AI for Logistics: Turning Complexity into Predictive Outcomes
Despite Artificial Intelligence (AI) entering beyond-hype territory, its true value proposition is far from evident for most logistics leaders today.
Availability of low-cost Artificial Intelligence (AI) services through cloud-based enterprise software has been a driver for experimentation and adoption. But its true value proposition is far from evident for many logistics leaders.
Headlines about AI’s "Matrix-like" domination over humanity add to the confusion. Most real-world applications are limited to intelligent virtual assistants that can act as a driving force for AI adoption but won’t deliver the business value…unless one is looking for showtimes.
AI’s value prop for logistics manifests in the white space between the grandiose and the simplistic—specifically, enabling predictions and proactive exception management.
The AI Imperative in Global Logistics
What if AI could tame the complexities of today’s supply chain, predict exceptions, and empower operational teams to proactively manage these exceptions before they occur?
Consider the supply chain complexities of a large U.S. manufacturer that ships products to a multitude of customers in countries worldwide. Potentially hundreds, if not thousands, of transportation lanes involving a mind-boggling number of partners—truckers, ocean and air carriers, 3PLs, customs brokers, country affiliates—have to be managed in harmony.
Increasing customer expectations for on-time in-full deliveries, backorder avoidance, quality of service, process compliance (e.g., cold chain), and the overall customer experience pressures margins and operational capabilities of 3PLs, carriers, and shippers.
The good news? Supply chain in all its complexities is organically filled with data. Every event in the product journey, such as a shipment milestone, hand-off, or document generates data that can be harnessed. AI can’t do it alone, but as a piece of the digitization puzzle, it can convert data to predictive insights.
Among the various fields of AI, machine learning (ML) is the most promising for global logistics.
Machine learning allows supply chain leaders to build predictive models that can learn and self-optimize from data or identify patterns on raw data by emulating logical reasoning. The ML model output could be lead time, on-time performance, service level, cost, or a combination of these, while the model inputs have to reflect the complexity of a given supply chain—lanes, modes, carriers, costs, historical performance, Incoterms, real-time feeds, IoT sensors. It all depends on the use case.
How to Get Started
AI needs data, lots of it. Thus, taking a digitization-first approach to establish a digital environment where data is electronically generated, or captured and stored, is key. IoT-generated data is good, but even data sitting in disconnected legacy systems or just exchanged via email can be a sufficient foundation by utilizing the right aggregation engine.
Developing "fast-fail" pilots is another important starting point. Build the right cross-functional internal team with deep expertise in the issues on hand and identify a partner that can support quick (4-12 week) proof-of-value projects. AI is not the only or always the best option, so accelerating discovery is key to target quick wins and re-evaluate.
Through enabling predictions and better exception management, AI is taming supply chain complexity and powering a digital transformation that brings more proactivity, efficiency, and results to the supply chain.