How do you debug a forecasting model today when the error is quite bad?

This is for a personal study that will end up becoming an in-depth article and possibly a fully open source solution ideally without the AI slop that we see these days.

Let's say you’ve trained a model and the result is worse than the business wants. What do you check next?

Do you break the error down by customer, product, location, or individual series? Check if it gets worse at longer horizons? Look for bias, volatility, intermittent demand or outliers?

Go back to the backtesting setup, metric, or baseline? Or do you usually start trying other models?

Also do the tools you use make this easy or do you end up building custom notebooks, tables, and plots every time?

Thinking about the last time this happened:

• What did you check first?
• What actually helped you find the problem?
• What did you have to build yourself?
• Did you end up changing the model, data, validation setup, metric, or business expectation?

I’m trying to understand how people diagnose bad forecasts beyond comparing one overall error score against another.

EDIT/UPDATE because it seems like this is not clear enough:

I’m not looking for an if-else checklist that can explain why any forecast is bad. The answer obviously depends on the data, objective, validation setup and the decision the model is supposed to support.

I’m exploring if there is room for a small open-source tool around forecast evaluation. Before building anything, I’m trying to understand which checks people repeatedly run after they already have predictions, what they still build manually, and what existing tools already handle well.

So I’m mainly interested in specific workflows from projects rather than a general formula for fixing a model.