When we think about “Smart Plants” we have an image of an interconnected network of smart devices, capable of sensing the environment, understanding and predicting trends and responding intelligently.
Analysts often describe the “Smart Plant” in terms of the enabling technology – for example, the IIoT (Industrial Internet of Things), industrial wi-fi / 5G, artificial intelligence, predictive analytics, cloud computing and more. It is probably fair to say that the technology choices for embedding “intelligence” into a manufacturing plant are still far ahead of the number of real-world practical applications, at least for now.
As engineers and technicians, we tend to analyse systems by breaking them down into their constituent parts. In this way, we believe that to simplify complex systems all we need is to understand each component’s function. While this type of analysis can be useful, other techniques can result in better insights, particularly when the components are interconnected as in a smart plant. In this article, I would like to very briefly introduce systems thinking and explain why it is a valuable technique to understand smart plants.
Systems thinking is not a new concept. It has been applied to the understanding of complex interrelated biological systems for many years. It also happens to be ideal for understanding complex, interrelated systems, such as a manufacturing company. Systems thinking sees all things as interconnected at some level and that it is through this interconnectedness, something more significant emerges.
A systems thinking mindset will avoid breaking down complexity into individual components. Instead, it considers the system as a whole, as a complex network of many interconnected elements. For example, a manufacturing plant is not something that operates in isolation. It is but one part of the value chain of a business, which is, in turn, a part of the wider supply chain network, which in turn forms part of a manufacturing cluster or ecosystem and ultimately operates within the economy as a whole. Each piece of the network has a role to play and is connected to the other parts. While each piece has its objective, the system as a whole itself also has an objective.
Some of the terminology used in systems thinking is important. Within a system, the individual parts combine to “synthesise” something new. Thus, an interconnected network’s effect is to create something more significant than the sum of the individual components. The result of this synthesis is the “emergence” of new outcomes. In our example, the network of sensors on the plant will synthesise information that will allow the plant to respond to changes from the supply chain network. More customised products might emerge from this system.
The interconnected nature of a system means that there are also dependencies between the parts and the information flows between them, creating “feedback loops”. These feedback loops have the result of either correcting and balancing the system or reinforcing something desirable. The feedback loops could be local within intelligent devices or software, or in the decisions of a controller in the control room. Or they could be across the supply chain such as demand planning and production scheduling. The coexistence of human and machine elements in these feedback loops are an important consideration.
Finally, “causality” in systems thinking is understanding how the individual things in a system influence each other. Understanding this cause-and-effect relationship will allow a deeper understanding of the overall dependencies and which feedback loops matter most within a system.
Systems thinking is an excellent conceptual tool for understanding how the smart plant will work in the future. It also provides a framework for viewing the physical plant as part of its network’s broader activity. When you view a business as a system, it becomes possible to analyse how the individual plant and business sub-systems relate to the overall objective. For example, how the enterprise resource planning (ERP), customer relationship management (CRM), supply chain management (SCM) relate to the plant systems (MES/MOM), process control systems, edge computing devices and smart sensors.
Systems mapping is a technique for identifying and mapping the things within a system to understand how they interconnect and how they influence the greater system behaviour. Many of the traditional tools we use (for example, business process mapping) often do not adequately describe how each part of the process interacts with the system as a whole. This is perhaps why some ERP implementations have a reputation of often creating rigid business processes that inhibit rather than enable the operations.
As process and instrument engineers, we love to think in hierarchies. Many process control models are described in layers, e.g. – the bottom layer is the field level/device layer, above that direct control, then supervisory control, then production control, and finally at the top, scheduling. I still believe that hierarchies play an important role in analysing and breaking down complex manufacturing systems. However, I would also motivate that in the future, a new set of tools and models will become just as, if not more, important. Systems thinking introduces a number of useful concepts, such as trending, causal loop diagrams, connected circles and more. It might be worth reading up on the subject if these are new concepts to you.
Many of us have suffered “death by Industry 4” over the past few years. The hype has been amplified by vendors offering solutions, “digital transformation” consultants, industry analysts and others. However, underpinning many smart manufacturing concepts (and the intelligent plant) are some fundamental shifts from linear systems towards interconnected networks. Systems thinking can provide the necessary techniques for understanding why these shifts are significant and give insight as to how to incorporate the power of interconnected systems in your business.
