1. Overview
Digital and Smart Manufacturing is a manufacturing knowledge area that connects manufacturing processes, equipment, operators, materials, quality, production planning, and maintenance information as digital data, and uses that data to monitor, control, analyze, and optimize the manufacturing floor.
If Manufacturing Systems connects processes, equipment, operators, logistics, and quality flow into one production structure, Digital and Smart Manufacturing collects, evaluates, and improves the status of that production structure through data.
Representative elements include the following.
CAD / CAM
CNC data
PLC control data
HMI / SCADA
MES
ERP
Production performance data
Equipment status data
Quality inspection data
Sensor data
IIoT
Digitalization of work standards
Process monitoring
Real-time production tracking
Digital work instructions
Barcode / QR / RFID tracking
Process simulation
Digital twin
Predictive maintenance
Process data analytics
Quality data analytics
Automated inspection
AI-based anomaly detection
Cybersecurity
Data standardization
The core of Digital and Smart Manufacturing is not simply “attaching sensors to a factory” or “applying AI.” Designers must consider product structure, process flow, equipment control, quality criteria, data collection points, data reliability, operator usability, maintainability, security, and cost-effectiveness together.
2. Role of Digital and Smart Manufacturing
The core role of Digital and Smart Manufacturing is to create a structure that understands the manufacturing floor through data and continuously improves production quality and efficiency.
Traditional manufacturing often depends on operator experience, handwritten records, final inspection results, and shop-floor intuition. However, as production structures become more complex and quality requirements become stricter, experience-based management alone makes it difficult to accurately identify process status, defect causes, equipment abnormalities, delivery delays, and cost variation.
The role of Digital and Smart Manufacturing can be summarized as follows.
Collects manufacturing data.
Monitors process status in real time.
Identifies equipment operating status.
Connects production performance with production plans.
Connects quality data with process conditions.
Tracks defect causes.
Manages material and part histories.
Digitalizes work instructions and work standards.
Predicts equipment failures and plans maintenance.
Analyzes production lines and process capability.
Connects automated equipment with information systems.
Shifts decision-making toward data-based judgment.
For example, in the manufacturing process of a cutting machine, it is not enough to simply machine and assemble parts. It should be possible to trace which material was used to produce each part, under which process conditions it was machined, which operator assembled it, which inspection results it passed, and under which equipment conditions it was produced. When this information is connected, the cause of a quality problem can be identified quickly and recurrence can be prevented.
3. Basic Principle
Digital and Smart Manufacturing works by connecting the physical flow of the manufacturing floor with a digital data flow.
The basic flow is as follows.
Important components are as follows.
Element | Description | Design Impact |
|---|---|---|
Product Data | CAD, BOM, specifications, drawings | Basis for production preparation and change management |
Process Data | Routing, work sequence, process conditions | Basis for manufacturing execution and quality tracking |
Equipment Data | Operating status, alarms, cycle time, load | Equipment maintenance and productivity analysis |
Quality Data | Inspection results, defect types, measured values | Quality improvement and root cause analysis |
Material Data | Receiving, storage, input, lot | Traceability and inventory management |
Operator Data | Work instructions, work history, confirmation records | Work standardization and responsibility tracking |
Control Data | PLC, HMI, sensor, actuator status | Automation and real-time control |
Analytics Data | Productivity, defect rate, OEE, bottlenecks | Improvement activity and decision-making |
Common principles of Digital and Smart Manufacturing are as follows.
Data must accurately reflect the actual process status.
Data without a clear collection purpose only increases management cost.
Data becomes meaningful only when connected to products, processes, equipment, and quality.
Automated equipment and information systems should not be separated.
A system that is difficult for shop-floor operators to use is difficult to sustain.
More important than real-time data is a reliable data structure.
Digitalization becomes more effective after process standardization.
Security and access authority must be considered from the initial design stage.
4. Classification of Representative Areas
Digital and Smart Manufacturing can be classified according to application scope and purpose.
Category | Representative Items | Main Purpose |
|---|---|---|
Digital Design Data | CAD, BOM, drawings, specification management | Connect design data to production |
Digital Process Planning | CAM, routing, work standards | Manage process preparation and execution criteria |
Machine Data Collection | CNC, PLC, sensor data | Collect equipment status and production performance |
Shop-Floor Control | HMI, SCADA, MES | Manage shop-floor execution and production |
Quality Data Management | Inspection data, SPC, defect history | Quality analysis and recurrence prevention |
Traceability | Barcode, QR, RFID, lot management | Track material, part, and process history |
Predictive Maintenance | Equipment condition monitoring, anomaly detection | Prevent failures and improve utilization |
Digital Twin | Process simulation, equipment model | Prediction, verification, and optimization |
Smart Automation | Robots, vision, automated inspection | Stabilize repetitive work and quality |
Data Analytics | Dashboards, AI analysis, root cause analysis | Support manufacturing decision-making |
Cybersecurity | Network security, authority management | Protect equipment and data |
These areas may appear to be independent technologies, but in actual manufacturing they must be connected as one data flow. CAD and BOM connect to process planning, process planning connects to work instructions, and work instructions feed back again through equipment data and quality data.
5. Digital Design Data
Digital Design Data means managing the design information required to manufacture a product in digital form. This includes CAD models, 2D drawings, BOM, specifications, material information, surface treatment information, tolerance information, and geometric tolerance information.
Major items of Digital Design Data include the following.
3D CAD model
2D drawing
BOM
Part number
Material specification
Surface treatment specification
Tolerances and geometric tolerances
Assembly structure
Change history
Approval status
Document version
Digital Design Data is important for the following reasons.
Design changes can be tracked.
Production preparation errors can be reduced.
BOM and purchasing information can be connected.
It can be expanded into CAM and machining data.
Assembly and inspection criteria can be clarified.
Design history can be checked when quality problems occur.
If design data is not well organized, the manufacturing floor may use incorrect drawings, confuse part numbers, produce old versions, use wrong materials, or apply inconsistent inspection criteria. Therefore, the starting point of Digital and Smart Manufacturing is not advanced AI, but accurate design data management.
6. Digital Process Planning
Digital Process Planning means defining manufacturing process sequence, work conditions, equipment used, tools, inspection criteria, and work standards as digital data.
Digital Process Planning includes the following items.
Process routing
Work sequence
Equipment assignment
Tool assignment
Fixture assignment
Work time
Machining conditions
Inspection location
Inspection items
Work standards
Quality criteria
Operator precautions
The roles of Digital Process Planning are as follows.
Converts design data into manufacturing execution.
Reduces operator variation.
Manages process change history.
Connects production planning with shop-floor execution.
Includes inspection criteria within the process.
Enables work performance and quality data to be connected.
When process planning is digitalized, it becomes easier to quickly identify which processes, tools, and inspection items are affected by a product change. It also helps maintain consistent work conditions in repetitive production.
7. CAM and CNC Data
CAM, Computer-Aided Manufacturing is a digital manufacturing technology that generates machining paths, tool paths, cutting conditions, and NC code based on CAD models. It is connected to CNC equipment and used to automate cutting, milling, turning, drilling, and grinding processes.
Major items of CAM and CNC data include the following.
Tool path
Tool list
Cutting conditions
Machining sequence
Material datum
Coordinate system
NC program
Simulation result
Collision review
Machining time estimation
Program version
The advantages of CAM are as follows.
Complex shapes can be machined accurately.
Tool paths can be reviewed in advance.
Machining time can be estimated.
CNC program errors can be reduced.
The same conditions can be maintained in repetitive production.
Items to watch in CAM and CNC data are as follows.
CAD model and actual drawing versions must match.
Coordinate systems and datum surfaces must be clear.
Interference between tools and fixtures must be reviewed.
Program change history must be managed.
Programs modified on the shop floor must be reflected in official data.
Machining results must be connected with inspection results.
CAM is not merely a tool for creating NC code. It is a key digital manufacturing stage that connects design data with actual machining.
8. PLC, HMI, and SCADA Data
In automated equipment, PLC, HMI, and SCADA become the center of manufacturing data collection and control.
PLC is a device that controls sensor inputs and actuator outputs. HMI is an interface through which operators check equipment status and set conditions. SCADA is a supervisory system that monitors the status of multiple pieces of equipment and collects control data.
Collectable data includes the following.
Equipment operating status
Automatic / manual mode
Cycle time
Alarm history
Sensor status
Motor status
Temperature
Pressure
Flow rate
Position
Speed
Current
Production quantity
Downtime
Emergency stop history
Work condition set values
The roles of PLC / HMI / SCADA data are as follows.
Checks equipment status in real time.
Automatically collects production performance.
Records alarms and stoppage causes.
Tracks changes in process conditions.
Connects quality data with equipment conditions.
Supports maintenance decisions.
In automated equipment, the important point is not only to control motion, but also to create a structure that records under what conditions the equipment operated and how it operated.
9. MES and ERP
MES, Manufacturing Execution System is a system that manages shop-floor execution. It manages production orders, work performance, equipment status, quality results, material input history, and operator history.
ERP, Enterprise Resource Planning is a system that manages company-wide resource planning. It manages purchasing, inventory, production planning, cost, accounting, shipping, and sales information.
The relationship between MES and ERP is as follows.
System | Main Role | Management Scope |
|---|---|---|
ERP | Resource planning and business management | Orders, purchasing, inventory, cost, accounting, shipping |
MES | Manufacturing execution management | Work orders, production performance, quality, equipment, operators |
PLC / SCADA | Equipment control and status collection | Sensors, equipment, alarms, control values |
The basic connection structure is as follows.
Without MES, production performance and quality information on the shop floor are often scattered across handwritten records or spreadsheets. In that case, delivery, inventory, defects, and work history become difficult to track accurately.
10. IIoT and Sensor Data
IIoT, Industrial Internet of Things is a structure that connects manufacturing equipment, sensors, controllers, and networks to collect process and equipment status data.
Representative sensors used in IIoT include the following.
Temperature sensor
Pressure sensor
Vibration sensor
Current sensor
Power sensor
Flow sensor
Position sensor
Proximity sensor
Vision sensor
Torque sensor
Load sensor
Humidity sensor
Noise sensor
The purposes of IIoT application are as follows.
Equipment condition monitoring
Process condition monitoring
Energy usage analysis
Quality anomaly detection
Preventive maintenance
Automatic production performance collection
Remote monitoring
Environmental condition tracking
In IIoT, the important point is not to install many sensors, but to clarify what data is being collected and why. Data collection without purpose only increases storage cost and management burden.
11. Traceability
Traceability is a structure that enables tracking of material, part, process, operator, equipment, inspection result, and shipping history. It plays a very important role in manufacturing quality and responsibility tracking.
Information managed in traceability includes the following.
Raw material lot
Part number
Supplier
Receiving date
Process history
Operator history
Equipment history
Inspection result
Defect history
Rework history
Assembly history
Shipping history
Customer delivery history
Representative tracking methods include the following.
Barcode
QR code
RFID
Laser marking
Serial number
Lot number
Database record
Inspection report linkage
Traceability is not only a means of finding responsibility after a problem occurs. It is a structure that allows the problem process to be identified quickly, the affected product range to be narrowed, and process improvement for recurrence prevention to be performed.
12. Quality Data and SPC
Quality Data Management means managing inspection results, measured values, defect types, and process conditions as data. When quality data is connected to process data, defect causes can be analyzed and process capability can be improved.
Representative quality data includes the following.
Dimensional measurement values
Surface roughness
Hardness
Leak test result
Electrical test result
Functional test result
Visual inspection result
Defect type
Rework record
Inspector information
Inspection equipment information
Inspection time
SPC, Statistical Process Control is a method for statistically managing process data to understand process variation.
Important items in SPC include the following.
Control chart
Mean value
Dispersion
Upper control limit
Lower control limit
Process capability
Abnormal pattern
Measurement repeatability
It is not enough to record only final pass/fail results as quality data. Actual measured values and process conditions must be recorded together to determine whether the process is stable.
13. Predictive Maintenance
Predictive Maintenance is a method of analyzing equipment status data to identify signs of abnormality before failure occurs and plan maintenance work.
Data used for predictive maintenance includes the following.
Vibration
Temperature
Current
Noise
Pressure
Flow rate
Motor load
Shaft position
Lubrication condition
Alarm history
Cycle count
Operating time
The purposes of predictive maintenance are as follows.
Reduces sudden equipment stoppages.
Plans maintenance timing.
Optimizes part replacement cycles.
Extends equipment life.
Detects equipment abnormalities before defects occur.
Improves production delivery stability.
Predictive maintenance does not require advanced AI from the beginning. In the early stage, large benefits can be obtained simply by systematically recording basic data such as alarm history, operating time, current changes, vibration increase, and temperature rise.
14. Digital Twin
Digital Twin is a concept that represents the status and behavior of real products, equipment, processes, and production systems as digital models, and connects them with real data for simulation, monitoring, prediction, and optimization.
Digital Twin can be applied to the following targets.
Product
Part
Equipment
Robot
Process
Production line
Factory layout
Logistics flow
Quality status
Energy usage
The purposes of Digital Twin include the following.
Review equipment motion
Process simulation
Production line bottleneck analysis
Operator movement analysis
Robot path verification
Collision review
Equipment status monitoring
Quality prediction
Maintenance planning
Production condition optimization
A Digital Twin is not a simple 3D model. It becomes meaningful only when connected to the status data of the actual system. A 3D model that is not connected to data is only a visualization asset and is difficult to regard as a Digital Twin.
15. Smart Automation
Smart Automation is an automation structure that goes beyond simple repetitive automation and uses sensors, vision, robots, control, and data analytics to respond to changes in work conditions and stabilize quality.
Representative components of Smart Automation include the following.
Industrial robot
Collaborative robot
Vision inspection
Automatic alignment device
Automatic feeder
Servo control
Force control
Torque control
Automatic measurement
Automatic defect separation
Sensor-based condition adjustment
Process data feedback
Smart Automation is advantageous in the following cases.
Repetitive work is frequent.
Operator variation affects quality.
Missed inspections must be reduced.
Dangerous work must be reduced.
Production performance must be collected automatically.
Product position and status must be judged automatically.
The core of Smart Automation is not that automated equipment solves everything by itself, but that process status is maintained reliably through sensors and data feedback.
16. Data Analytics and Dashboards
In Digital and Smart Manufacturing, data has no value by being collected alone. It must be analyzed and visualized so that it can be used to improve productivity and quality.
Representative analysis items include the following.
Production quantity
Actual performance compared with plan
Equipment utilization
Downtime
Cycle time
Defect rate
Defect type
Rework rate
Variation by operator
Bottleneck by process
Energy usage
Material usage
Delivery compliance rate
The roles of a dashboard are as follows.
Quickly identifies shop-floor status.
Visually shows problem processes.
Shows differences between production plan and actual performance.
Detects quality problems early.
Analyzes equipment stoppage causes.
Supports managerial decision-making.
A good dashboard does not simply show many graphs. It must quickly show the actual problems that need to be judged on the shop floor.
17. AI and Anomaly Detection
AI can be used in Digital and Smart Manufacturing for quality prediction, anomaly detection, image inspection, equipment failure prediction, and process condition optimization.
Representative AI application areas include the following.
Vision inspection
Surface defect detection
Defect prediction
Equipment anomaly detection
Predictive maintenance
Process condition recommendation
Production planning optimization
Demand forecasting
Energy usage optimization
Operator support
Items to consider when applying AI include the following.
Is there sufficient data?
Is the data quality reliable?
Does the data include enough defect cases?
Are process conditions connected to quality results?
Does it provide results that can be interpreted on the shop floor?
Is there a response structure when the AI makes a wrong judgment?
Does it conflict with existing inspection criteria?
Can the model be updated and maintained?
AI becomes effective after the data structure is organized. If data is inaccurate or process criteria are not standardized, reliable results are difficult to obtain even if AI is applied.
18. Cybersecurity and Data Reliability
In Digital and Smart Manufacturing, equipment, networks, servers, cloud systems, and operator terminals are connected, so cybersecurity and data reliability become important.
Items to consider for security include the following.
User authority
Equipment access authority
Network separation
Remote access management
Data backup
Log management
Malware response
Software updates
PLC and HMI protection
External USB usage control
Cloud connection security
Items to consider for data reliability include the following.
Sensor calibration
Measuring equipment calibration
Prevention of missing data
Time synchronization
Duplicate data management
Prevention of manual input errors
Data change history
Data approval procedure
As smart manufacturing systems become more connected, efficiency can improve, but security risks also increase. Therefore, security should not be treated as a later add-on, but as an initial system design condition.
19. Step-by-Step Adoption Strategy
Digital and Smart Manufacturing is not completed all at once. Especially for small and medium-sized manufacturers, it is more realistic to proceed step by step from process standardization and data collection rather than introducing every system at once.
A step-by-step adoption structure is as follows.
Items to consider during adoption include the following.
Are current processes standardized?
Which problem causes the greatest loss?
Is the data collection cost appropriate compared with the expected effect?
Can shop-floor operators use the system?
Can the system connect with existing equipment?
Is there a maintenance person in charge?
Is the system overly complex?
Are security and backup structures prepared?
Digitalization should not follow trends. It should be a step-by-step improvement strategy for solving actual manufacturing problems.
20. Application in Food Machinery
In food machinery and meat-processing machinery, Digital and Smart Manufacturing can directly affect quality, hygiene, traceability, assembly repeatability, equipment maintenance, and customer support.
Representative application items include the following.
Manufacturing history management for stainless steel parts
Welding and polishing process history management
Pickling and passivation treatment records
Blade manufacturing and inspection data management
Lot tracking of packing and seal parts
Electrical wiring inspection records
Pneumatic leak test data
Safety device inspection history
Finished product functional inspection records
Pre-shipment hygiene inspection records
Customer-specific equipment specification history
A/S replacement part history
Important data in food machinery includes the following.
Material of food-contact parts
Surface treatment history
Cleanability inspection results
Weld inspection results
Burr removal confirmation
Packing assembly status
Blade condition
Safety cover and interlock inspection
Motor, inverter, and PLC settings
Trial run results
Customer delivery specifications
For example, when manufacturing a meat slicer, inspection histories for the blade, feeding device, cover, electrical system, pneumatic system, safety device, and cleanable structure should be connected. Later, if a problem occurs at the customer site, it should be possible to check which specifications the machine was built to and which inspection results were passed before shipment.
21. Connection with Other Knowledge Tracks
Digital and Smart Manufacturing is very closely connected with other Knowledge Tracks.
21.1 Connection with Manufacturing Systems
Manufacturing Systems creates the physical production structure, while Digital and Smart Manufacturing collects and analyzes the status of that structure as data.
21.2 Connection with Tooling and Process Planning
Tools, fixtures, and process conditions become the basis for digital work standards and production data.
21.3 Connection with Manufacturing Quality
Quality data should be connected with process conditions, equipment status, and inspection results.
21.4 Connection with Assembly
Assembly processes can be connected with work history, fastening conditions, inspection results, and part traceability.
21.5 Connection with Control
PLC, HMI, sensor, servo, and inverter data are key data sources for smart manufacturing.
21.6 Connection with Sustainable Manufacturing
Energy, material, defect, and rework data can be analyzed to improve resource efficiency.
22. Core Thinking
The core thinking in Digital and Smart Manufacturing is as follows.
Smart manufacturing is not about installing many sensors or AI systems; it is about making manufacturing problems visible through data.
Data becomes meaningful only when connected to products, processes, equipment, and quality.
If a non-standardized process is digitalized, confusion can spread faster.
More important than real-time data is a reliable data structure.
AI becomes effective only after data quality and process standards are secured.
A system that is difficult for shop-floor operators to use is difficult to sustain.
Equipment control and information systems should not be separated from each other.
Traceability is a structure for narrowing down causes and preventing recurrence when quality problems occur.
Security and backup are basic conditions of a smart manufacturing system.
Good Digital and Smart Manufacturing design connects design data, process data, equipment data, quality data, and maintenance data into one flow.
23. Conclusion
Digital and Smart Manufacturing is a manufacturing knowledge area that connects manufacturing processes, equipment, operators, materials, quality, production planning, and maintenance information as digital data, and uses that data to monitor, control, analyze, and optimize the manufacturing floor. CAD, CAM, CNC, PLC, HMI, SCADA, MES, ERP, IIoT, Traceability, Digital Twin, Predictive Maintenance, Smart Automation, and AI-based analytics are all core elements of this area.
However, Digital and Smart Manufacturing is not simply the adoption of the latest technology. Designers must first organize product data, process criteria, work standards, inspection criteria, equipment data, and quality data. Only then can sensors, automation, MES, analytics, and AI be applied step by step to obtain practical results.
From the mechanical design perspective, the key points of Digital and Smart Manufacturing are as follows.
Which manufacturing problem should be made visible through data?
Are product data and process data connected?
Can equipment status and quality results be analyzed together?
Are work instructions and work standards digitalized?
Can material, part, assembly, and shipping histories be traced?
Is the control data of automated equipment connected with production information?
Is the collected data reliable?
Is the structure usable by shop-floor operators?
Are security and backup considered?
In food machinery, can hygiene, cleanability, safety, and traceability be assured through data?
Therefore, Digital and Smart Manufacturing is a core section in the Manufacturing Track that expands physical manufacturing systems into data-based manufacturing systems. It is closely connected with Manufacturing Systems, Tooling and Process Planning, Manufacturing Quality, Assembly, Control, and Sustainable Manufacturing.