Inventory Management.

Multi-location inventory, demand planning, replenishment automation — keeping the right stock in the right place at the right time, while minimizing working capital tied up in inventory.

Multi-location
Coverage
AI Demand
Planning
Auto
Replenishment
Working Capital
Optimized

What We Optimize

  • Multi-location inventory visibility
  • AI-driven demand forecasting
  • Automated replenishment workflows
  • Safety stock and reorder point optimization
  • Slow-moving and dead stock management
What It Is

A focused capability — delivered end-to-end

Inventory is working capital. Done well, it enables service levels; done poorly, it ties up cash and ages on shelves. We deliver inventory platforms and analytics that balance service and capital efficiency.

Multi-Location

Real-time visibility across DCs, warehouses, stores, in-transit

Demand Planning

Statistical and ML-driven forecasting at SKU-location granularity

Replenishment

Auto-calculated reorder points, safety stock, replenishment workflows

Allocation

Smart allocation rules for tight supply scenarios

Slow-Moving Mgmt

Identify, redistribute, or liquidate slow-moving and dead stock

Service-Capital Balance

Trade-off analysis between service levels and working capital

Engagement Model

How we deliver

01

Discovery

Assess your current state, identify gaps, scope the engagement against your goals and constraints.

02

Design & Build

Architecture, design, build, and integration — backed by CMMI 3 process maturity and CI/CD delivery practices.

03

Run & Optimize

Managed operations, continuous improvement, capability uplift, governance — partnership for the long haul.

← Back to Digital Transformation
Our Thinking

Perspectives on Inventory Management

How AI-powered demand forecasting and ERP-integrated inventory control are reducing carrying costs, eliminating stockouts, and turning inventory from a liability into a competitive asset.

📊
Insight

Demand Forecasting Accuracy: How AI Reduces Inventory Carrying Costs

Moving from statistical forecasting to machine-learned demand signals reduces excess inventory by 20–40% without increasing stockout frequency. The feature engineering and model choices that drive the improvement.

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Point of View

Just-in-Time vs. Safety Stock: AI-Optimised Inventory Strategies

JIT exposed its fragility in recent disruptions; safety stock is expensive. AI-optimised hybrid strategies — with dynamic safety stock parameters — offer a third path that balances resilience and cost.

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Whitepaper

Multi-Location Inventory: The ERP Configuration Playbook

Organisations with multiple stocking locations — warehouses, branches, 3PLs — face configuration choices that determine transfer efficiency, fill rate, and audit traceability for years after go-live.

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🗑️
Case Study

Eliminating Dead Stock: Predictive Analytics for Inventory Managers

A distribution company with £4M in annual dead stock write-offs reduced that figure by 78% in 12 months using demand sensing and automated markdown triggers. The implementation in detail.

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Let's build what's next — together.

Whether it's setting up your India GCC, modernizing your enterprise stack, or hiring 50 engineers in 30 days — we'd love to scope it with you.