Back to PortfolioE-commerce

Intelligent E-commerce Operations Platform

AI demand forecasting auto-triggers supplier purchase orders before stockouts — not after

🇺🇸 US🇦🇺 AU40% ops cost reduction · 23% fewer stockouts

Overview

AI-powered inventory and operations platform for a multi-region retail group operating 5 warehouses across the US and Australia. Replaced manual inventory management with an intelligent system that forecasts demand, automates supplier ordering, and optimises stock positioning across locations.

منصة مدعومة بالذكاء الاصطناعي لإدارة المخزون والعمليات لمجموعة تجزئة متعددة المناطق تدير 5 مستودعات. استبدلت إدارة المخزون اليدوية بنظام ذكي يتنبأ بالطلب.

The Challenge

The client was caught between two expensive problems simultaneously — overstock carrying costs (averaging $180K per quarter in dead inventory) and stockouts (losing estimated $340K quarterly in missed sales). Manual forecasting by their buying team was based on last year's performance and gut feel, with no ability to factor in external signals like seasonality, promotions, or competitor activity.

كان العميل يعاني من مشكلتين مكلفتين في آنٍ واحد: تكاليف حمل المخزون الزائد بمعدل 180 ألف دولار فصلياً، ونفاد المخزون بخسارة مقدرة 340 ألف دولار من المبيعات الضائعة.

What We Built

A unified operations platform with three modules: demand intelligence (AI forecasting + supplier automation), warehouse management (real-time stock levels, location optimisation, pick-path routing), and operations dashboard (cross-warehouse visibility, reorder alerts, supplier performance tracking).

منصة عمليات موحدة بثلاثة وحدات: ذكاء الطلب، وإدارة المستودعات، ولوحة العمليات.

Tech Stack

Next.jsPythonTensorFlowGCPPostgreSQLRedis

Key Outcome

40% ops cost reduction · 23% fewer stockouts

Start your project →

The AI Layer

The demand forecasting model analyses 18 months of sales history per SKU, cross-referenced with seasonal indices, promotional calendars, competitor pricing signals (scraped via API), and external data feeds (weather for seasonal categories, events for location-specific stock). It produces a 12-week rolling forecast per SKU per warehouse with confidence intervals. When forecast inventory falls below the reorder threshold — accounting for supplier lead time — the system automatically generates and submits a purchase order to the relevant supplier via their API. The buying team reviews only flagged exceptions, not every order.

يحلل نموذج التنبؤ بالطلب 18 شهراً من تاريخ المبيعات لكل وحدة SKU، متقاطعاً مع المؤشرات الموسمية وتقويمات الترويج وإشارات تسعير المنافسين والبيانات الخارجية. يُصدر توقعاً متدحرجاً لـ12 أسبوعاً لكل SKU لكل مستودع.

Results

  • Ops cost reduced 40% (combination of overstock reduction and automation savings)
  • Stockout rate reduced 23% in first quarter post-deployment
  • Dead inventory carrying cost reduced from $180K to $67K per quarter
  • 94% of purchase orders fully automated — buying team reviews exceptions only
  • Deployed across 5 warehouses in 2 countries in 45 days
- خفض تكاليف التشغيل بنسبة **40%** - تراجع معدل نفاد المخزون **23%** في الربع الأول بعد النشر - أتمتة **94%** من أوامر الشراء بالكامل