Okładka: Robot Foundation Models in the factory: Pi-Zero, OpenVLA, GR00T N1.5 — the end of teach-pendant programming
AIroboticsPhysical AIOmniMES

Robot Foundation Models in the factory: Pi-Zero, OpenVLA, GR00T N1.5 — the end of teach-pendant programming

For 50 years an industrial robot learned a single task through teach-pendant trajectory programming. In 2024–2026 three open foundation models for robotic manipulation arrived — Pi-Zero from Physical Intelligence, OpenVLA from Stanford and GR00T N1.5 from NVIDIA — promising to end that era. The operator shows the robot what to do in VR/AR through 5–10 demonstrations, and a generic model translates that into a real-time trajectory. This article walks through how it actually works today, where it is enough, where it is not, and the real implications for MES integrators across the EU.

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Okładka: Industrial data lake with Apache Iceberg + DuckDB + TimescaleDB: how a modern MES bridges hot, warm and cold storage
time-series dataTimescaleDBApache IcebergOmniMES

Industrial data lake with Apache Iceberg + DuckDB + TimescaleDB: how a modern MES bridges hot, warm and cold storage

TimescaleDB handles time-series from the last 1–90 days (hot) excellently, but at 5+ years of history the cost of PostgreSQL SSD and RAM grows faster than the business value. Apache Iceberg (a table format for the data lake) plus DuckDB (a local SQL engine) deliver a cheaper warm tier (90–730 days) and cold tier (>2 years) on S3 or MinIO. A 3-tier architecture cuts storage cost by 80–95% while keeping data queryable. This article shows how to build it for MES, where the limits are, what it costs, and how to migrate from a monolithic TimescaleDB.

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Okładka: CBAM for steel, aluminium and cement exporters: first definitive-period report by 1 August 2026 — what MES + EMS must measure
UEOmniMESCBAMEMS

CBAM for steel, aluminium and cement exporters: first definitive-period report by 1 August 2026 — what MES + EMS must measure

The CBAM definitive period started on 1 January 2026, and 1 August 2026 is the deadline for the first periodic report under the new regime. European companies in steel, aluminium, cement, fertilisers, hydrogen and electricity — whether importing into the EU or exporting to carbon-aware markets — need to start computing embedded emissions per tonne of product. This article walks through what data MES + EMS must produce, how to map it to PCF (ISO 14067), which tools actually work, what it costs and what the penalties are. Plus a six-week roadmap to the deadline.

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Okładka: TimescaleDB in OmniMES: how PostgreSQL hypertables handle 200M readings per day
time-series dataTimescaleDBPostgreSQLOmniMES

TimescaleDB in OmniMES: how PostgreSQL hypertables handle 200M readings per day

OmniMES uses TimescaleDB — the PostgreSQL extension with hypertables, native compression and continuous aggregates — as its database for industrial sensor time-series. The architecture handles 200 million readings per day, delivers 95% storage compression and 200–800 ms aggregations. This article walks through the hypertables concept, the OmniMES stack architecture, continuous aggregates and concrete production numbers.

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Okładka: Computex 2026: five NVIDIA/AMD/Intel announcements that will change MES in 2027 (GR00T N1.5, Cosmos, Omniverse Replicator)
AInvidiaOmniMESrobotics

Computex 2026: five NVIDIA/AMD/Intel announcements that will change MES in 2027 (GR00T N1.5, Cosmos, Omniverse Replicator)

Jensen Huang's Computex 2026 keynote (2 June) ran three hours and focused on industry — robotics, AI factories, digital twins. AMD and Intel countered the same day. From an MES perspective, five announcements actually change the technology map for 2027: GR00T N1.5 (robot foundation model), Cosmos 2.0 (world model for synthetic training data), the new Omniverse Replicator, AMD Ryzen AI Max+ as a Jetson alternative, and Intel Gaudi 3 for edge inference. This piece walks through each from a real deployment-pipeline perspective, not marketing.

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Okładka: NIS2 and the Polish KSC2 Act in 2026: how MES becomes cyber-compliance evidence for an EU factory
UEOmniMEScyberbezpieczenstwoNIS2

NIS2 and the Polish KSC2 Act in 2026: how MES becomes cyber-compliance evidence for an EU factory

The Polish amendment to the National Cybersecurity Act (KSC2), transposing the EU NIS2 Directive, enters real enforcement in the second half of 2026. Factories producing chemicals, food, machinery, automotive, electronics or medical devices — if they employ more than 50 people — are classified as "important entities" and fall under the full obligations package. This article walks through the ten Article 21 NIS2 requirements from an MES perspective: which functions already produce compliance evidence, what is missing, how every external API (OpenAI, cloud LLM, SaaS MES) grows your audit surface, and what to do concretely in the remaining months of 2026.

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Okładka: Time-series Foundation Models in MES: are TimesFM/Chronos/Moirai already beating your own XGBoost in failure prediction?
AItime-series dataXGBoostOmniMES

Time-series Foundation Models in MES: are TimesFM/Chronos/Moirai already beating your own XGBoost in failure prediction?

Two years after the first pretrained transformers for time series — TimesFM from Google, Chronos-Bolt from Amazon, Moirai-MoE from Salesforce — we finally have real answers to the question of whether a custom XGBoost pipeline for failure prediction in MES can be replaced by a zero-shot foundation model. This article walks through it without hype: what these models were trained on, the quality they deliver on real compressor sensor data, latencies on Jetson Orin and on a GPU server, when zero-shot is enough, and when fine-tuning is unavoidable — and what this means for the classic six-month ML project in a factory.

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Okładka: Digital Product Passport (DPP) enters the factory: ESPR and Battery Regulation 2027 — what MES must deliver by February
UEOmniMESDPPMES System

Digital Product Passport (DPP) enters the factory: ESPR and Battery Regulation 2027 — what MES must deliver by February

On 18 February 2027 the battery passport becomes mandatory for EV batteries, industrial batteries above 2 kWh, and LMT batteries — the first concrete moment when the Digital Product Passport (DPP) turns from EU concept into a hard production requirement. For an MES, this means exposing roughly seventeen attributes per unit: material origin, carbon footprint, recycled content, batch ID, durability, cell health data. This article walks through ESPR (2024/1781) and the Battery Regulation (2023/1542) without fluff: which MES functions already produce the data, what is missing, how to wire it up architecturally with GS1 Digital Link, and what to do in the nine months left before the deadline.

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Okładka: Local RAG in the factory: Phi-4 + sqlite-vec on Jetson Orin — an MES assistant without cloud data leakage
AIAI localOmniMESData Security

Local RAG in the factory: Phi-4 + sqlite-vec on Jetson Orin — an MES assistant without cloud data leakage

After a year of GPT-4 and Claude pilots in manufacturing, the honest question comes back: do we really have to ship process data to the cloud to get an MES assistant? In 2025–2026 the answer is no. Phi-4 (14B, Microsoft, MIT) at 4-bit quantization fits in 8 GB of VRAM, sqlite-vec gives you vector search in a single file with no server, and a Jetson Orin NX/AGX delivers 100–275 TOPS on the shop floor. This article walks through the concrete architecture, token-per-second benchmarks, 3-year TCO vs the OpenAI API, and what this means for AI Act, NIS2 and plant-level IT operations.

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Okładka: EU AI Act, August 2026: which MES functions qualify as "high-risk AI" — and what it means in practice
AIOmniMESUEAI Act

EU AI Act, August 2026: which MES functions qualify as "high-risk AI" — and what it means in practice

On 2 August 2026, the EU AI Act high-risk rules become fully enforceable. A subset of MES functions — predictive maintenance tied to machine safety, operator performance monitoring, AI in medical-grade quality control, and AI-assisted workforce decisions — may be classified as high-risk, with fines up to €15M for breaches. This article maps concrete MES modules to Annex I and Annex III, lists the seven obligations of high-risk operators, and gives a 3-month action checklist for plant owners.

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Okładka: Model Context Protocol (MCP): the new standard for AI integration with MES systems. How to open production data to LLM agents without vendor lock-in
AIAGENT AIOmniMESMCP

Model Context Protocol (MCP): the new standard for AI integration with MES systems. How to open production data to LLM agents without vendor lock-in

Model Context Protocol (MCP), released by Anthropic in November 2024 and adopted in 2025 by OpenAI, Google and Microsoft, has become the de facto standard for connecting LLM agents to external data and tools. For the MES world, it ends the era of "one integration per model" and starts an architecture in which a plant exposes its data once — and Claude, ChatGPT, Gemini, and home-grown agents all plug in. This article explains how MCP works, what to expose from an MES, how to design it safely (lethal trifecta, IT/OT zoning), and when MCP simply does not make sense.

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Okładka: Redash in OmniMES: Dashboards on MongoDB with SQL and REST API
OmniMESData VisualizationredashmongoDb

Redash in OmniMES: Dashboards on MongoDB with SQL and REST API

One of the recurring questions on MES projects is: how do you build analytical dashboards on data that lives in MongoDB, without rewriting the backend to a SQL database? In OmniMES we use Redash for this — for two reasons. First, Redash can consume a REST API as a data source. Second, it has a Query Results mechanism that lets you write full SQL on the result of any earlier query. The outcome is a combination that proves to be a game changer for many MES deployments: full SQL power (JOIN, CTE, window functions) over a NoSQL backend, with zero changes in the production layer. This article walks through the setup step by step — from configuring Redash, through querying the OmniMES REST API, to a SQL query that computes a vibration trend and a ready dashboard.

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Okładka: From Industry 4.0 to 5.0: How Human-Centric AI Is Redefining the Operator on the Production Floor
AIindustry50przemysł50Smart Factory

From Industry 4.0 to 5.0: How Human-Centric AI Is Redefining the Operator on the Production Floor

Industry 4.0 promised a "factory without people." The European Commission, the WEF, and a growing number of manufacturers are answering with Industry 5.0 — where the human stays on the floor but gets new tools: a cobot, an AI assistant, an exoskeleton, augmented reality. The 2026 operator is no longer the person handing over a component — they process information, supervise systems, and participate in decisions. This article shows how 4.0 and 5.0 actually differ, what the market data looks like (BCG, McKinsey, EU Joint Research Centre), who is really deploying human-centric AI (Bosch, Stellantis, Airbus), and where the barriers are — cognitive fatigue, compliance with ISO 45001 and the EU AI Act.

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Okładka: Agentic AI in Manufacturing: How Autonomous Agents Take Over the Factory in 2026
AIAGENT AIMESSmart Factory

Agentic AI in Manufacturing: How Autonomous Agents Take Over the Factory in 2026

Agentic AI is the next stage after generative AI. An agent does not just answer questions — it plans tasks, calls tools, adjusts line parameters, and reports the outcome. In 2026, the first factories are handing real operational decisions to agents: scheduling, maintenance planning, energy optimization. This article explains how an agent differs from a classical chatbot, which manufacturers have moved beyond pilot, what Gartner, McKinsey and Deloitte data actually says, and where the real barriers sit — control, auditability, and integration with MES/ERP.

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Okładka: Physical AI: Humanoid Robots on the Production Floor
AIroboticsSmart Factory

Physical AI: Humanoid Robots on the Production Floor

Humanoid robots have moved past the trade-show demo. In the last twelve months they have appeared on BMW assembly lines, in Amazon warehouses, and in Mercedes-Benz pilots. Physical AI — the software layer that lets a machine perceive the physical world, reason about it, and act on it — is reshaping how manufacturers think about automation. This article walks through where humanoids actually stand in 2026, what the market data really says, which companies have moved beyond pilots, and where the demo ends and real production begins.

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Okładka: AI Agents in Industry 4.0: From Automation to Autonomy
AGENT AIMES SystemOmniMES

AI Agents in Industry 4.0: From Automation to Autonomy

Agentic AI is redefining the trajectory of Industry 4.0. Traditional systems have largely been reactive—analyzing data and supporting human decision-making. Today, a new paradigm is emerging: autonomous agents that independently make decisions, optimize processes, and adapt to changes in real time. In modern smart factories, these systems integrate with MES, IIoT, and ERP platforms to create self-optimizing environments. Machines no longer wait for instructions—they identify issues, anticipate disruptions, and act before production is affected.

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Okładka: Data as the Foundation of Industry 4.0
Production DataOmniMESMES System

Data as the Foundation of Industry 4.0

The concept of Industry 4.0 is based on continuous monitoring of production processes and decision-making driven by real-time data. Without reliable information, it is impossible to implement predictive maintenance, energy optimization, automated planning, or meaningful OEE analysis. If data is delayed, incomplete, or inconsistent, digitalization becomes only superficial. IT systems may be in place, but they do not deliver real business value. That is why it is critical to build an architecture in which data is collected automatically, consistently, and centrally.

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Okładka: Pilot MES Implementation – How to Start and Why a Phased Approach Works Best
OmniMESMES SystemProduction efficiency

Pilot MES Implementation – How to Start and Why a Phased Approach Works Best

Implementing a Manufacturing Execution System (MES) rarely begins with a full-scale rollout across the entire production environment. In practice, the most effective approach is incremental—starting with a pilot implementation on a selected production line or area. This allows companies to validate the system in real operating conditions, collect data, and fine-tune the configuration before scaling across the entire machine park. More and more organizations choose a pilot not only for technical reasons but also for organizational ones. MES impacts the daily work of operators, production managers, maintenance teams, and planners. Therefore, implementation should be a controlled process rather than a one-time deployment.

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Okładka: 2026: The Year Agentic AI Transforms Industrial Manufacturing
AIMESAGENT AI

2026: The Year Agentic AI Transforms Industrial Manufacturing

Across global manufacturing, one theme has become increasingly clear: volatility is no longer something companies plan around — it is the environment they operate within. Manufacturers today face a complex combination of challenges: geopolitical uncertainty, ongoing supply-chain disruptions, increasing regulatory pressure, rising customer expectations. Each of these forces affects the entire production cycle — from planning and procurement to execution and delivery. In such conditions, organizations that have built strong digital foundations gain a significant competitive advantage.

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Okładka: AI in manufacturing: the real issue isn’t 90% accuracy. It’s data maturity.
AIOmniMESMES

AI in manufacturing: the real issue isn’t 90% accuracy. It’s data maturity.

In Industry 4.0 and 5.0 discussions, a common argument appears: AI models reach around 90% accuracy, and in industrial environments a 10% error can cost millions. The reasoning sounds compelling because it is framed numerically and linked to operational risk. However, this argument assumes something critical — the existence of a fully digitized factory. In reality, across many manufacturing plants in Poland and Europe — especially in the SME sector — that level of digital maturity simply does not exist.

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