The bullwhip effect remains operationally persistent despite decades of analytical research. Two computational deficiencies hinder progress: the absence of modular open-source simulation tools for multi-echelon inventory dynamics with asymmetric costs, and the lack of a standardized benchmarking protocol for comparing mitigation strategies across shared metrics and datasets. This paper introduces deepbullwhip, an open-source Python package that integrates a simulation engine for serial supply chains (with pluggable demand generators, ordering policies, and cost functions via abstract base classes, and a vectorized Monte Carlo engine achieving 50 to 90 times speedup) with a registry-based benchmarking framework shipping a curated catalog of ordering policies, forecasting methods, six bullwhip metrics, and demand datasets including WSTS semiconductor billings. Five sets of experiments on a four-echelon semiconductor chain demonstrate cumulative amplification of 427x (Monte Carlo mean across 1,000 paths), a stochastic filtering phenomenon at upstream tiers (CV = 0.01), super-exponential lead time sensitivity, and scalability to 20.8 million simulation cells in under 7 seconds. Benchmark experiments reveal a 155x disparity between synthetic AR(1) and real WSTS bullwhip severity under the Order-Up-To policy, and quantify the BWR-NSAmp tradeoff across ordering policies, demonstrating that no single metric captures policy quality.

翻译：尽管经过数十年的分析研究，牛鞭效应在运营实践中仍持续存在。两项计算缺陷阻碍了进展：缺乏用于多级库存动态（含非对称成本）的模块化开源仿真工具，以及缺乏用于在共享指标和数据集上比较缓解策略的标准化基准测试协议。本文提出Deepbullwhip——一个开源Python软件包，它集成了串行供应链仿真引擎（通过抽象基类提供可插拔的需求生成器、订购策略和成本函数，并采用向量化蒙特卡洛引擎实现50至90倍加速）与基于注册表的基准测试框架，该框架预装了一套经策展的订购策略、预测方法、六项牛鞭效应指标以及包含WSTS半导体账单的需求数据集。在四级半导体供应链上进行的五组实验揭示了：累计放大系数达427倍（1000条路径的蒙特卡洛均值）、上游层级存在随机滤波现象（CV=0.01）、超指数级前置时间敏感性，以及可在7秒内扩展至2080万个仿真单元。基准测试实验表明，在Order-Up-To策略下，合成AR(1)与真实WSTS数据间的牛鞭效应严重程度存在155倍差异，并量化了各订购策略下BWR-NSAmp的权衡关系，证明单一指标无法全面捕捉策略质量。