Industrial Revolution (INR) 4.0 – Top Courses that You Should Study in Malaysia to be Ready for it
- Industry 4.0, also known as the Fourth Industrial Revolution, is all about making business smarter and more automated.
- What technologies are driving Industry 4.0
- Top 20 Courses for a Career in Industry 4.0 in Malaysia
Industry 4.0 is signalling a change in the traditional manufacturing landscape. Also known as the Fourth Industrial Revolution, Industry 4.0 encompasses three technological trends driving this transformation: connectivity, intelligence and flexible automation.
Industry 4.0 converges IT (Information Technology) and OT (Operational Technology), to create a cyber-physical environment. This convergence has been made possible thanks to the emergence of digital solutions and advanced technologies, which are often associated with Industry 4.0.
These technologies are helping to drive manufacturing’s digital transformation through the integration of previously disparate systems and processes through interconnected computer systems across the value and supply chain.
Embracing Industry 4.0, digital manufacturing and the interconnectivity that comes with it opens a myriad of benefits for companies, including greater agility, flexibility and operational performance.
What are the implications of these future trends for key aspects
of the future workforce and workplace that would concern you as a student? To address this question, we take a closer look at the major factors that are expected to shape the world of work in the coming decades so that you can be prepared by choosing the right course to study so that you will be prepared for a career in the era of Industrial Revolution 4.0 in Malaysia and globally.
In doing so, our objective is not so much to predict the future but rather to understand what are the changes that technology is impacting jobs of the future. When we understand the future trends, we will know which courses to choose that will enable us to hone our skills to obtain a job that has high demand and salary.
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What is Industry Revolution 4.0?
Industry 4.0, also known as the Fourth Industrial Revolution, is all about making business smarter and more automated. Where the Third Industrial Revolution focused on switching mechanical and analog processes to digital ones, the Fourth Industrial Revolution focuses on deepening the impact of our digital technologies by making our machines more self-sufficient, able to “talk” to one another, and to consider massive amounts of data in ways that humans simply can’t—all in the name of efficiency and growth. Industry 4.0 technology represents a foundational shift in how businesses operate, as fundamental as the change from steam power to electricity in the Second Industrial Revolution.
Industry 4.0 is revolutionizing the way companies manufacture, improve and distribute their products. Manufacturers are integrating enabling technologies, including Internet of Things (IoT), cloud computing and analytics, and AI and machine learning into their production facilities and throughout their operations. These smart factories are equipped with advanced sensors, embedded software and robotics that collect and analyze data and allow for better decision making. Even higher value is created when data from production operations is combined with operational data from ERP, supply chain, customer service and other enterprise systems to create whole new levels of visibility and insight from previously siloed information. This technology leads to increased automation, predictive maintenance, self-optimization of process improvements and, above all, a new level of efficiencies and responsiveness to customers not previously possible.
Developing smart factories provides an incredible opportunity for manufacturers entering the fourth industrial revolution. Analyzing the large amounts of data collected from sensors on the factory floor ensures real-time visibility of manufacturing assets and can provide tools for performing predictive maintenance in order to minimize equipment downtime.
Using IoT devices in smart factories leads to higher productivity and improved quality. Replacing manual inspection with AI-powered visual insights reduces manufacturing errors and saves money and time. With minimal investment, quality control personnel can set up a smartphone connected to the cloud to monitor manufacturing processes from virtually anywhere. By applying machine learning algorithms, manufacturers can detect errors immediately, rather than at later stages when repair work is more expensive.
Industry 4.0 concepts and technologies can be applied across all types of industrial companies, including discrete and process manufacturing, as well as oil and gas, mining and other industrial segments.
Industry 4.0 optimizes the computerization of Industry 3.0
When computers were introduced in Industry 3.0, it was disruptive thanks to the addition of an entirely new technology. Now, and into the future as Industry 4.0 unfolds, computers are connected and communicate with one another to ultimately make decisions without human involvement. A combination of cyber-physical systems, the Internet of Things and the Internet of Systems make Industry 4.0 possible and the smart factory a reality. As a result of the support of smart machines that keep getting smarter as they get access to more data, our factories will become more efficient and productive and less wasteful. Ultimately, it’s the network of these machines that are digitally connected with one another and create and share information that results in the true power of Industry 4.0.
Malaysia Lacks the Talent to Work in Industry 4.0 Jobs
The Malaysian Ministry of International Trade and Industry (MITI) tabled the the National Policy for Industry 4.0 to help advance the countries’ businesses and factories. This will ideally help the local industries to increase productivity, efficiency, quality, and to also develop new skills and talent with the people.
According to MITI, Malaysia is currently somewhere in between Industry 2.0, which is mass production of items, and Industry 3.0, automation. It is a slow process that is facing many challenges such as the lack of awareness and understanding of Industry 4.0 and also the lack of standards and skillsets.
Industry 4.0 is the new approach to combining traditional manufacturing processes and technology such as the Internet of Things (IoT) to enable machines to capture and convey more data via machine-to-machine communications to enable businesses to make smarter decisions.
All these have to be mobilised by a workforce equipped with the necessary skill sets to develop systems, applications and services such as artificial intelligence, Big Data and advanced analytics, robotics and automation.
In terms of preparing the necessary skilled manpower (for Industry 4.0), Indonesia and Singapore are far ahead (of Malaysia) because they have specific programmes from abroad for their workers to learn from
Malaysia did not have a standard system to produce graduates with the necessary skills for Industry 4.0, Ganesh said the local university syllabuses were somewhat out of date and did not fulfill the requirements of Industry 4.0.
“After completing their studies, our (university) graduates have to be retaught to master 4.0 elements like additive manufacturing and robotics, that is, how to handle and manage robots and so on
Unfortunately, many of the local industries were still depending on manual labour to carry out their operations, he said.
He also said that Malaysia has to seek out foreign technology to enable it to approach Industry 4.0 due to the shortage of efforts locally to develop home-grown technology to meet the needs of the new industry.
What technologies are driving Industry 4.0?
- Data Science & Data Analytics
- Autonomous Robots & Advanced robotics
- Simulation/Digital Twins
- Horizontal and Vertical Systems
- Industrial Internet of Things (IIoT)
- Cybersecurity Technology
- Cloud Computing
- Additive Manufacturing (AM)
- Artificial Intelligence (Ai) & Machine Learning
- Augmented and virtual reality (AR/VR)
Data Science & Data Analytics
Data science combines the scientific method, math and statistics, specialized programming, advanced analytics, AI, and even storytelling to uncover and explain the business insights buried in data.
Data science is a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations. Data science encompasses preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.
Autonomous Robots & Advanced robotics
Robotics play a major role in the manufacturing landscape today. Automated manufacturing solutions should be a key part of any operation that strives for maximum efficiency, safety and competitive advantage in the market. Manufacturing robots automate repetitive tasks, reduce margins of error to negligible rates, and enable human workers to focus on more productive areas of the operation.
Robots used in manufacturing fill numerous roles. Fully autonomous robots in manufacturing are commonly needed for high-volume, repetitive processes — where the speed, accuracy and durability of a robot offers unparalleled advantages. Other manufacturing automation solutions include robots used to help people with more intricate tasks. The robot executes components of the process such as lifting, holding and moving heavy pieces.
Compared with conventional robots, advanced robots have superior perception, integrability, adaptability, and mobility. These improvements permit faster setup, commissioning, and reconfiguration, as well as more efficient and stable operations. The cost of this sophisticated equipment will decline as prices for sensors and computing power decrease, and as software increasingly replaces hardware as the primary driver of functionality. Taken together, these improvements mean that advanced robots will be able to perform many tasks more economically than the previous generation of automated systems.
Producers are now deploying advanced robotics as an essential element of advanced automation that enables the self-controlled factory of the future. Enhancing plant structures and processes with digital technologies can increase productivity and flexibility in both the factory and the supply chain, enabling producers to rapidly adjust to changing customer needs.
Simulations are used across industry to test products, systems, processes, and concepts. Often used during the design phase, simulations are often digital models using computer-aided design software applications. These models can be created in 2D or 3D to represent parts of a process or product, although they can also be created using mathematical concepts rather than computer-based models. The simulation works by introducing and testing different variables into the digital environment or interface to assess outcomes.
The digital transformation offered by Industry 4.0 has allowed manufacturers to create digital twins that are virtual replicas of processes, production lines, factories and supply chains. A digital twin is created by pulling data from IoT sensors, devices, PLCs and other objects connected to the internet. Manufacturers can use digital twins to help increase productivity, improve workflows and design new products. By simulating a production process, for example, manufacturers can test changes to the process to find ways to minimize downtime or improve capacity.
A digital twin is a virtual model that is created to accurately reflect an existing physical object. The physical object is fitted with sensors that produce data about different aspects of the object’s performance, for example on a wind turbine. This data is then relayed to a processing system and applied to the digital model. This digital model, or twin, can then be used to run simulations, study current performance and generate potential improvements that can then be applied back to the actual physical asset. A digital twin can also be created for non-physical processes and systems, mirroring the actual process or system and allowing simulations to be run based on real-time data.
The data used by digital twins is usually collected from Internet of Things (IoT) enabled devices, allowing for the capture of high-level information that can then be integrated into the virtual model.
A digital twin is, in effect, a virtual environment where ideas can be tested with few limitations. With an IoT platform, the model becomes an integrated, closed-loop twin that can be used to inform and drive strategy across a business.
A simulation replicates what could happen to a product, but a digital twin replicates what is happening to an actual specific product in the real world. Any changes to a simulation are limited to the imagination of a designer who needs to input any changes. However, because a digital twin offers real feedback, the designer can see if it is working as intended and then determine any improvements based on actual use. This translates from assets to other applications, such as for a manufacturing process, which can be assessed with real data to react to changing demands, requirements or business conditions. The difference is that while a simulation is theoretical, a digital twin is specific and actual.
Horizontal and Vertical Systems
When it comes to horizontal integration, Industry 4.0 envisions connected networks of cyber-physical and enterprise systems that introduce unprecedented levels of automation, flexibility, and operational efficiency into production processes. This horizontal integration takes place at several levels:
- On the production floor: Always-connected machines and production units each become an object with well-defined properties within the production network. They constantly communicate their performance status and, together, respond autonomously to dynamic production requirements. The ultimate goal is that smart production floors will be able to cost-effectively produce lot sizes of one as well as reduce costly downtime through predictive maintenance.
- Across multiple production facilities: If an enterprise has distributed production facilities, Industry 4.0 promotes horizontal integration across plant-level Manufacturing Execution Systems (MES). In this scenario, production facility data (inventory levels, unexpected delays, and so on) are shared seamlessly across the entire enterprise and, where possible, production tasks are shifted automatically among facilities in order to respond quickly and efficiently to production variables.
- Across the entire supply chain: Industry 4.0 proposes data transparency and high levels of automated collaboration across the upstream supply and logistics chain that provisions the production processes themselves as well as the downstream chain that brings the finished products to market. Third-party suppliers and service providers must be securely but tightly incorporated horizontally into the enterprise’s production and logistics control systems.
Vertical integration in Industry 4.0 aims to tie together all logical layers within the organization from the field layer (i.e., the production floor) up through R&D, quality assurance, product management, IT, sales and marketing, and so on. Data flows freely and transparently up and down these layers so that both strategic and tactical decisions can be data-driven. The vertically integrated Industry 4.0 enterprise gains a crucial competitive edge by being able to respond appropriately and with agility to changing market signals and new opportunities.
Industrial Internet of Things (IIoT)
The Internet of Things (IoT) is a key component of smart factories. Machines on the factory floor are equipped with sensors that feature an IP address that allows the machines to connect with other web-enabled devices. This connectivity makes it possible for large amounts of valuable data to be collected, analyzed and exchanged.
Manufacturing companies have not always considered the importance of cybersecurity. However, the same connectivity of operational equipment in the factory or field (OT) that enables more efficient manufacturing processes also exposes new entry paths for malicious attacks and malware. When undergoing a digital transformation to Industry 4.0, it is essential to consider a cybersecurity approach that encompasses IT and OT equipment.
The cost of a data breach in industrial manufacturing is among the highest of any industry. A single breach averages $5.2 million in the industrial sector, according to the 2019 Cost of a Data Breach Report by the Ponemon Institute. It can be much worse. When the WannaCry ransomware attack took place in May 2017, many manufacturing companies were hit particularly hard, with several automobile companies shutting down factories for days. Overall losses totaled in the billions of dollars.
Today, you have more open factory floors and supply chains. You must have granular visibility and controls, eliminating risks of unauthorized users, applications and data on the network. You also have to accept that nothing is perfect despite these controls, that threats can still get in.
You need provisions to quickly detect and prevent against attacks. For example, tools to automate threat detection and response, leveraging machine learning for IoT and Industry 4.0. The technologies that increase the attack surface are the same technologies that can automate cybersecurity detection and prevention. However, automation must be used strategically.
Cloud computing is a cornerstone of any Industry 4.0 strategy. Full realization of smart manufacturing demands connectivity and integration of engineering, supply chain, production, sales and distribution, and service. Cloud helps make that possible. In addition, the typically large amount of data being stored and analyzed can be processed more efficiently and cost-effectively with cloud. Cloud computing can also reduce startup costs for small- and medium-sized manufacturers who can right-size their needs and scale as their business grows.
Additive manufacturing (AM)
Additive manufacturing (AM) or additive layer manufacturing (ALM) is the industrial production name for 3D printing, a computer controlled process that creates three dimensional objects by depositing materials, usually in layers.
Using computer aided design (CAD) or 3D object scanners, additive manufacturing allows for the creation of objects with precise geometric shapes. These are built layer by layer, as with a 3D printing process, which is in contrast to traditional manufacturing that often requires machining or other techniques to remove surplus material.
AM is used to create a wide range of products across a growing number of industries, including:
AM is particularly suited to aerospace applications due to its weight saving capability and ability to produce complex geometric parts such as blisks.
A variety of materials are widely additive manufactured for the automotive industry as they can be rapidly prototyped while offering weight and cost reductions.
The medical sector is finding an increasing number of applications for additively manufactured parts, especially for bespoke custom-fitted implants and devices.
AI and machine learning
AI and machine learning allow manufacturing companies to take full advantage of the volume of information generated not just on the factory floor, but across their business units, and even from partners and third-party sources. AI and machine learning can create insights providing visibility, predictability and automation of operations and business processes. For instance: Industrial machines are prone to breaking down during the production process. Using data collected from these assets can help businesses perform predictive maintenance based on machine learning algorithms, resulting in more uptime and higher efficiency.
Augmented and virtual reality (AR/VR)
Industry 4.0 is increasing in recent years and is one of the main sectors where Augmented Reality and Virtual Reality technologies are being adopted.
In the context of Industry 4.0, Innovae augmented reality and virtual reality allow to empower the workforce and train operators to be more efficient in increasingly complex production processes.
In short, these technologies allow operators to obtain critical knowledge easily and visually, enabling the performance of tasks more efficiently.
The applications of augmented reality in Industry 4.0. are several and are aimed at supporting technicians in their real working environment.
Through augmented reality, the user can visualize step-by-step procedures of the task to be performed or even get visual instructions in real time from experts with remote assistance systems.
Currently, the presence of augmented reality in areas such as maintenance, assembly processes or quality control is already common and reference companies in various sectors are implementing systems based on augmented reality to revolutionize their industrial processes.
The demands of real-time production operations mean that some data analysis must be done at the “edge”—that is, where the data is created. This minimizes latency time from when data is produced to when a response is required. For instance, the detection of a safety or quality issue may require near-real-time action with the equipment. The time needed to send data to the enterprise cloud and then back to the factory floor may be too lengthy and depends on the reliability of the network. Using edge computing also means that data stays near its source, reducing security risks.
Which Courses are the Best for a Future Career in the Era of Industry 4.0 in Malaysia?
The International Labor Organization has estimated that almost 300 million jobs are at risk due to the coronavirus pandemic. Of those that are lost, almost 40% will not come back. According to research by the University of Chicago, they will be replaced by automation to get work done more safely and efficiently. Particularly at risk are so-called “frontline” jobs – customer service, cashiers, retail assistant, and public transport being just a few examples. But no occupation or profession is entirely future proof. Thanks to artificial intelligence (AI) and machine learning (ML), even tasks previously reserved for highly trained doctors and lawyers – diagnosing illness from medical images, or reviewing legal case history, for example – can now be carried out by machines.
At the same time, the World Economic Forum, in its 2020 Future of Jobs report, finds that 94% of companies in the UK will accelerate the digitization of their operations as a result of the pandemic, and 91% are saying they will provide more flexibility around home or remote working.
The world of work is in constant change. Email, video conferencing, and cloud sharing are now the norm and millions of people now work in the gig economy, rather than on structured payrolls. But perhaps the greatest debate about the future of work is centered on automation, artificial intelligence, and robotics, and their potential effects on jobs.
BETWEEN 3.3 million and 6 million jobs are expected to be created in Malaysia by 2030, but with the new age of automation Industrial 4.0, preparation and training are fast becoming the critical factor as the new workforce would need new skills.
Furthermore, the ever-increasing cost of living in Malaysia is making it challenging for fresh graduates and working professionals to support their lifestyle. In light of that, it would be important for students to plan ahead what career that you want to enter into so that you can choose a course that has future job demand and high salary in Malaysia.
By having a view of emerging job trends, it is hoped that students would be inspired to draw up study plans and select career choices and pathways as early as schooling years up to university level that will ensure success in future careers and work environments.
Top 20 Courses for a Career in Industry 4.0 in Malaysia
Check out the best careers that you can get today as well as the ones that are more futuristic:
- Computer Science
- Software development or Software Engineering
- Information Technology (IT)
- Data science
- Artificial Intelligence (AI)
- Internet of Things (IOT)
- Financial Technology (Fintech)
- Cloud Computing
- Game Development
- Network Computing
- Mobile Computing
- Augmented Reality (AR)/Virtual Reality (VR)
- Mechatronic Engineering
- Electrical & Electronic Engineering
- Mechanical Engineering
- Robotics Engineering
- Telecommunications Engineering