Company

Vision

M.AX specialist | AI Solution Company

Make the data, add the judgment, keep it sustainable.

VISION

Building factories
that decide

Manufacturing Intelligence for Autonomous Factory

Automation repeats a fixed motion. An autonomous factory reads the situation and decides.

What makes the difference is manufacturing data and manufacturing intelligence.

ITSCO has been producing data on the floor since 2016, and now builds factories that judge with it.

04
AUTONOMOUSAutonomous factoryReads the situation and decides
  1. Read
  2. Decide
  3. Act
03
Manufacturing intelligenceINTELLIGENCE
02
Manufacturing dataDATA · SINCE 2016
01
Automated factoryRepeats a fixed motionInput · Fixed rule · Same motion

Unified architectureData-driven execution

ITSCO Autonomous Factory Architecture

Beyond collection: a complete autonomous-manufacturing platform that learns from the floor's compound data and carries out the best decision itself.

Infrastructure built
SINCE 2016
Operating cycle
DX → AX → GX

TREND

What is happening on the floor

Manufacturing AI is moving from one company's choice to an industry's foundation. Four currents ITSCO is reading.

MARKET SHIFTITSCO's response
01

As the M.AX alliance widens sharply, manufacturing AI is being treated as shared industrial infrastructure rather than a few leading firms' experiment.

Shifting organization and process from single-company delivery to consortium-scale design.

02

The reach of manufacturing AI has moved past process optimization into production planning, supply chain, safety and inventory — the whole of a company's activity.

Moving the product axis from MES-centred delivery to enterprise decision support on a data lakehouse.

03

With an industrial-park AX group formed and the collecting, sharing and use of manufacturing data being formalized, the unit under discussion is the park rather than the plant.

Preparing a SaaS AX platform several companies use together.

04

The industrial-AI approach being pursued is to spread proven AI models by fine-tuning them to similar manufacturing processes.

Leaving build-to-order behind and making a reusable manufacturing AI product.

ITSCO ROADMAP

Three stages

Being certified, leading a program and selling a product are three different jobs. ITSCO aims to reach the third by 2027.

  1. STEP 01Capability confirmed

    M.AX specialist

    The stage at which the capability to deliver manufacturing AI has been formally confirmed. Smart-factory delivery and an AI track record qualify us to take part in M.AX programs.

    Core task

    Building exactly what was asked for

  2. STEP 02Leading the ecosystem

    M.AX lead company

    Not a participant but the company that designs the program and leads the consortium. Defining the customer's problem, setting the data structure, and assigning the roles of the participating organizations.

    Core task

    Deciding what should be built

  3. STEP 03The 2027 vision

    Manufacturing AI product company

    Not built anew for every project — proven manufacturing AI supplied as a product. A model proven on one process is fine-tuned to similar ones and spread.

    The leap

    Selling what was built once, again

STRATEGY

Five problems, five products

"Manufacturing AI" is too broad a phrase. There are five areas ITSCO has actually put products and people behind.

Select a card to see the detail
01Manufacturing AX / AI Factory
Unit-process optimization · Manufacturing Intelligence
STEP 1Cause
  • - Data silos
  • - Human error
STEP 2Cost
  • - Judgment lags in real time
  • - Data keeps being lost
STEP 3Answer
  • - A pipeline from real-time sensing straight to the decision

RESEARCH & DEVELOP

What we are building

Manufacturing AI does three jobs. It optimizes how things are made, it optimizes the resources they take, and it judges over both the way a person would. ITSCO's R&D is divided along those three axes.

01AI Factory Operations
Optimizing how things are madeProduction, quality and equipment joined in one set of data.
1
Production
Delivery forecastingProduction plan optimizationInput sequencing
2
Quality
Defect predictionCause analysisProcess condition advice
3
Maintenance
Anomaly detectionRemaining-life predictionMaintenance timing
PredictAnalyze the causeAdvise conditionsAuto-optimize
02AI Energy & Carbon
Optimizing the resources they takeEnergy and carbon computed on the same axis as production data.
1
Measure
kWh/EACO₂/EA
2
Predict
Energy forecastOptimal condition advice
3
Report
Carbon MRVCBAM/PEF reporting
MeasurePredictOptimizeRegulatory reporting
03Manufacturing Brain
Making the floor's judgment intelligentAn AI agent built for manufacturing that learns veterans' tacit knowledge together with operating data.
1
Data collection
PLC / sensorsMES / QMS / FEMS
2
Integration · judgment
AI AgentLakehouseManufacturing Knowledge
3
Action
Physics AIDigital Twin
DataJudgmentAction
Shared foundation:Process conditions · equipment state · raw materials · output records
Applied step by step, as far as the plant's data maturity allows.