示意预览ML demand forecasts that replace static reorder rules with dynamic min/max,
safety stock and EOQ across every Odoo warehouse.
什么是 AI Demand Forecasting & Auto-Replenishment for Odoo？
ML demand forecasts that replace static reorder rules with dynamic min/max, safety stock and EOQ across every Odoo warehouse. Built to order by ECOSIRE for Odoo 17, 18, 19 — indicative price from $999.00 USD; request a quote for a scoped proposal.
核心功能
按需定制，全程代劳
无需自行搭建——由 ECOSIRE 构建、安装并提供支持的可用应用。
- 1
您下单
以一次性构建价格开始。我们在启动时与您共同确定范围。
- 2
我们构建与安装
ECOSIRE 在您的 Odoo 上构建、配置并安装。
- 3
上线 + 支持
约一个工作周内上线，并包含两周上线支持。我们交付的代码中的缺陷免费修复。
技术规格
- Odoo 兼容性
- Odoo 17, Odoo 18, Odoo 19
- 版本
- 企业版和社区版
- 许可证
- 许可证需要确认
- Python 要求
- Python 3.10+
- 数据库
- PostgreSQL 12+
关于此产品
Odoo's native reorder rules force you to hand-set a fixed min and max on every product, in every warehouse, forever. Demand shifts, seasons turn, a promo lands — and your static thresholds quietly bleed cash: cash trapped in overstock on slow movers, and lost sales on the fast ones you under-buy. This build-to-order module replaces that guesswork with a machine-learning demand engine that reads your own Odoo sales history and recalculates optimal stocking policy on a schedule.
ECOSIRE designs, builds, installs and supports this as a net-new module tailored to your catalog, your warehouses and your Odoo version (17, 18 or 19; Community or Enterprise). It is NOT an off-the-shelf apps.odoo.com download — after purchase we scope your data, engineer the models and hand over a running, documented, supported system. Typical delivery is one working week.
Under the hood, a Python forecasting layer (Prophet-style time-series with seasonality and trend decomposition) trains on sale.order.line and stock.move history per product and per stock.warehouse. A new ecosire.demand.forecast model stores the predicted demand curve, while compute fields decorated with @api.depends derive dynamic minimum/maximum quantities, safety stock (from forecast error and your target service level) and Economic Order Quantity (EOQ) from lead time, holding cost and order cost. A scheduled ir.cron job refreshes forecasts nightly and — where you allow it — writes the results straight back into Odoo's own stock.warehouse.orderpoint reorder rules, so the standard Replenishment dashboard and procurement run keep working exactly as your team already knows them, just with intelligent numbers instead of static ones.
Every product is auto-classified — fast / medium / slow / dead — using ABC-XYZ style velocity and variability analysis, surfaced in filterable list and Kanban views (XML/OWL) so planners can see at a glance where cash is stuck and where stockout risk is climbing. A per-warehouse stockout-risk score flags SKUs likely to hit zero before the next receipt. When you're ready to act, one click generates draft purchase orders (or internal transfers) grouped by vendor, respecting MOQs and lead times, ready for your buyer to review and confirm.
Access is locked down with ir.model.access.csv plus record rules so planners see only their own warehouses, and a QWeb PDF replenishment report gives buyers and management a clean, printable order plan. A documented JSON-RPC/XML-RPC surface lets external BI, PLM or supplier systems read forecasts and push actuals. You keep full control: nothing auto-purchases without human confirmation unless you explicitly opt in.
你得到什么
- Complete, installable Odoo module (`__manifest__.py`, Python models, XML/OWL views, `ir.model.access.csv`, record rules, `ir.cron` jobs, QWeb report) targeting your Odoo 17, 18 or 19 instance
- Trained forecasting pipeline configured against your real sales history, with model parameters tuned to your catalog and seasonality
- Configuration of dynamic reorder policy, service levels, lead times and holding/order costs mapped to your actual products and warehouses
- Data-migration / backfill step that ingests historical `sale.order.line` and `stock.move` records to warm-start the forecasts
- Admin and planner documentation covering setup, day-to-day workflow, the JSON-RPC/XML-RPC API and troubleshooting
- Live training session for your inventory and purchasing team, recorded for onboarding future staff
- Deployment to your environment (staging first, then production) with a rollback plan and a validation checklist
- Post-go-live support window (bug fixes and tuning) plus a defined path for updates and Odoo version upgrades
这是给谁的
Inventory / Demand Planner
Owns min/max and reorder rules today and is drowning in per-SKU manual tuning. Wants dynamic thresholds, clear stockout-risk flags and dead-stock visibility so they stop fire-fighting and start planning.
Purchasing / Procurement Manager
Confirms POs and manages vendor relationships. Wants EOQ- and MOQ-aware, vendor-grouped draft orders and a clean printable plan, without losing final human sign-off on every purchase.
Operations Director / CFO
Answers for working capital and service levels. Wants less cash frozen in overstock, fewer lost sales from stockouts, and auditable forecast accuracy — all inside the Odoo system the business already runs.
AI Demand Forecasting & Auto-Replenishment for Odoo 如何比较
| 标准 | 伊科西尔 | 定制建造 | 竞争对手 | Odoo 原生 |
|---|---|---|---|---|
| Demand forecasting method | ML time-series (Prophet-style) trained on your own sales history, with seasonality and trend | Whatever you can build and maintain in-house — often a spreadsheet or basic average | Usually simple moving averages or fixed formulas, if any | None — you set static min/max by hand |
| Min/max & safety stock | Dynamic, recomputed nightly per warehouse from forecast error and service level | Possible but you own all the math and upkeep | Often static or semi-manual thresholds | Fully manual per product per warehouse |
| EOQ & MOQ-aware ordering | EOQ computed from lead time, holding and order cost; POs honor MOQ | Only if you code and validate it yourself | Rarely included, varies by app | Not available |
| Stock classification (fast/slow/dead) | Automatic ABC-XYZ style fast/medium/slow/dead with dashboard tiles | Manual analysis or custom reports | Sometimes basic ABC only | No built-in velocity classification |
| Writes back to native reorder rules | Yes — updates `stock.warehouse.orderpoint`, procurement run unchanged | Depends on your integration effort | Varies; some bypass native flows | N/A (rules are the manual native feature) |
| Tailoring & ownership | Built for your catalog, warehouses and version; you own the code | Fully yours but you carry all build risk and time | Generic; limited to the vendor's roadmap | Generic core behavior, no tailoring |
| Delivery & support | one working week build, install, training, support window, upgrade path | Your team's timeline and your team's support burden | Instant install but self-serve / ticket support | Included in Odoo, but no forecasting to support |
关于AI Demand Forecasting & Auto-Replenishment的常见问题
How long until the module is delivered and live?
This is a build-to-order module, not an instant download. After purchase we scope your sales history, catalog and warehouses, then engineer, install and validate the module — typically one working week depending on data volume and how many warehouses and edge cases are involved. We deploy to staging first, run it against your real history, then promote to production with a rollback plan. You'll get a delivery timeline confirmed in writing at kickoff.
What happens after go-live for support and updates?
Every build includes a post-go-live support window for bug fixes and forecast tuning, plus documentation and a recorded training session. Beyond that window we offer ongoing support and maintenance plans, and we handle updates and Odoo version upgrades (17 → 18 → 19) as a defined, quoted service. Because we build the module for you, we know the codebase and can extend or adjust it as your catalog and processes evolve.
Which Odoo versions and editions does it support?
We build for Odoo 17, 18 and 19, on both Community and Enterprise. The forecasting layer and dynamic policy engine are custom Python, so they don't depend on Enterprise-only apps — but if you run Enterprise we integrate cleanly with its inventory and purchase features. Tell us your exact version and edition at kickoff and we target it precisely.
Will this break or replace Odoo's native reorder rules and procurement?
No — it enhances them. Instead of bypassing Odoo, the module writes its computed min/max, safety stock and reorder quantities back into the standard `stock.warehouse.orderpoint` records. Your Replenishment dashboard, procurement run and existing routes keep working exactly as your team knows them; the numbers are just smarter. You can also run it in advisory-only mode where it never writes back automatically.
How accurate are the forecasts, and do I stay in control of purchasing?
Accuracy depends on your data history and demand stability, so we don't quote fictional numbers — instead the module tracks forecast accuracy (MAPE and bias) per product so you can see and audit real performance over time, and we tune the models during the support window. Purchasing stays human-in-the-loop by default: the system generates draft POs and transfers for your buyer to review and confirm. Full automation is available only if you explicitly enable it.
How does it handle multiple warehouses and data security?
Forecasts are computed per `stock.warehouse`, so each location gets its own demand curve, safety stock and reorder policy rather than a blended company-wide figure. Access is controlled with `ir.model.access.csv` and record rules, so a planner can be restricted to only the warehouses they own. External systems can read forecasts and post actuals through documented JSON-RPC/XML-RPC endpoints, keeping your data inside Odoo.
What data do you need from us to build it?
Primarily your Odoo sales and stock history — `sale.order.line` and `stock.move` records — plus vendor lead times, MOQs, and holding/order cost assumptions. The more clean history you have, the faster the models warm up. We handle the backfill and ingestion as part of the build, and we validate the forecasts against known periods before go-live so you can trust the output.
相关模块
AI Demand Forecasting & Auto-Replenishment
ML demand forecasts that replace static reorder rules with dynamic min/max, safety stock and EOQ across every Odoo warehouse.
- Historical-sales demand forecasting per product using a Prophet-style time-series model that decomposes trend and seasonality from your `sale.order.line` and `stock.move` history
- Per-warehouse forecasting so each `stock.warehouse` gets its own demand curve instead of one blended company-wide number
- Per-warehouse stockout-risk score that flags SKUs projected to hit zero before the next scheduled receipt
- Dynamic minimum/maximum quantities recomputed on a schedule and written back into native `stock.warehouse.orderpoint` reorder rules