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Demand-Forecasting, das Black Friday und Dienstag übersteht

Kontinuierliche Modelle für Peaks und Normalbetrieb—mit promotionalem Kontext, Planner-Override-Workflow und nativer ERP-Anbindung.

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The problem

Forecasts work for the average day and break on the ones that matter

  • Peak-day forecasts blow up on Tuesdays

    Models tuned for Black Friday and Christmas miss the quiet weeks; models tuned for normal days collapse on peaks. Planners maintain two parallel processes.

  • Promotions distort baselines

    Promotion lift is mixed with organic demand in historical data. The next forecast confuses cause and effect, and the planning team spends the cycle untangling it.

  • SKU-store granularity blows compute

    Real forecasts need SKU-store-day granularity. Most engines either give up on granularity or burn the warehouse compute budget.

  • No override workflow

    Planners know things the model doesn't—launches, weather, local events. Without a structured override and tracking, that knowledge stays in spreadsheets.

So funktioniert es

Models that scale, planners that own the override

Schritt 1

Ingest and reconcile

Sales, promotions, weather, events, and prices normalized; hierarchies built once and reused across SKUs, stores, and time.

Schritt 2

Forecast and explain

Hierarchical models with promotion lift decomposition and confidence intervals—planners see which factors drove the prediction.

Schritt 3

Override and publish

Planner overrides captured with reason codes; downstream replenishment uses both model and override—with full lineage to audit.

Flow adapts to your channels, ERP, and replenishment cadence.

Demand-Forecasting, das Black Friday und Dienstag übersteht

Leistungsumfang

Everything you needfor demand forecasting that scales

Hierarchical models, promotion lift, planner workbench, and ERP integration in one layer on Synapse—delivered with planners owning the override workflow.

Peak-aware models

Single model family that handles peaks (Black Friday, launches) and regular cadence without two parallel processes.

Promotion lift modeling

Decomposes historical lift from baseline; future promotions modeled with what-if simulation.

Hierarchical reconciliation

Forecasts coherent across SKU, store, region, and channel—no manual top-down/bottom-up.

SKU-store scale

Distributed inference on the largest catalogs without warehouse compute meltdown.

Planner override workbench

Reason-coded overrides with audit trail; model learns from overrides over time.

ERP and replenishment integration

Native connectors to SAP, Oracle, and replenishment engines—forecasts post directly to the plan.

Unterstützt von Synapse

Ergebnisse

What changes when this runs in production

Results vary by category mix, promotion intensity, and data quality. We scope honestly before we promise precisely.

−20–35%

stockouts on top-volume SKUs

Orientative—varies by category and current baseline.

−10–20%

overstock and markdown exposure

Orientative—based on early implementations.

+15–25%

forecast accuracy (MAPE) on peak weeks

Orientative—depends on promotional history quality.

So arbeiten wir

From first call to production—without the usual drag

Assess

Week 1–2

Map categories, promotion calendar, current forecast process, and the top accuracy gaps.

Design

Week 3–4

Define hierarchy, override workflow, replenishment integration, and accuracy KPIs.

Build

Week 5–9

Train and validate models, integrate ERP, design planner workbench, pilot on one category.

Scale

Week 10+

Roll out by category, tune promotion lift, govern override loop, expand to omnichannel.

Timelines vary by SKU count, channel mix, and promotional intensity.

Loslegen

Ready for forecasts that survive Tuesdays and Black Fridays?

No commitment. We start with a scoped session to map your categories, promotions, and current process.

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