Sevica, a logistics operator specialised in ecommerce fulfilment, worked with Crata AI to implement a platform that unifies data from its systems and applies predictive models to demand. Data consolidation became up to 80% faster and forecasting error fell by an estimated 20% to 30%.
Data scattered across systems that did not talk to each other
Sevica handles online orders for more than fifty retail brands from eight logistics centres across Madrid and Barcelona, moving over fifteen million units a year. Its own management software, Picko, gives each brand real-time traceability of its stock.
The forecasting side did not have that visibility. The information behind each estimate lived across older systems that did not communicate, so every forecast started with consolidating data by hand from different formats. Without a unified view, forecasting leaned on experience as much as on evidence, and at that volume, small deviations carried a real cost.
In a business where peak season is planned weeks ahead, that margin matters. Sizing teams, reserving space and committing to delivery windows all depended on estimates that arrived late and aged quickly.
A data platform that anticipates demand
With Crata AI, Sevica implemented a platform that brings information from its different systems into a single data layer and presents it in an operational dashboard the team uses daily. Predictive models run on top of that layer, anticipating demand and flagging anything that falls outside the expected range.
The real shift sits underneath the dashboard: planning decisions stopped relying on spreadsheets rebuilt every week and started relying on data that updates itself. The operations team now spends its time making decisions rather than gathering data.
The platform covers the data's full journey, from source system to operational decision:
- Integrates and unifies information from multiple systems into one layer.
- Shows the state of the operation in a dashboard updated in real time.
- Forecasts demand from historical records and the patterns of each brand.
- Automatically detects anomalies that depart from usual behaviour.
