Zero Unplanned Stockouts, Live in 6 Weeks
Made By Mary planned demand in spreadsheets while scaling into thousands of SKUs. Rudder Analytics built AI demand planning on its Fulfil ERP data: retail-timed forecasts, consolidated component buys, and one-click purchase orders back into Fulfil.
Forecast
12-month SKU forecasts, aware of promotions and retail timing.
Plan
Multi-level BOM demand resolved to components, with weeks-of-supply logic.
Auto PO
Grouped draft purchase orders written straight back into Fulfil, one click.
Demand Planning at Scale
Made By Mary is a direct-to-consumer jewelry brand. It pairs made-to-order personalization with ready-to-ship collections, and seasonal gifting, influencers, and promotions drive sharp demand spikes.
Scale raised the bar on planning. Thousands of SKUs and overlapping raw materials pushed beyond what spreadsheets could coordinate, and sales history sat apart from promo effects, so plans leaned on judgment more than data.
Rudder Analytics built AI demand planning on the brand’s Fulfil ERP data. The system produces retail-aligned 12-month SKU forecasts, consolidates multi-level BOM demand, and writes grouped draft purchase orders back into Fulfil.
Strain Across the Planning Chain
Manual spreadsheets could not keep pace with a fast-growing product mix. A planning transition added urgency to building something durable and repeatable.
Fragmented, Off-Cycle Forecasts
Sales history and promo effects sat in separate spreadsheets. The result was timing errors and scattered SKU-level forecasts.
Cash in the Wrong Stock
Buying inputs without consolidated demand tied up cash. That shrank the capacity to buy newer, faster-moving SKUs.
Double-Counted Components
One component fed several finished SKUs. Buyers placed duplicate orders and reconciled by hand across BOM levels.
Manual PO Creation
Creating and grouping purchase orders was slow and manual, with limited traceability once orders were placed.
Buys and Budget Apart
Purchase quantities and budget allocations were set separately, so buys did not always reflect cash priorities.
A Planning Handover
A planning transition left the process without a dedicated owner, adding urgency to a durable, documented system.
AI Planning on Top of Fulfil
Fulfil stayed the single source of truth, and the planning layer sat above it with no middleware rewrite. Forecasts, constraints, and inbound stock feed one buy engine that drafts purchase orders straight into the ERP.
ERP & Sales Data
Fulfil BigQuery schema and transactional exports, refreshed on schedule with ad-hoc pulls on campaign days.
Predictive Engine
12-month SKU forecasts with promo flags and 4-4-5 retail-week markers, scored on holdout validation.
Demand Plan
Category and SKU demand, BOM-resolved component needs, WOS logic, and manual overrides.
Auto PO Into Fulfil
An LLM agent turns the plan into grouped draft POs, approved in one click and logged in the ERP.
One Planning Dataset
Fulfil’s BigQuery schema and exports feed a central planning dataset. Scheduled ETL, ad-hoc campaign refreshes, and logged validation keep it current.
Retail-Timed Forecasts
Monthly 12-month SKU forecasts draw on 36–48 months of history, with promo flags, 4-4-5 markers, and per-SKU MAPE/WAPE confidence scores.
Plan-to-PO Quantities
Category and SKU demand come from recent contribution weights. The plan computes saleable and purchasable demand and supports scoped overrides.
One Line Per Component
Finished-good demand rolls into input needs across multi-level BOMs. Component demand aggregates into a single buy quantity, cutting order fragmentation and supplier costs.
Protected Working Capital
The plan surfaces quantity on hand, live open POs, expected end-of-period inventory, and Weeks-of-Supply. Target WOS sets the quantity needed while holding service levels.
One-Click, Auditable POs
An AI agent blends forecasts, safety stock, WOS targets, lead times, MOQs, and inbound POs into vendor-aware recommendations. Each PO posts to Fulfil, logged with user, time, and reason.
Six Weeks to Production
From kickoff to a production planning system in six weeks. Each stage shipped usable capability instead of waiting for a single cutover.
Data Integration
Fulfil schema and exports linked into one validated planning dataset.
Live Forecasts
12-month SKU forecasts with promo flags and retail-week markers, scored on holdout.
Demand Plan & BOM
Category and SKU demand, multi-level BOM consolidation, and WOS logic in place.
Auto PO in Fulfil
AI buy planning writing grouped, auditable draft POs into the ERP.
Impact in the First Quarter
Across the pilot and first three months live, early warnings and automated recommendations produced zero unplanned stockouts. Working capital came back, and the team scaled without adding planning headcount.
| Metric | Before | After | Impact |
|---|---|---|---|
| Deployment time | Spreadsheet planning | 6 weeks to live | new capability |
| Unplanned stockouts | Recurring | 0 | eliminated |
| Category forecast accuracy (wMAPE) | Spreadsheet baseline | ~25% better | +25% |
| Average on-hand inventory | Baseline | ~20% lower | −20% |
| PO create & approve time | Manual, per-line | ~80% faster | −80% |
| Planning & reconciliation time | Baseline | ~60% lower | −60% |
Made By Mary now plans on its own ERP data, promotion-aware and fully auditable. Buys track budget and cash, procurement cycles run shorter, and in-stock performance holds as the product mix keeps growing.
Data and AI for Brands Outgrowing Their Stack
Rudder Analytics builds that platform — from the first pipeline to reporting a board can act on.
