What are some of the best practices and pitfalls to avoid when designing and running a simulation experiment?

Simulations are only as good as your understanding of results and ability to utilize them. Recently I have analyzed outputs of series of simulations where 3 to 5 Regional Centers were connected with dozens of consolidation hubs. Originally this organization leaders wanted to test virtually everything. Without specific goal the analyst dive deep into details producing more than anybody can use. This is an example of money & time spent without any result.

Best Practices for Defining Objectives in Simulation Experiments: Be Specific: Clearly define what you aim to achieve. Align with Goals: Ensure objectives align with business or research goals. Measurable: Set quantifiable metrics for success. Feasibility: Ensure objectives are realistic and achievable. Pitfalls to Avoid: Vague Objectives: Avoid unclear or broad goals. Overcomplication: Don’t set overly complex objectives. Ignoring Constraints: Consider time, resources, and data limitations. Lack of Stakeholder Input: Involve relevant stakeholders early on. #DataScience #Simulation #BestPractices #AI #DataAnalysis #Research #BusinessIntelligence

Designing and running simulation experiments involves careful planning and execution to ensure accuracy, reliability, and meaningful insights. We should follow the below in an order to ensure best outsome and avoid pitfalls : Best Practice : 1) Start by defining clear and specific objectives for the simulation. Understand what insights or outcomes you aim to achieve. 2) Ensure that the assumptions made in building the simulation model align with the real-world scenario being simulated. Pitfalls to Avoid: 1) Failing to validate the simulation model against real-world scenarios can lead to misleading or inaccurate results. 2) Extrapolating simulation results beyond the scope of the model can lead to incorrect conclusions.

A defini??o do propósito é crucial de ser realizada antes da constru??o do modelo de simula??o. Responder à pergunta "que problema queremos resolver com a simula??o" é fundamental para direcionar os esfor?os, escolha de recursos, ferramentas e tecnologias a fim de cumprir com o propósito.

When designing and running a simulation experiment, best practices include clearly defining objectives, ensuring the model's validity through rigorous testing, and carefully selecting appropriate input data. It's crucial to perform sensitivity analysis to understand the impact of varying parameters and to document all assumptions and limitations transparently. Common pitfalls to avoid include overcomplicating the model, neglecting verification and validation steps, relying on poor-quality data, and failing to consider the uncertainty in the results. Regularly reviewing and refining the model based on results and feedback helps maintain accuracy and relevance.

Novas tecnologias, tais como gêmeos digitais, podem em muito colaborar com a simula??o e experimenta??o. Ocorrendo em ambientes cyber físicos, podem com precis?o estimar impactos com menor risco em rela??o à experimenta??o empírica.

1. Discrete-Event Simulation ?? - Best for: Dynamic, stochastic systems with discrete events - Pros: Models individual events and entities - Cons: May not capture feedback loops 2. System Dynamics ?? - Best for: Feedback loops and long-term trends - Pros: Analyzes continuous flows and system behavior over time - Cons: Less suitable for individual-level events 3. Agent-Based Simulation ?? - Best for: Systems with individual decision-makers - Pros: Models interactions between autonomous agents - Cons: Can be computationally intensive 4. Hybrid Simulation ?? - Pros: Flexibility to model different system aspects - Cons: More complex to design and implement

Para el dise?o de prototipos existen multiples aplicaciones que nos permiten en forma virtual crar y probar modelos. Luego, el resultado más optimo lo construimos y vamos ajustando, para finalmente "pulirlo", es decir, adicionarle las mejoras más finas. Muchas veces lo que en realidad virtual parece funcionar en la práctica resultan nuevos retos, sin embargo, el dise?o virtual nos hace ahorrar mucho dinero y tiempo.

This is a complex question and can vary wildly depending on your goals. What I’ve seen in my career in order of complexity is: 1. Monte Carlo Simulations: Good for assessing risks using existing data. Easy to implement but doesn't provide event-specific insights. 2. System Dynamics (SD): Best for strategic planning over time, capturing overall dynamics. Simplifies complex interactions, useful for larger, less detailed views. 3. Discrete Event Simulation:Detailed, perfect for identifying specific bottlenecks and testing scenarios. Requires extensive setup and data. 4. Agent-Based Simulation: Models individual behaviors within the network, adaptable and detailed but computationally intense. Start with the basics and go from there.

Selecting the appropriate simulation method is crucial. Whether it’s discrete-event simulation, Monte Carlo simulation, or agent-based modeling, the method you choose must align with your objectives and the nature of your system. Different methods have different strengths, such as handling randomness or capturing complex interdependencies. A common mistake is choosing a method that’s too simplistic or overly complex for your needs, which can skew results or make the model difficult to manage.

Actually validation and verification should be done by two sets of people: ?? Verification by another analyst or data scientist who has a fresh view on models created in the experiment. ?? Validation by somebody who has direct operational experience in the supply chain that you model. Verification should come first. Otherwise you risk showing to people who should 'buy in' outputs of experiment the uncertainty. Unfortunately, once under pressure I agreed to reverse order. It took me double the time to gain the operational team trust.

In my experience, validation and verification are non-negotiable steps. I’ve seen how an unverified model can lead to misleading conclusions. Therefore, I always invest time in comparing the model’s outputs against real-world data and peer reviews to ensure accuracy and reliability.

Once the model is built, the outcome of the same should be validated with the actual data. Say Iam building a logistical optimization model for an organization, the load factor and the cost output from my model should be inlined with the actual load factor and cost that the organization is incurring. Often such validation is done with the help of the operations teams which works on the ground level and adjustment in the model are made based on the inputs for better results

O nemawashi (constru??o de alinhamento - 'preparar o terreno para plantio') deve ocorrer durante a constru??o do modelo de simula??o. Isso busca trazer agilidade ao processo antecipando a constru??o de consenso e evitar que ao final o modelo seja invalidado.

Ensure that your simulation model accurately represents the real-world system by validating and verifying it. Validation ensures the model is capturing the correct behavior and relationships, while verification checks that the model is running as intended without errors. Best practices include comparing the model's outputs with historical data or running sensitivity analyses. A frequent pitfall is failing to thoroughly validate and verify, leading to inaccurate or unreliable results.

En una oportunidad nos tocó migrar de transportar vidrio a granel en contenedores Open Tops a contenedores HQ. Esto requirió: dise?o del sistema de carga frontal, dise?o de racks, elevadores, descargadores y logística inversa. Fue un proceso complejo que supuso mucha creatividad, inversión financiera y el dise?o de varios prototipos, pero al final, logramos un ahorro anual que superaba los tres millones de dolares, un ahorro logistico de un 40%. La clave: a) Constituir un equipo multidiciplinario. b) Evaluar multiples ideas por loca que parezcan c) Hacer el prototipo de la idea final. Testearlo, ajustarlo y poco a poco masificarlo para aprender del proceso. d) Revaluar el sistema y sus resultados, hacer los procedimientos operativos.

Once your model is validated, carefully design the simulation experiment. Define your variables, set up control groups, and determine the number of runs or replications needed to produce statistically significant results. Best practices include using randomized inputs to cover a range of possible scenarios and creating a detailed plan for how to manipulate the model variables. A common error is underestimating the importance of proper experimental design, resulting in biased or non-representative outcomes.

Best Practices: Plan a structured approach to running simulations, including input variables and scenarios. Use design of experiments (DoE) techniques to systematically explore the effects of variables. Ensure reproducibility by documenting all steps and settings. Pitfalls to Avoid: Running simulations haphazardly without a clear plan. Not considering all relevant variables and scenarios. Failing to document the experiment setup, making it hard to replicate.

Mais que o uso de métodos estatísticos, é importante que a simula??o seja validada empiricamente. é muito mais valioso a observa??o direta no gemba, e conversando com as partes interessadas diretamente ligadas ao objeto da simula??o do que confiar e considerar dados frios e sujeitos a outras influências. Como dizia Taiichi Ohno, os dados s?o importantes, mas ele colocava mais ênfase nos fatos.

Best Practices around analyzing your results: Define Clear Objectives: Know what you aim to achieve. Validate Input Data: Ensure data quality before simulation. Run Multiple Iterations: Use several trials to account for variability. Analyze Sensitivity: Understand how changes in inputs affect outputs. Use Visualization: Graphs and charts help interpret results. Pitfalls to Avoid: Ignoring Outliers: They can provide valuable insights. Overfitting Models: Avoid making your model too complex. Confirmation Bias: Don’t only look for data that supports your hypothesis. Neglecting Assumptions: Ensure your assumptions are valid. #DataScience #Simulation #BestPractices #Analytics #DataAnalysis

Pragmatismo: Números Versus Números. El análisis comparativo estadistico, de costos, riesgos y de oportunidad debe ser hecho de forma objetiva y cuantificable. Algunos factores a cuantificar no son tangibles, sin embargo, también deben ser considerados. Lo importante es, antes de sustituir un sistema vigente o actual con una innovación, debemos haber medido en forma objetiva los resultados a fin de comparar y decidir la total sustitución de un sistema por otro. Estos procesos son escalativos y hay que ir haciendo mejoras y ajustes hasta lograr la mayor perfección posible, por eso, hay que empezar con lo que tenemos e ir avanzando.

Thorough analysis of the simulation results is vital for drawing meaningful conclusions. Use appropriate statistical tools to evaluate the outcomes and test for significance. Visualizing data through graphs or charts can help communicate complex results more clearly. One pitfall to avoid is over-interpreting results without considering the inherent limitations of your model or failing to look for trends across multiple runs of the simulation.

Simulation is a great way to get convinced that the solution won't lead you to misery! Here are some aspects to consider when designing simulations: 1. What variable of the model do you need to flex & by how much? For example, you may want to put a variable, such as fixed value, capacity, etc., in one example, but demand forecast can vary within the tolerance defined. In another scenario, flip the variables to simulate. 2. How big of a supply chain/model are you simulating? I have been in projects where a bit more than what could be chewed was taken to finally realise the need for some simplification! 3. Consider how you will create mitigation plans for the simulation results—isn't that why you are simulating in the first place?

Here’s a short list with suitable emojis for best practices and pitfalls to avoid when designing and running a simulation experiment: Best Practices: 1. Define clear objectives and goals ?? 2. Use accurate and relevant data ?? 3. Validate the model with real-world scenarios ?? 4. Run multiple test scenarios ?? 5. Analyze and interpret the results thoroughly ?? Pitfalls to Avoid: 1. Ignoring key variables or constraints ?? 2. Overcomplicating the model unnecessarily ?? 3. Relying on inaccurate data ??? 4. Failing to review assumptions and limitations ? 5. Neglecting proper documentation and reporting ??

Simulations are powerful way to improve supply chain design and optimize your distribution network. Not only cost wise but fist and foremost resilience wise. Consider series of "catastrophy" or "distruption" scenarios. Simulate what will happen when one of your DCs goes down i.e. due to building fire, major sourcing route becoming unavailable i.e. due to war. See what will happen with your availability, inventory, costs. Show this to the board. Ask what is their risk acceptance level.

Simulations can be resource-intensive, so it's essential to balance detail with efficiency. Avoid overcomplicating your model by including unnecessary details, which can lead to longer run times and convoluted outputs. Always document each step of the process for future reference and repeatability, and be prepared to adjust your model as new information arises or as objectives shift. Lastly, be mindful of assumptions made in the model, as overly simplistic or incorrect assumptions can lead to misleading conclusions.