Industries / Logistics & Warehousing
Warehouse operations run on precision. A 10% forecast error compounds into overtime costs, missed SLAs, and idle labor. Attensus replaces spreadsheet-based planning with a forecasting engine that learns the operational rhythms of your facilities — holiday surges, post-peak depressions, client-specific volume patterns, and seasonal workforce dynamics.
The problem
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.
The failure mode is asymmetric. Volume typically surges 2–3 weeks before a major holiday — Black Friday, Christmas, Easter — 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 simultaneously — and Excel treats them as one.
Challenges we solve
Converting volume forecasts into headcount requirements across receiving, picking, packing, dispatch, and returns — each with different productivity rates, skill requirements, and shift constraints. 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 (hiring and training lead times) and scale down after — but the ramp-down curve differs from the ramp-up. Models that treat peaks symmetrically consistently over-staff the tail.
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; forecasting at the client level creates hundreds of independent series that need orchestration.
Volume surges begin weeks before a holiday, but the timing varies by holiday type and client. Easter is different from Christmas. Consumer goods clients spike earlier than B2B. The post-holiday depression varies in depth and duration. Trailing averages cannot capture any of this.
WMS exports arrive in semicolon-delimited CSVs, Latin-1 encoding, Danish comma decimals, inconsistent date formats, and missing value patterns that vary by system version. 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 package count with heavier items. Forecasting each metric independently misses these cross-metric dependencies.
The Attensus approach
Attensus is not a generic time series tool adapted for logistics. The engine understands the operational structure of warehouse networks — 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. Easter's 4-week post-holiday depression is captured automatically from the first year of training data.
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 recovery is faster but creates a secondary dip in mid-January. All of this is learned, not configured.
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 before commit. No data engineer required.
Each metric (hours, packages, lines, weight) receives independent forecasts from all six models, with ensemble weights optimized per metric. Cross-metric consistency is validated post-ensemble — if package count forecasts diverge from weight forecasts beyond historical norms, the system flags the anomaly.
Platform features
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. The engine learns from your decisions.
Model the impact of facility closures, client onboarding, and capacity expansions before they happen. "What if we lose Client X in Q3?" or "What if we add a night shift?" — 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 and trend summaries. Same data, different lenses — configured per role, not per user.
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, not just that it did.
What you forecast
The engine handles any numeric time series your WMS can export. These are the most common metrics in logistics deployments — each forecasted independently with cross-metric consistency validation.
Total and per-function (receiving, picking, packing, dispatch, returns). The primary input for shift planning and cost projection.
Inbound and outbound. Drives throughput planning, conveyor capacity, and staging area allocation.
A better proxy for pick complexity than package count. High line-count orders require more picker time per package.
Total kilograms through the facility. Correlates with equipment utilization, forklift hours, and physical strain metrics.
Financial overlay on operational forecasts. Enables margin analysis per workforce group and cost-per-package trending.
Any numeric series from your WMS: returns rate, dock-to-stock time, pick accuracy, or facility-specific KPIs.
Integration
Attensus ingests data from any warehouse management system that can produce a CSV export or API response. The ETL pipeline handles the format conversion, validation, and quality checks that typically require a data engineering team.
Blue Yonder, Manhattan Associates, SAP EWM, Körber, or custom systems. If it exports CSV, Attensus can ingest it. Configurable delimiters, encoding, decimal formats, and date patterns.
REST API endpoint for pushing daily actuals from your WMS. Enables automatic model retraining and drift detection without manual file uploads.
Every data upload gets a 500-row preview that validates column mapping, decimal conversion, date parsing, and metric assignment. You see exactly what the engine will receive before any data is written.
Automatic detection of missing values, outliers, duplicate records, and encoding errors. Quality score per column with specific remediation suggestions.
In production
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.