From Forecasting product to Forecasting System

Executive Sumary

Recognizing a need

One of the things recruiters and hiring managers care about most is: Can this person see around corners? This project demonstrates exactly that.

Toyota approached me with a need to modernize global vehicle demand forecasting. As a Principal Design Lead, I was responsible for defining the end-to-end forecasting experience, guiding product strategy, and leading UX delivery for a platform that would eventually serve forecasting teams around the world.

After the successful launch of Global Demand Forecasting (GDF), executives began exploring additional forecasting products for other areas of the business. Rather than continuing to create individual forecasting applications, I identified an emerging pattern and proposed a broader platform strategy: a unified Global Forecasting System capable of supporting any forecasting use case across Toyota.

While the platform vision was never funded due to budget constraints, the proposal shifted conversations from individual products to long-term ecosystem thinking and demonstrated how forecasting could scale across the enterprise. GDF ultimately launched successfully in six months and was highly praised by executive leadership for both its capabilities and user experience that made forecasting less intimidating for average users.

The Opportunity

Building One Forecasting Tool Was Easy. What Would 5 Look Like?

After the successful launch of Global Demand Forecasting, leadership approached the team with a second initiative: Accessory Demand Forecasting. As I reviewed the requirements, I noticed something important.

The workflows were nearly identical.

• Historical Data
• Covariates
• Model Selection
• Evaluation
• Forecast Outputs

The only thing changing was what we were forecasting. Instead of building another forecasting application, I began exploring what a shared forecasting platform could look like.

Building Global Demand Forecasting

Understanding the Forecasting Lifecycle

Before designing interfaces, I needed to understand how forecasting actually worked. After sitting next to data scientists, planners, and leadership teams, I mapped the complete forecasting lifecycle:
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1. Historical Data
2. Covariate Analysis
3. Model Selection
4. Evaluation
5. Forecast Generation
6. Explainability
7. Forecast Consumption

The challenge wasn’t simply generating forecasts. It was helping users trust and understand them. This became one of the central themes of the Global Demand Forecasting.
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Designing for Trust, Not Just Accuracy

Forecasting tools often assume users understand machine learning outputs. Most business users don’t. Throughout research, one recurring challenge emerged:

Users could see forecast results, but they often struggled to understand:

• Why a model was selected
• Which variables influenced results
• Whether forecasts should be trusted
• How confident they should be in decisions

To solve this, I introduced explainability throughout the experience.

The platform surfaced:

• AI generated insights and summaries
• Forecast accuracy metrics
• Model comparisons
• Covariate relationships
• Backtesting results
• Forecast rationale

Rather than treating explainability as an advanced feature, it became a core part of decision-making. This direction was showcased internally as a major differentiator for the platform

Building Global Demand Forecasting

In just six months, the team delivered a complete forecasting workflow that enabled users to:

Strategic Approach

Rather than creating a collection of disconnected forecasting tools, I proposed a Global Forecasting System (GFS).

My vision was to create a single forecasting ecosystem capable of supporting multiple business domains while leveraging shared workflows, governance, and forecasting infrastructure.

The platform would allow teams to forecast:
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• Vehicle demand
• Accessories
• Supply chain needs
• Inventory planning
• Future forecasting use cases

while maintaining a consistent experience and reducing duplicated effort.

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Expanding Beyond Forecast Creation

Another opportunity emerged during this work.

Forecasts were often created by data scientists but consumed by directors and VPs responsible for major business decisions.

I proposed introducing governance capabilities that would allow leadership to:
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• Review forecasts
• Approve recommendations
• Track performance over time
• Increase accountability across planning teams

This shifted forecasting from a technical workflow into a strategic business process.