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Manufacturing Execution System (MES) & Digitalization

Manufacturing Execution System (MES) & Digitalization:


MES is a combination of software system and hardware used in manufacturing to track and manage production processes in real-time. It includes functionalities such as basic production scheduling, job tracking, quality control, and inventory management. MES helps manufacturers to optimize their production processes, reduce lead times, increase throughput, improve product quality, increase visibility, and detects bottlenecks.

Digitalization in MSF refers to the use of digital technologies to transform and automate business processes. Digitalization involves the integration of digital technologies into all aspects of a business, including manufacturing, supply chain, paperless production… Digitalization enables manufacturers to collect and analyze data in real-time, automate routine tasks, and make informed decisions based on data insights. By optimizing production processes and minimizing waste, digitalization also can help reduce production costs.

While MES is a specific software system used in manufacturing, digitalization is a broader strategy that involves the use of multiple digital technologies, including MES, to transform and optimize manufacturing processes. MES is one aspect of digitalization, but digitalization goes beyond MES to include other technologies such as IoT sensors, artificial intelligence (AI), and machine learning (ML).


MSF Manufacturing Execution System (MES)

MSF MES helps to collect and analyze all production data in real-time, from workstations or WorkCentres, employees, tools usage, materials, quality (scrap, rework), sensors and much more. Real time data from workstation is shown on the Panel PC for the operator to see. All data from workstations and workcenteres are shown also in real-time in MES web app on the Dashboard page (and other pages). MSF MES is a fully functional system for managing manufacturing processes including production modules with product trees, production orders, job orders, basic labour management, monitoring of production processes and analysing stop causes in production. Using this module you can monitor the whole production in one single platform from various locations.


There are different versions of MES available to be used in the shop floor: Basic MES and Advanced MES:

Basic MES: Basic production information (employees (login, times), work orders (details, planned, durations), basic quality (scrap, rework), manual stop selection, interaction with pre-uploaded documents. Basic production OEE is available. Best suitable to be used for manual jobs, or workstation digitalization with MSF IoT card and machine digital signals.

Advanced MES: all from basic MES, plus stock transfer/request option, manual/automatic pallet system* (*with Supply Chain Planning), collecting data from sensors, connecting to PLC of workstations, monitoring sensor information, … Fully customizable panel themes are available. Maintenance team interaction* (*with Maintenance module only).



Types of shop-floor digitalization (MES):

Different combinations of MES digitalization are available, depending on the scenarios needed from the shopfloor. Shop floor can be digitized with either IoT card, Panel, or combination of Panel and IoT card.

1.) MSF Panel digitalization

Uses Smart Touch-Screen Industrial Panel (various sizes are available). Digitalization just with MSF Panel is available only in MES manual mode. These panels are usually put in strategic locations, for example WorkCentres, to digitalize multiple machines or manual job work places.

2.) MSF IoT digitalization

You can use MSF Smart I/O device and can be used like external PLC. This way you can also digitalize older machines without needing to put too much cost into upgrading you existing machine. One IoT can send collected data directly to server without the need of a Panel PC. Useful if you want to digitalize machines or sensors that are 24/7 in production, like compressors etc.

3.) MSF Panel + IoT

Another way to fully digitalize workstation is combination with panel and IoT card. With this way you’ll get full advanced options to collect data and interact with data from the panel. This will also give you possibility to calculate OEE and TEEP fully and correctly. Additionally, automatic and manual stops are collected, setup, counter, … Also you can input scrap are rework details. Other modules are able to work in parallel with MES combination: Maintenance module, APS module, SCP module, energy consumption module, …

4.) Panel / Panel + IoT for Manual jobs

If you want to put collect produced quantities from manual jobs in real-time, only Panel PC can be used. Additionally, buttons and switches can be connected to follow manual jobs in real time, not needing to interact with Panel so frequently.

4.) PLC digitalization

MSF MES can work with any kind of PLC, as long as it’s open for outside communication. There are many ways that a PLC can connect to external IoT device: Ethernet or Wi-Fi, Serial port communication (Modbus or RS-485), OPC-UA,  APIs or other integration methods. Many PLC integrations has already been implemented, with well known brands or smaller local ones (Siemens, Honeywell, Schneider, ABB, Allen Bradley, …)



Why should you take into consideration MSF Manufacturing Execution System (MES)?

  • Remove of paper (paperless factory), increasing productivity, finding bottlenecks,
  • For any type of production, sector, organization
  • Fully customizable
  • MES app works on-premise/cloud
  • Basic and advanced reports available
  • Can digitalize all workstations (old/new), manual jobs, and modernize old machines
  • Can collect and kind of data: machine speed, performance (workstation and employee), power consumption, …
  • One or multiple workstations per panel PC
  • Can digitalize with only IoT card or with only Panel PC
  • Managing stock movements automatically
  • Basic and advanced reports available from the data
  • More functionalities in combination with other modules (APS, 4PL, Maintenance, QC, …)
  • Real-time crisis scenarios automatically/manually trigger notifications and other modules
  • Can work independently to other modules
  • Available usage of external devices (scanners, …)
  • Panel PC Software supports all languages
  • Support employee login/log out


Shop floor digitalization refers to the broader concept of leveraging digital technologies to transform and optimize shop floor operations, whereas MES specifically refers to the software system designed for managing and controlling manufacturing operations. 

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There can be huge differences between different providers. For example, not all MES solutions are sufficient for different types of productions to follow at the same time. Good MES also needs to have capabilities to correctly digitalize various shop floors and scenarios, from old and new workstations, to manual jobs. Collecting correct data should be the priority, and collecting all the needed data to increase productivity and remove bottle necks should also be collected correctly. 

A Manufacturing Execution System (MES) is a computer-based system used in manufacturing industries to manage and control the execution of production operations on the shop floor. It serves as a bridge between the enterprise resource planning (ERP) system and the actual manufacturing process.

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A MES system works by integrating with various components of the manufacturing process and capturing real-time data to enable monitoring, control, and analysis. Here’s a general overview of how an MES system works:

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Meta Smart Factory MES (Manufacturing Execution System) and ERP (Enterprise Resource Planning) are two different software systems that serve distinct purposes in an organization’s operations. Here are the key differences between MSF MES and ERP:

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No, MES (Manufacturing Execution System) and ERP (Enterprise Resource Planning) are not the same. They are distinct software systems with different purposes and functionalities.

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MES (Manufacturing Execution System) and MRP (Material Requirements Planning) are two different systems that serve distinct purposes in the manufacturing process. Here are the key differences between MES and MRP:

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MES (Manufacturing Execution System) typically consists of multiple levels or layers that work together to provide comprehensive control and visibility over the manufacturing process. The specific levels may vary depending on the system architecture and implementation, but here are the commonly recognized levels of MES:

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The key features of a Manufacturing Execution System (MES) can vary based on specific system implementations and industry requirements. However, here are some commonly found key features of MES:

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MES (Manufacturing Execution System) production data refers to the information collected and generated by the MES system during the execution of manufacturing operations on the shop floor. This data captures various aspects of the production process, providing insights into the performance, efficiency, and quality of production activities. Here are some examples of MES production data:

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Integrating a Manufacturing Execution System (MES) with an Enterprise Resource Planning (ERP) system enables seamless data exchange and synchronization between the shop floor and the broader business operations. Here are some common approaches to integrating MES with ERP:

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About Manufacturing Execution System (MES)

Meta Description: Explore how Manufacturing Execution Systems (MES) drive operational efficiency in Industry 4.0. Learn about MES advantages, integration with big data, and the role it plays in ensuring regulatory compliance and quality control, Leveraging Big Data and Data Warehousing.

In the era of Industry 4.0, manufacturers are embracing advanced technologies to enhance operational efficiency and gain a competitive edge. One crucial tool at their disposal is the Manufacturing Execution System (MES), which plays a vital role in streamlining production processes, optimizing resource allocation, and ensuring quality control. In conjunction with the principles of Industry 4.0, MES leverages big data and data warehousing to unlock valuable insights and drive continuous improvement. Let’s explore the benefits and applications of MES, along with its integration with big data and data warehousing.

Understanding Manufacturing Execution System (MES) and Its Advantages

Manufacturing Execution System (MES) is a software-based solution that helps manage and control manufacturing operations on the shop floor. It connects various systems, equipment, and personnel to enable real-time monitoring, data collection, and decision-making. MES is a crucial component of Industry 4.0 as it enables digitalization and process optimization, driving operational efficiency and agility.

MES serves as a bridge between enterprise-level systems (such as Enterprise Resource Planning) and the shop floor, providing visibility and control over manufacturing processes. It facilitates data exchange, automation, and real-time insights, enabling manufacturers to optimize production, improve quality, reduce costs, and respond quickly to market demands.

MES enables digitalization by connecting machines, equipment, and systems through IoT technologies, allowing for real-time data collection and analysis. It automates manual processes, facilitates data exchange between different systems, and provides visibility into the manufacturing process. By optimizing processes, MES helps eliminate inefficiencies, reduce downtime, and improve productivity.

MES enables manufacturers to achieve process optimization by:

  • Streamlining workflows and reducing manual interventions
  • Enabling real-time monitoring of production activities
  • Automating data collection and analysis
  • Facilitating effective resource allocation
  • Enhancing quality control and compliance with regulations
  • Supporting predictive maintenance and minimizing equipment downtime

Implementing MES offers numerous advantages, including:

  • Improved production visibility: MES provides real-time data on production activities, allowing manufacturers to monitor operations, identify bottlenecks, and make informed decisions.
  • Enhanced resource utilization: MES optimizes the allocation of resources such as materials, labor, and equipment, reducing waste and maximizing efficiency.
  • Streamlined workflows: By automating and standardizing processes, MES eliminates manual errors, accelerates production cycles, and improves overall operational efficiency.
  • Better quality control: MES enables real-time monitoring of quality metrics, facilitating early detection of defects and ensuring compliance with quality standards.
  • Increased operational efficiency: Through data-driven insights, MES helps identify and eliminate inefficiencies, reduce downtime, and improve productivity.
  • Faster time-to-market: By streamlining processes, reducing lead times, and improving coordination, MES enables manufacturers to bring products to market more quickly, gaining a competitive advantage.

MES enhances operational efficiency and productivity in several ways:

  • Real-time monitoring: MES provides real-time visibility into production activities, allowing manufacturers to monitor key performance indicators (KPIs) and make proactive decisions to optimize operations.
  • Data-driven decision-making: MES collects and analyzes data from various sources, enabling manufacturers to make informed decisions based on accurate and timely information.
  • Workflow automation: MES automates manual processes, reducing errors and cycle times while improving overall process efficiency.
  • Resource optimization: By optimizing the allocation of resources such as materials, equipment, and labor, MES helps manufacturers maximize productivity and minimize waste.
  • Improved quality control: MES integrates quality management processes into production activities, ensuring adherence to quality standards, reducing defects, and improving overall product quality.
  • Continuous improvement: MES provides insights into production bottlenecks, inefficiencies, and areas for improvement, driving a culture of continuous improvement and operational excellence.

MES plays a critical role in ensuring regulatory compliance and quality control by:

  • Enforcing standardized processes: MES defines and enforces standardized operating procedures, ensuring consistent adherence to regulations and quality standards.
  • Capturing and storing relevant data: MES captures and stores production data, including critical quality parameters, traceability information, and regulatory documentation, providing a reliable audit trail.
  • Real-time monitoring and alerts: MES monitors production activities in real-time, enabling early detection of deviations from regulations or quality standards and triggering alerts for immediate corrective action.
  • Facilitating quality control processes: MES integrates quality control processes such as inspections, tests, and validations into the production workflow, ensuring quality at each stage.
  • Providing traceability: MES enables end-to-end traceability by tracking and documenting the movement of materials, components, and products, ensuring compliance with regulatory requirements.

Integrating Big Data into MES

MES collects and manages big data in manufacturing environments through various mechanisms:

  • IoT Sensors: MES integrates with IoT sensors embedded in machines and equipment, collecting real-time data on parameters such as temperature, pressure, vibration, and energy consumption.
  • Machine Connectivity: MES connects to machines and equipment via standardized protocols, allowing for the collection of data on machine performance, production rates, and maintenance needs.
  • Manual Inputs: MES allows operators and workers to input data manually, capturing information such as quality inspection results, production quantities, and downtime reasons.
  • Integrated Systems: MES integrates with other systems such as Enterprise Resource Planning (ERP), Product Lifecycle Management (PLM), and Supply Chain Management (SCM), consolidating data from multiple sources.

The sources of big data in MES include:

  • IoT Sensors: Data collected from sensors embedded in machines, equipment, and production lines.
  • Production Data: Information on production rates, cycle times, and process parameters.
  • Quality Control Data: Data from quality inspections, tests, and validations.
  • Inventory Data: Information on material stock levels, supply chain data, and traceability information.
  • Workforce Data: Data related to labor utilization, operator performance, and training records.
  • Integrated Systems Data: Data exchanged with ERP, PLM, SCM, and other systems.

Handling big data in MES comes with several challenges and considerations, including:

  • Data Volume Management: Managing large volumes of data generated from various sources and ensuring efficient storage and processing capabilities.
  • Data Integration: Integrating data from disparate sources and ensuring data consistency and accuracy.
  • Data Quality Assurance: Ensuring data quality by implementing data cleansing, validation, and verification processes.
  • Data Security: Implementing robust security measures to protect sensitive production and business data from unauthorized access or breaches.
  • Advanced Analytics: Leveraging advanced analytics techniques to extract actionable insights from big data and convert them into meaningful information for decision-making.

Data integration between MES and various data sources takes place through standardized protocols such as OPC-UA (Open Platform Communications – Unified Architecture) and APIs (Application Programming Interfaces). These enable seamless data exchange between MES and machines, equipment, and other systems, ensuring real-time data availability.

Integrating big data into MES enables data-driven decision-making by providing real-time insights into production processes, identifying bottlenecks, predicting maintenance needs, optimizing resource allocation, and improving overall operational efficiency. It helps manufacturers make informed decisions based on accurate and timely data.

Leveraging Data Warehousing in MES

Data warehousing is the process of collecting, organizing, and storing large volumes of structured and unstructured data from various sources in a centralized repository. In the context of MES, data warehousing plays a crucial role in enabling data analysis, reporting, and decision-making. It provides a comprehensive and unified view of manufacturing data, allowing manufacturers to extract meaningful insights and drive continuous improvement.

Data warehousing supports MES functionalities in several ways:

  • Centralized data storage: Data warehousing consolidates data from multiple sources, including MES, IoT sensors, production systems, and other integrated systems, creating a centralized repository for analysis and reporting.
  • Data aggregation and transformation: Data warehousing enables the aggregation and transformation of raw data into a standardized format, making it easier to analyze and derive insights.
  • Advanced analytics capabilities: Data warehousing platforms often include advanced analytics tools and techniques that enable manufacturers to perform complex data analysis, identify trends, patterns, and correlations, and make data-driven decisions.
  • Data visualization and reporting: Data warehousing provides tools for visualizing data through dashboards, reports, and interactive charts, allowing manufacturers to gain a clear understanding of key performance metrics and track progress towards goals.
  • Historical analysis: Data warehousing stores historical data over time, enabling manufacturers to perform trend analysis, identify long-term patterns, and make informed decisions based on historical performance.
  • Improved data accessibility: Data warehousing provides a centralized and organized data repository, making it easier for users to access and retrieve relevant information quickly.
  • Enhanced data analysis capabilities: Data warehousing platforms often offer advanced analytics tools that enable manufacturers to perform complex analysis, identify trends, and gain valuable insights from large volumes of data.
  • Real-time and historical reporting: Data warehousing allows manufacturers to generate real-time and historical reports, providing a comprehensive view of production activities, performance metrics, and quality data.
  • Efficient decision-making: Data warehousing facilitates data-driven decision-making by providing accurate and timely information, empowering manufacturers to make informed choices that drive operational efficiency and productivity.
  • Scalability and flexibility: Data warehousing platforms can handle large volumes of data and support the integration of additional data sources, allowing manufacturers to scale their operations and adapt to evolving business needs.

Future Trends and Innovations in MES and Industry 4.0

In the era of Industry 4.0, MES is evolving to meet the demands of increasingly connected and automated manufacturing environments. Some notable trends and advancements include:

  • Integration with emerging technologies: MES is integrating with emerging technologies such as artificial intelligence (AI), machine learning (ML), robotics, and augmented reality (AR) to further enhance automation, predictive capabilities, and real-time decision-making.
  • Digital twins and simulation: MES is leveraging digital twin technology to create virtual replicas of physical assets, enabling manufacturers to simulate and optimize production processes, predict outcomes, and perform “what-if” analyses before implementing changes on the shop floor.
  • Edge computing and edge MES: Edge computing, where data processing occurs closer to the data source, is gaining prominence in MES. Edge MES enables real-time data processing, reduced latency, and improved responsiveness, making it suitable for applications requiring immediate decision-making and action.
  • Cybersecurity and data privacy: As connectivity increases, cybersecurity and data privacy become critical concerns. MES is incorporating robust security measures to protect manufacturing data from cyber threats and ensuring compliance with data protection regulations.

In the future, MES holds the potential to deliver additional benefits in Industry 4.0, such as:

  • Predictive and prescriptive analytics: As MES continues to leverage big data and advanced analytics, it can further enhance its predictive and prescriptive capabilities. Manufacturers can proactively identify potential issues, predict maintenance needs, and prescribe optimal process adjustments to maximize efficiency and quality.
  • Autonomous and self-optimizing systems: With advancements in AI and machine learning, MES can evolve into autonomous systems capable of self-optimization. These systems can learn from production data, adapt to changing conditions, and make automatic adjustments to optimize performance and resource allocation.
  • Integration with supply chain partners: MES can expand its integration capabilities to include supply chain partners, enabling seamless data exchange, collaborative planning, and synchronized production activities. This integration can lead to improved supply chain visibility, agility, and responsiveness.
  • Enhanced human-machine collaboration: MES can facilitate enhanced human-machine collaboration through intuitive interfaces, augmented reality (AR), and wearable devices. These technologies can provide real-time guidance, support operators in complex tasks, and capture valuable insights from human-machine interactions.


By leveraging big data, manufacturers can:
  • Identify patterns and trends in production data to optimize processes.
  • Predict and prevent equipment failures by analyzing machine performance data.
  • Optimize inventory levels and supply chain management based on real-time demand signals.
  • Improve product quality by analyzing quality control data and identifying areas for improvement.
  • Implement predictive maintenance strategies to minimize equipment downtime and maintenance costs.
  • Optimize resource allocation and scheduling to maximize productivity and minimize costs.


Manufacturing Execution System (MES) plays a pivotal role in driving operational efficiency, productivity, and quality control in the context of Industry 4.0. By leveraging big data and data warehousing capabilities, MES enables manufacturers to gain valuable insights, make data-driven decisions, and continuously improve their processes. Integrating big data into MES empowers manufacturers to optimize production, reduce costs, enhance quality, and gain a competitive advantage in the manufacturing landscape.

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