What is Factory 4.0?

Around the world, and in practically every sector, manufacturing facilities are undergoing a major transformation. Digitization is changing the way we process materials and make products, and data is becoming the golden key that can open a door to technological possibilities with the power to completely reshape manufacturing.

The traditional manufacturing model is evolving into what is referred to as a “smart factory” or Factory 4.0 – a connected system that links machinery, personnel, maintenance activity, and analytics for a completely integrated approach to factory management.

Factory 4.0 leverages technologies and industry 4.0 components such as non-intrusive sensors, wireless connectivity, cloud computing, artificial intelligence, machine learning and others, to affect all phases of manufacturing business from raw materials processing, safety, and production, to quality assurance, packaging, and distribution.

The Building Blocks of Factory 4.0

There are numerous approaches for digitally transforming a manufacturing facility, but any typical Factory 4.0 solution will include the following core elements:


Sensor technology has developed greatly in recent years and today’s market of sensor providers offers a wide variety of low-cost sensors that can measure parameters that include temperature, pressure, light, vibration, water/lubricant quality, chemical content, liquid/solid levels and many more.

Depending on what they’re monitoring, sensors can be placed on or inside machines, at designated workstations, on devices carried by personnel, or in part of the factory’s existing systems such as the HVAC or security network.

Some use cases for industrial IoT sensors include:

  • Tracking the movement and position of raw materials, components, finished products, and valuable equipment throughout the factory
  • Quality assurance (optical testing and analytics)
  • Inventory: monitoring the supply of raw materials and spare parts
  • Identifying equipment behavior anomalies that could result in quality issues
  • Safety: sensors on machinery to restrict activity near personnel; sensors carried by personnel that measure potential environmental threats, lack of movement etc.

Connectivity Protocols

IoT connectivity protocols form the language of an IoT system. These communication standards allow data to be transferred and understood by the various components of the system – from the sensors to the cloud via PLCs and gateway devices; and finally to a software program for analysis.

Deciding on the correct protocol early on is critical for building a successful smart factory.

Cloud Computing

The cloud represents Factory 4.0’s main data center. Here, information collected from the sensors is stored, processed and analyzed. The cloud is also utilized for edge computing to further optimize data processing by minimizing the reliance on centralized processing nodes.

Analytics & Machine Learning

The large amounts of data captured continuously from the shop floor, and collected from historian systems, including information about every aspect of production. Data can be analyzed using statistical algorithms as well as by implementing machine learning techniques which automatically derive actionable insights from root-cause analysis and historical data.

This continual analytical activity triggers insights that lead to improved machinery performance, more efficient processes (eg. on-site transport, production line configuration, maintenance etc.), and reduced downtime.

Use Cases Drawing Manufacturers to Factory 4.0

Factory 4.0 is Changing the Way we Make Products and Process Materials

Implementing changes to a manufacturing system is a complex and costly process, but the use cases driving companies to adopt Factory 4.0 are highly compelling, with the potential to begin positively affecting ROI within a single quarter.

Improving OEE (Overall Equipment Effectiveness)

Analytics-driven insights enable the identification of the root cause of system issues. This understanding, based directly upon machine output data, allows management to hone in on areas that require changes while taking into consideration real-time availability of equipment, performance levels, and the quality of output.

From Corrective to Predictive Maintenance

Using predictive analytics to leverage the data collected from machines, it’s possible to monitor asset health to the point where equipment failure can be predicted, improving reliability and greatly reducing maintenance costs.

With Factory 4.0, manufacturers receive automatic alerts when anomalies occur and can optimize maintenance schedules to completely side-step machine failure.

This method of preempting system errors does away with the need for corrective or preventive maintenance, cutting labor costs and building a strong sense of reliability amongst customers.

Remote Asset Monitoring

A powerful use case for management, Remote Asset Monitoring offers improved visibility of the factory floor as well as mobile assets regardless of their location. Alerts about the condition of individual machines, equipment, and the factory environment are sent to stakeholders who can make data-driven decisions to increase efficiency and maintain compliance with regulations.

Enter the Digital Twin

A Digital Twin is a digital representation of an asset, process or facility; a visual model that offers real-time data about its physical correspondent. Digital Twins are the culmination of a number of technological capabilities that fall under the Factory 4.0 umbrella.

Digital Twin software offers full visualization of its “real-life” twin, allowing management to experiment with parameters and explore ideas for further optimization, without the risk of harming performance or damaging equipment.

The Many Benefits of Factory 4.0

Every production facility is different, and the nature of process manufacturing differs greatly from discrete manufacturing. That being said, there are a number of benefits to Factory 4.0 that are relevant across the board.

The Constant Pursuit of Quality

Using artificial intelligence along with input from management, Factory 4.0 continually learns how to optimize itself, reacting to changes in conditions in real-time, and running entire manufacturing processes autonomously.

Besides detecting risks, predicting failures, and preventing unplanned downtime, Factory 4.0 and predictive quality can help detect decreasing quality trends (increases in defects) and can suggest areas for improvement by identifying human, machine, or environmental factors that are affecting the number of defects.

Cutting Costs & Impacting the Bottom Line

The improved optimization brought on by Factory 4.0 technology cuts costs in a number of ways, leading to a leaner operation overall. Inventory can be managed in a much more precise manner since maintenance is far more predictable.

Repairs are proactive and timely, keeping machine health optimal. Since technicians know ahead of time the exact type of malfunction they’ll be working on, secondary damage is prevented and repairs are much quicker.

Having data on all aspects of the process also enables better-informed decisions regarding staff. This allows for more accurate employee allocations per task, preventing unnecessary spending on labor.

The “Why” behind Factory 4.0 is Crystal Clear

Replacing corrective maintenance with predictive maintenance is just the tip of the iceberg.

Factory 4.0 represents a new paradigm in how we produce materials and products. The use of Big Data and the high level of connectivity and control offered by smart factories allows manufacturers to focus on taking their operations, products, and services to the next level.

Interested in Factory 4.0 technology for your operation? Book a free demo to learn about Seebo’s smart factory solution for manufacturers.