Risk Modeling for Supply Chain & Operations with Monte Carlo: Building Robust Models in Excel and Python

Name: Risk Modeling for Supply Chain & Operations with Monte Carlo: Building Robust Models in Excel and Python
Brand: Independently Published
SKU: 9798198364493
Price: 3208.0 INR
Availability: InStock

by Alice Schwartz , James Preston

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ISBN13: 9798198364493
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: May 24, 2026
Pages: 444
Original Price: GBP 28.29
Language: English
Edition: N/A
Item Weight: 531 grams
BISAC Subject(s): Production & Operations Management

About The Book

Reactive Publishing

In today's volatile global markets, supply chain and operations professionals face unprecedented uncertainty. From demand fluctuations and supplier disruptions to price volatility and geopolitical risks, traditional deterministic models often fall short. This practical guide shows you how to build robust, data-driven risk models using Monte Carlo simulation, one of the most powerful techniques for quantifying uncertainty and making better decisions under risk.

You will learn how to:

Construct Monte Carlo simulations from the ground up in both Excel and Python
Model key supply chain and operations risks including demand variability, lead time uncertainty, price fluctuations, and inventory risk
Translate complex real-world problems into probabilistic models
Analyze simulation outputs to generate actionable insights and risk metrics
Compare deterministic vs. stochastic approaches and understand when Monte Carlo adds the most value
Build reusable, professional-grade models suitable for both quick analysis and enterprise environments

Written for supply chain analysts, operations managers, risk professionals, and data-savvy practitioners, this book bridges the gap between theory and hands-on implementation. Whether you work in manufacturing, logistics, retail, or procurement, you'll gain concrete skills to stress-test your supply chain and improve resilience.

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