Using simulation forecasting in business analytics

What is simulation forecasting?

Simulation forecasting uses simulation modeling to predict how different scenarios might unfold. It recreates a system's dynamics to evaluate how input changes affect outcomes in a controlled environment. This shapes predictions and guides decision-making.

Traditional forecasting methods rely on statistical analysis or predictive analytics to anticipate possible outcomes based on historical and current data. These techniques extrapolate known patterns to forecast what is likely to happen under similar circumstances.

That might sound similar to simulation forecasting, and in many ways, it is. Both approaches involve modeling potential futures, but the key difference is that simulation forecasting can use both real and synthetic data, not just historical records.

In practice, simulation modeling enables businesses to test scenarios with a low probability of happening in reality, or no historical precedent at all. This includes extreme events or novel combinations of factors, offering businesses a way to forecast beyond what past data alone could provide.

Simulation forecasting enables businesses to explore predictions in a safe, risk-free environment.

In the context of business analytics, both methods have unique roles to play. Predictive analytics anticipates trends based on past data, allowing for more accurate predictions and more proactive decision-making. Simulation forecasting enables organizations to understand broader potential possibilities to build more resilient plans in the face of uncertainty.

The core techniques of simulation forecasting

Users can approach simulation forecasting in various ways depending on their goals, but the process generally follows a few main steps.

First, identify the goal of the simulation forecast. Defining the purpose will narrow down the models and techniques to use, the tools or technology required and the system dynamics and outcomes to be modeled.

Next, construct the model. This involves creating an abstract representation of the system to be simulated -- often a business process or operation -- along with specifications, conditions and limitations it will adhere to when simulated. Simulation models can take several forms, but there are three main types:

• Static models. Change at discrete points in time.
• Dynamic models. Change continuously over time.
• Hybrid models. Combine both static and dynamic variables to represent complex systems.

Then comes model implementation, where the simulation is executed. The model can be run using specialized software and tools with various inputs. These inputs might represent different scenarios and conditions that allow observing the model's behavior under specified forecasts.

Finally, analyze the observed outcomes. This stage focuses on interpreting how the model behaved during simulation -- how it behaved over time or under certain conditions -- and what outcomes the forecasting produced. This analysis can reveal risks and opportunities that inform decision-making.

Executing simulation forecasting requires a broad set of technical and analytical skills. Statistical analysis is arguably the most important, since it is essential for building, running and interpreting simulation models. All simulation modeling and forecasting uses statistical analysis and mathematical interpretation. Other important skills include data preprocessing, model selection and evaluation, model optimization, simulation integration, validation and monitoring and predictive analytics.

Simulation model types used in business forecasting

Simulation forecasting relies on various model types. Choosing the right model depends on the system being simulated, the type of data available and the specific outcomes the organization wants to analyze:

• Discrete-event models. This model aims to simulate a system over time, where changes occur at specific points over the simulation. It is often used in time series forecasting, where the goal is to predict future values at regular intervals based on discrete-time data.
• Agent-based modeling. It simulates the behavior of an individual actor, or agent, within a system. An agent could be a person, a piece of equipment or anything else that can act within a system. Each agent operates based on a ruleset, and their interactions produce emergent behaviors and interactions that can inform decision-making. This model is often used to predict market crashes, changes in supply and demand or how customer sentiment will change based on unique scenarios.
• Continuous simulation. It models systems that change the state of a system over time. These models change the state variables continuously, unlike discrete-event models that change the state variables at different points in time. For example, this model can show how revenue shifts over time in response to gradually changing inputs such as demand or pricing.
• System dynamics. It is a subset of continuous simulation that focuses on how the specific elements of a system interact over time. The results are often visually represented using stock and flow diagrams to visualize how changes in one area, such as sales volume, affect another, such as inventory depletion. Understanding the rates of change in this model can help forecast stock and flow and form more effective business strategies.
• Monte Carlo simulations. This model uses probability distributions and random sampling to model uncertainty and risk analysis. It is based on roulette, which was popularized in Monte Carlo casinos. It's often used in finance, manufacturing and risk analysis to identify potential failure points or quantify the likelihood of various scenarios.
• Hybrid simulations. As the name suggests, hybrid simulations combine other models and simulation techniques to better represent a system and its variables. For example, a hybrid model might comprise static and dynamic variables or discrete and continuous elements. This is usually done to simulate large-scale systems with diverse processes that are more complex than singular models can represent alone.

Synthetic vs. real data in simulation model construction

When building simulation models, it's important to understand the differences between using synthetic and real data.

Synthetic data is artificially generated data designed to replicate the structure and statistical properties of real data. It's often extrapolated from historical data sets or through generative algorithms and can be used to make a copy of real data that separates out sensitive details. For example, a synthetic data set might generalize personal information in a financial data set to protect against privacy and security risks. Synthetic data can introduce inaccuracies or anomalies since the data is generated rather than collected from reality.

Real data is sourced from actual historical data or current data sets. Its authenticity reflects real-world behavior and events, making it valuable for producing insights that align closely with observable patterns. However, it also poses challenges, including privacy concerns, regulatory risks and data quality issues from data collection. There are also cases where real data might not be available, such as simulating an event that has not happened or happens rarely.

Constructing a model for simulation forecasting requires understanding these advantages and limitations before deciding what data to use, as the type will affect the outcomes, reliability and any insights derived.

Benefits of simulation forecasting

• Improved foresight. Simulation forecasting enables businesses to explore predictions in a safe, risk-free environment isolated from the real world. It allows for the testing of complex systems and hypothetical scenarios to understand how changes in variables affect expenses. This foresight makes addressing what-if scenarios easier and account for more uncertain factors, which informs stronger decision-making.
• Greater adaptability. Companies can identify flaws early and adjust by testing ideas before implementing them. Since forecasting models are simulated, changes can be tested and implemented faster without real-world consequences. This gives businesses more flexibility, agility and adaptability to respond to rapid changes in customer needs or market conditions.
• Stronger strategic planning. Businesses can use simulation forecasting to plan for various short- and long-term scenarios by identifying trends and predicting behaviors over time. This makes it easier to create plans for everything, including inventory management and production scheduling. Businesses can better understand future possibilities and adjust their planning by studying the relationships between interrelated variables and how dependencies may affect decisions.
• Cost and resource optimization. These advantages contribute to cost and resource improvements. Rather than making uninformed decisions, organizations can simulate multiple paths before investing in one. This reduces wasted resources by experimenting with possible decisions in a safe, controlled environment to prevent the need for expensive course corrections.

Limitations of simulation forecasting

• Accuracy. Simulation forecasting involves estimating and predicting outcomes based on assumptions about the relationships between variables. As a result, the outputs might not be entirely accurate, and decisions based solely on simulation results should be made with caution. The use of synthetic data may further affect reliability, depending on how closely it reflects real-world conditions.
• Complexity. Developing and applying simulation models can be difficult. Building models often requires advanced mathematical methods, specialized data inputs and custom tooling. Interpreting results adds another layer of difficulty, especially for organizations without the in-house expertise.
• Cost. Simulation forecasting can be expensive to run, particularly at scale. It often requires specialized software and computing equipment to operate and skilled personnel to manage the computations and analyze the results. These costs add up quickly, making simulation less accessible for organizations that don't run them regularly.
• Data requirements. Producing useful insights depends on the quality of data used, the timeliness and completeness of the input data. Simulation models require accurate and comprehensive datasets. Otherwise, limited data will skew results and introduce errors, affecting probabilities and likelihoods. Synthetic data must be generated carefully to ensure it's as close to real data as possible.

Simulation forecasting in real-world use cases

Simulation forecasting supports real-world planning and decision making by modeling risks, outcomes and operational variability.

In finance, simulation forecasting is used to anticipate performance trends and generate what-if scenarios at scale. These simulations guide businesses to refine their financial strategies, adapt to changing patterns and develop contingency plans.

In healthcare, simulation forecasting models public health events such as seasonal infection surges during flu season. Forecasts inform resource planning, staffing levels and equipment stock levels to better manage demand fluctuations.

In the aerospace industry, simulation forecasting is used to predict equipment and component maintenance needs, including timing and costs. It estimates schedule maintenance intervals, scheduling, equipment downtime and equipment replacement or failure expenses.

In manufacturing, simulation forecasting models production variability. For example, Monte Carlo simulations explore different outcomes that can occur across thousands of possible scenarios. Simulating each scenario and determining the likelihood helps businesses develop process improvements, optimize production and minimize risk.

When used effectively, simulation forecasting enhances business analytics by grounding decisions in tested hypothetical trends and future scenarios. This enables organizations to stay one step ahead of the competition while keeping operational costs low. However, to gain the full benefits of the technique, it's essential to understand the limitations of simulation forecasting and the nuances of model construction.

Jacob Roundy is a freelance writer and editor with more than a decade of experience with specializing in a variety of technology topics, such as data centers, business intelligence, AI/ML, climate change and sustainability. His writing focuses on demystifying tech, tracking trends in the industry, and providing practical guidance to IT leaders and administrators.