replenishment vs SAS Intelligent Inventory Management

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.

SAS Intelligent Inventory Management is a web-based suite for retail and consumer packaged goods companies planning sizes, purchasing and inventory. It combines SAS Size Optimization and SAS Inventory Optimization, using historical sales, analytics and machine learning to produce size, demand and inventory intelligence. Size features create demand-based profiles, support event scenarios, detect active size ranges and work across hierarchy levels. Products can be grouped by hierarchy or attributes, with new products matched to attribute profiles. Pack recommendations can apply usage and variation constraints, combine packs with bulk and account for supply-chain costs, lifecycle changes and promotions. Inventory planning calculates stock levels across the supply chain and supports replenishment policies based on lead times, costs and target service levels. What-if analysis compares scenarios, proposal costs and policy effects across single- and multi-echelon networks. SAS says the Viya version can deploy on Microsoft Azure and Amazon Web Services, but SAS Intelligent Inventory Optimization is unavailable on Viya from release 2024.04 onward.