Industry 4.0 is transforming the world of manufacturing. The convergence of digital, biological and physical innovations are lowering operating costs, increasing flexibility and responsiveness, reducing waste and improving product quality. These technologies are opening new doors for innovative, risk-taking companies and threatening to leave slow adopters behind.




Integrated systems, automated factories, AI and big data, additive manufacturing and so much more. It may seem like the commercialization of these new technologies is years away but the fact is, it’s here now.

In Canada, this high-tech revolution will mean new ways of organizing our businesses and a new, skilled labour pool to run our factories, machines and manage our data and technology infrastructure. Tomorrow’s solutions are needed today.

Canada’s business climate is primed for an industrial revolution. The spirit of competitive collaboration is one of the fundamental advantages of doing business on the prairies. It’s this same spirit that represents the key to the innovation diffusion that will launch a revolution.




Advanced Manufacturing is a generic term that does not have a universally accepted definition. Industrie 4.0, Advanced Digital Manufacturing, and Cyber-physical production Systems (CPPS or CPS) are all terms that are used to describe the next evolution of manufacturing practices. At CME , we use a widely accepted terminology that includes nine key technologies that make up the foundation of Industry 4.0.

This website is not intended to provide an exhaustive description of advanced digital technologies but rather present examples within each that will enable a broader understanding of the world of advanced manufacturing.



CME’s Technology Investment Program aims to help Ontario manufacturers automate production, reduce their environmental footprint, and increase their competitiveness in the global market.

The program provides direct financial assistance to SME manufacturers in southern Ontario in the form of a matching contribution of up to $50,000 (a maximum of 50% of eligible costs, which can include capital equipment, materials, training on new equipment, installation of new equipment, professional service fees, as well as a company’s direct labour devoted to the completion and management of the project) to support the investment in long-term production assets and technology that increase capacity and reduce emissions on production.

Want to learn more about the Technology Investment Program? Click here. 


Investing in new productive assets and technology is critical to keep manufacturers competitive. Over 40% of manufacturers identify uncertainty on the Return on Investment and Cost/Benefit as the main reasons they are not investing in new technology. CME’s Technology Assessment Program will help companies understand which technologies are best suited for their needs and provide greater certainty on investments.
The Technology Assessment Program connects SME Manufacturers in British Columbia with LEAN Facilitators to get customized Technology Assessments.
CME’s LEAN Facilitators will work on-site with your Company to create a customized plan on how to improve your productivity by capitalizing on existing assets and guiding your team in implementation of the latest advanced manufacturing technologies including hardware, software and cloud computing.
Upon completion of the Assessment, the Facilitator will recommend an action plan that will enable your business to make informed decisions to assess, purchase, adopt, implement, train and / or maintain new technologies.
Want to learn more about the Technology Assessment Program for BC? Click here


Working with the Prairies Canada Economic Development Agency (PrairiesCan) and other key partners, the initiative will support individual companies and enhance the manufacturing network through advisory services, benchmarking assessments, and competency development to accelerate the competitiveness of Manitoba’s manufacturing sector with an emphasis on technology readiness assessments and adoption.

Want to learn more? Click here




Facilitated, peer-to-peer learning networks that meet monthly to share best practices, tour facilities and more.


The Smart Manufacturer's Guide to Capital Equipment Purchases


EMBRACING CHANGE: Industry 4.0 and Canada’s Digital Future in Manufacturing


Lean AM Readiness Assessment and Advisory Services available on a region by region basis.



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.