The decision layer on top of your systems — live in weeks, not months. Your business runs on an ERP and channel tools, but the plan runs on spreadsheets: buy sheets, forecast tabs, allocation workbooks held together by tribal knowledge. Plantryx replaces the spreadsheet layer — your systems stay — with AI-native demand planning, supply planning, and a control tower.
Works alongside the systems and channels you already operate on: NetSuite · Microsoft Dynamics · SAP · Oracle · Shopify · Amazon · and others · See all integrations
The ERP holds the orders, the BI shows the dashboards — but the real planning system is Excel: deciding what to buy, how much, and where to put it happens in workbooks. If any of these sound familiar, you're exactly who we built Plantryx for.
The forecast lives in one workbook, the buy plan in another, allocation in a third — linked by copy-paste and the one person who knows how it all works. It breaks when they're out, a formula drifts, or two versions collide in email.
Bestsellers stock out while slow movers age into markdowns and write-offs. The overstock and the lost sales are both real — and both invisible until the quarter closes.
Thousands of SKUs across DTC, wholesale, and marketplaces — each channel with its own velocity, seasonality, and lead time. One forecast tab per planner can't keep up, and nobody trusts the rollup.
Vendor lead times run months, so every PO is a bet placed against promos, seasonality, and demand you haven't seen yet — with no way to test the downside before the cash is committed.
Planning decisions are too dynamic for spreadsheets — and too cross-functional for disconnected workbooks.
Three modules that work as one platform — replacing the spreadsheet layer while your systems stay.
Forecasts built the way consumer demand actually works — by channel, season, and promotion, at the level you plan.
Buy and replenishment planning that doesn't stop at "you're low" — it gets you to a funded, approved buy.
The executive layer across demand, supply, and inventory — one picture of what changed and what it costs.
Plantryx doesn't ask planners to review everything — it tells them what's worth reviewing, with the analysis already done. The weekly cycle shrinks from days of spreadsheet triage to a ranked queue, and decision lag closes with it.
Every refresh surfaces what changed: demand spikes, supplier slippage, material shortages, promise-date risk, excess building — before they surface in a weekly meeting.
Issues are ranked by business consequence — expected value, urgency, and days to impact — not by who shouts loudest.
Every issue is quantified before anyone opens a spreadsheet: revenue at risk, cost to recover, time to failure.
Each issue carries a recommended action with a confidence score, plus alternatives to compare — owned and worked in one shared workspace.
Approved actions flow back into your workflow. Routine moves can auto-execute within policy thresholds your team sets; everything else waits for a planner.
Most tools bolt a chatbot onto old workflows. Plantryx redesigns the workflow so signals become decisions. Here's specifically where AI does the work — automating friction, never the planner's judgment.
The platform detects each item's demand pattern — seasonal, intermittent, trending, lumpy — and applies the best-fit model from a library of 25+ models spanning statistical forecasting, machine learning, and ensembles. Models retrain as new data arrives, so forecasts adapt instead of decaying. Every forecast is a range backed by a confidence level and explainable drivers — not a single point you're forced to trust.
AI watches the signals consumer demand actually moves on — sell-through and POS, marketplace and web trends, promo response — detects shifts early, and flags them with their forecast impact, so the plan reacts in the same cycle instead of next month's review.
Build named scenarios on the levers that actually move a brand's plan — a price change, a landed-cost increase, a volume push — scoped to a vendor, category, or product. See margin, revenue, and volume impact side by side, run a supply feasibility check before committing, then adopt the winning scenario into the plan. Non-destructive and fully auditable.
When AI diagnoses an issue or writes a narrative, every number in it is traceable to a published fact from your ERP — the order, the PO, the inventory position. No black-box scores, no hallucinated impacts. If the data can't support a claim, the platform says so instead of making one up.
Open any flagged issue and the AI co-planner walks you through it: what happened, the evidence behind it, and selectable costed action options. Ask it to adjust a forecast, rebalance an allocation, or draft the vendor follow-up — then compare tradeoffs and act. In planning meetings, ask it questions in plain language: why a forecast moved, where excess is building, which channel is driving the miss.
The AI recommends and drafts; actions execute only with planner approval — or automatically, within policy thresholds your team has pre-approved.
Planner overrides are logged with reasons and outcomes. AI learns which adjustments improved accuracy — so institutional knowledge compounds in the platform instead of walking out the door with a planner.
Pre-built ERP connectors, out-of-the-box templates, and hands-on onboarding from supply chain practitioners — no multi-quarter implementation, no army of consultants.
Most brands can't put a number on what their planning gaps cost — in markdowns, stockouts, and trapped cash. That number is exactly what the diagnostic delivers, from a focused data extract, no integration required.