Warehouse operations run on precision. A 10% forecast error compounds into overtime costs, missed SLAs, and idle labor. Attensus replaces spreadsheet-based planning with an engine that learns holiday surges, post-peak depressions, client-specific volume patterns, and seasonal workforce dynamics.
Most warehouse operations forecast with trailing averages in Excel. This works passably during stable periods but fails precisely when accuracy matters most: peak seasons, holiday transitions, and client onboarding ramps.
Volume typically surges 2–3 weeks before a major holiday, but staffing demand follows a different curve because worker productivity changes under surge conditions. After Easter, volumes consistently drop for four weeks, but trailing-average models don't capture this because the depression occurs at a different calendar position each year.
Multi-client 3PL operations add another layer. Each client has distinct volume patterns, contractual SLA requirements, and seasonal profiles. A warehouse serving five clients is effectively running five independent forecasting problems — and Excel treats them as one.
Converting volume forecasts into headcount requirements across receiving, picking, packing, dispatch, and returns — each with different productivity rates. A 5% volume error becomes a 12% staffing error when it compounds through the planning chain.
Black Friday, Christmas, and Easter create volume spikes of 30-80% above baseline. Staffing must ramp before volume arrives. But the ramp-down curve differs from the ramp-up — models that treat peaks symmetrically over-staff the tail every time.
3PL operators serve multiple clients from shared facilities. Each client has distinct volume patterns, SLA commitments, and seasonal profiles. Forecasting at the facility level hides client-level variance that drives staffing misalignment.
Volume surges begin weeks before a holiday, but timing varies by type and client. Easter is different from Christmas. The post-holiday depression varies in depth and duration. Trailing averages cannot capture moving-date holidays.
WMS exports arrive in semicolon-delimited CSVs, Latin-1 encoding, Danish comma decimals, and inconsistent date formats. Before you can forecast, you must survive the ingestion — and most tools cannot.
Hours, packages, order lines, and weight are correlated but not linearly. A surge in package count with smaller average weight has different staffing implications than the same count with heavier items. Independent forecasting misses these dependencies.
Attensus is not a generic time series tool adapted for logistics. The engine understands workforce groups, metric hierarchies, holiday calendars, and the specific data formats that logistics systems produce.
The engine maintains a comprehensive calendar of public holidays, school breaks, and commercial events across Nordic countries. It learns the specific lead time, magnitude, and recovery pattern for each event type — independently per facility and client.
After every major peak, volumes don't return to baseline immediately. The engine models the recovery curve — its shape, duration, and variation by peak type. Christmas recovery takes 3 weeks; Easter takes 4. Black Friday creates a secondary dip in mid-January.
The ETL pipeline handles semicolon-delimited CSVs, Latin-1 and UTF-8 encoding, Danish comma decimals (stripping thousand-separator periods, then converting comma to dot), and inconsistent date formats. A 500-row inline preview validates every transformation.
Each metric receives independent forecasts from all six models, with ensemble weights optimized per metric. Cross-metric consistency is validated post-ensemble — divergence beyond historical norms triggers an anomaly flag.
Automatic identification of unusual volume events — client promotions, system errors, one-off surges — before they distort future forecasts. Anomalies are flagged for human review: keep, exclude, or adjust.
Model the impact of facility closures, client onboarding, and capacity expansions before they happen. The engine propagates the change through all affected workforce groups and metrics.
Operations managers see shift-level forecasts and anomaly alerts. Finance sees cost projections and variance analysis. Executives see facility-level KPIs. Same data, different lenses — configured per role.
Automated narrative summaries of forecast changes, detected anomalies, and upcoming peaks. Delivered to each stakeholder with context appropriate to their role. The briefing explains why the forecast changed.
A Scandinavian 3PL operating 6 warehouses with 97 workforce groups replaced Excel-based forecasting with Attensus. Result: 3.2% average MAPE (down from 14%), 8 FTE reduction per facility, €340K annual savings, and forecast generation time from 2 days to 6 minutes.
3.2%
MAPE
8 FTE
Saved per site
€340K
Annual savings
6 min
Forecast time
Send us a sample CSV from your WMS. We'll run it through the engine and show you your MAPE within 48 hours — no commitment, no integration work.