Logistics Planning: How to Make Sure AI Delivers on its Promise

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“Action speaks louder than words” holds weight in many contexts, especially for the promises of supply chain AI. The gap between future expectations and realities with AI hinges on one underestimated element: logistics execution. 

While AI provides the intelligence needed for managing the complexities of collaborative decisions in a digital supply chain, it’s powerless to execute its brilliant plans. For that, supply chain AI needs some muscle, which is where logistics saves the day for AI.

AI can return the favor by boosting logistics performance if the AI-logistics relationship is approached correctly with digitalization. This article outlines how to do that so your company can ensure AI will deliver on its promises.

Why the AI-Logistics Relationship is Crucial

Logistics manages the flow of goods, data, and financial capital through supply chains and AI’s brilliance into actionable results. AI can significantly improve logistics decisions, profitability, efficiency, and sustainability. That’s the promise, but the present reality is far different.

Is Supply Chain AI Headed for Trouble 

The stark contrast between potential and reality is evident in a startling statistic from Gartner that reveals only 7% of supply chains can execute decisions in real-time. 93% of companies cannot translate AI’s potent decision-making into meaningful action, rendering AI worthless for logistics execution.   

And that brings us back to the original point: Actions speak louder than words.  AI is an alluring solution with persuasive answers and perfect plans, but the reality check is the final result. For supply chains, that comes from logistics execution, and three basic requirements must be met for AI to succeed.

Three Requirements for Successful Supply Chain AI

Agile logistics processes are essential for executing AI’s decisions, and companies must implement digital solutions that fulfill the following three requirements for that to happen:

  1. End-to-end data Visibility:  Awareness of changing logistics requirements, constraints, and risks is a prerequisite for effective planning. This data fuels digital planning and decision-making solutions; real-time visibility is the gold standard.

  2. Holistic Planning: Data must be analyzed to formulate cross-functional plans that optimize enterprise operations.  Effective planning spans a horizon ranging from minutes to several months ahead.  The best supply chain solutions are composable, allowing tighter integration with existing systems, and they use a digital twin to create a uniform, holistic model. Planning algorithms that consider the collective impact of changes in execution, both immediate and long-range, are crucial to achieving optimization.  

  3. End-to-End Agility: Integrated technology, logistics strategies, and vendor arrangements must allow agile development and execution. Planning directs the execution of these solutions; this functionality is the actionable response to data visibility. 

How do we bridge this gap to realize AI’s full potential? Let’s look at one solution, the flexis Cloud, to understand some of the capabilities that make this straightforward.

flexis Cloud Maximizes Supply Chain AI Effectiveness

Supply chain planning is central to bridging visibility and agility, and AI greatly enhances these planning capabilities when appropriately implemented with logistics execution. The flexis cloud provides a framework that allows companies to accomplish this with existing enterprise systems, maximizing AI effectiveness.  

Holistic Transportation Planning  A digital network is collaborative by nature, and it requires a holistic planning approach that evaluates resources and constraints across a supply chain. An outside-in process that examines the impact of execution from a supply chain partner’s perspective is imperative in the digital supply chain, favoring collaboration. Effective planning must work for your company and its partners, and AI decisions must be rooted in this perspective.

Scenario-Based Planning: Continuous planning runs extensive simulations to proactively evaluate inbound and outbound transportation using multiple transportation modes and distribution sites. This forward-thinking planning mitigates potential risks and seizes opportunities.

Sustainability: Carbon emissions calculations and reporting are automated for each shipment, making emissions compliance effortless. Companies can juxtapose these measurements against corporate sustainability goals and integrate them into transportation planning. 

User Customization: Every business is unique, and the flexis solution allows users to define operational parameters and business rules for each location. User customization through a data-driven design allows greater flexibility and fine-tuning AI decisions, resulting in more effective planning and execution.

Transportation Optimization: Lightning-fast AI decisions are not the bottleneck in the digital supply chain; it’s logistics constraints: workflow, fluctuating carrier capacity, traffic congestion, and more. Optimizing transportation minimizes these delays, improving logistics agility. flexis’ SCM Transportation Planning and Scheduling addresses this critical aspect of planning, considering logistics constraints in every decision.  


As supply chains increasingly rely on AI for decision-making, robust logistics execution will determine whether its decisions bear fruit. In the final analysis, the action – the tangible results – speaks volumes.  The flexis cloud offers a proven framework that enables supply chain AI to deliver on its promises. 

If you want to learn more get your Guide to AI

In this Guide you will learn:

  1. Why AI will define the future of the global supply chain?

  2. AI applications for manufacturers

  3. AI integration with S&OP

  4. Keys to successful implementation