Industry 4.0, the Industrial Internet, smart manufacturing, digital factory, ...
Despite enthusiasm for digital manufacturing, few companies have realized its potential at scale, according to our new survey. Six success factors can help, McKinsey argues.
Historically, Industrial Automation and Control Systems (IACS) were largely isolated from conventional digital networks such as enterprise ICT environments. Where connectivity was required, a zoned architecture was adopted, with firewalls and/or demilitarized zones used to protect the core control system components. The adoption and deployment of Internet of Things (IoT) technologies is leading to architectural changes to IACS, including greater connectivity to industrial systems. This paper reviews what is meant by Industrial IoT (IIoT) and relationships to concepts such as cyber-physical systems and Industry 4.0. The paper develops a definition of IIoT and analyses related partial IoT taxonomies. It develops an analysis framework for IIoT that can be used to enumerate and characterise IIoT devices when studying system architectures and analysing security threats and vulnerabilities. The paper concludes by identifying some gaps in the literature.
The paper aims to provide high-level guidance for architects of cyber-physical enterprises such that the nature of interactions within it as a system can be largely self-determined based on system self awareness and dynamic self-configuration, and a set of foundational guiding principles, rather than being pre-defined by an external designer or architect. The paper investigates the suitability of typical development life cycles and architectural challenges in the context of dynamic cyber-physical systems intending to utilize the power of the Internet of Things, and then goes on to define desired attributes of such systems, which need to guide suitable core architectural choices. Application of the findings is exemplified through a case study, followed by synthesis of issues and implications for further research.
Artificial intelligence (AI) is a hot topic in business technology, and industrial companies have taken notice. By deploying the right combination of AI technologies, producers can boost efficiency, improve flexibility, accelerate processes, and even enable self-optimizing operations. A BCG analysis found that use of AI can reduce producers’ conversion costs by up to 20%, with up to 70% of the cost reduction resulting from higher workforce productivity. Producers can generate additional sales by using AI to develop and produce innovative products tailored to specific customers and to deliver these with a much shorter lead-time. AI is thus integral to the factory of the future, in which technology will enhance the flexibility of plant structures and processes.
The acatech Industrie 4.0 Maturity Index focuses on four key areas, each of which has two fundamental principles attached to it. The main challenge for companies wishing to implement Industrie 4.0 is to put these principles into practice by developing the various capabilities described in this study. The goal is to generate knowledge from data in order to transform the company into a learning, agile organisation and enable rapid decision-making and adaptation processes throughout every part of the business and across all business process areas. The acquisition of this agility provides companies with a significant competitive advantage in a disruptive environment.
This publication examines the opportunities and challenges, for business and government, associated with technologies bringing about the “next production revolution”. These include a variety of digital technologies (e.g. the Internet of Things and advanced robotics), industrial biotechnology, 3D printing, new materials and nanotechnology. Some of these technologies are already used in production, while others will be available in the near future. All are developing rapidly. As these technologies transform the production and the distribution of goods and services, they will have far-reaching consequences for productivity, skills, income distribution, well-being and the environment. The more that governments and firms understand how production could develop in the near future, the better placed they will be to address the risks and reap the benefits.
In a fast-paced world it can be difficult to see the big changes even as they unfold in front of us. Looking back at 2017, in what ways did emerging technologies significantly impact the world in the past 12 months? We found five signposts indicating that the Fourth Industrial Revolution indeed transformed our lives and societies in 2017 – and five areas where transformations are yet to come.
For those of us fortunate enough to come together at Davos this year, the Fourth Industrial Revolution promises gains in scientific knowledge, human health, economic growth and more. But for most people around the world, the prospect of a future in which robots and computers can perform many human jobs is a source of profound personal concern. As president of an institute with ‘technology’ in its name and ‘the betterment of humankind’ in its mission, I take these concerns seriously. Every past technology wave ultimately produced more jobs than it destroyed and delivered important gains, from higher living standards and life expectancy to productivity and economic growth. Yet many fear that this time the change may be so fast and so vast, and its impact so uneven and disruptive, that it may threaten not only individual livelihoods but the stability of society itself. This outcome is not inevitable. The future is in our hands. Indeed, deliberate, coordinated action is exactly what smoothed the way for such transitions in the past. If we want the advance of technology to benefit everyone, however, we need to take action right away. We must proactively and thoughtfully reinvent the future of work.
In the 47 years since I founded the World Economic Forum, I have witnessed first-hand that when we change the way we talk, we begin to think differently too. Likewise, changing the way we think leads to changes in the way we act. This is true for all of us – whether you are a private citizen at home or making consequential decisions as a head of government, the language we use and the way we think about the world shapes our subsequent behaviour.
If the vision of Industry 4.0 is to be realized, most enterprise processes — manufacturing, product development, customer relations, and the workplace itself — must become fully digitized. A critical element will be the arrival of the digital supply chain.
Future manufacturing is becoming “smart” - capable of agilely adapting to a wide variety of changing conditions. This requires production plants, supply chains and logistic systems to be flexible in design and reconfigurable “on the fly” to respond quickly to customer needs, production uncertainties, and market changes. Service-Oriented Architecture (SOA) provides a promising platform to achieve such manufacturing agility. It has proven effective for business process adaptation. When combined with the emerging Internet of Things (IoT) technology and the concept of cyber-physical production systems, it is expected to similarly revolutionize real-time manufacturing systems. This paper proposes a new concept of cyber-physical manufacturing services (CPMS) for service-oriented smart manufacturing systems. In addition, we propose a modeling framework that provides appropriate conceptual models for developing and describing CPMS and enabling their composition. Specifically, the modeling framework separates service provision models from service request models and proposes the use of standardized functional taxonomies and a reference ontology to facilitate the mediation between service requests and service consumptions. A 3D-printing use case serves as an example implementation of an SOA-based smart manufacturing system based on our proposed modeling framework.
The purpose of this article is an attempt to develop the concept of a business model dedicated to companies implementing technologies of the Industrial Internet of Things. The proposed concept has been developed to support traditional companies in the transition to the digital market. The study was based on the available literature on the impact the Industrial Internet of Things has on the economy and business models.
The wave of the fourth industrial revolution (Industry 4.0) is bringing a new vision of the manufacturing industry. In manufacturing, one of the buzzwords of the moment is "Smart production". Smart production involves manufacturing equipment with many sensors that can generate and transmit large amounts of data. These data and information from manufacturing operations are however not shared in the organization. Therefore the organization is not using them to learn and improve their operations. To address this problem, the authors implemented in an Industry 4.0 laboratory an instance of an emerging technical standard specific for the manufacturing industry. Global manufacturing experts consider the Reference Architecture Model Industry 4.0 (RAMI4.0) as one of the corner stones for the implementation of Industry 4.0. The instantiation contributed to organizational learning in the laboratory by collecting and sharing up-to-date information concerning manufacturing equipment. This article discusses and generalizes the experience and outlines future research directions.
The concept of an open enterprise architecture that links plant floor operations with business operations across an entire corporate entity has been around for a while in many industrial sectors. But making this concept a reality remains challenging. This is particularly true for those companies that lack huge IT staffs or budgets. IIoT, with its compelling promise of accessing, aggregating, and analyzing data from previously stranded assets and systems to improve decision support and thus business performance, represents a further disruption.
As it is today, many of product lifecycle processes, from design, to process planning and engineering, manufacturing are siloed because different software tools, models and data representations are used, and often by many different teams across different organizations and geographic locations. To achieve the goals of smart manufacturing, these product lifecycle processes and manufacturing functions need to be connected and integrated to increase process automation, responsiveness and efficiency, and to reduce human errors. Furthermore, because of connected smart products, this lifecycle is now being extended beyond the four-wall of the factories, into customers’ operation environment. Digital thread refers to the communication framework for integrating production functions across the product chain and integrating product data for digital models. It does so by enabling data flow and integrated view of the product’s data throughout its lifecycle across different stages, from design, to manufacturing, and now to operation, and even to end-of-life and recycling of the product
Achieving change in a world ever more defined by complexity is difficult. We face an array of complex ‘wicked’ problems, from an ageing population to climate change to intergenerational cycles of poverty. It can often seem that these challenges are insurmountable and that we lack the ability to make meaningful change. To find opportunity in challenge will require reimagining the ways that we currently think about innovation and design. The narrative around a ‘fourth industrial revolution’ risks narrowing the focus of innovation to technology which would locate innovation-led growth solely in the outputs of universities and research institutes, or technology clusters like Cambridge’s Silicon Fen. While these are a vital piece of the UK’s innovation jigsaw, they are not the whole picture. Enterprises large and small across sectors and regions need to also be part of the innovation mix. The UK has long been at the forefront of design, a rich heritage that permeates a diverse range of sectors. Design thinking methodologies are deployed in service, policy and governance design across sectors, not merely product design. Harnessing the power of this creative capacity will be crucial to generating the innovative solutions required to tackle pressing social challenges. But design thinking alone will not be enough. The core insight of this paper is that solving our most complex problems will require augmenting design thinking with a systems thinking approach as the basis for action. While design thinking has proved itself to be successful in the realm of creating new products and services, the challenge is how to support innovations to enter and actively shape the complex systems that surround wicked social challenges. Great design doesn’t always generate impact. As we show in this report, innovations attempting to scale and create systemic change often hit barriers to change, sending them catapulting back to square one. We call this the ‘system immune response’. The particular barriers will differ dependent on context, but might be cultural, regulatory, personalitydriven or otherwise. This report argues that innovations for the public good are susceptible to the system immune response because there is a deficit of systems thinking in design methodologies. This report introduces a new RSA model of ‘think like a system, act like an entrepreneur’ as a way of marrying design and systems thinking. At its most simple, this is a method of developing a deep understanding of the system being targeted for impact and then identifying the most promising opportunities to change based on that analysis – the entrepreneurial part. By appreciating factors like power dynamics, competing incentives and cultural norms, innovators can prepare themselves for barriers to change, and find the entrepreneurial routes around them to successfully affect system change.
The IoT is already boosting a significant amount of innovation across various industries by providing near real-time insight into rich and contextual environmental data across a wide range of complex scenarios, such as the industrial internet, smart homes and cities, energy management, agriculture, intelligent transport systems, connected health and smart retail. To identify the essential building blocks of IoT architecture, it’s helpful to review the IoT reference architectures that have been created by several bodies and industry consortia.
The pervasive diffusion of Information and Communication technologies (ICT) and automation technologies are the prerequisite for the preconized fourth industrial revolution: the Industry 4.0 (I4.0). Despite the economical efforts of several governments all over the world, still there are few companies, especially small and medium enterprises (SMEs), that adopt or intend to adopt in the near future I4.0 solutions. This work focus on key issues for implementing the I4.0 solutions in SMEs by using a specific case example as a test bench of an Italian small manufacturing company. Requirements and constraints derived from the field experience are generalised to provide a clear view of the profound potentialities and difficulties of the first industrial revolution announced instead of being historically recognised. A preliminary classification is then provided in view to start conceiving a library of Industry 4.0 formal patterns to identify the maturity of a SME for deploying Industry 4.0 concepts and technologies.
IIoT and Smart manufacturing – a twin-movement of digitalization: The Industrial Internet of Things (IIoT) and smart manufacturing are two parallel developments driven by the same core technology advances – the ubiquitous connectedness and widespread computation – that drive and are driven by the internet, and the seamless information sharing and optimal decision-making they enable.
As manufacturing processes become increasingly digital, the digital twin is now within reach. By providing companies with a complete digital footprint of products, the digital twin enables companies to detect physical issues sooner, predict outcomes more accurately, and build better products.
Industry 4.0 after the initial hype: Where manufacturers are finding value and how they can best capture it
McKinsey report on its Industry 4.0 Global Expert Survey, exploring changes in attitudes towards Industry 4.0 and progress made in its implementation.
By integrating ERP and manufacturing data for more accurate demand forecasts, companies can reduce inventories by avoiding overproduction.
This essay/article was written for a Theories II class with professor Neil Leach. The essay talks about how we are in this new era of architectural design where the technology around us has been helping us so much in the actual design of projects. Parametricism is a new way of finding a code in nature and actually finding information for it to create a completely new design. It is very interesting in the fact that this new revolution we are in will completely change our notions of cities and buildings and actually connect us more as people.
The world is changing. There’s no way around this fact. The Fourth Industrial Revolution is now. And, whether you know it or not, it will affect you. Billions of people and countless machines are connected to each other. Through groundbreaking technology, unprecedented processing power and speed, and massive storage capacity, data is being collected and harnessed like never before. Automation, machine learning, mobile computing and artificial intelligence — these are no longer futuristic concepts, they are our reality. To many people, these changes are scary. Previous industrial revolutions have shown us that if companies and industries don’t adapt with new technology, they struggle. Worse, they fail.
The Internet of Things (IoT) has been a long time coming, but as with so many software and cloud-driven markets today, the curve from hand-waving to pervasive adoption is set to be remarkably steep. Network-driven markets increasingly tend to be pretty close to winner takes all (think Google in Search, Apple in phones, Facebook in social, Snapchat in dogear-driven Augmented Reality) which makes timing and effective, community-driven execution all the more important. Which brings us to IBM. Wait. What? IBM? OK bear with me here.