From Forklifts to Machine Learning: Why Mars’ 80% Shipping Win Is the AI Use Case That Actually Lands
Everyone one is looking for the killer AI app. It's not about the application but the workflow.
Introduction
Last week, we said AI is the new forklift—shifting infrastructure from a cost center to a business enabler (read the post here). But not all AI is created equal.
The most impressive example in that post didn’t come from a large language model. It came from traditional machine learning. And it delivered real results: Mars cut shipping costs by 80% by using Celonis' process intelligence platform.
This isn't generative AI writing haikus or summarizing emails. This is predictive AI optimizing real-world operations. It’s also a perfect case study for IT leaders and vendors who want to build a business case for infrastructure investment.
Let’s break it down.
Section 1: The Mars-Celonis Case — Traditional ML in Action
Mars, the global consumer goods company behind M&Ms and pet food, partnered with https://supplychain360.io/ai-revolutionizes-supply-chain-operations-benefits-and-challenges/ to overhaul its supply chain. The result? An 80% reduction in shipping costs.
This wasn’t magic—it was math.
Celonis’ Machine Learning Workbench powered the analysis. Mars fed in shipment volume, route history, and loading patterns. The platform used traditional ML—not generative models—to identify inefficiencies in how trucks were being loaded.
Instead of sending half-full trucks across the country, Mars used Celonis to predict better consolidation opportunities. Fewer trips. Lower fuel costs. Reduced emissions. Higher margins.
What powered it? Not ChatGPT. Not LLMs. Just traditional supervised learning and real-time process mining—running on scalable infrastructure that could handle high-frequency logistics data.
This is the kind of “AI” that lands with a CFO. It doesn’t hallucinate. It saves money. And it depends entirely on modern infrastructure to deliver results.
Section 2: Traditional ML vs. Generative AI — Why Infrastructure Enables Both
It’s easy to get caught up in the generative AI hype cycle, but let’s be clear: traditional ML still dominates enterprise AI use cases. It predicts, classifies, and optimizes. That’s what drove Mars’ 80% win.
Compare the two:
If you want to build anything AI-related that isn’t just a demo, infrastructure is the difference between an experiment and enterprise-grade execution.
This brings us back to our original thesis:
If the infrastructure can’t support the AI roadmap, the roadmap doesn’t happen.
Mars made it happen because their infrastructure could support it.
Section 3: Lessons for IT Leaders and Vendors
Mars’ win is your budget case template.
For IT leaders: Stop pitching infrastructure as power savings or hardware refresh. Start framing it as the foundation for AI wins like this. Tie it to real numbers. Mars cut shipping costs by 80%. That’s your opener.
For vendors: Don’t lead with specs. Lead with outcomes. Show how your solution helped a company reduce waste, launch faster, or increase margins. Don’t just tell buyers your product is “AI-ready.” Show them it’s already AI-proven.
If you're selling into a CFO-led environment, traditional ML may be your best ally. It's deterministic, explainable, and directly tied to outcomes. And it makes the case for investing in the infrastructure that powers it.
🧭 Conclusion and Call to Action
Mars’ 80% shipping cost reduction isn’t a GenAI magic trick—it’s a traditional ML success story, powered by the right infrastructure.
This is the kind of AI story that actually moves budget. It speaks to business outcomes, not technical ambition. It reframes infrastructure as a growth driver, not a sunk cost.
So if you're in IT and pitching a refresh, take a page from Mars:
Lead with outcomes
Back it with infrastructure
Show how AI isn't just "coming"—it's already delivering
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