Blue Yonder Demand and Supply Planning vs replenishment
Blue Yonder Demand and Supply Planning
blueyonder.comreplenishment
github.comSide by side
| Figure | Blue Yonder Demand and Supply Planning | replenishment |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| API | Yes | — |
| Plans published | 1 | 1 |
| Spec score · Inventory Optimization Software | 7.4 (#11) | 7.7 (#6) |
| Purpose | Provides all-in-one demand, supply and inventory planning for supply-chain organizations. | The project describes replenishment as a Python library for stock replenishment simulation, policy optimization, and decision visualization. |
Plans
Blue Yonder Demand and Supply Planning
- Demand & Supply PlanningPriced on request
In their own words
Blue Yonder Demand and Supply Planning
Blue Yonder Demand and Supply Planning 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.