A Manufacturing Execution System (MES) is a computerized system used in manufacturing to track and document the transformation of raw materials to finished goods. MES provides information that helps manufacturing decision-makers understand how current conditions on the plant floor can be optimized to improve production output.
MES primarily tracks activities on the manufacturing floor in real-time. MES collects vast amounts of data from machines and production lines, analyzes the data, and provides actionable intelligence to help guide decision-making. All of this real-time data provides the visibility manufacturers need to understand what is happening on the shop floor, allowing them to improve quality control and increase productivity.
The architecture of an MES refers to the underlying technology that the framework enables for these real-time tracking, analysis, and intelligence capabilities. This includes both the software components and how they integrate with plant floor equipment and other enterprise systems. Understanding MES architecture helps organizations deploy systems that best meet their manufacturing objectives.
This guide provides a comprehensive overview of key components in a typical MES architecture. It covers:
- Core MES software components and functions
- Integration capabilities with machines, sensors, and other data sources
- Interfaces with Enterprise Resource Planning (ERP) and other higher-level systems
- Support for manufacturing analytic tools
- Role of IIoT and plant connectivity
- On-premise vs cloud deployment options
- Core MES Software Components
The core software components of MES architecture provide the essential functions for real-time manufacturing tracking, analysis, and intelligence. While the exact MES software components may vary by custom software architecture services vendor and deployment type, typical architecture includes:
Data Collection and Acquisition
This module gathers real-time data from a variety of sources, including machines, sensors, process equipment, quality systems, and more. It manages interfaces, communications protocols, and data routing to aggregate raw data into actionable information.
Manufacturing Modeling
The manufacturing execution system architecture must include capabilities for virtual modeling of the physical manufacturing environment. This enables monitoring and analysis relative to the as-designed workflow. Models facilitate schedule tracking and provide the baseline for overall equipment effectiveness (OEE) metrics.
Scheduling and Dispatching
These components manage production schedules and material use while optimizing machine-to-machine and plant floor-to-warehouse workflows. The schedule and dispatch modules allocate resources to balance efficiency with production goals.
Inventory Tracking
MES architecture requires accurate tracking of raw materials, work-in-process, and finished goods. This facilitates materials planning, supports just-in-time methods, and improves overall inventory management across the facility.
Document Control
All specifications, standard operating procedures (SOPs), manufacturing instructions, and related documentation are maintained within the manufacturing execution system architecture. This facilitates training, ensures consistency, and provides traceability relative to regulatory compliance.
Real-time Tracking and Genealogy
At its core, MES architecture enables understanding what is happening on the manufacturing floor in real-time. This requires linking data from production runs to specific product genealogy and components used.
Performance Analysis
A wide range of plant floor equipment effectiveness and manufacturing productivity metrics are calculated from the MES data. Standard metrics include overall equipment effectiveness, schedule compliance, throughput, and much more.
Alert Management
Configurable alerts triggered by out-of-range manufacturing data enable rapid response to emerging issues. Alert management facilitates corrective actions that minimize downtime and mitigate quality concerns.
Integrating with Data Sources
In order to perform its tracking and analytic functions, MES architecture includes technologies to integrate with numerous systems and data sources on the plant floor, including:
Sensorsmetrics,
Data from production equipment sensors and process sensors flows into the MES software modules. Sensor data provides key manufacturing metrics including cycle times, downtime, machine speeds, temperatures, pressures, and much more.
Control Systems
MES architecture can get setpoints, production counts, alarm statuses, and other supervisory data by connecting to industrial and process control systems. This facilitates detailed genealogy tracking and granular OEE analysis.
Process Equipmentmetrics,
Direct equipment integration provides manufacturing execution system architecture with real-time production analytics. Data from equipment such as forklifts, conveyors, robotic arms, and other intelligent assets helps optimize workflows.
Quality Systems
MES integration with quality data sources improves closed-loop corrective action processes. Whether from an instrument, tester, gauge, or manual inspection, MES architecture manages quality data for genealogy and analysis.
Lab Systems
Where required, MES architecture supports bi-directional data exchange with laboratory information management systems (LIMS). This facilitates activities where manufacturing workflows have a dependency on or provide inputs to laboratory analysis procedures.
To enable these many integrations, MES architecture leverages industrial communications protocols and information technologies. This includes Ethernet, WiFi, cellular, OPC, MTConnect, Modbus, SQL, and more. The specifics depend on the nature of the equipment data sources and the deployment environment.
Connecting to Higher-Level Systems
In addition to plant floor data sources, MES architecture also includes standard interfaces to connect with higher-level systems for context and extended analysis of manufacturing information, including:
ERP Interfhigher-level
MES architecture can put manufacturing details in the context of orders, business goals, and finances by exchanging data back and forth with enterprise resource planning (ERP) systems. Integrating MES and ERP improves scheduling, performance analysis, inventory management, and more.
SCADA/Historian
By linking data from manufacturing operations in MES software to process historian tools and SCADA systems, operational metrics can be gathered across the whole company. This provides unified dashboards and supports advanced analytics initiatives.
CMMS
Interfacing computerized maintenance management system (CMMS) software with MES architecture allows deeper insights into asset utilization and maintenance efficiency. This drives improvements in overall equipment effectiveness (OEE) and related productivity metrics.
Analytics Platforms
MES software leverages plant floor data to generate manufacturing intelligence. Connecting MES data to analytics platforms expands the analysis capabilities even further. This allows data scientists to apply machine learning and other advanced methods to uncover optimization opportunities.
The Cloud Factor
The connectivity methods and interface technologies enabled in MES architecture provide the pathways for accessing data anywhere across connected digital infrastructure. This facilitates cloud-based solutions for handling compute-intensive MES data workloads with flexible scalability.
As manufacturers increasingly leverage cloud platforms and SaaS solutions, MES architecture evolves to split key software components between on-premise and cloud:
On-Premise MES Components:
- Local plant floor connectivity
- Real-time functions
- Modeling and visualization
Cloud-Based MES Components:
- Data aggregation
- Historical data retention
- Analytics and reporting
- Administrative functions
This hybrid approach balances the need for cloud scalability while retaining the real-time responsiveness of an on-premise system. As Industry 4.0 and smart manufacturing initiatives progress, architectures will continually adapt to leverage the best attributes of cloud and edge computing.
The Role of IIoT
The Industry 4.0 revolution currently underway in manufacturing is built on the Industrial Internet of Things (IIoT). This refers to the intelligent connectivity of production machinery, operational systems, and product lines using internet technologies. IIoT capabilities are increasingly embedded into modern manufacturing devices and equipment.
As IIoT expands on the plant floor, MES architecture evolves to leverage these new data sources and connectivity pathways:
- Smart Machines. IIoT-equipped machines transform standalone equipment into transparent, optimized assets. MES architecture readily integrates with smart machine data.
- Connected Infrastructure. IoT technologies weave connectivity into the very fabric of the production environment. MES architecture rides atop this infrastructure to access and analyze data.
- Edge Computing. To support real-time response requirements, MES solutions increasingly leverage edge computing tools located near data sources. This facilitates localized handling of time-sensitive functions.
As IIoT reshapes manufacturing technology, it brings tighter integration between MES architecture and the underlying data sources. This greater connectivity expands the visibility and control MES solutions provide.
Conclusion
MES architecture is evolving rapidly, driven by Industry 4.0 innovations. Connectivity is expanding, equipment is getting smarter, and data platforms are moving to the cloud. Yet even as technology progresses, MES architecture must retain its core functions: providing real-time plant floor visibility, actionable analytics, and manufacturing intelligence.
As this guide outlines, companies can leverage reference architectures for typical MES deployments. But every manufacturer has unique data environments and analytical objectives. Defining architecture aligned to these specific needs allows MES solutions to deliver maximum value. And as Industry 4.0 continues redefining manufacturing possibilities, the underlying architecture ensures these smarter factories run at peak efficiency.
