AI in Manufacturing – Applications of Big data, IOT and Cloud Manufacturing

building factory

The movement towards Industry 4.0 is seen as revolutionary…A VISION AND JOURNEY of the convergence and application of 9 digital industrial technologies – A bridge to Industrial assets and digital technologies…

Artificial Intelligence, being domain agnostic has many applications- call it AI in healthcare or AI in banking or AI in manufacturing and the list grows on. In short AI is everywhere.

Artificial Intelligence is radically changing the realm of Manufacturing. The world of Manufacturing is closely connected with engineering and designing of products. From raw materials to final products sent out for delivery, each step requires 24×7 surveillance and monitoring. With the ever-increasing demand and the evolving challenges, the industry experts have foreseen a future with AI, where robots take over most of the assembling, moving, packaging, transporting and other physical tasks, changing the role of the human workforce.

Industry 4.0 – The path for digitization in Manufacturing sector

Industry 4.0

With the incorporation or blend of IOT into the production and manufacturing environment a new era is born, the period of ’Industry 4.0’. The term was coined to mark the fourth industrial revolution, which fosters the very niche of Smart Factory and with it, many new technologies have recently evolved like Cyber Physical System, Big Data, Internet of Things, Internet of Services, Cloud Computing, Virtualisation, Semantic Web, etc.

The term Industry 4.0 is defined as “The fourth industrial revolution applying the principles of cyber-physical system (CPS), internet and future-oriented technologies and smart systems with enhanced human-machine interaction paradigm.”

Some examples of Industry 4.0 are machines which can predict failures, know the process of manufacturing of the product and trigger the maintenance process autonomously or self-organised logistics which can react to any unknown and unexpected changes in production.

Challenges confronted by Manufacturing Companies:

The traditional manufacturing systems will not be able to cope with the current challenges like short product-life cycle, dynamic demand and highly customised products. The challenges are vast and require modern approaches. Let us look at these challenges a bit closely.

Labour Shortage

Of late most of the manufacturing companies are facing a serious issue of skilled labour shortage issue. It’s expected that nearly 4.6 million manufacturing jobs will become available over the next decade – and nearly 2.4 million are expected to remain vacant. Today, 6 out of 10 open skilled production positions are unfilled.

Cyber Security

With huge volume of data collected and stored, the security of data becomes a major concern, as technological advances often leads to increase in cyber-criminal activity. But the majority of manufacturers ignore the concern, thereby relying on outdated security systems which are incapable of dealing with the volume and complexity of threats. This leaves many of them vulnerable to costly security breaches.

Environmental challenges


Ever since the First Industrial Revolution in the late 17th century, the manufacturing industries have been dealing with waste management issues. With little or no measures taken to reduce waste production, the pressure to reduce waste production in factories and implement the concepts of sustainable development emphasises the need to think strategically about environmental issues. Strategic decisions regarding product designs, processing technologies and managerial systems are major determinants in the environmental performance of manufacturing firms.

Global Competition

According to Global Manufacturing Competitive Index 2018, the US does not hold the top spot, China holds it, and India is a ranking at 107. With the 4th Industrial Revolution, humanity has entered a new phase and the global race for domination is heating up.

The world economic forum has introduced the Global Competitiveness Index 4.0 contributing to global thinking by incorporating the notion of Industry 4.0. A report by Deloitte suggests that advanced manufacturing has a strong multiplier effect on GDP and future of manufacturing is advanced technology and talents.

The Key Components of Industry 4.0

Big Data

Big Data is structured or unstructured raw data stored in multiple disparate formats. With the increased use of internet and social networks, the amount of data has increased exponentially. A big data repository offers substantial potential to store and retrieve data, at any level. This can benefit companies in many ways – by improving and identifying the root cause of failures in real time, by improving operations and implementing advanced data analytics to increase yield and by generating customer offers based on their buying habits. Big data analytics can reduce process flaws, save time and reduce cost. In the past manufacturers have been using techniques like six sigma (operational excellence or zero defects) and lean methods to reduce waste and improve the variability in the production process. However, certain industries like the pharmaceutical, chemical or mining, encountering extreme swings of variability, are also part of the process. Given the complexity of the process, a more granular approach in diagnosing and correcting the flaws is needed.


IOT refers to the infrastructure of interconnection among devices. IIOT, which stands for Industrial Internet of Things, is a technology in which different types of sensors are attached to physical assets. These sensors gather data, which is transferred wireless, stored in cloud and then machine learning is used to perform analytics to take actions, improve the overall process, detect flaws and predict any system failures. For instance, if a machine breaks down the connected sensors will automatically pinpoint the issue and trigger a service request immediately. Predictive maintenance can reduce equipment downtime and improve safety of human operators. IIOT can transform traditional manufacturing supply chains into dynamic, interconnected systems – a digital network supply (DNS) which can make factories more efficient. By forming a bridge between physical and digital world, Digital Supply Networks can drive actionable improvements through advanced analytics based on insights gathered from disruptive data from various sensors.

Cloud Manufacturing

It is a new paradigm developed from cloud computing, IOT, virtualisation, service-oriented technology and advanced computing technology. In other words, it is an industrial version of cloud computing. Manufacturing resources and data can be stored and managed in a network, to be made available to all, thus saving money and time by being extremely functional and sustainable. Such platforms can be used by various suppliers to offer and combine value added services, thus improving the overall efficiency of the business. It is the process of utilising the well-established manufacturing resources, such as the ‘Enterprise Resource Planning’, through the cloud. Resources can be viewed, updated and applied at any time and any place. Cloud manufacturing helps businesses unplug themselves for faster growth and developments.

Cyber Physical System

The foundation of Big Data, IOT and Cloud Manufacturing has led to the integration of physical and virtual world, and this integration is known as Cyber Physical System. It is characterised as a “physical and engineering system whose operations are monitored, controlled, coordinated and integrated by a computing and communicating core”. Simply put, CPS is about intersection and not the union of the physical and cyber world. The potential of CPS to change every aspect of life is vast. The concept of autonomous cars, robotic surgery, intelligent building and smart factory has already emerged. Although, CPS and IOT share the same basic architecture, CPS presents a higher combination and coordination between physical and computational elements. Researchers are developing Digital Twin, a software which exemplifies Cyber Physical System. Digital Twin is the virtual model of process, product or service, and in turn allows analysis of data and monitoring of systems to head off problems.

Thomas Kaiser, SAP Senior Vice President of IOT, put it this way: “Digital twins are becoming a business imperative, covering the entire life-cycle of an asset or process and forming the foundation for connected products and services. Companies that fail to respond will be left behind.”

Smart Factory

The implementation of IOT and CPS technologies have paved the way to Smart Factory. Researchers consider smart city as the heart of Industry 4.0.

As defined by Agnieszka Radziwona – “Smart factory is a manufacturing solution that provides such flexible and adaptive production processes that will solve problems arising on a production facility with dynamic and rapidly changing boundary conditions in a world of increasing complexity. This special solution could, on one hand, be related to automation, understood as a combination of software, hardware and/or mechanics, which should lead to optimisation of manufacturing resulting in reduction of unnecessary labour and waste of resource. On the other hand, it could be seen in a perspective of collaboration between different industrial and non-industrial partners, where the smartness comes from forming a dynamic organisation”.

It is a broad concept of manufacturing which represents a leap forward from more traditional automation. The concept of smart factory is still in its infancy and is the so-called future factory or the learning factory. The smart factory, integrates the new technologies to improve the performance, transparency, handiness and quality of manufacturing process.

The core idea is to add intelligence to manufacturing, to sway away from the traditional paper and spreadsheet maintenance towards connected wireless technologies in order to foresee a more productive, customised and safe future in the manufacturing environment. A future where manufacturers or suppliers spend more time in improving the process than finding and detecting faults in it. Using digital work-flows, pick-to-light solutions, video downtime analysis and digital data management, we head towards the AI-manufacturing-revolution.