Technology Adoption

Additive manufacturing is a general description for technologies that build by adding layer upon layer of material to make a finished piece; whether that material is plastic, metal, or concrete. It includes a number of constituent technologies, (predominantly additive which include metallic and non-metallic additive manufacturing systems). Additive manufacturing is rapidly being introduced in sectors like aerospace and medical, although its adoption is quickly spreading across the board. Additive manufacturing is considered to be a “clean technology” as it typically requires less energy for manufacturing and generates far less manufacturing scrap. Additive manufacturing can allow SMEs to integrate with advanced manufacturing operations in Canada and abroad.

Next-generation ERP/MRP systems are being used to control all aspects of production, optimizing value chain operations within vertically-integrated manufacturers and offering opportunities for broader supplier horizontal integration. The vast potential here lies in fully-integrated supply chains, seamlessly linking internal and external suppliers in real time. Every manufacturing operation requires inputs. Equipment to generate products must accommodate resource availability, machine/tool availability and wear, as well as meet the rapidly changing needs of customers. ERP/MRP systems are linked directly to the factory floor in a cybersecure manner with the customer; whether supplier, integrator or end-user. ERP/MRP systems are the central processing unit of the factory ensuring that customer demands are met with quality products on time and within budget.

Unlike their predecessors, the robotics and automation of Industry 4.0 are autonomous or semi-autonomous with dispersed but centrally-monitored decision making. A common misconception is that automation reduces job opportunities for humans. Given the current aging workforce demographics, smarter machines will enable industry to meet current business production challenges, and grow their businesses with fewer available people. Robots, cobots (computer-controlled robots designed to work alongside humans) and other next-generation integrated systems will certainly change the workplace in a positive manner. People will have more stimulating jobs and have a more direct role in driving efficiency, reducing downtime, while improving safety and productivity. The needs of tomorrow’s 24/7 smart factories are already beginning to dramatically change the manufacturing ecosystem, as manufacturing becomes once again a desirable career choice.

Augmented Reality (AR) is impacting all aspects of modern manufacturing from safety to quality assurance, design feasibility assessment to remote asset performance management. While virtual reality immerses the operator in an entirely virtual environment, augmented reality immerses the operator in an environment that may include virtual as well as real life assets and features. For instance, within an augmented reality environment, a technical expert on one continent can help troubleshoot a system on another continent where a more junior and/or inexperienced worker is performing maintenance that is being guided by digital manuals as well as the remote expert. This example also illustrates how safety, just-in-time, and other training can be supported through AR constructs.

Imagine the ability to simulate an entire value chain, then improve it with real-time process optimization. As with so many other Industry 4.0 concepts, simulation and modelling applies to every phase of a product life cycle, from initial design and test, through manufacturing, to retirement of the product, and environmental remediation. One example that demonstrates the depth of Industry 4.0 simulation and modelling can be illustrated in the digital twin. A digital twin is a computational simulation of a manufacturing system or component, and, in a digital factory all mechanical components will be simulated and work alongside their physical twins and human partners. The performance of the digital twin can be continuously compared with the performance of the physical asset in order to assess and manage deviations:, for instance, a tool bit changed when its performance is degraded. Likewise, when the production line needs to change from one component to another, all involved production elements can be managed to optimize the production process.

Most people are now somewhat familiar with the concept of cloud computing, the storage of data on remote servers to which different levels of access can be granted to various users. This enables open systems management of large volumes of data. The challenge is simply the quantity of data that needs to be moved and stored, and the speed with which data communications can be achieved. In a similar manner, Fog computing refers to decentralized data storage that while distributed, is typically not as widely distributed nor as accessible as in the Cloud. Fog computing involves the manipulation and storage of vast quantities of sensor data closer to the sensor, such that the data that is moved beyond the fog is of somewhat more manageable size. Essentially, application data is reduced and stored within Fog nodes which then can transfer data packets to Cloud nodes as needed.

Although differentiated somewhat by end-purpose, the internet of things (IoT), and the industrial internet of things (IIOT) refer to a network of sensors, machines, systems and products which capture, manipulate and store large datasets that are used in design, design validation, manufacture, and asset performance management phases of a products life cycle. By some estimates there are approximately 20 billion devices currently connected to the internet and by 2020 there will be an additional 4 billion. Add up the numbers, factor in the many sensors that are used on each device and the issue of big data becomes self-evident.

As indicated in the IoT/IIoT, the massive number of devices connect to the internet, and a host of intranets, an incredible amount of data is being generated, stored and used to manage manufacturing processes, identify end-user requirements and product performance in-use. The study and use of this data offers an opportunity to manufacturers, their value chains, and, users of their product for real-time cyber physical integrated decision making. The sheer volume of the data, however, makes the human identification and analysis of trends and information all but impossible. Machine learning is a branch of artificial intelligence that is devoted to enabling machines to autonomously capture and analyse data, identify trends and make recommendations for prioritized decisions concerning all aspects of a product’s design and use.

With some many systems integrated and online, cybersecurity is fundamental to all Industry 4.0 technologies. IT and engineering competencies will take drivers seat in the factory of the future where job requirements will increasingly appear more related to information and communication technologies than mechanical manufacturing. For all of the advantages noted in the previous technologies identified, there are corresponding security issues. Production lines can be disrupted intentionally or accidentally by value chain elements or competitors. Cybersecurity is a pervasive requirement for which there are numerous approaches, but as experience has shown an impermeable cyber shield is extremely difficult to achieve.