It was a classic case of good software falling short in practice
A major soft drink bottler in Latin America had invested in a promising solution to improve the quality of supply chain planning decisions. On paper, it was the perfect fit. But in reality, it just wasn’t delivering. In conversations with management and the folks actually operating the tool, the core issue became obvious to us: It took hours—sometimes days—just to organize the data needed to run a scenario.
Requests piled up. Executives were kept waiting. And when push came to shove… yep, good ol’ Excel came to the rescue.
And this isn’t a one-off.
Models are faster. Algorithms are smarter. Cloud infrastructure is more scalable. AI is everywhere.
You'd think we’d be light-years ahead when it comes to helping organizations plan better, run better, compete better.
But we’re not.
Like in the example above, too many projects fail to deliver on their promise. Robust planning tools gather dust. And the old, familiar spreadsheet quietly sort of gets the job done.
So, what separates the successful projects from the many that fizzle?
The answer, my friend, is blowing in the wind. Or rather buried under dashboards, maps, ants, 5,500 input coefficients, BI, AI, and the latest buzzword salad.
Today’s planning tools can optimize costs, profits, service levels, distance, and CO₂ emissions, among other things. But how good are they at optimizing the one resource that matters most to the people who actually interact with them—time?
Decision-makers don’t have time. They’re barely managing the time they have. So, when a request comes in at 5:30 p.m. to analyze a new situation, they’re not looking for perfection or “optimal” solutions. They want to get it done, do it right, and get home before 8.
What they need is a tool that:
- Protects them from making poor decisions
- Doesn’t require hours of prep just to get started
In other words, a tool that understands:
It’s too late (in the day) for optimal. Just make it good—and make it quick.
Let’s be fair—it’s often not the tool’s fault. There are a few simple but powerful principles that can make this work, starting with scoping the problem right and choosing the right kind of tool. I’ll share more on that in an upcoming post.
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