A book called The Fourth Industrial Revolution by Dr Schwab describes how the fourth revolution is fundamentally different from the previous three. The fourth revolution combines the physical, digital and biological worlds. These new technologies will impact all disciplines, economies and industries.
The Fourth Industrial Revolution demands that CEOs take responsibility for the massive transformation of their businesses and for the extraordinary impact that this transformation will have on wider society.
– Pierre Nanterme, Accenture CEO
Already in 2016 The World Economic Forum at Davos stated that almost half of the jobs in the USA were at risk, because of Industry 4.0. However, Industry 4.0 also has great potential to drastically improve the efficiency of business and organizations and help regenerate the natural environment through better asset management, potentially even undoing all the damage previous industrial revolutions have caused. According to the report issued by Deloitte, over the next decade, the U.S. could be short of 12 million skilled workers. As a result of this, manufacturers are looking for ways to increase manufacturing efficiencies by doing more with less.
What is Industry 4.0
PICTURE 1: Industrial Revolutions
The first industrial revolution brought mechanization through water and steam power. The second revolution was mass production and assembly lines using electricity. The third was the adoption of computers and automation. Now, the Fourth Industrial Revolution is upon us. It is a fusion of fast technological breakthroughs in the physical, digital, and biological spheres. Technology breakthroughs in fields such as 5G, the Internet of Things (IoT), Big Data, Analytics, Artificial Intelligence and Robotics are significant on their own, but when combined we are talking about the enormous benefits of Industry 4.0.
- IoT. The Internet-of-Things (IoT) are where devices such as industrial sensors connect to the internet and to each other. IoT requires connectivity. 5G helps the evolution of IoT by improving the interaction between different platforms as well as enabling more devices to become connected.
- 5G. 5G will recode the whole concept of mobile connectivity. 5G brings a new-market disruption, which opens a completely new blue ocean of opportunities. 5G pushes industries to seek entirely new use cases for connectivity. 5G offers a completely different performance. 5G pushes a shift from the hardware business to the software business. 5G will change how people use and communicate with technology. It will change how different technologies communicate with each other. All this will take place faster and more reliably than ever before. Mobile internet will be faster than ever. High bandwidth and low latency will transform whole industries through new ways of connecting production processes and products.
- Big Data. There is more data available than ever. As the amount of data increases, new innovations and technologies to utilize the promise of this data are constantly created. Big Data can be filtered and turned into Smart Data. Fast Data aims for real-time data processing, where data is processed when it arrives. In the field of Big Data technologies like Artificial Intelligence, Machine Learning and Deep Learning can be used to analyze the data.
- Software ecosystems. With 5G, IoT, Big Data and Artificial Intelligence, the differentiating factor will be software capabilities. New capabilities are all about thinking outside the box. Ingenuity regarding what can be done with technologies, and how they can be applied to solve tomorrow’s challenges. This is a crucial mindset across every technology-related industry.
PICTURE 2: Industry 4.0 is a fusion of fast technological breakthroughs in the physical, digital, and biological spheres. It is not a single innovation, it is all the innovations combined.
The key to achieving the potential of Industry 4.0 is a collaboration between stakeholders from (traditional) industries and technology partners. This requires a new agile mindset and cultural shift. In short, Industry 4.0 will bring a new productivity shift, while smart machines keep getting smarter as they get more data, and factories will become more efficient, productive and less wasteful.
We Are the First Generation that Can End Poverty, the Last that Can End Climate Change.
– UN Secretary-General Ban Ki-moon
Forbes vs Uros
Markus Meukel from McKinsey & Company dropped big numbers regarding the promise of IoT at the annual Nordic IoT 2019 keynote speech. According to Mr Meukel “IoT is the most impactful technology revolution” creating an estimated 10 trillion annual turnover globally by 2025. Sounds insane, doesn’t it. However, just this week a Finnish IoT company Uros presented its growth figures.
And Uros CEO Jerry Raatikainen stated “What we are seeing now is the IoT market growing; the sector will be huge. We will leap into the year 2022 with the same rate of growth”. Mr Raatikainen is saying that a small technology company from Northern Finland will hit 50 Billion turnover in a few years. This underlines the effect the Industry 4.0 phenomenon offers for large companies. Industry 4.0 will change the game big time.
Industry 4.0 Examples
- Logistics: Optimize the routes of individual parcels and vessels (car, truck, ship) for efficiency and productivity. Automate everything. For example, Kalmar aims to optimize terminal processes. Zalando to use robots to pick packages. DHL is investing big time in Industry 4.0.
- Manufacturing: Smart manufacturing is a major application of the Internet of Things (IoT). For example, Fastems provides numerous references, where they have automated manufacturing lines around the world. Another interesting case example is the Bosch factory, where implementing radio-frequency identification-based tool management, embedding sensors to machines and analyzing real-time machine data and inventory, the factory renovated its manufacturing infrastructure and is able to understand and eliminate output losses and predict machine interruptions before they occur. Lean manufacturing principles, which form the backbone of smart factories, are being increasingly integrated into everyday operations. Adoption of lean manufacturing and the transition to data-led practices can increase the competitive edge of manufacturing. To improve manufacturing efficiency, one must backtrack from the customer. In today’s rapid cycle of customer demand, the linear design-manufacture-market-sell process does not increase competitive advantage, because demand forecasting is never as effective as ongoing collaboration. When customers are participating continuously in product design and make updates to their orders, they achieve a more productive relationship.
- Process Industry: The Industrial Internet offering by Valmet combines process technologies, automation and services, which will turn the benefits of the Industrial Internet into reality. Metso is focusing on providing sustainable solutions for the processing and flow of natural resources by means of Industry 4.0 and Digitalization.
- Food Production: Autonomous farming is taking agriculture to the next level. Agriculture 4.0 uses artificial intelligence to analyze the data from satellite imagery, weather forecasts, flying drones and IoT sensors on the field for crop modelling and for precision agriculture. For example, John Deere is pushing the boundaries on what can be achieved in the area of autonomous farming machinery.
- Mining: Sandvik AutoMine already provides full fleet automation with automatic mission and traffic control capability. Sandvik OptiMine analytics system creates a transparent model of the mining operations improving efficiency and safety.
- Business Disruption: Rolls-Royce has pioneered a pay-by-use approach in its jet-engine business; other manufacturers have followed. Rolls-Royce has turned from an RnD and manufacturing company to a data analytics company. Some say, that Rolls-Royce started the whole subscription economy of today’s Netflix and Spotify culture.
- IPR Business: Today, many manufacturing companies have deep expertise in their products and processes but lack the expertise to generate value from their data. SAP offers consulting services that build on its software. Qualcomm makes more than half of its profits from intellectual-property royalties. Manufacturers might offer consulting services or other businesses that monetize the value of their expertise.
We Are Creating a Better Future
Adopting Industry 4.0 technologies will help countries and businesses achieve sustainable growth. Industry 4.0 capabilities will enable faster design, novel products, reduced risks, optimized processes and the elimination of waste. New technologies will contribute to companies’ economic success. But they will also fulfil a social purpose, by contributing towards improving people’s lives. New technologies like artificial intelligence and edge computing can make people’s work less error-prone and create more room for creative tasks. These are technologies that will enable us to stay successful.
The promise of Big Data has finally gained momentum. There is more data available than ever. While the amount of data increases, new innovations and technologies to utilise the data are constantly created. Big Data describes increasing amounts of data, which is constantly collected. Big Data can be filtered and turned into Smart Data. Smart Data can be described as cleansed, filtered, and prepared for the context form of Big Data. Work is about batches and off-line processing, where you capture-store-process data in overnight batch jobs. Fast Data reduces the time between capture and process. Fast Data aims for real-time data processing, where data is processed as soon as it arrives.
Data technologies are evolving. Both the way data is processed and the timeline when data is analyzed are changing. Data is collected from countless systems and analyzed in real-time. Data includes transaction data and business data, IoT metrics, operational information, and application logs.
The Fast Data revolution has been made possible by two underlying revolutions of Big Data and Smart Data. Big Data systems like Hadoop made it possible to store huge constant volumes of unstructured data on commodity hardware. For example, the Cassandra storage technology began from the need of Facebook to store high volumes of data. Currently, on Facebook, 2000 photos per second are uploaded. However, the challenge that Facebook faced, starts to be an every-day challenge for any tech company working with high volumes of transactional data. The practical implications of having access to Fast Data are huge for any organization.
3V’s of Data
- Volume. Big Data emphasizes the volume of data
- Velocity. Fast Data emphasizes the velocity of data. Modern enterprise needs to make data-based decisions in real time
- Variety. Smart data emphasizes the variety of data. Enterprise needs a wide variety of data for making decisions suitable for the context.
While some large enterprises have made efforts to build data warehouses, most organizations still leave the majority of their unstructured data unused. The key big data adoption inhibitor is a knowledge gap. Many companies don’t have an understanding of how to create business value based on fast-data systems and many don’t have the skills to build this type of system.
Data Analysis Before, After and During
Data can be used to analyze past, current and future.
Analytic options can be categorized into high-level buckets, which complement each other:
- Descriptive Analytics: Insight into the past. Uses data aggregation and data mining to provide insight into the past and answer: “What has happened?”. Reports for historical insights.
- Diagnostic Analytics: Similarly to descriptive analytics but with an aim to drill down to isolate all confounding information: “Why did it happen?”. Root cause analysis.
- Predictive Analytics: Understanding the future. Uses statistical models and forecasts techniques to understand the future and answer: “What could happen?”. Forecasting of demand and output.
- Prescriptive Analytics: Advice on possible outcomes. Uses optimization and simulation algorithms for advice: “What should we do?”
No matter, what the business is about, data-analytics can be used to understand the past, predict the future and even to suggest the best possible ways forward.
A modern fast data architecture is based on four cornerstones.
- Acquisition: Fast and reliable data acquisition, where data enters the system from numerous sources. The focus must be on performance and dealing with back pressure, where data is generated faster than it is consumed. From a technology perspective, this means using streaming APIs and messaging solutions like Apache Kafka, Akka Streams, Amazon Kinesis, Oracle Tuxedo or ActiveMQ/ RabbitMQ/ SoniqMQ/ JBossAMQ
- Storage: Flexible storage and querying, where both logical and physical data storage is taken into consideration. There is no single good answer on how to model the data. Textual data falls into RDBM, but not all data is text. The main driver for the increase in the volume of data is the growth of unstructured/non-text data due to the increased availability of storage and the number of complex data sources. Unstructured data currently forms up to 80% of enterprise data. While the unstructured data has no identifiable internal structure, the data models must be based on solving the use cases will be created through experiments. E.g. Redis and Cassandra are one option for running fast-data, where the speed of a Redis combined with the performance of Cassandra makes working with data fast and easy. From a technology standpoint, we are using Apache Cassandra, Apache Hive, Amazon DynamoDB, Couchbase, Redis, MemSQL, MariaDB/MongoDB.
- Processing: Sophisticated processing and analysis are nowadays usually implemented as a hybrid between traditional batch processing and modern stream processing. Traditional ETL processes are run as batches and real-time online processes as streams. The same goes with the location of processing; It is common practice to combine in-memory and on-disk processing to reach the optimal results. In-memory processing can be accomplished via traditional databases or via NoSQL data grids. Data proximity discusses whether data is available locally. The best performance is reached when processing local data. Data locality is the idea of moving computation to the data rather than data to the computation and data gravity mean considering the overall cost associated with data transfer. From the technology aspect, we have solutions like Apache Spark/Flink/Storm/Beam and Tensorflow.
- Presentation: Presentation combines technology, science, and art. Presentations can be divided, for example, into notebooks, charts, maps and graphics. Common technologies here are Apache Zeppelin, Jupyter, Tableau, D3.js and Gephi.
|Big Data||Fast Data|
|Automakers improve the efficiency and safety of cars using data-analysis during car design||Automakers improve the efficiency and safety of cars using connected, autonomous cars to optimize the route and speed based on location, traffic and predictive maintenance information|
|Health care system suggests for a diagnose based on a historical dataset||Health care system predicts a heart attack based on real-time data|
|Warehouse determines what products to order based on analysis of the previous quarter||The online store offers personalized recommendations real-time based on customer behaviour|
|Credit card company creates risk models based on demographic data||Credit card company reacts real-time on potential credit fraud|
|Aeroplane manufacturer improves the maintenance plan of aeroplane based on data-analysis of historical data||Aeroplane manufacturer suggests maintenance activities real-time regarding technical status, location, routes and coming time tables of the plane|
|Cargo companies optimize parcel routes based on historical data||Cargo companies optimize ship and truck routes real-time based on weather, traffic and content of the vessel|
|Telecom operator adjusts the base-station location and configurations based on earlier collected data.||Telcom operator combines fast data and 5G network slicing to split a single physical network into multiple virtual networks and apply different policies to each slice to offer optimal support for different types of services and real-time charging based on usage.|