Lokad vs supplychainpy
Lokad
lokad.comsupplychainpy
supplychainpy.orgSide by side
| Figure | Lokad | supplychainpy |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Web | Yes | — |
| Windows | — | Yes |
| macOS | — | Yes |
| Linux | — | Yes |
| API | — | Yes |
| Self-hosted | — | Yes |
| Plans published | 1 | 1 |
| Spec score · Inventory Optimization Software | 7.4 (#12) | 7.9 (#3) |
| Founded | 2008 | 2016 |
Plans
In their own words
Lokad
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.
supplychainpy
supplychainpy is a Python library for supply chain analysis, modeling, and simulation, aimed at commercial inventory and demand-planning analysts. Its inventory features include ABC XYZ classification, safety stock, reorder levels, economic order quantity, service-level targets, and replenishment recommendations. Forecasting options include simple exponential smoothing and Holt’s trend-corrected exponential smoothing; ARIMA models remain under development. The reporting suite presents charts, tables, KPIs, inventory summaries, and recommendations, with a bot for querying analysis in natural language. Inputs can be CSV or text files, Pandas DataFrames, or data supplied through a database ETL process. The maker describes use with Pandas, Jupyter, Matplotlib, openpyxl, and xlwings. Installation is available through pip or from source, and the reporting suite can run locally with configurable host and port settings. Command examples cover Linux, Mac, and Windows. The project is licensed under BSD-3 and is free. Its maker warns that the library is in early development and unsuitable for production because its API may change.