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Process Automation in Manufacturing: PLC, MES and Workflows
By Martin Brandel · Last updated: March 2026
What Is Process Automation in Manufacturing?
Process automation in manufacturing is the use of technology to replace manual, repetitive, or error-prone activities with automated systems. In a factory, this applies at multiple levels: machines are automated with PLCs and robots, production information is automated with MES and IoT, and business processes are automated with ERP and workflow systems.
The term "process automation" is often associated with industrial automation: PLCs controlling machines, robots welding car bodies, conveyor systems transporting parts. But in modern manufacturing, a large portion of the remaining manual effort is not on the machine itself. It is in the information processes around the machine: recording what was produced, documenting downtimes, calculating KPIs, reporting order status to the ERP, notifying maintenance when a machine stops, compiling shift reports. These information processes consume significant time, introduce errors, and create delays. Automating them with an MES (Manufacturing Execution System) is the next step in process automation after the machines themselves are automated.
Three Levels of Process Automation in Manufacturing
| Level | What is automated | Technology | Examples |
|---|---|---|---|
| Machine-level automation | The physical production process: material handling, machining, assembly, welding, painting, packaging. Machines perform production steps without human intervention. | PLC (Siemens, Beckhoff, Allen-Bradley), industrial robots (KUKA, Fanuc, ABB), CNC controllers, SCADA, DCS, conveyor systems, pick-and-place systems. | An injection molding machine runs a fully automatic cycle: mold closes, material injects, part cools, mold opens, robot removes part. No operator intervention needed during the cycle. |
| Information-level automation (MES) | The production information processes: data collection, KPI calculation, downtime recording, alarm notification, order tracking, ERP feedback, quality documentation, shift reporting. | MES, IoT gateways, OPC UA, cloud platforms, real-time dashboards, notification systems, ERP integration interfaces. | Machine states, production counts, and cycle times are captured automatically from PLCs via IoT gateways. OEE is calculated in real time. Order completions are reported to the ERP automatically. No manual data entry needed. |
| Business-level automation (ERP/Workflow) | The business processes: procurement, order management, invoicing, scheduling, capacity planning, material requirements planning. | ERP systems (SAP, Microsoft Dynamics, proAlpha, Infor), APS (Advanced Planning and Scheduling), workflow engines, RPA (Robotic Process Automation). | When a customer order is entered in the ERP, it automatically generates production orders, reserves materials, and schedules capacity. No manual planning steps required for standard orders. |
Most manufacturing companies have invested heavily in level 1 (machine automation) over the past decades. Machines are highly automated. But the information processes between and around the machines (level 2) often remain manual: operators fill out paper forms, production managers compile Excel reports, shift leaders phone maintenance when a machine stops. This information gap between automated machines and manual data processes is where the biggest remaining productivity potential lies.
Manual vs. Automated Production Information Processes
| Production information process | Manual (without MES) | Automated (with MES) | Time saved per shift |
|---|---|---|---|
| Machine data collection | Operator records machine states, downtime reasons, and production counts on paper or in Excel at end of shift. Data is estimated, rounded, and incomplete. | Machine states, counts, and cycle times are captured automatically from PLC signals via IoT gateway. Every state change is recorded with exact timestamp. | 15 to 30 minutes per operator per shift. |
| OEE calculation | Production engineer collects data from paper forms, enters it into Excel, calculates OEE manually. Available next day or next week. Often based on incomplete data. | OEE is calculated in real time from automatic machine data. Updated with every production cycle. Available on dashboard instantly. | 30 to 60 minutes per production engineer per day. |
| Downtime notification | Operator notices machine stopped. Walks to phone or looks for maintenance technician. Delay of minutes to hours before maintenance is informed. | Machine stop is detected automatically. Notification sent to maintenance technician's phone within seconds. Escalation if no response within defined time. | 5 to 30 minutes reaction time per downtime event. |
| Order status reporting to ERP | Operator or shift leader enters order completions into ERP terminal at end of shift. ERP data is always behind actual production by hours. | Order completions are reported to ERP automatically as production progresses. ERP shows real-time order status. | 10 to 20 minutes per shift leader per shift. |
| Shift report compilation | Shift leader compiles shift report from paper notes, machine logbooks, and memory. Subjective. Incomplete. Available at shift handover. | Shift report is generated automatically from MES data: production counts, downtime events, quality results, open issues. Objective and complete. | 15 to 30 minutes per shift leader per shift. |
| Quality documentation | Quality inspector records inspection results on paper. Scrap and rework are tracked in separate lists. Data compilation for quality reports takes hours. | Scrap and rework are captured at the machine. Inspection results are recorded digitally. Quality KPIs are calculated automatically. Reports are available instantly. | 20 to 40 minutes per quality engineer per day. |
MES as Production Information Automation
An MES automates the information layer between the machines (PLC/SCADA level) and the business systems (ERP level). It replaces manual data collection, manual calculations, manual notifications, and manual reporting with automated, real-time processes.
| Automation function | What the MES automates | Customer example |
|---|---|---|
| Automatic machine data collection | Machine states, production counts, cycle times, and alarms are captured from PLC signals via IoT gateways (OPC UA or digital I/O). No manual entry. No operator effort. | Brita: digital machine signals captured automatically for output and downtime monitoring across 2 plants. Carcoustics: 500+ machines connected via IXON IoT devices and MQTT. |
| Automatic KPI calculation | OEE, availability, performance, and quality are calculated in real time from automatic machine data. No Excel, no manual formulas, no end-of-shift compilation. | Meleghy: OEE captured at critical process steps across 6 plants in 4 countries. Real-time dashboards replace manual KPI tracking. |
| Automatic alarm notification and escalation | When a machine stops or an alarm fires, the MES sends a notification to the responsible person (email, push, SMS). If no response within a defined time, the notification escalates to the next level. | Neoperl: SPS-based alarm capture with automatic downtime classification. Alarms correlated with quality defects for root cause analysis. |
| Automatic ERP feedback | Order completions, quantities, times, and status are reported to the ERP automatically. Bidirectional integration eliminates manual ERP entry. | Schmiedetechnik Plettenberg: InforCOM ERP integration. Production orders flow from ERP to MES; completions, quantities, and status flow back automatically. Meleghy: bidirectional SAP R3 integration via ABAP IDoc. |
| Automatic downtime detection and classification | Every machine stop is detected automatically from signal changes. If PLC alarms are available, the downtime reason is classified automatically. No manual downtime logging. | Neoperl: 10% fewer downtimes through automatic detection and classification. Klocke: piece counts and downtimes captured via DI gateways without LAN infrastructure. |
| Automatic report generation | Shift reports, production reports, and quality reports are generated automatically from MES data. No manual compilation. Reports are available at shift end or on demand. | SYMESTIC provides automated production reports, shift summaries, downtime Pareto analyses, and quality trend reports. All generated from automatic data. |
Machine Connectivity: The Foundation of Automation
Information-level automation requires a data connection from the machine to the MES. Without this connection, the machine is an isolated automation island: it runs automatically, but the information about what it does remains locked inside the PLC.
| Machine type | Connection method | PLC modification required? | Installation time |
|---|---|---|---|
| Modern PLC with OPC UA | IoT gateway reads machine data via OPC UA protocol over Ethernet. | No. OPC UA server on PLC is used as-is. | 2 to 4 hours per machine. |
| PLC with Ethernet (no OPC UA) | Gateway communicates via proprietary protocol (e.g., S7 protocol for Siemens). Reads data directly from PLC registers. | No. Data is read from existing PLC memory. | 4 to 8 hours per machine. |
| Legacy machine (no digital interface) | Digital I/O gateway taps into existing electrical signals (24V): running signal, cycle complete signal, alarm signal. | No. Signals are tapped from existing wiring. | 2 to 4 hours per machine. |
| Injection molding machine (EUROMAP) | Standardized EUROMAP 63/77/83 interface provides standardized data set. | No. Standardized interface. | 2 to 4 hours per machine. |
The critical point is that no PLC program change is required and no production interruption is needed. The IoT gateway is added alongside the existing machine control. This means information-level automation can be implemented without disrupting the existing machine-level automation. At Klocke, all packaging lines were connected via digital I/O gateways, each operating on LTE with no factory LAN required. At Carcoustics, 500+ machines across 7 countries were connected within 6 months.
Process Automation and OEE
Process automation at the information level directly improves OEE in three ways:
| OEE factor | How automation helps | Measurable impact |
|---|---|---|
| Availability | Automatic downtime detection and immediate notification reduce the time between machine stop and maintenance response. Automatic alarm Pareto identifies the most frequent causes for systematic elimination. | Meleghy: 10% reduction in downtime, 5% improvement in availability. Carcoustics: 4% reduction in downtime, 8% improvement in availability. Neoperl: 10% fewer downtimes, 8% higher availability. |
| Performance | Automatic cycle time monitoring detects micro-stops and speed losses that manual recording misses. Real-time target vs. actual comparison enables immediate intervention when performance drops. | Meleghy: 7% improvement in output. Brita: 7% improvement in output. Klocke: 12% improvement in output. |
| Quality | Automatic scrap and rework tracking with defect classification provides real-time quality data. Alarm-quality correlation identifies which machine events cause which defects. | Neoperl: 15% less scrap through alarm-quality correlation. 15% productivity gain through targeted quality actions. |
Frequently Asked Questions About Process Automation
What is the difference between machine automation and production information automation?
Machine automation (PLC, robots, CNC) automates the physical production process: the machine performs production steps without human intervention. Production information automation (MES, IoT) automates the data processes around the machine: data collection, KPI calculation, notifications, ERP feedback, reporting. Both are needed. A fully automated machine that still requires manual data entry and Excel reporting is only half automated.
Does process automation replace operators?
Information-level automation does not replace operators. It replaces the manual data entry, paper-based documentation, and reporting tasks that operators and shift leaders perform alongside their actual production work. Instead of spending 15 to 30 minutes per shift filling out forms, operators can focus on operating and improving the production process. The data is more accurate, more complete, and available faster.
Can old machines be integrated into automated information processes?
Yes. Machines without any digital interface can be connected using digital I/O gateways that tap into existing electrical signals (24V running signal, cycle complete signal). No PLC modification is needed. No production interruption is required. This provides the basic data (machine state, production count, downtime) needed for automatic OEE calculation and downtime monitoring.
What is the difference between process automation and RPA?
RPA (Robotic Process Automation) automates office and IT processes: copying data between systems, filling out forms, processing emails. It operates at the business/IT level. Manufacturing process automation operates at the machine and production information level: PLCs control machines, and MES automates production data processes. Both are forms of process automation, but they operate in different domains and solve different problems.
How long does it take to automate production information processes?
With a cloud-native MES, the first machines can be connected and real-time dashboards can be operational within days. Klocke scaled from one packaging line to all lines in 3 weeks. Meleghy scaled from one plant to 6 plants in 6 months. The speed depends on machine connectivity (modern PLCs with OPC UA are fastest), the scope of automation (KPIs only vs. full order management), and ERP integration complexity.
About the author:
Martin Brandel
MES Consultant at SYMESTIC. Over 30 years in industrial automation. Dipl.-Ing. Nachrichtentechnik.
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