One of the lasting changes from the COVID-19 pandemic is rethinking how companies address supply chain risk. Underpinned by technologies, risk analytics have become embedded in supply chain decision-making, offering a competitive advantage of a more agile and resilient supply chain than their competition.
Supply chain management and transport teams are now demanding information beyond ‘where is my shipment’ to ‘when will it arrive at the next stop?’ and ‘are there any risks of a delay?’ As such, the ability to predict shipment ETAs has become increasingly valuable.
However, their accuracy and reliability depend heavily on the quality of the data captured and the sophistication of the technology and computational methodology used.
In this article, we will explore why data quality is essential for RTTV platform performance, both in calculating predictive ETAs and carrying out comprehensive analyses on flows to generate and leverage it for comprehensive analyses on flows and generate valuable insights into a supply chain’s operations and processes.
The challenges posed by lack of data
When shippers experience delays in getting shipments out for delivery or use resource-intensive communication to reach teams and customers, it degrades the experience and decreases efficiency. If issues occur on the production line or stock runs out, it causes operational and planning headaches for supply chain managers. This can have a knock-on effect if delays occur due to docking bay mismanagement and congestion at ports. All of these are issues that, in the current climate, shippers would ideally love to avoid.