GAINS vs replenishment

GAINS DEO is a supply chain decision platform connecting strategic design, planning, and execution for manufacturers, distributors, and retailers. Its capabilities combine AI and machine learning, optimization, workflow automation, data science, decision science, and distributed systems engineering. Listed functions include demand planning, inventory optimization, replenishment, scenario planning, supplier management, logistics planning, and fulfillment management. GAINS says its Demand Forecast Factory creates period-specific AI models up to 60 months out, and that demand planning can improve forecast accuracy by up to 35%. The company also says its machine-learning-driven replenishment can automate up to 90% of buying decisions. GAINS Connect exchanges data in real time with systems including SAP, Oracle NetSuite, and Salesforce through APIs and webhooks. It supports recommendations such as purchase, transfer, and work orders. Deployment options include cloud and on-premise, along with extensions to legacy environments or assembled industry-specific solutions. GAINS describes phased rollouts through its Proven Path to Performance methodology and says it has a 100% implementation success rate. Support resources include application guides, reporting information, a video library, and GAINS University training. Pricing is available on request.

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.