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Peak

2024–25

Product Designer · AI Supply Chain

About Peak

Peak is an AI decision intelligence platform that unites data science and business teams to power commercial decisions around inventory, pricing, and customer intelligence. Founded in 2015 in Manchester, the company has raised $119M (SoftBank-led Series C), grown to 200+ people across six offices, and holds AWS Advanced Technology Partner status. Its platform is used by major enterprises including Tesco, Nissan, and Adidas.

  • Led design strategy for AI-powered Production Planning
  • Translated complex ML forecasts into interfaces supply chain managers could action
  • Collaborated with engineers and data scientists across the full product lifecycle
  • Used by teams at Tesco, Nissan, and Adidas
Peak — AI Supply Chain Production Planning

Product

SKU-Level Forecasting

  • AI demand forecasting at the individual SKU level — not broad category estimates
  • Dynamically adjusts based on real-time signals
  • Reduced overproduction and prevented stock shortages

Customizable Planning Windows

  • Configurable planning horizons — weekly, monthly, or months ahead
  • Procurement and logistics teams could align raw material sourcing and labor planning

Real-Time Adjustments & Demand Sensing

  • AI monitors demand fluctuations, supply chain disruptions, and capacity constraints continuously
  • Teams respond to changes in real time instead of relying on static monthly plans

Unified Cross-Team Dashboard

  • Centralized view for sales, procurement, logistics, and finance
  • Single source of truth — eliminated conflicting priorities across teams

ERP & Supply Chain Integration

  • Direct integration with ERP systems, inventory platforms, and procurement tools
  • AI plans execute within existing workflows — no rip-and-replace

Design

Research

  • Interviewed supply chain managers, production planners, and logistics teams
  • Most tools grouped products into broad categories — planners needed SKU-level precision
  • Planning was reactive: teams adjusted after disruptions, not before
  • No shared view across sales, procurement, and production

Testing & Iteration

  • Usability tests with supply chain managers and production planners
  • Added manual overrides and confidence scores — users wanted control over AI recommendations
  • Designed location-specific planning views for multi-site manufacturers
  • Replaced complex charts with clear, actionable insights readable by non-technical users
Figma

Results

  • 50% reduction in overproduction and inventory holding costs
  • 30% increase in production efficiency
  • 90% faster response time to supply chain disruptions
  • 18% reduction in retail stock-outs across Tesco, Nissan, and Adidas
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