Splice Insights
Technology, Logistics and Supply Chain Perspectives

In logistics, the "integration problem" is a cocktail of two specific issues. First, the Information Gap: "I don’t know what I need to know." Second is the Connection Gap: "I can’t get the data to where it needs to be." In logistics, these gaps are rarely isolated. They combine to create a black hole that results in demurrage fees, missed sailings, and frustrated customers. Splice offers a suite of compatibility tools, including AI, designed for the fragmented world of freig

LLM hallucinations: We’ll explain what they are, how they impact logistics workflows, and why deterministic automation platforms like ours are still the most reliable approach for real-world operational integrity.

Splice breaks down the core cost drivers of agentic operations, how to model them accurately, and how to evaluate whether an agentic approach is cost-effective for your logistics use case.

To build truly AI-powered logistics workflows, we need a universal translation engine capable of bridging the gap between rigid legacy data and the chaotic, unstructured reality of the supply chain. Rules-based translators and GenAI agents are the combination to drive tremendous results.

In logistics, agents shouldn’t replace humans, they should empower them. human-in-the-loop agent design offers the best of both worlds: Speed and intelligence from AI, and safety and oversight from human experts

Most logistics APIs were never designed to be used by an AI agent. We break down the core incompatibilities between typical logistics APIs and agentic AI, and how to adapt your systems to overcome them.

It’s feasible to create agentic AI for logistics powered by large language models (LLMs). Still, going from concept to a working logistics AI agent is not trivial. It requires a different architecture, and in this article, we break down the components required to build an AI agent for logistics, including what current limitations you need to plan for.

As logistics companies explore artificial intelligence (AI) to increase efficiency and reduce costs, a critical question emerges: What kinds of problems are well-suited to agentic AI? And equally important: Which are not? Agentic AI is most useful when workflows involve unstructured data, unclear instructions, or frequent exceptions. In other cases, use workflow orchestration, a capability inherent in Splice's platform.

APIs are emerging as the preferred choice for modern supply chains over EDI. This article explains why.

Interoperability is a central tenent of Splice. When data moves easily between systems, the result is faster processes and lower costs.

Splice Exports now includes rail receiving dates at 170+ intermodal terminals, helping exporters, forwarders, and truckers align with ERD and vessel cut-off times.

Event-driven automation creates a lot of value because it can affect so many aspects of shipping and logistics.

Our 2024 AgTC presentation on receiving dates and the various reasons they are hard to set and track. Spoiler alert: Using Splice will help!

Let’s get into what earliest receiving dates (ERD)and cut-off dates entail and why they matter.

Export visibility data are key to success. Earliest receiving dates, cut-offs, and vessel schedules underpin good management and planning.

This article delves into the intricacies of export demurrage at marine terminals, exploring its causes, costs, and potential solutions.

Drayage is critical to export container logistics. A key factor to success are timely updates to the earliest receiving dates.

February 26, 2024 — Greenabl, the logistics industry's leading partner in achieving global supply chain decarbonization, has joined...

In today's rapidly evolving digital landscape, businesses in supply chain, shipping and logistics are continually seeking ways to...

Splice recently announced a partnership with Turvo, and it speaks to the challenges of aligning data in supply chain and logistics.