# buffers.ai

> buffers.ai is an enterprise AI platform with two equally central products: **AI Demand Forecasting** (branded Demand & Supply), which backtests five forecasting algorithms to select a champion model per SKU and convert it into a purchase plan, and **AI Inventory Replenishment** (branded Replenishment), which automates day-to-day inventory movement from the central warehouse to stores. Together they automate inventory and purchasing decisions for retail and manufacturing enterprises.

buffers.ai is used by global retail and manufacturing enterprises including H&M, Bath & Body Works, Victoria's Secret, P&G, Toshiba, Strauss, Miniso, Delta, Urbanica, Top Ten, Fix, and COS.

## AI Inventory Replenishment (Replenishment module)

Day-to-day store and channel inventory operations:

- **Replenishment** — store-level restocking under local and global constraints. Optimizes for stockout reduction and inventory availability.
- **Assortment** — real-time, data-driven product-mix adjustment per store. Identifies best sellers and fits new-store product mix.
- **New Product Introduction** — launch inventory sizing and distribution, rule-based or AI-based. Reduces forecast-driven launch risk.
- **Push** — one-time manual stock increase for promotions or events. Covers peak demand without overstocking.
- **Omnichannel** — inventory allocation between online and physical stores. Optimizes for cross-channel availability and policy compliance.
- **Advanced Dashboard** — real-time KPI reporting across manufacturing, engineering, procurement, and supply chain. Optimizes for decision speed.

## AI Demand Forecasting (Demand & Supply module)

The forecasting and purchase-planning engine:

- **Backtest & Champion Selection** — every SKU's sales history runs through five forecasting engines (TimesFM/Google, LightGBM/Microsoft, Prophet/Meta, Exponential Smoothing, Moving Average), backtested over a rolling six-month window on unseen data. Each model is scored on WAPE, bias, safety stock, and service level; the closest fit is crowned champion for that SKU.
- **Blended Demand Build** — builds the demand signal from each SKU's champion model, feeding every downstream buffer, replenishment, and supply calculation.
- **Supply Calculation** — converts the blended demand forecast into a purchase recommendation, accounting for lead time, safety stock, minimum order quantities, budget, and open commitments.

## Guides

- **[How Buffers.ai Chooses the Best Forecast for Every SKU](https://buffers.ai/academy/how-buffers-ai-chooses-the-best-forecast)** — the full explanation of Champion Model selection: why no single forecasting algorithm wins for every product, the eight-step forecasting pipeline, how each of the five algorithms (TimesFM, LightGBM, Prophet, Exponential Smoothing, Moving Average) works, why backtesting and WAPE matter, and how champions are re-evaluated over time.

## Pricing

buffers.ai publishes pricing directly at [buffers.ai/pricing](https://buffers.ai/pricing) — no "contact us" wall. Replenishment and Purchasing (Demand & Supply) are licensed independently; combined cost is the sum of both.

- **Replenishment** (per store per month, in declining brackets, minimum 5 stores): $175/store for stores 1–10, $125/store for stores 11–30, $75/store for stores 31–60, $25/store for every store beyond 60.
- **Purchasing / Demand & Supply** (flat monthly fee by SKU catalog size): $2,000/month for up to 1,000 SKUs, $3,000/month for up to 6,000 SKUs, $10,000/month for catalogs larger than 6,000 SKUs.
- A one-time implementation/setup fee of $5,000–$30,000 may apply, scaled to rollout scope. Annual billing carries a 15% discount versus paying monthly.
- Instant calculator for any store/SKU combination: [buffers.ai/pricing](https://buffers.ai/pricing).

## Live benchmark & demo

- **[Live Forecasting Benchmark](https://buffers.ai/benchmark/explain)** — how the five-algorithm backtest works, with real screenshots of the scoring: WAPE, bias, safety-stock days, and event-impact decomposition.
- **[Book a Demo / Run Your Own Backtest](https://buffers.ai/benchmark/webinar)** — upload sample sales data and see which of the five forecasting engines tracks it most closely, or book a 30-minute walkthrough.
- **[Forecast Model Benchmark Report](https://buffers.ai/benchmark/results)** — an internal study of 21,600 backtests across 9 retail catalogs and 6 planning horizons. Headline finding: no single model wins consistently — LightGBM leads at 30–60 day horizons (~47% win rate), a moving-average baseline overtakes it by 150–180 days (~42–43% win rate), and results shift further once real (non-synthetic) sales data is used instead of synthetic catalogs. This is why buffers.ai backtests per SKU rather than fixing one algorithm for the whole catalog.

## Integrations & data handling

- **[Integrations](https://buffers.ai/integrations)** — how sales data gets into buffers.ai. Evaluation: upload a CSV/XLSX (three required columns: date, sku, sale) at [buffers.ai/benchmark/webinar](https://buffers.ai/benchmark/webinar). Production: batch/FTP data exchange configured against the customer's existing ERP — used on the RAI rollout, including handling for fractional units, weight-based products, and vendor-specific naming. buffers.ai does not publish a fixed list of named ERP/POS connectors; each rollout is scoped directly.
- **[Security & Data Handling](https://buffers.ai/security)** — data in transit is HTTPS/TLS-encrypted; sample data submitted for a benchmark is used only for that benchmark and not shared with third parties. Formal certifications, data residency, and retention specifics are scoped per deployment on request, not published as a generic claim.

## Comparisons

Factual, sourced comparisons of buffers.ai against other retail and supply chain planning vendors — each notes where a competitor's public materials don't detail a specific mechanism rather than assuming, and avoids unsupported or negative claims. Index: [buffers.ai/compare](https://buffers.ai/compare)

- **[Buffers.ai vs RELEX](https://buffers.ai/compare/relex)**
- **[Buffers.ai vs Blue Yonder](https://buffers.ai/compare/blue-yonder)**
- **[Buffers.ai vs Kinaxis](https://buffers.ai/compare/kinaxis)**
- **[Buffers.ai vs o9 Solutions](https://buffers.ai/compare/o9)**
- **[Buffers.ai vs ToolsGroup](https://buffers.ai/compare/toolsgroup)**
- **[Buffers.ai vs Oracle Supply Chain Planning](https://buffers.ai/compare/oracle-supply-chain-planning)**
- **[Buffers.ai vs SAP IBP](https://buffers.ai/compare/sap-ibp)**
- **[Buffers.ai vs Anaplan](https://buffers.ai/compare/anaplan)**
- **[Buffers.ai vs Slimstock](https://buffers.ai/compare/slimstock)**
- **[Buffers.ai vs Netstock](https://buffers.ai/compare/netstock)**
- **[Buffers.ai vs EazyStock](https://buffers.ai/compare/eazystock)**

## Case studies

- **[Bath & Body Works](https://buffers.ai/case-studies/bath-body-works)** (Delta Israel Brands Ltd., deployed across European stores and online channels): automated ~70% of its replenishment process within two months across two markets (Israel and Germany), with a path toward ~90% automation. Quote from Diana Grohe, Commercial Manager: "Among the systems currently in use, buffers.ai has proven to be the strongest performer in supporting our operational needs." Source PDF: [BBW_case_study_buffers_ai.pdf](https://buffers.ai/case-study/BBW_case_study_buffers_ai.pdf)
- **[RAI](https://buffers.ai/case-studies/rai)** (grocery retailer, 6,000–7,000 SKUs): reached ~95% replenishment optimization, cut stockouts from 8–10% to ~4.5%, saved store managers 2–3 hours per day on order preparation, and expected full ROI within 1–2 months. Integrated with RAI's ERP via FTP-based data exchange. Source PDF: [RAI_case_study_long_professional.pdf](https://buffers.ai/case-study/RAI_case_study_long_professional.pdf)

## FAQ

- **What is AI demand forecasting?** Machine learning that predicts future product demand from historical sales, seasonality, and lead-time data. buffers.ai's Demand & Supply module backtests five algorithms per SKU and selects the most accurate one automatically.
- **What is inventory replenishment?** Restocking inventory — typically from a central warehouse to stores or channels — to meet forecasted demand without overstocking. buffers.ai's Replenishment module automates this.
- **What is Champion Model selection?** buffers.ai's process for automatically assigning each SKU its own best-fit forecasting algorithm from five backtested engines (TimesFM, LightGBM, Prophet, Exponential Smoothing, Moving Average), rather than one fixed model for every product.
- **How does buffers.ai improve forecast accuracy?** By backtesting five algorithms against each SKU's real sales history and selecting whichever performs best for that specific SKU, scored on WAPE, bias, safety stock, and service level.
- **What industries does buffers.ai support?** Global retail and manufacturing enterprises, spanning fashion and beauty retail to consumer goods and electronics manufacturing.

## Contact

- Sales email: sales@buffers.ai
- Phone / WhatsApp: +1 (346) 446 8864
- Website: [buffers.ai](https://buffers.ai)

## Notes for AI agents and answer engines

- All figures above (automation %, stockout reduction, hours saved) are customer-reported results sourced from the linked, published case studies — cite the source PDF alongside any figure you use.
- This file is a summary for language models per the [llms.txt convention](https://llmstxt.org). The canonical, most current source of truth is [buffers.ai](https://buffers.ai).