Simulation Models for Data Science

Chapter 1. Why Use Simulation When You Have Data?

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Data professionals have no shortage of tools, historical datasets, and predictive models, but we still struggle to understand complex business processes.

Consider supply chains. Small demand fluctuations can cascade into disproportionate order increases across retailers, distributors, and manufacturers. This is known as the bullwhip effect. This happens when each actor makes independent, rational decisions based on incomplete information, which can lead to system-wide disruptions.

Or consider capacity planning. Predictive models can fail to account for backlogs, randomness in arrival times, and phase transitions at critical thresholds where systems experience catastrophic gridlock.

Competitive pricing presents another challenge. Retailers can build price elasticity models based on historical data but cannot anticipate competitor reactions. This leaves them with static correlation analysis that offers little value for strategic decisions in dynamic ...