Netstock Inventory Optimization vs replenishment
Netstock Inventory Optimization
netstock.comreplenishment
github.comSide by side
| Figure | Netstock Inventory Optimization | replenishment |
|---|---|---|
| Price from | $900/mo | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Plans published | 1 | 1 |
| Spec score · Inventory Optimization Software | 7.5 (#8) | 7.7 (#6) |
| Purpose | Netstock provides inventory planning and optimization software that uses predictive insights and recommendations to balance inventory investment and respond to supply and demand changes. | The project describes replenishment as a Python library for stock replenishment simulation, policy optimization, and decision visualization. |
Plans
Netstock Inventory Optimization
- Netstock Inventory Optimization$900 a month
In their own words
Netstock Inventory Optimization
Netstock Inventory Optimization has no write-up on Specifiction yet; its figures are above.
replenishment
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.