The three part article is titled : “The Wave of Industry 4.0”

　　In the first part we take a historical perspective how the wave of Industry 4.0 has been building up. The second part looks at the current scenario how Siemens has transformed manufacturing in the context of Indus 4.0. In the third part we look at the scenario how Industry 4.0 impacts the millennial workforce. This will shape the future scenario.

　　Part I: Evolution of Digitalization in Industry 4.0

　　Part II: Digital Enterprise Suite from Siemens Industry Software

　　Part III: Millennials Bridging the Skilling Gap in Industry 4.0

　　The ideas expressed in Part I and Part III are personal. Ideas expressed in Part II are based on the Digitalization initiatives and the resources thereof available in Siemens Industry Software.

　　This three part article is written by :

　　Abhijit A Barua

　　Part I: Evolution of Digitalization in Industry 4.0

　　(In this part we go back into the past to look at the evolution of Industry 4.0)

　　Historians and economists have often opined, civilizations undergo social, business and political revolutions every 250 years. It is worthwhile to explore whether such a phenomena can be observed in Science and Technology by taking a historical odyssey going back 500 years to the 1500s. Such historical observations do reflect a thread of scientific and technological innovations leading to Industry 4.0 and Digitalization.

　　In the 1500s Europe came out of despair and darkness from the Bubonic plague. During that time there were silver linings with the spread of knowledge being revolutionized with the invention of the Gutenberg printing press. This was an epochal invention comparable to the impact of the modern day internet. Some of the other inventions from the Middle Ages were telescope and microscope. During that time there were many scientific discoveries coming from Copernicus, Galileo, John Napier, Fermat, Robert Boyle, Sir Isaac Newton. There was an intellectual innovation giving birth to modern anatomy, astronomy, biology, chemistry, mathematics and physics.

　　Societies were also undergoing revolutionary changes. In Europe, with evolution of the Protestant Movement many people dispersed to settle across geographies as immigrants. This was the time when Christopher Columbus discovered Americas, leading to the birth of United States.

　　Cognitive psychologists have observed that learning in an individual happens in four stages. These are the four stages how our brain comprehends. The first step is perceptual, when we first sense by listening or reading. The second phase is conceptualization when we observe and rationalize applying the laws of science. The third phase is visualizing and the fourth phase is application. So perceptual – conceptual – visual – application.

　　My historical odyssey, made me to observe that knowledge of science till around the 1500s was primarily perceptual with subjects like Philosophy taking the lead. From 1500s to the mid 1700s primarily conceptual.

　　Starting around mid 1700s we could observe the brewing of a new revolution. This revolution, from a learning perspective was primarily visualization and application. This was the Industrial Revolution. Application of knowledge was the genesis of our modern day Engineering. Engineers build by applying the concepts of science.

　　The Industrial Revolution starting from around 1750s can be divided into four phases of developments. The first phase of Industrial Revolution till around second half of 1800s was around mechanical machines for example the spinning jenny by James Hargreaves , steam engine by James Watt and sewing machine by I M Singer. The second phase was around electro mechanical machines for example the electric motor, light bulb, telephone. The Internal Combustion engines were also developed during the second phase. The third Industrial Revolution started around the 1960s with relays, transistors and semiconductors leading to the era of computers.

　　From the first decade of 2000s we are in the fourth phase of Industrial Revolution. We refer to this as Industry 4.0. Let us try to understand what is happening in Industry 4.0.

　　In the first three Industrial Revolutions we were only building machines which were tools to make us more productive and efficient. An automobile is a tool to drive from Point A to Point B. An aircraft is a tool to fly from City A to City B. All such tools are controlled by human beings in operations.　　

　　In Industry 4.0 we are building machines which functionally represent how a human brain works. What is unique about the human brain. The human brain understands patterns, has the power of reasoning and has command over language. This means we are building machines which can perceive, reason and communicate or act. They are at times autonomous from human controls. Examples are driverless cars, pilotless planes and drones. And this is the reason why we refer to them as smart machines or intelligent machines. We also communicate with these machines. Many of the smart cars today have pre-crash warnings. The cars alert us by tightening of the seat belt and awakening us if we are asleep. Innovators are building machines which can communicate with us reading our emotions. For example visual sensors which operate by reading our emotions by the dilation of our pupils.

　　Well renowned inventor and futurist, Ray Kurzweil in his book ‘The Singularity is Near’ highlighted the merging of technology with human intelligence with numerous examples. This merging is happening by our increasing understanding of how the human brain functions. Efforts are to reverse engineer the functions of the brain. Ray Kurzweil gave the example of the retina in the eye which is 2 centimeters wide and a half millimeter thick primarily functioning for image capturing. The retina captures 10 million image detections every second. The estimate is that 100 computer instructions are required to recreate each such detection at human levels of performance. This means to replicate this function of the retina we require 1000 MIPS (Million Instructions Per Second). Similarly the number of computations that the human brain does is 10¹⁶. With such computational estimations, an analogy can be made to the computation power in computers to animal brains. A supercomputer 50 years back could do 0.25 MIPS equivalent to the intelligence of a bacteria or a worm. The IBM Blue Gene computer in 2007 could do Million Giga Flops per second i.e. 10¹⁵ instructions per second which is estimated to be equivalent to the brain power of a mouse. By early part of next decade processor technologies will be developed which can do 10¹⁶ instructions per second. And towards 2030s it is estimated that machines for example a robot, will be developed which can pass the ‘Turing Test’. Such a machine will be indistinguishable from a human being.

　　Increasingly skills will be determined by the ability to work with smart and intelligent machines. Post the ubiquity of Turing Test certified computers or machines, human skills will be determined by its ability to complement the machine cognition. Humans failing to complement or supersede the abilities of the machines will be below average. Well known economist, Tyler Cowen refers to this scenario as “Average is Over”.

　　Developments in processor technologies to support such massive levels of computations are being contributed by 3D molecular computing, DNA computing, quantum computing and spintronics. Many of the traditional transistor technologies are being disrupted as logic gates are being defined at atomic dimensions. Innovators are working by adopting such technologies on nano robots or nanobots which can travel through human blood vessels and will be connected through communication protocols with other nanobots.

　　These disruptive digital developments have forced organizations across industries to innovate and leverage digitalization technologies. For many organizations, failing to do so, it will be a question of survival.

　　Part II: Digital Enterprise Suite from Siemens

　　(In this part we look at the current scenario and how Siemens has transformed manufacturing through Digitalization)

　　Manufacturing Industry has been optimizing processes in product design, production planning and production. Software tools have aided in some parts of the processes. CAD and CAE in product design and validation. In production planning software applications have been used for factory layout, through put analysis, robotic simulation and programming, quality analysis. In production MES software applications aided the processes.

　　PLM software applications supported the underlying process threads and established the pervasiveness of a common digital thread from Product Design to Production. For example the BOM ( Bill of Materials ) can be generated as a conceptual BOM in Product Design and taken into Manufacturing and Requirements Planning. A virtual thread has been developed replicating in parallel to the physical thread in manufacturing industry. And in many areas the virtual and the physical threads were merging. Still in some of the areas there were disconnects between the virtual and the physical thread. One such area was prototyping and testing with physical models being built. This disconnect is compounded by products becoming increasingly complex with the use of embedded software and electronics. Moreover the market forces demanded products to have multiple variants to meet the personalized requirements of multiple customer segments.

　　The need is a common Digital Platform which is a comprehensive merger of the virtual and the physical world. Siemens PLM has introduced the Digital Enterprise Suite of Software forming such a platform. The inception of the platform is the merger of the Product Lifecycle Management (PLM) software with the powerful automation technologies in Totally Integrated Automation (TIA) portal. This merger of PLM and TIA built the thread from Product Design to Production Planning to Production Engineering to Production Execution.

　　Massive volume of data is generated across the platform. This data comes through IOT devices which will be inter-connected. The data can be used intuitively as a feedback loop. To this end Siemens has introduced Mindsphere , an open IOT operating system. Mindsphere provides an open Platform as a Service and is part of the Digital Enterprise Suite.

　　Thus the Digital Enterprise Suite is a fully integrated software solution across customers’ entire value chain from initial conceptual design, manufacturing planning and execution through services and support of both the products and plants that produce them.　　

　　The outcomes of the applications of the Digital Enterprise Suite will be in the form of:

　　· Connected Enterprises

　　· Smart Factory Digitalization

　　· Internet of Things

　　· Big Data, Intelligent Systems

　　· Additive Manufacturing

　　· Augmented Reality

　　· Cyber Physical Systems

　　· Robotics and Automation

　　The application of the Digital Enterprise Suite by customers is by building digital twins which are smart virtual models. Such a digital twin of a product will carry all the necessary information for their own production. Siemens has demonstrated the capabilities by setting up its own smart factory in Amberg.

　　The holistic view of digitalization in manufacturing in the Digital Enterprise Suite of Siemens PLM can be looked at from three stages of developments.

　　1. The Ideation stage wherein product design is conceptualized. At this stage is the Digital Product Twin including all design elements of a product viz:

　　- 3D models ( using CAD systems )

　　- System Model ( using system engineering product development )

　　- Bill of Materials

　　- 1D, 2D, and 3D analysis models ( using CAE systems )

　　- Digital software design and testing ( using ALM systems )

　　- Electronic design

　　Such a digital product twin supports complete virtual product design and validation doing away with building physical prototypes.

　　2. The Realization stage, wherein how a product will be manufactured through production planning is defined. At this stage is the Digital Production Twin. The digital production twin carries all information on the product’s :

　　- Manufacturing Process , how it will be produced

　　- Production facility model, carrying information on the plant , production and assembly lines

　　- Production facility automation model describing how the automation system ( SCADA, PLC, HMI etc. ) will support production

　　The Digital Production Twin will support throughput analysis, optimization of assets and quality parameters. It can be further simulated for virtual commissioning.

　　3. The Utilization stage wherein the performance of the product once it is put to use is monitored. At this stage is the Digital Performance Twin which forms a feedback loop on the actual performance of the product. Performance data at this stage can be analyzed with the Digital Product Twin and Digital Production Twin for further optimization of the complete process. This also gives newer insights to innovation.

　　The Digital Enterprise Suite from Siemens PLM enables customers to leverage digitalization to gain benefits in terms of shortening time to market, greater production flexibility, reducing costs and improving quality.

　　Siemens has installed globally millions of devices ( 30 million automation systems, 70 million contracted smart meters, 800 thousand connected products). This is complemented by being the leading global provider of Product Lifecycle Management ( PLM ) and Manufacturing Operations Management ( MOM ) software, systems and services with over 15 million licensed seats and more than 140,000 customers worldwide. This successful legacy has enabled Siemens to be the only IOT provider to drive closed loop innovation on the Digitalization Enterprise Suite through Digital Twins of Product, Production and Performance. This is the Digitalization of Manufacturing.

　　Part III: Millennials Bridging the Skilling Gaps in Industry 4.0

　　(In this part we look at the impact of Industry 4.0 going ahead in the context of the millennials who will be joining the workforce and will shape the future)

　　In Malcolm Gladwell’s ‘Outliers’, I came across two interesting observations. First is the list of 75 all time richest people in the world including names like Sultan of Brunei to Queen Cleopatra, Bill Gates to Mukesh Ambani. Of these 75, there are 25 names form the US. And among these 25 names, 14 are born in a gap of 9 years from 1832 to 1840. Some of these among the 14 are John Rockefeller, Andrew Carnegie, JP Morgan.

　　The second example is of Bill Gates, Steve Jobs, Eric Schmidt, Vinod Khosla. All were born in 1955.

　　One definite commonality that we can draw among these two groups of people is during the times of their successes there have been the influence of an Industrial Revolution.

　　For people born between 1832 to 1840 it was the Second Industrial Revolution. For people born in 1955 it was the Third Industrial Revolution.

　　In the context of the Fourth Industrial Revolution the group to have the most impact are the millennials born in the window of 1985 to 2000. They are the age group coming into the workforce during Industry 4.0.

　　Some of the underlying transformational technologies influencing Industry 4.0 are:

　　1. Augmented Reality or Virtual Reality

　　2. Advanced Robotics

　　3. Additive Manufacturing

　　4. Cloud Computing and Big Data

　　5. Data Security

　　6. Artificial Intelligence and IOT

　　7. Automation of Knowledge through Machine Learning

　　8. Autonomous Systems

　　9. Blockchains

　　10. Genetics and Nano Technologies

　　Especially for students keen to work in cutting edge technologies these are some of the areas to focus. These technologies have helped organizations to solve engineering problems beyond the realm of reality. The NASA Curiosity Rover successfully landed on the Martian surface in 2012. It weighed 900 kgs, travelled inter-planetary for 352 million miles over 253 days maneuvering an extraordinary Entry – Descent – Landing. Only way the NASA scientists could design and validate was by simulating in a virtual environment using some of the technologies as highlighted above.

　　With the advancements and developments of new technological arenas one of the biggest challenge for countries and organizations is having the right talent. Many countries in their education initiatives have started focusing on STEM (Science, Technology, Education, Mathematics). As per the World Economic Forum report, ‘Future of Jobs’ most of the in-demand opportunities or specialties in industries did not exist 5 to 10 years back. The report highlights that the biggest driver of change in employment is technological. It further predicts that the maximum growth in job opportunities will be in STEM related fields.　

　　In terms of talent in STEM globally, Germany has the highest rating basis the survey from Deloitte Global in 2016 Global Manufacturing Competitive Index. It further observed one of the possible reason is in the German education of dual system, combining class room trainings with work experiences.

　　Manufacturing in India is the largest employment sector. India came up with the ‘Make in India’ initiative to bring up the manufacturing contribution to GDP from the current level at 16% to 25% by 2020. This is expected to create 100 million additional jobs in the manufacturing sector by 2022.

　　By 2021 India will become the youngest country in the world with 64% of its population being millennials. Average age of an Indian in 2020 will be 29 years. All millennials are digital natives. They will be taking India into the next growth economy.

　　India has one of the highest growth in STEM graduates. But many will require industry experiences to bridge the skill gaps to be productive.

　　As per the 2016 Global Manufacturing Competitive Index, 2016 survey the topmost driver of manufacturing competitiveness is talent. The survey also projects India is expected to move to the 5th rank among countries globally by 2020 from its current ranking of 11th.

　　India has to focus more on STEM curriculum in the context of technologies being used in Industry 4.0. As per the UNESCO 2013 report the number of researchers per million population in India is 157. This is low in comparison to other competing nations.　　

　　Another perspective is a country’s spend on R&D as percentage of GDP. As per the Industrial Research Institute 2016 report in India 0.85% of GDP is invested into R&D. This is low in comparison to other competing countries.　　

　　Curriculums in colleges should be increasingly aligned towards research and addressing industry challenges. For example in the Automotive Industry in India, focus has been on complying to BS VI emission norms. For example students in Mechanical Engineering or Automobile Engineering should be working on research papers connecting the concepts imbibed in their semesters to how it will effect BS VI. One example, to meet BS VI norms automobiles require light weighting to increase fuel efficiency. So industry is researching on the use of materials with lighter weight. Most of the modern cars have started using bodies made of aluminium and composites. This means for a student studying Metallurgy research paper should focus on properties of aluminium for stamping and die design. Similarly welding of aluminium. The objective is towards inculcation of research orientation.

　　People enter into the workforce around the age of 25 years and retire at the age of 60 to 65 years after a work life of around 40 years. Developments in health sciences predict average life expectancies starting in the developed countries to move to around 100 years. This means people will have to work for around 60 to 65 years on entering the workforce at 25 years. But technologies keep changing. There were no Whatsapp, Twitter, Google Cars 10 years back. This means for millennials to keep their skills relevant they have to continuously learn and re-learn acquiring new skills.

　　Among the millennials some will be successfully riding the wave of Industry 4.0 to be the next Steve Jobs or Bill Gates. And maybe from India in a flatter world.

　　References:

　　1. ‘Civilization, The West and the Rest’ by Niall Ferguson

　　2. ‘The Demographic Cliff’ by Harry S. Dent , Jr

　　3. ‘The Singularity is Near’ ( When Humans Transcend Biology ) by Ray Kurzweil

　　4. ‘The Design of Future Things’ by Donald A. Norman

　　5. ‘Phenomenology’ ( Basing Knowledge on Appearance ) by Avi Sion

　　6. ‘Average is Over’ by Tyler Cowen

　　7. Siemens Digital Enterprise by ied Russwurm in ‘The Digital Enterprise’ by Karl-Heinz Streibich

　　8. ‘Outliers’ by Malcolm Gladwell

　　9. ‘Future of Jobs’ report from World Economic Forum

　　10. ‘2016 Global Manufacturing Competitive Index’ from Deloitte Global and the Council on Competitiveness

　　11. ‘2016 Global R&D Funding Forecast’ from Industrial Research Institute ( IRI )

　　12. ‘Economic Survey 2015 – 16’ from Government of India