and exception paths behind ERP records, orders, inventory, customers, suppliers, production, fulfillment, finance, service, quality, and reporting data.

Design the connection path

Define integrations, data contracts, cleanup, cloud controls, workflow rules, audit logging, and human review before delivery starts.

Govern production use

Set validation gates, rollback paths, operating owners, monitoring, and improvement loops so the AI-connected workflow can be trusted after launch.

Production Data Audit

Inventory production data sources -- MES, PLCs, paper logs, ERP work orders. Identify what's measured, what's missing, and what's measured but not used. In an AI-first scope, this defines the records, permissions, and workflow rules so AI, dashboards, forecasting, and reporting workflows answer from the same operational truth.

Metric Definition

Define OEE calculations, cycle time standards, scrap categories, and performance benchmarks. Align operations and production leadership on the definitions. In an AI-first scope, this defines the records, permissions, and workflow rules so AI, dashboards, forecasting, and reporting workflows answer from the same operational truth.

FAQ

Questions that usually decide the scope.

These answers help separate a real AI-connected delivery plan from a generic technology discussion.

How does this service connect to AI?

Metrotechs starts with the AI outcome, then maps the trusted operating data, systems, permissions, and workflow rules needed for that outcome. The service matters because data ownership, definitions, quality checks, permissions, and refresh paths are settled before AI or reporting becomes authoritative.

Do we have to replace existing systems first?

What has to be ready before delivery starts?

Do we need an MES to use production analytics?

How do you handle manual production processes?