Factory 4.0 Defined
Manufacturing facilities globally are undergoing major transformations in virtually every sector. The way products are made and materials processed is changed by digitization, and data has become the key that opens doors to technological possibilities. This could lead to manufacturing being reshaped completely.
Traditional manufacturing models are slowly evolving into what is known as Factory 4.0 or “smart factories.” These are connected systems that link together personnel, machinery, analytics and maintenance activities, resulting in factory management that is completely integrated.
Factory 4.0 is used to leverage technologies and industry 4.0 components, including wireless connectivity, non-intrusive sensors, artificial intelligence, machine learning, cloud computing and others. It ultimately affects all manufacturing process phases, including safety, raw materials processing, quality assurance, production, distribution and packaging.
Factory 4.0 Building Blocks
Although there are many approaches for transforming manufacturing facilities digitally, typical Factory 4.0 solutions normally include at least these core elements:
In recent years, sensor technology has been developing in leaps and bounds, and nowadays the sensor market provides a wide selection of low-cost sensors that can be used to measure numerous parameters, including pressure, temperature, vibration, light, lubricant and water quality, solid and liquid levels, chemical content, and much more.
Depending on what is being monitored, sensors may be placed inside or on machines, on devices carried by people, at workstations, or in any component of a facility’s existing systems such as security networks or HVAC systems.
Industrial IoT sensors may be used to:
- Track the position and movement of components, raw materials, valuable equipment and finished products.
- Assist with quality assurance, e.g. analytics and optical testing.
- Monitor inventory, e.g. spare parts and raw materials supply.
- Identify anomalies in equipment behavior that may cause quality issues.
- Assist with safety issues, e.g. machinery sensors restricting activity close to personnel; personnel sensors measuring lack of movement, environmental threats, etc.
Protocols used for Connectivity
The language of any IoT system consists of the protocols used for connectivity. These communication protocols allows for data to be transferred and understood by the system’s various components, including from the sensors via gateway devices and PLCs to the cloud; and finally to software programs for analysis.
It is critical to select the correct protocol early on in order to build a smart factory that will be successful.
The cloud can be seen as the main data center of Factory 4.0. Information received from sensors is stored in the cloud, after which it is processed and analyzed. Data processing is also further optimized via the edge computing facilities the cloud provides. This minimizes reliance on nodes used for centralized processing.
Machine Learning and Analytics
The huge amounts of data continuously received from a shop floor, together with that gathered from legacy systems, includes information about all aspects of the operation. Process-Based machine learning techniques and statistical algorithms can be used to analyze data. Historical data and root-cause analysis can also be used to generate automatically derived actionable insights.
This analytical activity continually reveals insights that results in machine performance improving, processes becoming more efficient, e.g. maintenance, production line configuration and on-site transport, and downtime reducing.
Use Cases Driving Adoption
Although it is a costly and complex process to implement changes to any manufacturing system, the use cases for Quality 4.0 and Factory 4.0 are highly compelling. This drives organizations to adopt quickly, especially as it has the potential to have positive effects on ROI within a quarter.
Overall Equipment Effectiveness Improvement
The root cause of system issues can be identified with insights driven by analytics. The understanding is based directly on machine output data, and this enables management to focus on areas that need changing, while being able to take into consideration the output quality, performance levels and availability of equipment in real time.
Doing Predictive Rather than Corrective Maintenance
If predictive analysis is used to leverage data received from machines, asset health monitoring can be performed to the level where it becomes possible to predicted equipment failure. This greatly reduces maintenance costs and improves reliability.
When anomalies occur, manufacturers receive automatic alerts with Factory 4.0 and can therefore optimize maintenance schedules to such a degree that machine failure is completely eliminated.
This technique for predicting system errors eliminates the need for both corrective and preventive maintenance, resulting in reduced labor costs while building a strong foundation of reliability.
Monitoring Assets Remotely
Monitoring assets remotely offers a powerful use case for management. It improves visibility of mobile assets and the factory floor, irrespective of their location. Alerts about the status of the factory environment, equipment and individual machines are sent to relevant stakeholders who can then make decisions driven by data, thereby maintaining compliance with regulations and increasing efficiency.
Digital Twins are digital representations of a facility, process, or asset. This visual model offers real-time data on its physical counterpart. Digital Twins are the result of various technological capabilities that form part of Factory 4.0.
Digital Twin software enables full visualization of the actual twin, enabling management to explore ideas for further optimization and experiment with parameters, without damaging equipment or risking the degradation of performance.
Factory 4.0 Benefits
Production facility all differ, and discrete manufacturing is vastly different from process manufacturing. In spite of this fact, there are several benefits of Factory 4.0 that are relevant, irrespective of the specific details of the process.
Factory 4.0 uses input from management together with artificial intelligence to learn how to optimize itself continually. It reacts to conditions changing in real-time, and autonomously runs the whole manufacturing process.
Factory 4.0 detects risks, predicts failures, and prevents unplanned downtime. Using predictive quality, it helps detect trends where quality decreases (increasing defects) and is able to suggest areas where improvement can be achieved by identifying environmental, machine, or human factors that affect the number of defects.
Improving the Bottom Line by Cutting Costs
Factory 4.0 technology improves optimization and this cuts costs in various ways, which in turn leads to an operation that is leaner overall. Inventory can be controlled much more precisely as maintenance is more predictable.
Repairs are done timely and proactively, ensuring that machine health is kept optimal. As technicians will know in advance the exact type of defect they’ll be fixing, secondary damage is eliminated and repairs are done much faster.
As areas of loss are identified, and focused actions that reduce inefficiencies and product defects are prescribed, production waste can be prevented. Automated root cause analysis and predictive analysis are used to identify potential process failures that could lead to wastage.
Process engineers then use predictive simulation to test production parameters, and determined set points that should be used for optimized throughput and quality.
Informed decisions regarding staff can also be made based on data on all aspects of the process. This enables very accurate employee task allocations, preventing unnecessary labor costs.
The Reasons for Factory 4.0 are Clear
Using predictive maintenance instead of corrective maintenance is just one small part of the picture.
Factory 4.0 ushers in a new paradigm on how products and materials are produced. Using Big Data and the high level of control and connectivity presented by smart factories enables manufacturers to take their services, products and operations to the next level.