Inventory Intelligence and Predictive Demand Planning
The Challenge
Client's core business personalises and sells accessories across multiple e-commerce channels.
Each base product evolves into many downstream SKUs. One single product can become a hundred SKUs. And inventory and sales must be tracked separately across Shopify, Shopee, TikTok Shop, and POS systems, with no single source of truth. The challenges :
- SKU transformation complexity
- Fragmented multi-channel data
- Ungrounded planning decisions
Commerce platforms are built to transact products, not to model how products evolve. They could not represent SKU transformations, preserve product lineage, or unify inventory across marketplaces. Excel was used to bridge the gaps, but became unscalable and unreliable.
Management lacked a reliable view of true inventory positions, cross-channel performance, and demand behaviour across transformed SKUs.
The Solution
A unified operational and data layer including:
- Unified inventory and sales data across channels
- Canonical product model with SKU lineage tracking
- Consolidated multi-channel performance intelligence
- Foundation for grounded demand and production planning
Fragmented commerce tools and spreadsheets were replaced with a central operational intelligence platform, where products, inventory, and performance are understood and modelled, not just listed and sold. This required the platform to be trusted as the single source of truth.
Data ownership and decision rights were redefined, regular planning cadences replaced ad hoc Excel compilation, and buying decisions shifted from gut instinct to grounded, data-driven recommendations.
The Results
Illustrative estimates for this type of transformation are below. These are not measured against the Client's actuals, and depend on these operating model changes actually taking hold.
- Inventory cost reduction: 10–15%, from a unified view of inventory positions in place of fragmented, channel-specific tracking
- Stock-outs: >20% fewer, by replacing intuition-driven ordering with grounded, consolidated demand signals