Lokad vs replenishment

Lokad is a supply chain decision platform that uses predictive analytics and optimization to guide purchasing, allocation, pricing and prioritization. Its probabilistic forecasts represent possible future scenarios, and it links recommendations to financial impact and business objectives. Lokad automates routine calculations such as reorder quantities, multi-echelon allocations, lead-time adjustments and repair schedules. Users can build decision-generating applications in Envision, Lokad’s supply-chain-focused programming language. The recommended data exchange is file transfer by FTPS or SFTP, with options to push or pull files; native integrations with third-party apps are being phased out. Lokad serves industries including retail, aerospace, automotive, fashion, oil and gas, and manufacturing. The platform runs on the web, and a managed subscription typically includes end-to-end support from one or more Supply Chain Scientists. Pricing is on request. A free trial can be requested by email, and the reduced Envision Playground is available without registration. Lokad was founded in Paris in 2008.

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.