replenishment vs Trinamix Multi-Echelon Inventory Optimization

replenishment is a Python library for simulating stock replenishment, optimizing policies, and visualizing policy behavior. It is designed for teams comparing policies against historical or synthetic demand. Listed optimization workflows include safety stock based on mean forecasts, k*RMSE or k*MAE, and percentile targets. For k*RMSE and k*MAE, safety stock can optionally increase when lead-time forecast quantities rise above a baseline. The library supports moving between CSV or DataFrame inputs and simulation configurations, and it can show visual comparisons of policies on replenishment time series. The README includes notebooks for policy variants, synthetic data generation, and loading stock replenishment data. Installation is through PyPI with `pip install replenishment`. The project is tested on Python 3.10, 3.11, 3.12, and 3.13; listed dependencies include pandas, Polars, PyArrow, matplotlib, ipykernel, and scienceplots. The package uses the MIT license and is classified as Alpha. The project welcomes contributions. Minimum order quantities, case-pack constraints, and promotion-aware demand handling are listed as areas for future contribution.

Trinamix Multi-Echelon Inventory Optimization is a cloud-based web solution for positioning inventory across plants, distribution centers and retailers. It operates as a microservice with Oracle supply planning and models interdependent supply-chain stages using mathematical algorithms and historical behavior. Planners can adjust parameters, simulate inventory levels and assess effects across the network. The optimization infers postponement and risk pooling, accounts for demand and supply disruptions, and calculates safety stock by comparing marginal inventory utility with inventory positions. Stochastic forecasting estimates possible outcomes and probabilities for inventory targets, while machine learning converts supply and demand uncertainty into variability. An inventory-entitlement mechanism separates stock drivers such as errors and disruptions. A genetic algorithm optimizes inventory holding under budget constraints. Integrations include Oracle Cloud SCM, ERP systems and third-party systems such as SAP; Trinamix also lists the product on Oracle Cloud Marketplace. The maker identifies retail, consumer goods, manufacturing and high-tech industries as users. Pricing is on request. Trinamix lists the company as founded in 2009 and headquartered in the United States, while also identifying San Jose, California.