Supply Chain Analytics
Most supply chain analytics projects stop at the warehouse. The dashboard shows inventory levels and inbound shipments. It doesn't show the demand signal that's driving the replenishment, or the supplier performance that's constraining it. Visibility stops where the data connection ends.
What we hear from operators
The problems we solve
Inventory decisions are made without demand context
Warehouse teams see stock levels. They don't see the demand forecast that should be driving replenishment decisions. Safety stock is set by gut feel or by a rule of thumb that predates the last market shift. The result is overstock on slow-moving SKUs and stockouts on fast movers — simultaneously, in the same warehouse.
Supplier performance isn't tracked until after the impact is felt
When a supplier delivers late, the first signal is often a production line that's about to stop or a customer order that's about to be short-shipped. The delivery performance data exists in the purchase order system. It's not aggregated, not trended, not used in supplier reviews. By the time the pattern is visible, it's already caused operational damage.
Days inventory outstanding is a finance KPI, not an operational one
DIO is reported to the CFO monthly. It isn't connected to the SKU-level decisions that drive it. The finance number goes up; operations gets an email. Nobody can trace which SKUs, which locations, which supplier constraints drove the increase. The metric is tracked but not managed.
Who this is for
Who supply chain analytics is built for
The roles that feel the problem first — and what we build for each of them.
Supply Chain Director
The problem
Visibility stops at the warehouse. The dashboard shows stock and inbound, but not the demand signal driving replenishment or the supplier performance constraining it.
What we build
A demand-to-delivery model in Microsoft Fabric feeding Power BI, connecting ERP, WMS, TMS and supplier data through Azure Data Factory.
One demand-to-delivery view, not a warehouse-only dashboard
Demand / Supply Planner
The problem
Safety stock is set by gut feel or a rule of thumb that predates the last market shift, so you overstock slow movers and stock out on fast movers at once.
What we build
Safety-stock recommendations from real demand and lead-time variability in Power BI over Microsoft Fabric, with automated replenishment triggers.
+15–25% forecast accuracy
Logistics Manager
The problem
A supplier delivering late first shows up as a production line about to stop — the delivery data sits in the PO system but is never trended into a review.
What we build
Supplier on-time scorecards from SAP MM in Power BI, with Power Automate alerts when a vendor trends below SLA.
Supplier risk flagged before it stops the line
COO
The problem
Days inventory outstanding is reported to the CFO monthly but is disconnected from the SKU-level decisions that drive it — the number moves and nobody can say why.
What we build
DIO decomposed to SKU, location and supplier constraint in Power BI on Microsoft Fabric, making a finance KPI operational.
DIO traced to the SKUs and locations that move it
Measurable outcomes
What changes after implementation
Specific shifts from delivered supply chain analytics work — the before, and the after.
Inventory visibility: weekly stock count → daily live position by SKU and location
Stock decisions made from current data. Slow-moving stock identified early enough to act before write-off becomes the only option.
Supplier on-time performance: relationship-managed → data-driven monthly scorecards
Delivery performance tracked by supplier, commodity, and lead time category. Monthly scorecards generated automatically. Renegotiations backed by 12 months of delivery data.
Stockouts: reactive response → proactive 3-week early warning
SKUs projected to breach safety stock within 3 weeks flagged automatically. Procurement acts before the shortage impacts production or customer delivery.
By market
Supply Chain Analytics — market-specific pages
Each page below covers what supply chain analytics looks like specifically in that market — the local ERP landscape, compliance context, and the operational patterns we actually see there.
Singapore & Malaysia
United Kingdom
North America
By industry
Supply Chain Analytics — industry-specific pages
How supply chain analytics applies to the specific systems, metrics, and operational challenges of each vertical.
FMCG & Retail
FMCG and retail data problems concentrate at two points: the demand signal and the shelf.
Explore →
Logistics & Supply Chain
Logistics operations in India, the GCC, and Southeast Asia share a common data challenge: high transactional volume, multi-party execution (3PL, 4PL, last-mile carriers), and a fragmented visibility picture.
Explore →
Technology stack
Start with a conversation, not a proposal
First call is 30 minutes with Amit. We ask about your systems, your team, and your most pressing operational problem. You get a clear view of where the gap is and what closing it looks like. No slides. No pitch deck. No obligation to proceed.