Using the Right Tools to Address Complexity
Limitations Dealing with Complexity
The world is getting more and more complex, but our ability to deal with it is limited.
We are constantly being inundated with much more information than our human brain was designed to be capable of handling, so we are inclined to look for easier solutions by removing or simply ignoring the implications of our actions that are not directly attributable to our initial goal. Add in a tendency to consider only the short-term effects of our actions to the equation, and you have a recipe for disaster.
Defining Complexity
But let's look at this step-by-step: what does complexity mean and why do traditional tools offer insufficient support in finding appropriate solutions?
A system can be defined as complex when it features these three main characteristics:
- Multiplicity – there are many players who act and make decisions about the system;
- Interdependence – these elements (players) are linked to each other;
- Diversity – these elements differ in terms of behavior, goals and decisions.
The greater the number of elements to consider, the greater their variety and the more interdependent they are, the more complex the problem is.
Contextualizing Complexity
What does this mean? Well, it means that the decisions you have to make and the solutions you are looking for must be contextualized. In other words, having a clear idea of the goal to be achieved is not enough; you need to make your decisions based on the context in which you are operating.
Basically, when dealing with complexity, you can succeed only if you:
- Take the impact of possible external causes into account;
- Understand how your actions will change the system status – the so-called feedback effects.
Let’s use the business enterprise to demonstrate the need for contextualization when dealing with complexity. The context includes the supply chain: a network (multiplicity) aimed at distributing products and/or services from the supplier to the final customer (diversity) through an organized flow of materials, information and money (interdependence) in a continuous effort to balance needs and available resources.
Manual Tools vs. Complexity
Now, think about the way your organization makes decisions, plans investments, defines actions to improve production and distribution or identifies the correct inventory policy. Which tools are used? I'm sure most of you have thought about or use spreadsheets for this purpose. Don't get me wrong, I’m not against Excel, but I don’t think it’s suitable to represent the inner complexity of each supply chain.
We reflect our own mental pattern in the "Excel sheet," but since it’s not possible to replicate the system dynamics, we are tempted to simplify the problem, isolate it, and omit feedbacks, time delays and accumulations. As a result, we end up ignoring the complexity of the specific problem and its context.
Simulation Tools vs. Complexity
Much more powerful simulation tools that allow you to model a system from an experimental point of view have already been available for several years. By replicating the functioning of different processes linked together, they capture the system dynamics and show the system’s evolution in virtual time.
For example, simulation models have been used to study the supply chain phenomenon known as the “Bullwhip Effect,” defined as the demand distortion that travels upstream in the supply chain from the retailer through to the wholesaler and manufacturer due to the variance of orders which may be larger than that of sales. Today, the same concepts can be found in advanced supply chain management courses focused on teaching participants how to optimize supply chain management using simulation as well as how collaboration across networks can be fundamental for business success.
When dealing with complex problems, Simulation Models are definitely the most reliable and low-cost way to test various possible decisions and evaluate their effects (even in the long-term).
The Digital Twin vs. Complexity
Over the years, our team has helped leading organizations and their managers create simulation models that were able to help them correctly assess the impact of their strategic decisions on the entire corporate ecosystem.
Now, we believe it is the right time to build from these experiences and create tools that also help to constantly monitor process performance and take timely corrective actions by evaluating the impact they will have over time “offline.”
A detailed simulation model with these capabilities is called a Digital Twin, a term that emphasizes the close connection between the digital solution and the physical asset to which it refers. Thanks to new technologies, the data collected on the field feeds the Digital Twin (almost) in real time, allowing users to obtain an up-to-date picture of the system’s actual operating status, which can then be used to test different decision-making scenarios in a risk-free environment.
The concept of Digital Twin was originally formed in 2002 at the University of Michigan in the context of Product Lifecycle Management (PLM). Though the terminology has changed over time, the concept has remained fairly stable. It is based on the idea that a physical system can be digitally recreated through its information and logics.
The two (physical and digital) systems remain tightly coupled throughout the entire lifecycle and evolve together. The information processed by the digital copy would be a "twin" of the information that was embedded within the physical system. At its optimum, any information that could be obtained from the study of a physical asset or actual process should be able to be obtained from its Digital Twin.
Therefore, the Digital Twin is defined as a set of virtual information that fully describes a physical product, system or process and enables to evaluate its current and future status or operation. Regardless of its abstraction level, the Digital Twin is a near-real-time digital image of a physical system that helps facilitate the monitoring and optimization of business performance.
According to analysts, the use of Digital Twins is becoming more and more common. By the end of 2022, more than 75% of companies deploying the Industrial Internet of Things (IIoT), for example, will have at least one Digital Twin installed in production. Let me be clear – such a rapid growth in the adoption of this technology cannot be attributed to intense marketing and training activities carried out by vendors alone. The fact is that Digital Twins have proven to generate real business value and have become part of the digital strategies of today’s leading and most innovative industrial organizations. Where does your organization stand?
