4R Systems Intelligent Inventory Optimization vs replenishment

4R Systems Intelligent Inventory Optimization is a cloud-delivered web suite for retailers managing supply-chain and inventory decisions. It covers demand planning, replenishment, allocation, assortment, and markdowns. Forecasting accounts for promotions, seasonality, pricing, and localized demand while identifying underlying demand, including lost sales. Replenishment models multi-echelon inventory and recommends stock by SKU and location. Allocation recommendations consider stock, weather trends, inbound orders, and future requirements. Assortment tools use store clusters, product attributes, and local demand to suggest store-specific mixes. Markdown optimization recommends timing and depth for end-of-season and end-of-life reductions. Planning as a Service combines AI tools with strategic planning and execution support. Customers use a web portal after establishing data exchanges; the One Predictive Engine integrates with core operating systems, although specific systems are not named. The vendor lists auto parts, convenience, apparel and footwear, hardware, and home furnishings retailers. Pricing is not published; visitors are directed to request a demo.

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.