replenishment

Replenishment is a Python library for analysts who want to compare stock policies against historical or synthetic demand. Its practical appeal is a focused set of safety-stock workflows; it is a weaker fit when replenishment decisions depend on ordering constraints or promotion-aware demand.

Overview

The project centers on modeling and comparing replenishment policies, with workflows that connect demand data to simulation configurations. It gives Python teams room to explore policy behavior, but its Alpha status and stated future contribution areas matter for anyone considering it in an operational process.

Key features

Replenishment simulates policies against historical or synthetic demand and supports visual comparisons across replenishment time series. Optimization workflows include mean-forecast safety stock, k*RMSE and k*MAE, and percentile-target policies. That selection lets analysts compare several buffer-setting approaches; for k*RMSE and k*MAE, an optional adjustment can increase safety stock when lead-time forecast quantities rise above a baseline.

CSV and DataFrame inputs can be connected to simulation configurations. Included notebooks cover policy variants, synthetic data generation, and stock data loading, giving contributors and analysts concrete examples to adapt. Installation is through PyPI with pip install replenishment.

The project is tested on Python 3.10, 3.11, 3.12, and 3.13. Its dependencies include pandas, Polars, PyArrow, matplotlib, ipykernel, and scienceplots, so teams should account for that Python data and visualization stack. Minimum order quantities, case-pack constraints, and promotion-aware demand handling are identified as areas for future contribution; teams that rely on those rules will need additional work beyond the library’s current focus.

Pricing

The Open source library plan is free. It is an MIT-licensed Python package, with no seat or usage caps stated in the plan. The free plan suits teams able to install and work with a Python library; it does not provide a separate paid tier with broader capabilities. The README welcomes contributions and links to setup, testing, pull request guidance, and the issue tracker.

Platforms

Replenishment is distributed as a Python package through PyPI and is tested on Python 3.10 through 3.13. The project metadata lists pandas, Polars, PyArrow, matplotlib, ipykernel, and scienceplots as dependencies.

Who it's for

It suits analysts and development teams that can work in Python and want to simulate demand scenarios, compare safety-stock policies, or build on the included notebooks. Teams looking for a packaged operational workflow around minimum orders, case packs, or promotions should look elsewhere or plan to extend the project, since those are future contribution areas.

Pros and cons

Pros

• Several policy optimization workflows, including mean-forecast, error-based, and percentile-target approaches, support comparative analysis.
• Historical and synthetic demand simulation, visual policy comparisons, and CSV or DataFrame workflows form a coherent toolkit for Python-based exploration.
• The package is free, MIT-licensed, installable through PyPI, and tested across Python 3.10–3.13.

Cons

• Alpha development status may be a concern for teams that need a mature operational dependency.
• Minimum order quantities, case-pack constraints, and promotion-aware demand handling remain future contribution areas, limiting its fit for workflows governed by those rules.
• Its Python package and dependencies make it best suited to teams already working in that ecosystem.

Alternatives

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supplychainpy is another free option, with its 0.0.5 source package available through PyPI.

Stockpyl is a free, MIT-licensed open-source Python package and a reasonable alternative to evaluate alongside Replenishment.

Manhattan Active Inventory Replenishment is a paid option for readers comparing web or API offerings.

4R Systems Intelligent Inventory Optimization uses custom pricing, with a demo request as the pricing route.

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Decion Demand Planning has custom pricing tailored to selected capabilities, scope, users, integrations, and implementation needs.

E2open Planning uses custom pricing, with pricing details available by contacting E2open.

Verdict

Choose Replenishment if your team works in Python and wants a free, MIT-licensed way to simulate demand and compare safety-stock policies. Its focused analytical workflows are the reason to choose it; its Alpha status and unfinished support for ordering constraints and promotions are the reasons to look elsewhere when those rules shape day-to-day replenishment.