But how are the ETAs, which the whole
industry is starting to rely on, calculated? You can make business decisions
based on ETAs only if you are confident
in their reliability.
This whitepaper gives
a behind-the-scenes overview of the infrastructure and components necessary
to create high-quality, reliable ETA predictions.
Chapters included
Core concepts In order to understand the technical playing field of ETA calculations.
Building blocks The most important factors that are taken into consideration at ETA calculation.
Getting started There are certain technical and organizational steps that stakeholders need to take.
Chapters included
Core concepts In order to understand the technical playing field of ETA calculations.
Building blocks The most important factors that are taken into consideration at ETA calculation.
Getting started There are certain technical and organizational steps that stakeholders need to take.
Key takeaways
ETA quality
ETA quality and applicability in business processes increase significantly only when we add derived insights and domain knowledge into the mix, which are based upon in-depth logistics expertise, data science, and machine learning.
Secret ingredient
Historical data is Sixfold’s “secret ingredient.” As we track more and more shipments, our algorithms can pick up various patterns in carrier and warehouse behaviours — rest stop durations and locations, tendencies of individual carriers and even vehicles, frequent delays at specific sites. These observations help us reduce average uncertainty in ETA prediction by a magnitude compared to the standard ETA providers — given enough shipments.
ETA credibility
For supply chain leaders, it’s important to acknowledge the various aspects that give ETA credibility. Decision makers need to understand how the smart use of historical data and machine learning brings out the real value and benefits for organizations. This all helps to build critical business processes around visibility — would it be from an inbound radar for incoming transports or dynamic warehouse management to shorten waiting and dwelltimes.
ETA quality and applicability in business processes increase significantly only when we add derived insights and domain knowledge into the mix, which are based upon in-depth logistics expertise, data science, and machine learning.
Historical data is Sixfold’s “secret ingredient.” As we track more and more shipments, our algorithms can pick up various patterns in carrier and warehouse behaviours — rest stop durations and locations, tendencies of individual carriers and even vehicles, frequent delays at specific sites. These observations help us reduce average uncertainty in ETA prediction by a magnitude compared to the standard ETA providers — given enough shipments.
For supply chain leaders, it’s important to acknowledge the various aspects that give ETA credibility. Decision makers need to understand how the smart use of historical data and machine learning brings out the real value and benefits for organizations. This all helps to build critical business processes around visibility — would it be from an inbound radar for incoming transports or dynamic warehouse management to shorten waiting and dwelltimes.
Get your free copy of ETA Whitepaper Series part 2 now!