The system collects data, creates its own analytical models, makes decisions and optimizes the factory. This is why Industry 4.0 is also called the “Smart Factory.”
Let’s see how this could work.
Our sample company – which is based on a real-world scenario – produces a grain-based fuel in bulk. It also produces various co-products, some of which are higher margin than the primary products. Interestingly, profits can be maximized when primary product yield is slightly suboptimal.
Humans can’t effectively optimize production for profit. The mathematical models and degree of process control micromanagement make it impractical, if not impossible. Optimization would demand almost continuous adjustments based on weather conditions, equipment processing parameters, water quality, forecasted demand, raw material quality, finished good and co-product quality requirements and supply costs, among other things.
It’s this type of scenario where Industry 4.0 systems would excel. A self-guiding system could quickly analyze the inputs and vary production parameters to optimize profits.
The history of Industry 4.0
Industry 4.0 was conceived as part of a German government strategic initiative to computerize manufacturing with the goal of protecting the country’s position as a manufacturing powerhouse.
In April of 2011, a working group presented a draft white paper with the thesis that Germany can continue to thrive as a manufacturing hub, notwithstanding its high-wage economy. The authors proposed a framework based on smart, cyber-physical systems that connect equipment, software and people. The final report was presented at Hannover Messe in April of 2013.
Our process-manufacturing scenario is what the framers of Industry 4.0 had in mind – intelligent product customization in mass production environments, where the customizations are delivered by self-optimizing systems.
The urgency for Canadian manufacturers
For many Canadian manufacturers, the question isn’t whether they should transform. It’s whether they can survive without implementing Industry 4.0 systems.
Canada is a relatively high-wage economy. Apart from some recent (and hopefully temporary) trade frictions with our neighbours to the south, global trade barriers are being knocked down, providing foreign competitors with easier access to our markets. And, to further press the issue, global trading partners who used to compete on cost alone are now competing on cost, value and service dimensions.
China, for example, recognized that its window to continue building an economy on the back of cheap, low-quality goods was closing. In 2015, China released a strategic plan – Made in China 2025 – aimed at upgrading strategically important industries. It also targets Industry 4.0 sectors, including: robotics, AI, IoT, smart appliances and machine learning.
According to Oxford Economics and BCG research, China accounted for 27 per cent of the world’s value-added manufacturing output at the end of 2017, which was 1.7 times more than the U.S. and 4.4 times more than Germany. According to that same research, China’s exports have increased dramatically, even while its labour cost base has increased.
To remain competitive, Canadian manufacturing companies need to invest in next-generation industrial and business models to protect and expand their market share.
The four pillars of Industry 4.0
Industry 4.0 is that next-generation framework. If we reflect on the evolution of business technologies, the central theme has always been to automate and integrate processes with a view to eliminating transactional and decision-making frictions.
MRP (materials requirements planning) sought to remove frictions between sales and operations by time-phasing purchase and manufacturing material requirements with demand for those resources. ERP (enterprise resource planning) came next. This innovation removed finance and accounting frictions by integrating the operational transactions to general ledger, accounts payable and accounts receivable modules.
Today, we’re seeing previously siloed manufacturing control and execution systems being integrated with enterprise software systems – systems that drive production, equipment maintenance, quality and inventory processes.
What makes these integrated systems “Industry 4.0” is the layering of artificial intelligence that has autonomous capability to learn from data, make decisions and optimize transactional and execution system processing.
When designing your organization’s Industry 4.0 environment, you need to consider four overlapping principles, according to the authors of Design Principles for Industrie 4.0 Scenarios:
- Interconnection. The systems need to connect people, machines, sensors, devices and software through the Internet of Things (IoT) and allow them to communicate with one another.
- Information transparency. The data collected through interconnection needs to be made available to operators for decision-making.
- Technical assistance. The intent is twofold: 1) to shift low-value tasks from people to cyber-physical systems, and 2) for systems to provide people with information to make timely and effective decisions.
- Decentralized decisions. The systems need to be able to make their own decisions and take autonomous action.
And, perhaps most importantly, the system needs the capability to decide and act autonomously. For example, when the system’s artificial intelligence “brain” discovers a higher profit mix of primary and co-product, it needs to be able to automatically adjust production.
Getting your organization ready for Industry 4.0
It’s against this backdrop of urgency for Canadian manufacturers that my firm Pemeco Consulting has partnered with Manufacturing AUTOMATION. We’re collaborating on a multimedia content hub including columns and webinars that will be filled with best practices and templates that you can use to plan and execute your Industry 4.0 transformation plan. We’ll cover:
- The basics. You’ll learn about the building blocks, and how they piece together, including: IIoT, AI, enterprise software and control systems.
- Planning. You’ll gain access to templates that you can use to map and budget for changes to your organizational structure, information architecture and business processes.
- Implementation. This scale of transformation is disruptive and risky. You’ll see guides that cover change and project management, data governance, testing and training.
- Risk management. Privacy, cybersecurity and automation-related job losses are but a few of the big-time challenges and risks. We’ll help you to address the issues and mitigate the risks.
Jonathan Gross is the managing director at Pemeco Consulting. He helps his clients architect and implement technology environments that integrate ERP with the edge.
This article originally appeared in the March/April 2019 issue of Manufacturing AUTOMATION. Read the digital edition.