Industry 5.0 is no longer just about automation but about smart use of data. With IoT and MES, every machine produces endless streams of signals. The challenge is not in collecting them, but in analyzing and using them in real time. Vector databases and AI are increasingly at the heart of this process.
In the era of industrial digital transformation, the global human-machine interface (HMI) market reached USD 24.5 billion in 2024 and is projected to grow to USD 55.2 billion by 2033, with a compound annual growth rate (CAGR) of 9.7%. At the same time, the simulation software market expanded from USD 19.95 billion in 2024 to an expected USD 36.22 billion by 2030 at a CAGR of 10.4%. These dynamic trends reflect a fundamental shift in the approach to optimizing production resources.
The integration of the Sparkplug B protocol in MES systems enhances energy efficiency and reliability in production.
The digital transformation of manufacturing requires a strategic approach to managing production batches. The use of Manufacturing Execution Systems (MES) combined with Industry 5.0 technologies can reduce downtime by 50% and cut maintenance costs by 40%, according to the latest Deloitte research.
Modern industry is at the heart of digital transformation, where Manufacturing Execution Systems (MES) with real-time data analytics form a cornerstone of Industry 5.0. According to a 2023 McKinsey Global Institute report, companies that deploy advanced analytics in production achieve, on average, a 15–20% productivity increase and a 25% reduction in defects.
The Polish market for operator panels and HMI systems is worth ~PLN 120 million annually. Yet, only 15% of factories in Poland are fully automated, highlighting vast potential for companies ready to invest in modern batch management technologies.
ROI: up to 15% efficiency gains and 10–20% better energy storage performance – this is the real power of AI in modern energy. In the era of Industry 5.0, where technology merges with a human-centric approach, the renewable energy sector is growing at an unprecedented pace – with 18.6 GW of additional solar capacity installed in the first nine months of 2024. Manufacturing Execution Systems (MES) enhanced with machine learning are becoming a cornerstone of this transformation, delivering both business and technical benefits.
Industry is on the brink of a new revolution. Industry 5.0 marks an era of human–machine collaboration that prioritizes sustainability, where AI is not merely a tool but a strategic enabler of transformation. Unlike Industry 4.0, which focused on automation and digitalization, Industry 5.0 emphasizes human–machine collaboration, sustainable development, and the integration of ESG (Environmental, Social, Governance) principles with advanced technologies.
In the world of industrial IT, where technical documentation grows faster than peak-season production, finding specific information becomes a challenge. That’s why we decided to combine two powerful tools: LangChain and Outline, creating a smart documentation assistant that understands user queries and responds based on the current knowledge base.
In the age of Industry 5.0, where energy efficiency becomes a key differentiator, manufacturing enterprises face unprecedented challenges. According to the new EU Directive 2023/1791 on energy efficiency, member states must achieve an average annual energy savings rate of 1.49% between 2024–2030. This means that companies must radically rethink their approach to energy management. A crucial tool in this transformation is the optimization of the OEE (Overall Equipment Effectiveness) index through intelligent energy management in MES-class systems. In both Industry 4.0 and 5.0, OEE plays a key role in ROI planning, reliability, and production stability.
Integrate MES with IoT to enhance quality control and reduce production downtime.
The global Internet of Things (IoT) market reached USD 714.48 billion in 2024 and is projected to grow to USD 4,062.34 billion by 2032, representing a CAGR of 24.3%. At the same time, the Industrial IoT (IIoT), valued at USD 289 billion in 2024, is expected to reach USD 847 billion by 2033 with a CAGR of 12.7%. These impressive figures reflect a fundamental shift in modern industry toward Industry 5.0.
The use of Web3 and blockchain technology in managing industrial data and promoting renewable energy sources (RES). This article discusses energy consumption monitoring, automated settlements, energy tokenization, and peer-to-peer trading, emphasizing the benefits of using Web3 in the industry.
A comparison of InfluxDB and MongoDB for industrial data collection. This article discusses the use of InfluxDB for time-series data analysis, machine monitoring, and IoT, as well as MongoDB as a universal database for MES systems. Practical tips on when to choose each database.
Comparison of PyTorch, TensorFlow, XGBoost, LightGBM, and CatBoost in industrial data analysis. The article discusses the use of PyTorch in machine failure prediction, time series data analysis, and compares it with other tools in terms of flexibility, performance, and applications.
An overview of two approaches for collecting industrial machine data: implementing an MES system such as OMNIMES by Multiprojekt, and using public datasets from platforms like Statista and Kaggle. Practical solutions for building predictive maintenance models.
Comparison of TensorFlow, XGBoost, LightGBM, and CatBoost algorithms in fault detection in MES systems. Practical applications, time-series and tabular data analysis, and performance evaluation in terms of speed and computational resources.
Practical application of XGBoost, LightGBM, and CatBoost algorithms in MES systems for fault detection. A description of data analysis methods, machine state classification (production, failure, planned, and unplanned downtime), and real-time notifications implementation to minimize downtime.
The Ollama project is an open, free software that allows running local AI models, specifically large language models (LLMs), on your own computer or server. Due to its low hardware requirements, this software is accessible to medium and small businesses, providing access to 93 AI models without significant limitations.
In today's world, the rapid development of technology and increasing market demands make efficient production management crucial for a company's success. Systems like Omnimes offer advanced tools for process optimization, including data visualization, which supports analysis and decision-making.
Learn how to integrate the GPT-4 API with a web application step by step to leverage advanced data analysis features. Example in Python and Flask, along with settings and data security.