Can you trust AI to forecast your inventory? Here’s how to get it right

Inventory forecasting has always been a high-stakes balancing act. If you overstock, you tie up cash and waste space. Understock, and you risk lost sales, upset customers, and supply chain chaos.

AI promises to take the guesswork out of it by using your past data, external trends, and real-time inputs to forecast demand and recommend the right levels of inventory. 

But here’s the catch: if your team doesn’t trust the system’s predictions, they won’t use it.

That’s where explainability comes in. A good AI system doesn’t just tell you what to do, it helps you understand why.

The role of explainability in inventory forecasting

Let’s say your system recommends ordering 500 extra units of a product. Naturally, your team wants to know why. 

Is it because of a sudden spike in regional sales? A seasonal pattern? A promotional campaign?

Explainable AI makes those reasons visible. It shows which factors—like sales trends, supplier lead times, or store locations—led to that recommendation. That level of insight helps your team build confidence in the system and take action faster.

So how do you get there?

Building trust with AI starts by designing the system with explainability in mind. From the data you use to the interfaces your team relies on, every part of your AI forecasting system should support transparency and trust.

What a trustworthy AI forecasting system needs

Here’s what to look for (and what we help build) when designing an AI-powered inventory forecasting system your team can trust.

1. Clarity of purpose

Start by defining what success looks like. Do you want to keep shelves stocked, avoid overordering, or make promotions more predictable?

Your goals shape everything from the data you collect to how the AI is evaluated.

2. Good, usable data

Your system is only as good as the data it learns from. That includes:

• Sales history with seasonal patterns
• Product attributes (SKUs, sizes, categories)
• Supplier lead times and delivery reliability
• External factors like weather, promotions, or regional events

You don’t need perfect data to start. But you do need it to be clean, relevant, and connected if you want to build momentum.

3. Transparent predictions

There are powerful AI models out there, but many operate like a black box. You get an output, but no clue how it was calculated. That’s not good enough.

We use explainability techniques like Shapley Additive Explanations (SHAP) to show which variables most influenced each prediction. Think of it like a confidence report that lets you see why the system said what it did.

For example, if the system forecasts 100 units of a product next week, SHAP might break it down like this:

• +30 units from an upcoming promotion
• +20 units due to a sales spike in the region
• -10 units because of expected bad weather
• +60 baseline (average demand)

This kind of breakdown gives your team confidence in the AI’s logic and helps them make informed decisions faster. 

4. Interfaces your team can actually use

It’s one thing for a system to generate smart forecasts. It’s another for your team to understand and act on them.

Dashboards should clarify, not confuse. The best ones turn those SHAP insights into visuals your managers can quickly grasp, showing:

• Which products are projected to spike (and why)
• What’s driving the change—e.g., a promo, a trend, or an anomaly
• How tweaks to inputs (like pushing a sale by a week) affect the forecast

This is where design and AI come together: to turn complex analysis into clear next steps. When explainability is built into the interface, teams are more likely to trust and adopt the system.

5. Room to learn and improve

Even the best AI system should keep learning. That means testing forecasts against actual outcomes, gathering user feedback, and making updates as conditions change. The system should get smarter over time, while staying grounded in your business context.

Why it’s time to prioritize AI transparency

AI adoption is picking up speed, and so is regulation. If you're using AI to make decisions that affect people, products, or money, you can expect more scrutiny.

States like California and Illinois, and cities like New York, have already introduced rules around AI transparency. And more are on the way.

Companies that invest in clear, explainable systems today will meet future requirements and build trust with the teams using them.

How we help

At MichiganLabs, we design and build AI-powered systems that your team can trust.

That means:

• Bringing the right data into one place
• Choosing models that are accurate and transparent
• Creating dashboards that turn forecasts into clear next steps
• Keeping pace with changing AI standards and regulations

We do more than build software. We help you make smarter decisions and explain why they make sense.

If you’re exploring AI for inventory forecasting, we’d love to hear what you’re working on. Whether you’re just starting or looking to refine an existing system, we can help you move forward with confidence. Contact us to schedule a discovery call.