The global Industrial Internet of Things (IIoT) market is expected to reach US$195 billion by 2022, growing from US$113 billion in 2015, at a compound annual growth rate of 7.89 percent between 2016 and 2022, according to a market research report by Markets and Markets. A key factor identified is the need to implement predictive maintenance techniques in industrial equipment to monitor their health and avoid unscheduled downtimes in the production cycle.
In the metrology segment, the view for Industry 4.0 in terms of part inspection is to increase quality and maximise throughput, whilst reducing costs right down the production line, making manufacturing processes faster and more accurate. Multi-sensor metrology alone is not enough to seize the maximum potential of the production line; integration of autonomous processes and hardware as well as complete connectivity is needed to fully embrace manufacturing of the future.
Four Industry 4.0 trends will be discussed in this article—from big data, predictive maintenance, augmented reality to cybersecurity. Industry players should be aware of these trends as they have already begun to affect many aspects of industrial automation going forward.
Industry 4.0 is the no longer the future of the industry, and the time is now for companies to implement intelligent manufacturing practices.
1. Big Data/Data Analytics
Big data describes the large volume of data, both structured and unstructured. Insights from big data can enable better decisions to be made—deepening customer engagement, optimising operations, preventing threats and fraud, and capitalising on new sources of revenue. The majority of data created between now and 2020 will not be produced by people, but by machines as they communicate with each other over data networks.
The insights gained from big data analytics and the IIoT to drive greater manufacturing intelligence and operations performance is considered essential by 68 percent of manufacturers, according to a recent survey by Honeywell. This highlights that manufacturers are increasingly aware of the importance in big data analytics and its potential in the industry.
Exponential Growth In Big Data
The global big data and business analytics market will grow to US$203 billion over the next few years, according to a report by International Data Corporation. The growth forecast for the global big data and business analytics market through 2020 is led by manufacturing and banking investments. The rate at which data is being generated is rapidly outpacing the ability to analyse it, according Dr Patrick Wolfe, a data scientist at the University College of London. The complex nature of the information created requires solutions capable of addressing data security, privacy and flexibility issues. Dr Wolfe added that it is key to turn these massive data streams from liability into strength.
Machine tool manufacturer Mazak has developed its own data collection and analysis system called SmartBox to connect machine tools securely and intelligently. The company uses this system at several of its own facilities worldwide. Most recently, the i-Smart Factory concept in their Singapore production facility is based on the company’s accumulated knowledge on factory management.
Tomohisa Yamazaki, president of Mazak, stated that rising personnel costs is a social problem not only in Japan but also in other countries as the labour force population declines. As such, one of the most crucial issues for the manufacturing industry is to keep increasing productivity by investing in advanced production technology.
Data Analytics To Reduce Machine Downtime
Employing data analytics ensures machines machines
are kept running at optimum level.
A survey of manufacturing executives in the US by Honeywell revealed 67 percent of respondents have plans to invest in data analytics. The executives viewed data analytics as a fundamental component of the IIoT, and as a solution to unplanned downtime and lost revenue.
The survey revealed companies are feeling pressure to continue working under threats of unscheduled downtime and equipment breakdowns, which was viewed as the most crucial factor in maximising revenue. Employing data analytics to ensure machines are kept running at optimum level could vastly reduce and even eliminate unplanned downtime.
2. Predictive Maintenance
Predictive maintenance foresees when equipment breakdowns might arise, and it prevents machine breakdowns by carrying out maintenance. When repairs and maintenance are planned, it could save manufacturing companies 12 percent in cost savings, whereas a loss as much as 30 percent could be incurred when unplanned repairs occur, according to research by the World Economic Forum and the consultancy Accenture.
With predictive maintenance, manufacturers can lessen maintenance and servicing costs, and boost reaction times within disruptive production processes.
The unchanging objective in metal cutting manufacturing is to further increase productivity, creating added value for the customer. Heller, a milling machines and systems manufacturer, has developed its own system to improve transparency of its current machine status, by evaluating data to allow purposeful diagnostics which yields higher productivity and reduces machine downtimes. The visualisation of specific information, including status displays of axes, spindles or other assemblies, enables users to determine wear and take preventive measures in order to avoid unscheduled downtimes.
Real-time Condition Monitoring
Machine and sensor data can be catalogued and displayed in real time using Industry 4.0 software, which provides support for condition monitoring. Data visualisation is not confined to the control station, and can be accessible on any platform everywhere—from tablets, smartphones, and bigger screens, both on the production floor and in the cloud.
When the software has determined an imminent maintenance task from the pre-set specifications, the information would be sent immediately to maintenance staff. After maintenance has been carried out, staff can note down tips to improve subsequent maintenance works.
3. Augmented Reality
Augmented reality (AR) is an enhancement of a real-time display using real images alongside computer generated information. AR is associated with Industry 4.0 practices relating to smart manufacturing, and has tremendous potential to influence manufacturing industries. With augmented reality, challenges which arise with conventional 3D measurement can be eliminated.
For example, Keyence’s XM Series handheld probe coordinate measuring machine allows for an operator to perform 3D measurements via an onscreen interactive visual guide and touch probe. Augmented-reality guidance images are created automatically, and the system overlays the measurement points along with their 3D elements.
Shared programmed work instructions and measurement results in consistent measurement regardless of the operator, environment or other circumstances.
Potential Usage Scenarios
AR has numerous uses, involving different types of operations that can be executed on the factory floor—manufacturing activities such as production, and support processes such as maintenance and training.
“Some companies are concerned or even hesitant to adopt industry 4.0 practices as they are not even at the Industry 3.0 stage. However, it is possible to jump straight from Industry 2.0 to 4.0. For example, to improve standard operating procedures among plant staff that are still using physical papers for instructions, they could instead make use of augmented reality to simplify and learn new procedures,” said Lim Yew Heng, partner and managing director, The Boston Consulting Group. Some potential usage scenarios of AR are as follows:
- Operations: any kind of operation which requires some step by step procedure can benefit from the adoption of AR—installation, assembly and machinery tool change.
- Maintenance and remote assistance: AR is efficient at reducing execution times, minimising human errors and sending the relevant performance analytics to maintenance staff.
- Safety management: AR allows risk and safety of operators and equipment to be managed.
- Design and visualisation: AR provides tools that improve design, prototyping and visualisation in the design phase.
- Training: for companies where training is a critical process involving many field technicians, AR-guided training can be effective at training staff, especially in the beginning where there is a learning curve.
- Quality control: AR support in quality control processes enables staff to determine if products meet manufacturing standards.
4. Cyber Security
Augmented Reality holds vast potential and it is the
future of manufacturing.
The integrated nature of Industry 4.0-driven operations means that cyberattacks can have devastating effects, evident in the unprecedented “WannaCry” global cyberattack in May this year. Cyber security strategies should be secure and fully integrated into organisational and information technology. Picking the right cybersecurity provider is essential in ensuring data is protected.
“Some of our clients have come to us and said they do not think they will be able to put up their data on the cloud as they have very sensitive data. Within their operating database, there are certain data that are more sensitive, and there are those which contain less sensitive information,” said Mr Heng, partner and managing director, The Boston Consulting Group. “They could start out with putting less sensitive data on the cloud and understand how it works first, and understand how cybersecurity providers can help them. From there, they can move towards a more balanced approach.”
Data Sharing: Increased Access To Data
Companies should consider which data should be shared and how to protect the systems, and which data that is proprietary or have privacy risks. Companies should leverage tools such as encryption for data which are at rest or in transit, to safeguard communications should they be intercepted or if the systems are compromised.
It is important for manufacturing companies to perform risk assessments across their environment—including enterprise, DSN, industrial control systems, and connected products. Data evaluations should then be applied to update cyber risk strategies.
Sensitive data are not limited to sensor and process information; it also includes a company’s intellectual property or even data related to privacy regulations.
As more IoT devices are connected to networks, the risk of potential attack increases, along with risk from compromised devices. The first step companies should take is to discover all assets, especially industrial controllers. Picking the right cybersecurity provider who understands what your company needs is essential in protecting your data against cyberattacks. Transparency is important for companies with highly sensitive data therefore, ensure that third-party cybersecurity providers inform you where the information goes.
The Right Strategy Is Important
“Companies have to ask themselves why they want to create a fully automated manufacturing factory, and what value it creates for the end users. Once the staff in the company knows why this is being done, it will change the company’s culture, and they will start focusing on value delivered to the customers,” said Scott Maguire, global engineering director, Dyson. “At the end of the day, these are big investments, and companies have to plan strategies for the long-term and be willing to change their company culture.”
Manufacturing companies should embrace the positive disruptive changes that Industry 4.0 practices can bring. A digitalisation strategy which is tailored to your company’s needs should be mapped out, and disseminated to staff so they understand it is a part of the company’s new culture.
APMEN Feature, July 2017