Dukes Supply Chain BI Dashboard: end-to-end visibility from PO to container.

Project facts & technologies

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Project name

Dukes Supply Chain BI Dashboard — End-to-End Supply Chain Visibility

Client

Dukes India

Industry

Supply Chain, Manufacturing, Logistics

Use case

Unified BI platform integrating procurement, inventory, logistics, and shipping data

Frontend stack

HTML, CSS, JavaScript

Storage stack

Hybrid MySQL (transactional core) + MongoDB (semi-structured, time-series)

Source systems integrated

ERP, vendor portals, freight & logistics systems, warehouse dispatch

Analytics layer

Descriptive analytics (what is happening) + predictive analytics (what is about to happen)

Coverage

PO issuance through container-level shipment — no blind spots between functions

Dashboard surfaces

Executive overview, PO-to-container view, inventory cost, delivery performance, predictive alerts

Stakeholder users

Supply-chain leadership, procurement, logistics, warehouse, finance

Inventory outcome

25% reduction in inventory cost

Delivery outcome

40% improvement in on-time delivery

Predictive capability

Bottleneck forecasting, shipment-delay prediction, demand signals

Why is supply chain visibility such a hard problem to fix with reports alone?

Almost every mid-market and enterprise supply chain runs into the same structural problem. Procurement runs on the ERP. Vendor communication runs through a separate portal — or several. Freight and logistics live in their own system, often a different one per carrier. Warehouse dispatch runs on yet another platform with its own data model. Each function has the data it needs to do its own job. None of them has the data to see the chain as a whole. The result is a supply chain that looks coherent on the org chart and fragmented in reality.

Conventional BI tools partially solve this. A standard reporting platform can pull ERP data and produce a procurement dashboard. The harder problems are the integration shape — getting vendor portals, freight systems, and warehouse data into the same substrate without forcing a rip-and-replace; the storage shape — handling both the structured transactional data of POs and shipments and the semi-structured event data of in-transit logistics; and the analytics shape — going beyond descriptive reporting into predictive forecasting that gives operators time to act before a bottleneck becomes a missed delivery.

What problem does the Dukes Supply Chain BI Dashboard solve?

Dukes India faced challenges with disconnected data systems across procurement, inventory, logistics, and shipping, creating blind spots between PO issuance and container-level shipment. This led to poor planning, shipment delays, and limited ability to predict bottlenecks. AiSPRY designed the platform to solve a specific set of operational challenges together:

Key challenges

• Disconnected source systems — ERP, vendor portals, freight systems, and warehouse dispatch each ran in their own silo, producing data that nobody could join across functions.
• PO-to-container blind spot — between a PO being issued and a container arriving at the warehouse, there was no single view of where the order was, who had it, and when it would land.
• Poor planning — supply-chain planning depended on stale, partial data pulled together manually from multiple systems.
• Shipment delays — delays surfaced as exceptions only after the fact; there was no upstream signal that gave operators time to redirect or expedite.
• No bottleneck prediction — the platform stack supported looking backward (what already happened) but not forward (what is about to happen).
• Cross-function silos — procurement, inventory, logistics, and shipping each had their own metrics; nobody had a unified scorecard.
• Inventory carrying cost — without precise visibility into in-transit and incoming stock, the company over-stocked to compensate, tying up working capital.
• On-time delivery accountability — with fragmented data, accountability for missed delivery commitments was fragmented too — no single team owned the end-to-end outcome.

How does the Dukes Supply Chain BI Dashboard work?

AiSPRY developed a unified Business Intelligence platform that integrates data across ERP, vendor portals, freight systems, and warehouse dispatch into a single, drill-down view of the Dukes India supply chain. The platform follows a five-stage architecture: source-system integration, ETL pipeline and unification, hybrid MySQL + MongoDB storage, a descriptive + predictive analytics engine, and HTML / CSS / JavaScript dashboards built for daily use across procurement, inventory, logistics, warehouse, and finance.

See end-to-end supply chain visibility in action

A walkthrough of the Dukes Supply Chain BI Dashboard — source-system integration, hybrid MySQL + MongoDB storage, executive overview, PO-to-container tracking, and predictive bottleneck alerts.

— Watch the walkthrough

Dukes Supply Chain BI — descriptive + predictive supply chain intelligence

Click to play · Unified visibility from PO to container

• Unified integration — ERP, vendor portals, freight, and warehouse data consolidated into one substrate
• Hybrid storage — MySQL for transactional core, MongoDB for semi-structured event data
• Descriptive + predictive — what is happening and what is about to happen on the same surface
• Drill-down to source — every KPI traces back to the underlying transaction record

What does the Dukes Supply Chain BI architecture look like?

The platform follows a five-stage BI pipeline that takes fragmented source-system data and converts it into a single, drill-down supply-chain view with both descriptive and predictive analytics layered on top. Stage 1 — Source systems: ERP, vendor portals, freight and logistics, and warehouse dispatch feed the platform with their domain data. Stage 2 — Data integration: a connector framework and ETL pipelines pull data from each source with schema mapping, master-data alignment, and data quality checks. Stage 3 — Hybrid storage: MySQL holds the structured transactional core, MongoDB holds semi-structured and event-style data, with indexing optimized for dashboard query speed. Stage 4 — Analytics engine: the descriptive layer computes KPIs across procurement, inventory, logistics, and shipping; the predictive layer runs forecasting and bottleneck-detection models. Stage 5 — BI dashboards: an HTML / CSS / JavaScript dashboard layer delivers an executive overview, PO-to-container view, inventory cost dashboard, delivery performance dashboard, and predictive alert surfaces.

What constraints shaped the design?

Building a BI platform for a real supply chain — across four functions, multiple source systems, structured and semi-structured data, and operators who need to act on what they see — imposes a specific set of constraints that an off-the-shelf reporting tool cannot meet. AiSPRY engineered around four:

What measurable results does the Dukes Supply Chain BI Dashboard deliver?

The platform was engineered against two headline metrics — inventory cost and on-time delivery — both moved sharply in the right direction. Beyond the headline numbers, the platform also shifts the operating practice of supply-chain management from fragmented and reactive to unified and forward-looking.

Dukes Supply Chain BI — frequently asked questions