AI demand forecasting for Odoo MRP — predicts requirements, feeds MPS/MRP, and flags bias. Built-to-order for Odoo 17/18/19. Built to order by ECOSIRE for Odoo 17, 18, 19 — indicative price from $799.00 USD; request a quote for a scoped proposal.
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AI demand forecasting for Odoo MRP — predicts requirements, feeds MPS/MRP, and flags bias. Built-to-order for Odoo 17/18/19.
自分での設定は不要 — ECOSIRE が構築・インストール・サポートする実働アプリです。
一回限りの構築料金から始めます。キックオフで一緒に範囲を決めます。
ECOSIRE がお客様の Odoo 上で構築・設定・インストールします。
約2〜4週間で稼働し、リリース後のサポート期間が付きます。
Most SMB manufacturers still plan production the same way they did a decade ago: a spreadsheet, a gut-feel safety stock, and a monthly scramble when a bestseller runs dry or a slow mover fills the warehouse. Odoo's Master Production Schedule (MPS) and MRP are excellent execution engines, but they only replenish against the demand numbers you feed them — and if those numbers are a manual guess, MRP faithfully propagates the guess into purchase orders and manufacturing orders.
The AI Production Forecasting & Demand Planner closes that gap. It's a build-to-order Odoo module that learns demand patterns from your own sales history — trend, seasonality, promotions, and intermittent SKUs — and writes machine-generated forecasts directly into Odoo's planning layer. Instead of a planner typing "forecasted demand" by hand into the MPS, a scheduled forecast run populates it, so MRP and reordering rules replenish against a statistically grounded prediction rather than an educated guess.
This is not an app you download and self-install from apps.odoo.com. ECOSIRE designs, builds, and deploys the module against YOUR Odoo database, YOUR product catalog, and YOUR sales patterns. Over a typical 2–4 week engagement we model your data, tune the forecasting logic to your SKUs, wire it into MPS/MRP, and hand you a documented, tested, supported module you own.
Under the hood it's real Odoo engineering, not a bolt-on. A custom demand.forecast model (a proper models.Model with stored and @api.depends computed fields) holds forecasts per product, location, and period. An ir.cron automated action runs the ML pipeline on a schedule you choose (nightly or weekly). Forecast values are pushed into product.mps / the MPS demand line and made available to stock.warehouse.orderpoint so replenishment reflects the prediction. Every record is governed by ir.model.access.csv plus record rules so planners see only their warehouses, and dashboards use standard views (list/pivot/graph) plus OWL components for the forecast-vs-actual chart. Accuracy metrics (MAPE, bias) are computed automatically and surfaced as QWeb PDF reports and Discuss/activity alerts when a SKU's forecast drifts.
The result: fewer stockouts on the products that actually sell, less cash frozen in overproduced slow movers, and a planning process that improves every month as the model retrains on fresh actuals. It runs on Odoo 17, 18, and 19, on Community or Enterprise (MPS itself is a Community feature), and integrates with the Manufacturing, Inventory, Sales, and Purchase apps you already run.
Owns the monthly production and purchasing plan and is tired of guessing safety stock in spreadsheets. Wants Odoo's MPS to be fed by a defensible forecast instead of gut feel, and needs visibility into which SKUs the model gets right or wrong.
Accountable for service levels and working capital at the same time. Fights the stockout-vs-overstock trade-off constantly and needs SKU- and category-level forecasts, scenario planning, and accuracy metrics to defend inventory decisions in S&OP reviews.
Responsible for the Odoo instance and wary of unsupported third-party apps. Wants a clean, documented module that respects Odoo conventions (proper models, security records, crons), installs across 17/18/19, and comes with a support contact rather than a mystery zip file.
| 基準 | エコシエール | カスタムビルド | 競合他社 | オドゥー ネイティブ |
|---|---|---|---|---|
| Forecasting method | ML on your sales history — trend, seasonality, and intermittent-demand handling, tuned to your SKUs | Whatever your developer builds; quality depends entirely on their forecasting expertise | Fixed statistical method baked into the app, not tuned to your data | |
| Feeds Odoo MPS/MRP automatically | Yes — machine-generated forecasts written into MPS demand lines and reordering rules | Possible, but you pay to design and build the integration from scratch | Sometimes; varies by app and often needs manual export/import | |
| Forecast accuracy & bias tracking | Built in — MAPE and bias per SKU via compute fields, plus automated drift alerts | Only if explicitly scoped and built | Basic or absent in most marketplace apps | |
| Scenario planning (optimistic/base/pessimistic) | Included — compare variants and promote one into MPS | Extra build effort on top of base forecasting | Rarely offered | |
| Fit to your catalog & processes | Purpose-built against your data, warehouses, and cadence during a 2–4 week engagement | Fully bespoke, but you carry all specification and QA risk | Generic — you adapt your process to the app's assumptions | |
| Support, ownership & version coverage | Full source ownership, documentation, training, warranty window; runs on Odoo 17/18/19 | You own it but also own all future maintenance and bugs alone | Vendor-dependent; may lag Odoo version upgrades or be abandoned | |
| Time to value | Built, integrated, and supported in ~2–4 weeks — no self-implementation | Months, plus hiring or diverting a skilled Odoo/ML developer | Fast to install but slow to trust; tuning and integration are on you |
This is a build-to-order engagement, not an instant download. Typical delivery is 2–4 weeks from kickoff, depending on the state of your sales history and how deep the MPS/MRP integration needs to go. We work in stages: data assessment and forecast modeling first, then module build, then deployment to a staging database for your sign-off, and finally production go-live. You'll have checkpoints along the way rather than a single hand-off at the end.
Every engagement includes a defined post-go-live support and warranty window for bug fixes, forecast tuning, and questions from your team. Because you receive the full source code, the module is yours to keep and extend. We also offer ongoing support and Odoo version-upgrade retainers (for example, moving your build from Odoo 17 to 18 or 19) as a separate arrangement so the forecasting logic keeps working across future Odoo releases.
It works on both. The Master Production Schedule (MPS), MRP, reordering rules, and the ORM/security/cron building blocks we rely on are available in Odoo Community, and the module targets Odoo 17, 18, and 19. If you're on Enterprise, it also plays cleanly with the Enterprise Manufacturing and Inventory features. We confirm the exact edition and version during scoping so the delivered build matches your instance.
We don't publish a blanket accuracy number because it depends entirely on your demand patterns — steady, seasonal products forecast far better than brand-new or highly erratic SKUs. What we do commit to is measuring accuracy honestly: the module computes MAPE and bias per SKU automatically and shows forecast-vs-actual so you can see where it performs well and where human judgment should override it. For modeling we typically want 12–24 months of Odoo sales/delivery history; the more clean history you have, the stronger the seasonal signal.
No. The module writes forecasts into the planning layer (MPS demand and forecast inputs) and leaves your MRP execution, BoMs, routings, and reordering-rule logic intact — MRP simply replenishes against better numbers. Planners keep full manual override on every forecast line, changes are logged in the chatter, and access is scoped by record rules so nobody sees warehouses they shouldn't. We deploy to staging first and validate against your real data before anything touches production.
Yes. Planners can edit any forecast line directly, and the module ships with optimistic/base/pessimistic scenario variants you can compare side by side and promote into the MPS. Every override is auditable. If you later want new scenario logic or additional drivers (promotions, new-product introductions, external demand signals), those extend the same `demand.forecast` model — and since you own the code, either your team or ECOSIRE can build them.
AI demand forecasting for Odoo MRP — predicts requirements, feeds MPS/MRP, and flags bias. Built-to-order for Odoo 17/18/19.