Case study · Department store

How a multinational apparel retailer compressed planning from three weeks to one day

A team of 12 spent three weeks every month producing forecasts. With RabbitHawk, all forecasts now run in a single day, shifting a reactive, spreadsheet-driven process to an adaptive, goal-led decision engine.

Client: Multinational apparel & department-store retailer
Industry: Department store
Country: USA
Revenue: Just under US $100M

Measured outcomes

The results of the engagement

Reduction in new inventory: 52%Reduction in new inventorywith no loss of sales revenue
Forecast accuracy improvement: 96%Forecast accuracy improvementvs legacy forecasting
Reduction in manual workload: 90%Reduction in manual workloadfrom weeks to hours
Decision cycle: 3 wks → 1 dayDecision cyclefrom debate to coordinated action

Results from this multinational retailer engagement. Outcomes vary by data, workflow and operating context.

Before: ERP-rich, planning-poor

Numbers everywhere. One trusted decision, nowhere.

The business was ERP-rich but planning-poor. A leading ERP proved rigid and slow to adapt, so the planning team had reverted to Power BI and spreadsheets. Numbers were everywhere; a single, trusted decision was nowhere.

The planning bottleneck

Three weeks of debate, every month

Each monthly cycle consumed weeks of debate and reconciliation. Twelve people spent three weeks arguing over whose forecast was correct: time spent reconciling numbers instead of acting on them, while long lead times and tight cashflow windows punished every delay.

The customer

A distributed supply chain under cashflow pressure

A multinational department-store operator with revenue just under US $100M. The company designs locally and outsources production across the USA, South America and Asia: a distributed supply chain with long lead times and tight cashflow windows. Its leading ERP proved rigid and slow to adapt, so the team had reverted to Power BI for planning. Each monthly cycle consumed weeks of debate and reconciliation, with teams arguing over whose forecast was correct.

The solution: an adaptive layer, not a replacement

RabbitHawk on top of the existing ERP

RabbitHawk deployed its forecasting & optimization engine as an adaptive layer on top of the existing ERP, with no rebuild. It ingests client data exports, forecasts by style × color × store × brand, surfaces ageing inventory, projected stock-outs, MOQ impacts and margin risk, then exports optimized purchase orders back to the ERP: a closed loop from data to action.

Multi-level forecasting

SKU, category, store and region, each with cross-functional visibility.

Probabilistic forecasting

Risk-aware predictions that show not just what is likely, but what is possible.

Goal-driven optimization

Purchasing that balances cashflow, margin, revenue and constraints (MOQ, lead time, risk, budget).

The workflow

A closed loop, with humans in control

Forecasting and optimization run as one governed sequence, and every recommendation is reviewed by a planner before it returns to the ERP.

Connect

Ingests client data exports from the existing ERP, with no rebuild and no migration.

Forecast

Forecasts probabilistically by style × color × store × brand, with honest ranges.

Optimize

Surfaces ageing inventory, projected stock-outs, MOQ impacts and margin risk, then optimizes purchase orders against cashflow, margin and constraints.

Approve

Planners review the assumptions, evidence and expected impact behind every recommendation, so humans stay in control.

Export

Optimized purchase orders are exported back into the ERP, closing the loop from data to action.

“It’s the first time our meetings started with what’s possible rather than what went wrong.”

Planning Director·Multinational apparel & department-store retailer

Why it worked

Impact, not incrementalism

Extension, not replacement
Human-centerd automation
One coherent planning truth
Probabilistic planning
Constraint-aware optimization

Extension, not replacement

RabbitHawk extends the ERP rather than displacing it: “add intelligence without rebuild” delivers immediate ROI.

Goal-aligned optimization

Optimizes for cashflow, GMROI and revenue within real constraints, not pure accuracy. The system learns what success means for the retailer.

Human-centerd automation

Eliminating manual drudgery redirects expertise toward scenario planning and proactive risk management.

Demonstrable ROI

Outcomes quantified in both time and capital saved: practical, compounding returns over time.

What other retailers can learn

Transferable lessons

You do not need to replace the ERP to fix planning; add an adaptive intelligence layer on top of it.
Optimize for the outcome (cashflow, margin, service) rather than forecast accuracy alone.
Keep planners in the loop: auditable recommendations beat opaque automation for trust and adoption.

What’s next

Phase two expands RabbitHawk in two directions: a top-level UI that focuses daily decisions on broad company goals, and store-specific micro-analysis that finds opportunities in local demographics and buying behavior.

See the retail solution

Could this be your planning story?

Tell us about your planning environment and the decisions you need to improve, and we’ll show you how RabbitHawk would approach it.

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