Simulation for Digital Transformation

Welcome to Thayer School of Engineering at Dartmouth’s Simulation for Digital Transformation. This in-depth course will equip you with the skills and tools needed to model, analyze, and optimize complex systems, helping organizations navigate uncertainty and make impactful, data-driven decisions. As part of the Digital Transformation for Data Analytics Certificate, this course focuses on discrete event simulation and other techniques essential for addressing the challenges of modern digital transformation. Simulation plays a critical role in bridging predictive and prescriptive analytics by forecasting outcomes and identifying optimal actions to achieve desired results. Whether improving operational workflows, optimizing resource allocation, or designing better customer experiences, simulation provides the foundation for informed decision-making in dynamic environments. This course introduces you to the fundamentals of simulation, starting with the key concepts of probability and uncertainty modeling. You’ll learn to generate random variables using techniques like the inversion and rejection methods, building robust models that reflect real-world variability. By mastering discrete event simulation, you can design event-driven models that incorporate simulation clocks, system states, events, transitions, and end conditions, allowing you to analyze and optimize systems across industries. You’ll use tools like Python and SimPy through hands-on exercises to build and implement simulation models for real-world scenarios. From optimizing traffic flows to managing inventory and scheduling, the course demonstrates how these techniques apply to various business challenges. You’ll also explore how to validate and verify simulations, ensuring they provide trustworthy and actionable insights. As the course progresses, you’ll tackle more complex problems, incorporating advanced methods for simulating random variables and addressing multi-objective goals. Real-world case studies, such as coffee shop customer flow and repair facility optimization, will challenge you to think critically and apply your skills to practical scenarios. By the end of the course, you’ll be equipped to use simulation as a predictive tool and a prescriptive analytics framework to recommend the best courses of action in uncertain environments. The capstone practicum consolidates your learning, allowing you to develop and analyze a complete simulation project. You’ll apply all the techniques learned, from random variable generation to sensitivity analysis, culminating in a professional report with actionable recommendations for stakeholders. Guided by Professors Vikrant Vaze and Reed Harder, this course blends theory, coding, and real-world applications to prepare you to lead data-centric initiatives. Whether you're a seasoned professional or new to analytics, Simulation for Digital Transformation will empower you to make smarter decisions, manage risk, and drive innovation in today’s fast-changing digital landscape. 3b:T503,

• ● Master Discrete Event Simulation : Develop and implement event-driven simulation models in Python using tools like SimPy to analyze and optimize real-world systems.● Generate Random Variables : Apply techniques like the inversion and rejection methods to simulate uncertainty and model complex scenarios effectively.● Design Trustworthy Simulations : Learn how to validate, verify, and refine simulation models to ensure accurate and actionable results for decision-making.● Optimize Complex Systems : Use simulation to efficiently improve workflows, allocate resources, and evaluate multi-objective solutions in diverse industries.● Bridge Predictive and Prescriptive Analytics : Leverage simulation as a tool to not only predict outcomes but also recommend optimal strategies in dynamic environments.

• Course: Fundamentals of Digital TransformationSkils: Basic knowledge of Python