High-Tech Software Cluster companies are involved in developing some of the most advanced smart software in the world, enabling their customers to rapidly benefit from digitisation and gain competitive advantage in their markets. From Digital Twins through to Model Driven Engineering, software technologies are changing the way that we do business and the only certainty is that they will profoundly impact the performance of your organisation.
Developing a strategic understanding of these technologies is essential to the future of your business and key to mastering the perfect digital storm that surrounds us. This article provides you with a starting point to understand the most important software technologies driving innovation in industry. You certainly won’t need to embrace every technology described here, but the right selection and combination of a few will definitely help your business gain competitive advantage and achieve higher returns.
The High Tech Software Cluster can help you develop a smart software strategy uniquely attuned to the needs of your business and can provide the products, competencies and knowledge necessary to successfully digitalise your business.
Industrial Internet of Things
The Industrial Internet of Things (IIoT) is a network of intelligent machines, computers, devices and objects in a manufacturing environment that collect and share huge amounts of data. The collected data is sent to a central service on the Internet where it is combined with other data and then shared with end users in a helpful way. This data also raw input material for data analytics, machine learning and artificial intelligence applications. IIoT can greatly improve connectivity, efficiency, scalability, time savings and cost savings for industrial organizations.
Cloud computing is the delivery of services to store, manage and process data through a network of remote computers on the Internet. It makes computer system resources available on demand without having to host and directly actively manage a local computing infrastructure. Cloud computing can greatly improve innovation speed and business scalability and flexibility. As the services are offered on a pay-per-use basis, it also reduces operational and capital investment costs.
Connecting our machines, our homes and our critical business processes to the internet means we’re adding an entrance to these systems accessible from all over the world, and so we need to put security measures in place to keep out malicious actors. Just like our physical entrances, it’s no use bolting on a lock if the door frame is of poor build quality; we need to apply software security from day one throughout the development process. Training, tooling and proper processes are required in order to build secure software.
AI (artificial intelligence), machine learning and deep learning are closely related – actually, they basically refer to the same concept for the majority of today’s applications. There are subtle differences between though. Artificial intelligence is an umbrella term for computer systems that are able work in the real world rather than in highly constrained environments specifically tailored to accommodate them. Understanding spoken language, interpreting images and guiding robots through a factory are all examples of AI.
Machine learning is a derviative of AI and where a computer is specifically trained to perform a task by being exposed to (a lot of) examples of the task instead of explicitly told what to do. Finally, deep learning is a particular, practical technique to perform machine learning that draws from concepts in biology. It usually requires more computing power than other approaches, but is able to make sense of more complex inputs.
Most of the recent advances in machine learning, and, in turn AI, are the result of the rapid developments in deep learning. But keep in mind that both fields are actually broader; your spam filter is a good example of a learning system without the ‘deep’ moniker.
Image recognition has made huge strides in the past years thanks to advancements in machine learning. Whereas programmers traditionally had to search for specific combination of features in the pixel output streams from camera’s – with limited success – we can now simply train the computer by example, getting much better results. Drones for surveying chemical plants, intelligent driving assistance systems and automated inspection systems in the factory are made possible by image recognition.
Industrial applications can also benefit from the use of simulations, for example to train new personnel. Virtual reality (VR) is a computer-generated environment which users can enter using immersive head-mounted displays combined with head and hand tracking sensors. The virtual environment responds to the user’s actions, making it a quick and cost-effective way for industrial companies to train employees but also expand the skill set of the existing workforce.
Augemented reality is a variant of VR that mixes a simulation with a physical environment. Using augmented reality (AR) text, graphics, audio and other virtual enhancements can be integrated in the real production environment. Using a head-mounted display, AR overlaps the user’s environment with useful information, helping him or her to master new processes. For example, in virtual guided assembly, 3D images of virtual objects are integrated into a real-world workspace, to provide workers with the means to follow correct assembly procedures and reduce errors.
A digital twin is a complete, fully functional, virtual representation of a product, machine or production line. It enables users to interact with a life-like facsimile of a cyber-physical system that exhibits exactly the same behaviour as the real thing. Unlike a simulation, it runs the actual application software that will drive its real-world counterpart. Using a digital twin enables the application software to be tested as if in real-world conditions, without these conditions being available. This improves the efficiency of the engineering process and shortens the time from idea to realization.
Model-driven engineering (MDE) is a way to develop software by automatically generating computer programs from models instead of writing them by hand. It focuses on capturing at an abstract level all the topics related to a specific problem domain, rather than the computing concepts. By hiding unnecessary detail the approach keeps complexity manageable and makes understanding of and communication about the system simpler and less error prone. Model driven engineering also enables software systems to be realised on a “Correct by Construction” basis since models can be subjected to rigourous analytical verification and can also be validated against system requirements, reducing the chances for mistakes even further. MDE thus greatly increases productivity and product quality.
Continuous integration (CI) is the practice of driving development teams to make small changes to their software and integrate the changes into a shared repository several times a day. Each check-in is then automatically verified. This ensures that new additions don’t break the functioning work that came before them, and developers who do in fact break something can be notified immediately. By integrating regularly, errors can be detected quickly and located more easily and new features can be released more frequently. CI leads to better collaboration, improves software quality and speeds up innovation.
DevOps is a way of creating software that brings together development (Dev) and operational IT (Ops) through alignment of their philosophies, practices and tools. By building a culture of collaboration between these historically disjunct teams it aims to shorten the development life cycle while delivering features, fixes and updates frequently in close alignment with business objectives. The promised benefits include increased trust, faster software releases, ability to solve critical issues quickly and better manage unplanned work. DevOps helps both development and operations to be more efficient, innovate faster and deliver higher value to businesses and customers.
Servitization is the shift in business model from selling products to selling an integrated product and service offering that delivers value in use. Instead of being perceived as an expense, services are viewed as an opportunity to generate more revenue by better serving the customer. This involves the innovation of organisational capabilities and processes to better create mutual value. The provision of product-centric services provides a main differentiating factor in the marketplace. Servitization brings higher business margins and pushes performance in customer intimacy, operational excellence and product leadership.
Blockchain in essence is a way to reach agreements among several participants without relying on a central broker, judge or maintainer. This can be as simple as agreeing to store a bit of information or as complex as agreeing to execute some computer code when some conditions are met (a smart contract). Or, as with Bitcoin, agreeing to transfer some tokens. Each new agreement is added to a public database of which everyone shares a copy, cryptographically linked with what’s already there and validated in a cheat-proof way by the other participants. Once it’s part of this ever growing chain, a change in even one bit will stick out like a sore thumb, making fraud impossible. Information is strongly encrypted though, so even though the database is public, the information in it is not. Although best known for cryptocurrency applications, blockchain holds promises for areas such as logistics and finance.
Companies that master the challenge of innovating in software will win the digitalisation race and establish an unassailable competitive advantage. The High Tech Software Cluster helps industrial companies to master digitalisation and realise smart products, services and business models by using software technology to drive innovation in their business.