Smart factories use connected equipment and devices to allow for evidence-based decision-making to optimise efficiency and productivity throughout the manufacturing process.
Delivering an agile, iterative production process can extend the capabilities of both devices and employees, leading to lower costs, reduced downtimes and less waste in the manufacturing industry.
Identifying and then reducing or eliminating underused or misplaced production capabilities increases efficiency and output with little investment in new resources.
The benefits of digitalising a factory include those related to planning, quality control, product development and logistics as each is assessed and optimised based on real feedback.
There are also long term benefits to be gained through the introduction of machine learning to the process. By collecting and analysing data, it is possible to schedule preventive and predictive maintenance - based on accurate real-life information - to avoid production line shutdowns.
There are four levels that can be used to assess your journey through the improvement process to becoming a smart manufacturer:
1. Level One: Basic Data Availability
At this level, a factory or facility is not really ‘smart’ at all. There is data available but it is not easily accessed or analysed. Data analysis, where it is done, is time consuming and can add inefficiencies to your production process.
2. Level Two: Proactive Data Analysis
At this level, the data can be accessed in a more structured and understandable form. The data will be centrally available and organised with visualisation and displays assisting with its processing. This all allows for proactive data analysis, although there will still be a level of effort involved.
3. Level Three: Active Data
At this level, the data can be analysed with the assistance of machine learning and artificial intelligence, creating insight without as much human supervision. The system is more automated than at level two and can predict key issues or anomalies to proactively predict potential failures.
4. Level Four: Action-Oriented Data
The fourth level builds on the active nature of level three to create solutions to issues and, in some instances, undertake action to alleviate a problem or improve a process with no human intervention. At this level, data is collected and analysed for issues before solutions are generated and, where possible, actioned with very little human input.
Smart factories use a variety of different technologies related to the fourth industrial revolution (Industry 4.0) to optimise smart manufacturing processes.
These technologies include:
Sensors
Sensors on devices and machines are used at specific stages of the manufacturing process to collect data that can be used to monitor processes. For example, sensors can monitor temperature or other variables and either self-correct any problems or alert staff. These sensors can be linked to a network to provide joined-up monitoring across several machines.
Cloud Computing
Storing and processing data collected from the sensors is achieved through cloud computing. This if more flexible and cheaper than traditional on-site storage, allowing large amounts of data to be uploaded, stored, and assessed to provide feedback for decision-making in real time.
Big Data Analytics
As more data is collected, it is possible to use it to provide insights into how a production process is performing. Big data allows for error patterns to be spotted and predictive quality assurance undertaken with a greater degree of accuracy. This data can be shared between different factories or even organisations to solve common problems and further optimise processes.
Virtual and Augmented Reality
Augmented reality is a digital technology that involves digital information being overlayed across reality and viewed via a smartphone, while virtual reality is a more immersive virtual world that requires special glasses. Both of these technologies can help smart factory operators to organise products, production tasks and the maintenance and repair of equipment.
Digital Twins
A digital twin can be used to represent a process or physical object and simulate performance in the real world. This can lead to efficiency improvements while also aiding control and operations planning.
The Internet of Things (IoT) is where devices, machines and/or processes are connected through Internet data communication systems so that they can share information with other machines and people.
Typically using sensor technologies and cloud computing, the Industrial IoT (IIoT) automates a lot of the work required to track and identify improvements in a manufacturing process.
IIoT is part of what has been termed ‘Industry 4.0’ and involves the computerisation of many traditional industries, including manufacture. The smart factory brings together digital and physical systems with the Internet of Things. These systems include wireless connection, sensors, and data collection programs.
The constant monitoring afforded by an effective IIoT-enabled workplace will not only help reduce costs and time for production processes, but can also improve the safety of the production environment by monitoring for potential failure and allowing for predictive maintenance, as well as reducing the physical demands on workers. Using machine learning to optimise production processes can also reduce energy consumption, offering wider environmental benefits too.
The key principles behind the factory of the future are connectivity alongside data analysis and diagnostics; leading to less shutdowns, improved processes and optimised facilities.
A smart factory is based around using the latest technologies and connectivity to drive improvements to processes.
Using technologies such as IoT and artificial intelligence allows for a more responsive, yet also predictive, line; making the most of the available resources to deliver cost-effective and efficient manufacturing.
Upgrading a factory so that is ‘smart’ can seem like it would be an expensive exercise, but you can make fast and effective changes without having to replace every machine in your manufacturing chain.
If you assess your manufacturing chain and pick out the most important parts, you can quickly make changes that will benefit the entire process. Analysing these key areas may then provide information as to what should be improved next.
This analysis should be undertaken with a diverse team driving it, including specialists in different areas of the business. The more you can involve the workforce in the improvements, the more effective the changes will be. Employees may also need training to ensure they can use any new equipment. Indeed, rather than needing fewer people in the workforce, the skills your employees require will change as they monitor systems, collate data and action improvements, inspections or repairs.
Engineers will need to work with management and I.T. systems specialists to find areas for upgrading, and a plan should be drawn up to look into optimising processes, increasing sales, reducing costs and saving time across the whole manufacturing process.
Since smart factories are reliant on computing and digital systems, cyber security needs a special mention.
Data protection and privacy are vital for any business and, as soon as industry is digitised, cybersecurity needs to be addressed. In some instances, industry will share data with other companies for the benefit of everybody, for example, with safety issues. However, your components, processes and other data need to be protected from accidental error or even deliberate hacking.
Cybersecurity issues may create a further cost that needs to be considered when deciding if your smart factory benefits are worth the expense in setting up.
Smart factories use a range of different technologies to create connected manufacturing that is able to collect and assess process data and deliver improvements to efficiency, safety and more.
This optimisation can include procedural improvements, inspection and maintenance improvements, logistics, timing, and even staff utilisation.
Using the Internet of Things alongside data analytics and sensors, a smart factory can become an active part of the move towards Industry 4.0, with improvements to be felt across the entire production process.
However, given the cost of upgrading equipment, setting up secure systems and retraining staff, not every employer will be able to justify the expense against the benefits.
The decision to make a factory smart needs to engage all areas of a company, but ultimately also needs to be based on an accurate comparison of whether it is worth it for your particular facility or business model.
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