Approach
Cluster What Matters
We turn noisy SKU and customer data into a few actionable segments so you can set policies that match reality. Not averages.
Data & Analytics – The Nitty Gritty Approach
Inputs
- Transactional data, such as 24–36 months of POS, outbound, inventory
- Master data, such SKU and customer masters, lead times & supply
- Historical system parameter settings, KPIs
How we segment
- Supported by machine learning
- Multi-criteria clustering (volume, seasonality, margin, criticality, etc.)
- Automatically updates demand segments in real time
- Create a data structure, or “DNA”, for every SKU and customer
Outputs you get:
- Not all products are created equal.
- Not all customers require the same level of service.
- There is rarely a one-size-fits-all solution.
- Policies, targets, and effort need to differ by segment.
Key Example Lifts
Our clients see measurable improvements within the first cycle of engagement. Here are some of the most common performance lifts achieved through our supply chain optimization process:
5%
Customer Service Level – Protecting certain demand segments more vulnerable or unpredictable
20%
DC Capacity Alleviation – Redistributing stocking locations by segment based on service need
15%
Inventory Reduction – Identifying and addressing SKU segments (e.g., domestic/import, or high/low-velocity) driving excessive inventory
18%
Operational Cost – Identifying EOQ or batch size, buffer, delivery frequency, shelf capacity, pack size by segment, optimizing labor end-to-end
Process that Works
Data. Decisions. Delivery. Each owned, coordinated, and measurable.
Track A —
Health Check
Track B —
As-Is & To-Be process mapping
Track C —
Delivery
Why This Works
Our approach isn’t a framework that sits on the shelf—it’s designed to stay active, accountable, and adaptive inside your operations.
- No “model shelfware”; rules live directly in your systems
- Least-privilege access with clear owners and built-in rollback
- Continuous improvement embedded in the operating cadence
Why work
with Segmatics?
From day one, you work directly with the practitioner who will do the work. We start with your reality—messy order lines, lumpy lead times, and hard constraints. Our job is to make better decisions obvious: which items should be buffered, which customers should be promise-first, and which nodes actually need stock. Every assumption is documented in plain language and parameters are wired into your stack with the lightest possible touch.
When something drifts, you’ll see it in the dashboard before it becomes a fire. The plan survives contact with the warehouse floor—because that’s where we test it.