The Role of Simulation in Informed Decision-Making

In today’s demanding world where there is little tolerance for error, there are decisions that need to be made daily for complex systems with often unpredictable outcomes. Behind these decisions are simulations that play a vital role in engineering across many different industries and organizations.

Simulations are useful imitations of real-world processes, systems, or parts of systems in their environment over time. The models used for simulations can provide insight into the behavior of systems under different parameters and conditions, which organizations can use to better visualize outcomes, guard against risks, and optimize processes before any new implementations are carried out.

In this blog, we will explore the role of simulation in informed decision-making, specifically its ability to reduce risk, improve efficiency, and enable optimization. 

Simulation

According to the International Council of Systems Engineering's (INCOSE) definition, simulations are “the process of using a model to predict and study the behavior or performance of [the system of interest]” (Walden, 2023, pp. 192-193).

In real-world applications, simulations are a powerful tool that uses mathematical models to create a digital representation of a system, from which scenarios can be tested to determine their potential outcomes. Because of these abilities, simulations are of particular value to organizations in industries such as finance, manufacturing, and healthcare.

In addition, as growing systems become more interdependent and multifaceted in the modern era, simulation plays an increasingly important role in helping organizations enhance their understanding of their system processes. This understanding can allow system stakeholders to visualize the implications of their decisions in a controlled, risk-free setting, which helps to facilitate identifying patterns and relationships.

Benefits

According to a blog by business consulting firm BTS, simulations provide several specific benefits: reducing risk, reducing cost, and improving productivity (Why Tomorrow's Top Leaders Are Relying on Business Simulation for Decision-making, 2024). 

Risk Reduction

By simulating processes, organizations can identify potential issues with the system and also in the decision-making process.

For example, in the manufacturing sector, engineers can identify production bottlenecks and consider critical points of failure in manufacturing processes. Recognizing these issues before they actually happen enables organizations to proactively approach risk, providing opportunities for resilience and redundancy in their operations.

Cost and Time Efficiency

Virtual simulation scenarios can save time and resources compared to traditional real-world experiments. Physical experiments often require significant financial investments, testing facilities, and long timelines to organize and deliver results. In comparison, digital simulations allow engineers to explore multiple scenarios quickly and affordably.

An example is airframe design, where simulations can estimate the impacts of air drag on wing structures, minimizing the need for physical models each time. Furthermore, simulations can even enable rapid testing and what-if analyses. In a design-of-experiments study, engineers can experiment with different variables, adjust parameters, and deliver results quickly for use in the decision-making process.

Optimization & Productivity

In the same line of thought from the previous section, testing multiple variables can help organizations optimize their strategies.

Simulations can incorporate various conditions, providing insights into optimal solutions in various complex problems and environments. For instance, in logistics, companies can run simulations to optimize routing and scheduling, driving benefits such as reduced transportation costs and efficient delivery schedules.

Simulation Types

Understanding the different types of simulations is crucial for effectively applying the benefits of simulation in decision-making. Several types of simulation are particularly useful, namely Monte Carlo, Discrete Event, and System Dynamics. 

Monte Carlo

Monte Carlo simulations are often used for probabilistic analyses in complex systems. These simulations are executed by generating random samples to simulate a range of possible outcomes. The results provide insight into the likelihood of various scenarios.

Discrete Event

Discrete Event simulations are good for visualizing processes in systems. These simulations are executed by modeling system operations as a sequence of events, which can also represent the different changes in the states of the system. The results provide insight into how outcomes are linked to the systems and subsystems that interact with each other over time.

System Dynamics

System dynamics are used to understand feedback mechanisms, loops, and delays within systems. These simulations rely on polynomial equations to describe systems, which attempt to model the complex interactions and reveal any unintended or unexpected consequences. The results provide insights into the long-term impacts of changes to complex systems.

Practical Uses of Simulation

Simulations are very versatile, which is evident due to their usage in countless applications across various industries.

In healthcare, system dynamics can be used to model population dynamics such as disease, infection, and growth, which can help government agencies predict the impacts of infectious pathogens.

In logistics and transportation, discrete event simulations can be used to model deliveries and passenger throughput, which can help airlines find the impacts of late arrivals and departures.

In finance, Monte Carlo simulations can be used to quantify risk assessments and guide portfolio management, helping investors manage volatile markets. 

SPEC Innovation’s Sopatra

SPEC Innovations developed Sopatra, a tool that can help facilitate simulations. Augmenting our original flagship tool, Innoslate, Sopatra can quickly import and digest your existing standard operating procedures (SOPs) and convert them into verifiable Action Diagrams.

Sopatra’s Action Diagrams are executable and customizable, capable of running both Monte Carlo and Discrete Event simulators. Sopatra can also compute metrics from SOPs such as Procedure Buffer Time (PBT), Allowable Operating Time Window, and Probability of Failure to Complete.

With all these features, Sopatra is a sophisticated tool that allows organizations to efficiently visualize and optimize their processes for informed decision-making.

Simulation plays a critical role by enabling better informed and confident decision-making across many different domains and industries. They help us to better understand complex systems, reduce risks, enable efficient use of resources, and provide opportunities for optimization.

As organizations increasingly rely on data-driven approaches, reliance on simulation and digital models will only grow. Embracing the power of simulation will be critical to achieving success in the foreseeable future.

Sources

Walden, D. (2023). Modeling, Analysis, and Simulation. In Systems Engineering Handbook (pp. 192–193). Wiley.

Why Tomorrow’s top leaders are relying on business simulation for decision making. BTS. (2024, January 31). https://bts.com/insights/why-tomorrows-top-leaders-are-relying-on-business-simulation-for-decision-making/#:~:text=Simulations%20are%20a%20great%20tool,collaboration%2C%20and%20overcome%20communication%20barriers.