In my previous role, I led a project simulating the impact of a new supply chain model on our logistics network. I began by gathering detailed data on current operations, then designed the simulation model using specialized software. Throughout the project, I collaborated with cross-functional teams to ensure accuracy and relevance. The simulation results provided actionable insights, leading to a 15% efficiency improvement in our supply chain operations. This experience honed my skills in project management, data analysis, and stakeholder communication.

Ensuring accuracy involves rigorous validation and verification processes. I start by cross-referencing real-world data with simulation outputs to identify discrepancies. I then refine the model iteratively, incorporating feedback from subject matter experts. Additionally, I use statistical methods to validate the model's predictive capabilities. Regular calibration and sensitivity analysis are also crucial to maintain reliability. By adhering to these practices, I ensure that the simulation results are both accurate and trustworthy.

To effectively communicate complex simulation results, I focus on simplifying the data into clear, actionable insights. I use visual aids like charts and graphs to illustrate key findings. I also prepare concise reports with executive summaries that highlight the most critical outcomes. During presentations, I ensure to explain technical terms in layman's terms and engage stakeholders with interactive discussions. By making the information accessible and relevant, I help stakeholders understand the implications and make informed decisions.

When encountering unexpected results, I first conduct a thorough analysis to identify the root cause. This often involves revisiting the model's assumptions and inputs. If necessary, I consult with experts to validate the findings. I then document the anomaly and its potential causes, suggesting corrective actions. If the anomaly is significant, I may recommend additional simulations or further data collection. My approach is to remain flexible and open to revising the model to ensure it accurately reflects the real-world scenario.

In a previous project, our client requested a shift in focus from cost optimization to risk assessment midway through the simulation. I quickly adapted by incorporating risk factors into the model, such as supply chain disruptions and market volatility. I also adjusted the simulation parameters to reflect these new priorities. This required additional data collection and collaboration with risk management experts. Despite the tight deadline, I successfully delivered a revised model that met the client's new requirements, demonstrating my ability to pivot and deliver under pressure.