Overview

The Challenge
"0 to 1" project to help new business owners launch their products. The client wanted an AI-driven dashboard that selects the best suppliers, freight, and logistics partners while exposing the "invisible costs" of the supply chain.

My Role
Sole Product Designer (3 Weeks).

The Outcome
A scalable, flexible dashboard that transforms a chaotic spreadsheet process into a clear "Viability Score" and actionable profit roadmap.

The Core Conflict: Data Density vs. Decision Fatigue

My research into logistics revealed a product can go through 20+ different ways but all narrowed downed to 4 distinct stages: Supplier, Freight, Warehouse, and Delivery.

• The Problem:
  Displaying partner details alongside a granular price breakdown created a cognitive overload. The initial wireframes required a "Long Scroll," forcing users to lose context between selecting a partner (top of page) and seeing the cost impact (bottom of page).
  
  

• The "Junior" Mistake Avoided:
  I rejected the idea of jamming everything into one view, which would have increased cognitive load and bounce rates.

The Critical Design Pivot

I split the interface into two focused modes using a Tab System: "Supply Chain Partners" (Selection) and "Profit Breakdown" (Analysis).

1. The Risk:
   Tab switching can cause memory loss—users might forget why their profit changed after switching tabs.
   
   

2. The "Mature" Solution:
   I implemented a Sticky Profit Margin component after heuristic evaluation.

   • How it works: Even when the user is deep in the "Partners" tab selecting a new freight forwarder, the "Profit Margin" stays visible and updates in real-time.

   • Result: Users get immediate feedback on their decisions without needing to switch tabs constantly.

Business Logic & Simulation

I designed two simple features to move beyond "passive data" into "active decision making":

1. The Viability Score (Top Right)

   • Purpose: To prevent analysis paralysis.

   • Logic: Uses onboarding data (Product Idea + Source Location + Sell Location) to give a simple "Go/No-Go" rating. It tells the user immediately if their business model is sound before they dive into the math.
     
     

2. The Profit Simulator (Bottom Panel)

   • Insight: New entrepreneurs often panic about upfront costs (Customer Acquisition Cost - CAC).

   • Interaction: I used sliders to visualize the relationship between CAC and Breakeven Time.

   • Value: This "Play" interaction helps users understand that spending more on marketing (higher CAC) might actually reduce their breakeven time by driving volume. It turns anxiety into strategy.

Refining the Logic: Heuristics & Guardrails

Beyond the macro layout, I implemented specific UI mechanics to handle the complexity of supply chain relationships and prevent errors:

1. Error Prevention (Conditional States): To prevent invalid configurations, I used disabled states. For example, "Amazon Delivery" remains locked unless "Amazon FC" is selected in the Warehouse step. This acts as a guardrail, ensuring users build a realistically viable supply chain.
   
   

2. Contextual Cost Attribution: In the Profit Breakdown, I explicitly restated the Selected Partner’s Name next to their specific line items. This reduces cognitive load—users don't have to mentally track "Who is charging me for this fee?" while analyzing the numbers.
   
   

3. Source Transparency Tags: I added subtle tags like "Supplier Provided" or "3PL Provided" on logistics cards. This clarifies ownership instantly—if a Supplier also handles Freight, the user sees they can streamline their chain of command.
   
   

4. The "Human Element" (Optimization Tips): An AI tool can calculate costs, but it can't close deals. I added an "Optimization Tips" card that prompts users to take offline action—specifically reminding them to negotiate or ask for volume discounts.

Validation

Access to real users was limited and has no budget. To validate the hierarchy without live users, I generated Attention Heatmaps to test if the layout matched the entrepreneur's psychological needs.

• The Hypothesis: Entrepreneurs are "hunters" first and "explorers" second. I predicted their immediate anxiety would drive them to find the "Verdict" (Is this viable?) before diving into the "Details" (Who are the partners?).
  
  

• The Evidence: The heatmap confirmed this behavior. The most intense "Red Zones" were on the Viability Score and Profit Summary at the very top—bypassing the partner selection cards initially.
  
  

• The Success: The heat diffuses sequentially downwards: Verdict > Partners > Optimization. This confirms the design successfully guides users from "Panic" (Can I afford this?) to "Action" (How do I optimize?), preventing cognitive overload.

Final Design

View prototype

Retrospective

• Win: Successfully mapped a complex multi-stage logistics model into a UI that feels simple enough for a first-time business owner.
  
  

• Improvement for V2: Currently, if the "Viability Score" is low, the user hits a dead end. In the next iteration, I would design an "Improve My Score" feature that suggests specific levers to pull—such as "Switch to a slower freight option to save 12%" or "Increase initial order volume to unlock supplier discounts." This turns a negative result into a constructive strategy session.