We all know that the last few years have been tough for many global supply chains. At the same time, customer expectations around shipment delivery times and status updates only climb higher. For this reason, along with decades of global disruption events hammering our supply chains, has seen shipment visibility become an increasingly important topic for organizations.
Common supply chain challenges include:
- More fragmentation and complexity
- Increasing costs of transport
- Process inefficiencies, caused by blind spots, where shipment statuses are unknown
The impacts of these challenges on business performance are significant, potentially causing inventory shortages, late penalties, lost revenue, and damage to brand reputation.
Fortunately, it’s now more accessible than ever to track orders as they travel throughout global supply chains, in real time, thanks to the availability of faster and more reliable connectivity, affordable IoT devices, and more compatible systems and software platforms.
Our platform offers end-to-end shipment visibility at any point across the supply chain, so that enterprise supply chain management can have the same shipment delivery experience for their supply chain shipments as they’ve come to expect at home from online retailers, offering better service, transparency and real-time traceability.
Although knowing where a shipment is brings a raft of benefits, the question that supply chain managers and transport teams ask themselves is shifting from ‘Where is my shipment?’ to ‘When will my shipment arrive?’ and ‘Will it be on time?’. For this reason, being able to predict ETAs for shipments is becoming more and more important.
The accuracy and reliability of these predictions depends on the quality of the data captured, and how sophisticated the technology and computational methodology used is. Dataiku, a specialist company helping organizations to systemize their use of data and AI, is helping Shippeo increase its pace of innovation, by offering improved ways to ingest, clean and transform data, design and train ML models, and manage model lifecycles.