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Driving OTIF warehouse performance with A.I.


The logistics department of this international, industrial company, worked with KPI’s for the shipment of spare parts to which they referred to as OTIF: On Time In Full. This KPI represents the percentage of shipments which arrive at their destination on time, compared to the promised delivery time at the moment of ordering. CROPLAND was asked to investigate the root causes of OTIF failures in the logistical process and to determine the key elements that impact the client’s OTIF.

Objectives of the cooperation

To identify the determinants of OTIF, different datasets from multiple business processes like order picking, invoicing, purchasing, warehouse picking, shipping, … were connected and analysed using advanced BigData Analytics algorithms. Next, an A.I. algorithm was implemented that runs live predictions on the operational ICT system.


As a result of this, our client was able to realise their OTIF goals by identifying the root causes of OTIF failures. Because of this improved customer communication, their customer loyalty increased noticeably.

Technology Data wrangling I R I Artificial Intelligence

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