Supply Chain Analytics in New York
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.
End-to-end supply chain visibility isn't a luxury. It's what separates the companies that absorb disruption from the ones that get disrupted. US-based clients tend to have higher awareness of modern data stack tooling — Snowflake, dBT, and Databricks are well-understood at the technical level. The challenge is more often the domain knowledge gap: understanding how to apply those tools to the specific operational environment of a manufacturing plant in Dubai or a supply chain network spanning India and Southeast Asia. That's the combination MDI provides.
What we hear from operators
The problems we solve
These aren't hypothetical pain points assembled from industry reports. They're observations from actual plant floors, warehouse ops, and finance desks — written down because they come up in almost every first conversation.
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.
How we work
Our approach
01
Build the demand-to-delivery data model
One integrated view from the demand signal — forecast, confirmed orders, point-of-sale data — through the supply plan, purchase orders, inbound shipments, goods receipt, and final delivery. Every hand-off point in the supply chain represented in one model. The data sources connected: ERP, WMS, TMS, supplier portals, customer EDI.
02
Deliver the operational supply chain dashboard
Inventory by SKU, location, and age. Inbound POs with expected delivery vs promised date. Supplier on-time performance by vendor and commodity. Days cover by SKU against current demand forecast. The exceptions surfaced automatically — the POs at risk of late delivery, the SKUs below safety stock, the suppliers trending below SLA.
03
Layer in optimisation and early warning
Once the visibility is trusted, we add the intelligence layer: safety stock recommendations based on actual demand variability and supplier lead time variability. Replenishment triggers automated from the live demand and stock data. Supplier risk scoring that flags concentration risk before a disruption makes it obvious.
What changes
Outcomes
These are specific, measurable shifts — not benefit statements. Every outcome listed here has been achieved with a client.
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.
Technology stack
Common questions
What buyers ask us
These are questions that come up in almost every first or second conversation. If yours isn't here, it will be in the first call.
We have multiple warehouses across different countries. Can the dashboard handle that?
Multi-site, multi-currency supply chain analytics is standard in the markets we serve. The data model is designed from the start to handle multiple warehouses, multiple legal entities, and multiple currencies. Consolidation happens at the data layer — the dashboard shows global, regional, or site-level views from the same underlying model.
Our suppliers don't share data electronically. Does that limit what we can do?
It limits real-time supplier visibility, but not the rest of the supply chain analytics. We can still build demand-to-delivery tracking on your side of the supply chain — your POs, your inbound shipments, your goods receipts. For suppliers without EDI or API capability, we typically start with a simple supplier portal for delivery confirmations, which is lower effort than full integration and captures the most critical data point: will this PO arrive on time.
We already have an S&OP process. Is this analytics or is it replacing our process?
This is analytics that makes your S&OP process better, not a replacement for it. The S&OP process — the meeting cadence, the cross-functional decision-making, the consensus forecast — stays the same. What changes is the quality of the data going into it. Demand signals are more current. Supply constraints are visible earlier. The conversation in the S&OP meeting shifts from "what do the numbers say" to "what do we do about what the numbers are telling us."
Ready to move
Start with a conversation, not a proposal
First call is 45 minutes. No deck. 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 obligation.