Challenge — Inbound Logistics Volume Variability & Planning Risk
In manufacturing and production environments, inbound logistics volumes are rarely stable. The actual quantity of goods delivered by suppliers fluctuates due to vacation periods, seasonal influences, and other external factors. This volatility creates major challenges for supply chain planning and operations management: overdelivery can cause warehouse congestion, higher inventory, and increased storage costs, while underdelivery raises the risk of material shortages, production downtime, and reduced service levels. The goal is to reliably predict delivered quantities to enable better production planning.
Solution — Data-Driven Inbound Delivery Forecasting with Time-Series ML
We build a forecasting model that reliably predicts inbound delivery volumes by combining supplier-available data with publicly available indicators. The approach uses advanced time-series machine learning models, including Autoregressive Transformer architectures, to capture seasonality, trend patterns, and volatility—supporting more robust planning for inbound flows and production.
Impact — Customer Satisfaction, Warehouse Utilization & Reduced Waiting Times
- Up to 25% increase in customer satisfaction through improved planning
- Around 20% improvement in warehouse utilization
- Minimized waiting time in warehouse operations