Winmore · AI-Powered Forecasting Platform

Choosing the right carrier shouldn't depend on spreadsheets.

Selecting the right carrier is a critical part of logistics planning. Before Winmore, teams relied on spreadsheets to compare carrier performance, forecast demand, and evaluate operational fit. I designed an integrated forecasting platform that brought this workflow into one experience, enabling planners to make faster, more informed carrier decisions.

Project Details

Role Senior UX DesignerScope End-to-End Product DesignTimeline 3–4 MonthsDomain Logistics · AI Forecasting

The Opportunity

Every shipment demands a different decision.

No two shipments are the same. Capacity, delivery timelines, carrier reliability, and historical performance all influence which carrier is the best fit. The challenge was to simplify these complex decisions and help planners choose the right carrier with greater confidence.

Drag to Compare

Before

Forecasting Concept – AllocationAI

View 1 – Weekly volume forecast per lane

Customer	FAK Terms	Loading Location	POL	POD	Unloading Location	Commodity	Contract Type	Carrier Required	Equipment
Customer ABC	FOB	Wuhan	Shanghai	Rotterdam	Venlo	Electronics	FAK	MSC	20ft
Customer ABC	FOB	Wuhan	Shanghai	Antwerpen	Brugge	Electronics	FAK	MSC	20ft
Customer ABC	FOB	Shenzhen	Shenzhen	Rotterdam	Rotterdam	Electronics	FAK	MSC	20ft
FAK	FOB	Shenzhen	Shenzhen	Rotterdam	Rotterdam	FAK	FAK	None	20ft


View 2 – Weekly overview of past performance + future plan, incl. actual bookings

Customer	FAK Terms	Loading Location	POL	POD	Unloading Location	Commodity	Contract Type	Carrier Required	Equipment
Customer ABC	FOB	Wuhan	Shanghai	Rotterdam	Venlo	Electronics	FAK	MSC	20ft
Customer ABC	FOB	Wuhan	Shanghai	Antwerpen	Brugge	Electronics	FAK	MSC	20ft
Customer ABC	FOB	Shenzhen	Shenzhen	Rotterdam	Rotterdam	Electronics	FAK	MSC	20ft
FAK	FOB	Shenzhen	Shenzhen	Rotterdam	Rotterdam	FAK	FAK	None	20ft

After

↔

Spreadsheet workflow vs. Winmore

The Journey

One shipment. Multiple possibilities. One confident decision.

Every carrier offers different strengths. Winmore transforms complex logistics data into a visual journey, making it easier to compare options, understand trade-offs, and select the carrier that delivers the best overall outcome.

This animation was created specifically for the case study to illustrate the carrier evaluation journey.

Designing the Experience

From structure to layout.

Once the product's foundation was defined, the next step was translating it into an information hierarchy, a set of guiding principles, and early layout explorations.

Product Decisions

Key decisions I made to shape the information architecture and experience.

Forecast Overview (KPIs)

Placed at the top so planners can quickly assess performance and spot changes.

Forecast Data Table

Made the primary workspace where most planning and updates happen.

Trend Analysis

Supports deeper analysis without leaving the context of the data.

Alerts

Kept visible alongside analytics to surface issues before they escalate.

AI Insights

Positioned at the bottom to inform decisions, not distract from them.

Design Focus

The principles that guided every design decision in this module.

Make planning glanceable

Surface the right metrics and trends so planners can understand at a glance.

Reduce back-and-forth

Bring planning, analytics, and alerts into one place to avoid context switching.

Highlight exceptions first

Help users focus on what needs attention, not search for it.

Support faster decisions

Design for clarity and confidence so planners can act without hesitation.

Early Exploration

Explored multiple layout directions to find the right balance of clarity and depth.

EXPLORATION A

Chart prominent, alerts on the side.

↓

EXPLORATION B

Table focused for detailed planning.

↓

FINAL DIRECTION

Balanced layout with table + chart and alerts in view.

VIEW

Final Solution

Bringing the Forecasting Platform to Life

The final solution is organized into four connected modules that mirror the way planners work—from getting a high-level view of forecasting health to creating forecast data, monitoring operational performance, and evaluating forecasting accuracy over time. Together, they create a unified experience that supports faster planning, clearer insights, and more confident decision-making.

Module 01 — Forecast Command Center

Forecast Command Center

This is where planners gain a high-level understanding of forecasting health before diving deeper into individual workflows. The dashboard consolidates key metrics, trends, alerts, and AI-generated insights into a single decision-making hub, enabling users to quickly identify issues and prioritize actions.

Module 02 — Managing Forecast Data

Managing Forecast Data

This module serves as the operational workspace for creating and maintaining forecast data. It enables planners to organize forecast inputs, apply seasonal adjustments, and manage shipment forecasts in a structured and scalable table experience.

Module 03 — Monitoring Operational Performance

Monitoring Operational Performance

Forecasts are only valuable when compared against real operational outcomes. This module helps planners evaluate forecast performance by comparing planned and booked volumes, making it easier to identify variances and investigate potential issues.

Module 04 — Measuring Forecast Accuracy

Measuring Forecast Accuracy

Improving forecast quality requires continuous evaluation. This module provides historical accuracy metrics and trend analysis, allowing planners to measure forecasting performance over time and identify opportunities for refinement.

Bhagyashree R.

Project Impact

Delivering a More Efficient Forecasting Experience

The redesign transformed forecasting from a fragmented process into a connected workflow that supports better operational decisions.

35%

Improved Workflow Efficiency

25%

Reduction in Usability Issues

Highly Adopted

Positive Internal Reception

Unified Experience

Connected Forecasting Workflow

Good enterprise design does not remove complexity—it makes complexity easier to navigate.

Next Case Study

From Forecasting to Human Connection — Native

View Case Study