replenishment

replenishment is a Python library for simulating stock replenishment, optimizing policies, and visualizing results. It is intended for teams evaluating replenishment approaches against historical or synthetic demand. Optimization workflows include safety stock based on mean forecasts, k*RMSE or k*MAE, and percentile targets. For the k*RMSE and k*MAE approaches, safety stock can optionally increase when lead-time forecast quantities rise above a baseline. The library supports workflows between CSV or DataFrame inputs and simulation configurations, and it can display policy comparisons on replenishment time series. Installation is documented through PyPI with `pip install replenishment`. The project says it 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. Its repository includes notebooks for policy variants, synthetic data generation, and stock replenishment data loading. The README welcomes contributions and links to setup guidance and an issue tracker. Minimum order quantities, case-pack constraints, and promotion-aware demand handling are listed as areas for future contribution.

replenishment focuses on simulation, policy optimization, and visualization for stock replenishment in Python. Its Alpha status and listed future work areas are worth considering when assessing fit.

This library suits Python teams comparing stock replenishment policies using historical or synthetic demand. It is also relevant to contributors interested in its listed future work areas.

Overview

replenishment is a free, open-source Python library for modeling stock replenishment decisions. It suits analysts and engineering teams that can work with Python and want to compare policies against historical or synthetic demand. The trade-off is a focused analytical package rather than a finished operational replenishment system.

Key features

The library simulates replenishment policies over demand scenarios and visualizes competing results on replenishment time series. That combination is useful when a team needs to see how a policy changes inventory behavior before adopting it, rather than relying only on summary metrics.

Its optimization workflows cover mean-forecast safety stock, k*RMSE, k*MAE, and percentile-target policies. These options let analysts compare several buffer-setting approaches. For the k*RMSE and k*MAE methods, safety stock can optionally increase when lead-time forecast quantities rise above a baseline, giving the buffer a way to respond to elevated expected demand.

Data workflows connect CSV or DataFrame inputs with simulation configurations. That is practical for teams whose source data arrives in files or tabular Python objects. Repository notebooks cover policy variants, synthetic-data generation, and stock-replenishment data loading, so users have worked examples to adapt. Installation is a standard pip install replenishment command.

The boundaries matter for operational use. Minimum order quantities, case-pack constraints, and promotion-aware demand handling are identified as future contribution areas. If those rules materially affect purchasing decisions, the library will require additional work around the model rather than providing them as established capabilities.

Pricing

The single Open source library plan costs free. It is a Python package under the MIT license, with no paid tier or seat charge. This is the right fit for individual analysts, research groups, and teams that can maintain Python workflows themselves. The zero price does not remove the engineering cost of integrating data, validating policies, or adding business constraints.

Platforms

replenishment is distributed through PyPI for Python use. The project says it is tested on Python 3.10, 3.11, 3.12, and 3.13. Its dependency set includes pandas, Polars, PyArrow, matplotlib, ipykernel, and scienceplots, so adopting it also means accommodating that data, plotting, and notebook stack.

Who it's for

This is a strong match for supply-chain analysts, data scientists, and developers who need a programmable environment for policy experiments. It is especially appropriate when historical or synthetic demand is available and the goal is to compare safety-stock strategies or visualize time-series outcomes. Teams seeking a turnkey planning application, broad business-user interface, or built-in handling for order multiples and promotions should look elsewhere or plan substantial extension work.

Pros and cons

Pros

• Several safety-stock approaches: mean-forecast, error-based, and percentile policies support meaningful policy comparisons.
• Forecast-responsive buffering: optional increases tied to lead-time forecast quantities can make error-based buffers more adaptive.
• Flexible data path: CSV and DataFrame workflows fit common Python data pipelines.
• Clear cost and license: the MIT-licensed package is free to use and modify.
• Current Python coverage: testing across Python 3.10 through 3.13 helps teams on recent runtimes.

Cons

• Operational constraints are unfinished: minimum order quantities, case packs, and promotion-aware demand are future work areas.
• Alpha maturity: teams need to allow for change and contribute fixes or features as their workflows expand.
• Python-centered delivery: organizations wanting a packaged planning application for nontechnical users will face a poor fit.
• Dependency overhead: the pandas, Polars, PyArrow, plotting, and notebook stack adds environment-management responsibility.

Alternatives

For a broader category view, see Inventory Optimization Software.

supplychainpy is another free, open-source Python package, distributed through PyPI, and is a sensible alternative when its API, Linux, macOS, Windows, or self-hosted availability better matches your deployment.

Stockpyl is a free MIT-licensed open-source Python package to consider when its Linux and API availability suits your environment.

Choose ZebraDesigner instead for barcode-label and tag design and printing, including a free Essentials plan.

Creo is a paid, multi-platform design product with floating licensing for organizational users.

o9 Digital Brain is a paid enterprise SaaS option with customer-scoped pricing across business complexity, users, functions, solutions, and implementation.

Manhattan Active Inventory Replenishment is a paid API and web offering without a free plan.

Bluemingo Production Planning & Scheduling is a paid web product sold through its sales team.

E2open Planning is a paid planning product with pricing handled by e2open.

Verdict

Choose replenishment if you need a no-cost, MIT-licensed Python toolkit for simulating demand scenarios, optimizing safety stock, and comparing policies visually. Look elsewhere when replenishment decisions depend on minimum orders, case packs, promotions, or a production-ready interface; its Alpha maturity and those unfinished areas make it better for analytical development than direct operational deployment.