![]() The experiment shows results for running a stochastic model or a model with stochastically varied inputs sampled from certain distributions. The results of these runs could be displayed on one diagram illustrating different behavior with a certain subset of parameter values.Ĭompare runs experiment is similar to parameter variation but instead of input values automatically changing according to a predefined algorithm, a user can interactively control the inputs and ultimately compare results.įor level 3 situations, AnyLogic offers a Monte Carlo simulation experiment. The variation is performed automatically and includes several single model runs. Parameter variation experiment runs a model with different parameters and analyzes how they affect the model behavior. Sensitivity analysis runs a simulation model multiple times varying one of the parameters and shows how the simulation output depends on it.Įxample of the sensitivity analysis results For situations falling into the level 2 category, there are sensitivity analysis, parameter variation, and compare runs experiments. This helps see the impact of business decisions before implementing them in the real world.įor various levels of uncertainty, there are experiments available in AnyLogic. With simulation modeling you can handle time and causal dependencies, explaining why things happen. ![]() Simulation-based experiments that help with uncertainty Most real-life scenarios that are dynamic in nature usually belong to level 2 or 3 and can be addressed with simulation modeling. These uncertainties may interact in ways so unpredictable that no plausible range of scenarios can be found. The company will confront several uncertainties concerning technology, demand, and relations between hardware and content providers. McKinsey gives an example of a telecommunications company deciding where and how to compete in the emerging consumer multimedia market. Situations that fall into this category are rare and they tend to migrate to levels 2 or 3 over time. In contrast to level 3 scenarios, it’s impossible to identify a range of potential outcomes, let alone scenarios within a range. Multiple dimensions of uncertainty interact to create an environment that is virtually impossible to predict. When modeling a café, a simulation engineer would need to take these ranges of variables into account. To identify a range of potential futures, there are a limited number of key variables, and the outcome may lie somewhere within that range.įor instance, a café owner knows from their observations that the first guests usually arrive at any time from 8:30 to 10 am and there could be any number of customers from 1 to 5 entering at the same time. With a simulation model, managers can run various what-if scenarios to test and analyze how the modeled system would perform and assess possible risks. Many businesses facing major regulatory or legislative change confront this level of uncertainty. The future is one of a few alternative discrete scenarios, but you’re unsure which of them will eventually happen. A clear enough futureĪt this level, the environment is so stable and slow-changing that a simple forecast of the future could be precise enough for strategy development. The uncertainty that remains after the best possible analysis undertaken falls into one of the four categories below.įour levels of uncertainty. Identifying the right level of uncertainty can help managers and consultants develop actionable strategies that protect a company against threats. But uncertainty itself can have different levels. Many managers are bound to make decisions under uncertainty – acting based on often imperfect observations and with unknown outcomes. Read more: How to set randomness in your simulation model.
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