replenishment

Overview

Replenishment is a Python package for modeling stock decisions rather than managing them through a finished planning interface. It suits analysts and teams who want to compare replenishment policies using demand data and are comfortable building a workflow in Python. Its analytical focus is useful, but Alpha maturity and unaddressed operational constraints make it a cautious choice for production-critical planning.

Key features

The library simulates policies against historical or synthetic demand, then supports visual comparison across replenishment time series. That combination helps teams explore how a policy behaves under different demand scenarios, though it is oriented toward analysis rather than a complete inventory-operations system.

Optimization workflows include mean-forecast safety stock, k*RMSE or k*MAE, and percentile-target policies. For k*RMSE and k*MAE, safety stock can optionally increase when lead-time forecast quantities rise above a baseline. This gives analysts several buffer-setting approaches to compare, with a specific mechanism for responding to higher forecast quantities.

Data workflows connect CSV or DataFrame inputs with simulation configurations. The repository includes notebooks on policy variants, synthetic data generation, and stock-data loading, useful starting points for Python users adapting the library to their own analysis. Installation is through PyPI with pip install replenishment. The package is tested on Python 3.10, 3.11, 3.12, and 3.13; its dependencies include pandas, Polars, PyArrow, matplotlib, ipykernel, and scienceplots.

Minimum order quantities, case-pack constraints, and promotion-aware demand handling are future contribution areas. Teams whose decisions depend on those rules should look for a more complete fit or plan to extend the package. Its MIT license and invitation to contribute make customization possible, but the Alpha classification is reason to assess suitability before relying on it for constrained workflows.

Pricing

Open source library — free. The free Python package is MIT-licensed, with no paid tier described. It fits individual analysts and teams able to install and work with a Python library; there is no separate lower plan with reduced features. The cost is not the main trade-off: users need the technical capability to build their own workflow and should account for the package’s Alpha status and the stated gaps around operational constraints.

Platforms

Replenishment is distributed as a Python package through PyPI, rather than as a standalone desktop or mobile application. Its tested Python versions are 3.10 through 3.13. The listed dependencies span data handling, visualization, and notebook work, so it is best suited to an existing Python analysis environment.

Who it's for

Choose replenishment if your team can work in Python and needs to compare stock policies against historical or generated demand, explore safety-stock approaches, or visualize replenishment behavior. It is less suitable when you need a ready-made operational system or decisions must directly account for minimum orders, case packs, or promotion effects.

Pros and cons

Pros

• Multiple policy-optimization workflows: mean-forecast safety stock, k*RMSE or k*MAE, and percentile targets let analysts compare different buffer-setting approaches.
• Scenario and visual analysis: historical and synthetic demand simulations paired with time-series comparisons support policy exploration.
• Flexible data path: CSV and DataFrame inputs can be connected to simulation configurations, with notebooks for common exploration tasks.
• MIT-licensed and free: teams can use the package without a subscription and are invited to contribute.

Cons

• Alpha status: teams should assess its readiness before making it a dependency for critical planning.
• Operational constraints remain gaps: minimum order quantities, case packs, and promotion-aware demand are future contribution areas.
• Python-oriented delivery: users need a Python environment and the ability to assemble their own analysis workflow.

Alternatives

Inventory Optimization Software is a useful category starting point if you want to compare options beyond a Python library.

Choose ZebraDesigner instead if your need is basic barcode label and tag design and printing, including support for Zebra Link-OS and Link-OS Basic printers; it is a different kind of tool, with paid plans as well as a free Essentials plan. supplychainpy is another free Python package distributed through PyPI to consider alongside replenishment. Stockpyl is a free, MIT-licensed Python package credited to Lawrence V. Snyder.

Creo, Manhattan Active Inventory Replenishment, Bluemingo Production Planning & Scheduling, E2open Planning, and GAINS are paid alternatives for readers comparing other software options; their listed pricing is custom pricing or available through a sales or demo request.

Verdict

Replenishment is a good fit for Python-capable analysts who want a free, MIT-licensed way to simulate demand scenarios, compare safety-stock policies, and visualize results. Its strongest reason to choose it is that focused analytical workflow; its strongest reason to look elsewhere is Alpha status combined with future-work gaps in constraints such as case packs and promotions.