Overview
Supply chain management is fundamentally an information problem. The physical goods — in transit, in warehouse, in production, with suppliers, on their way to customers — are visible only through the data that systems generate as they move. When that data is fragmented across the ERP, the WMS, the TMS, the carrier systems, the supplier portals, and the spreadsheets that fill the gaps between them, the supply chain is visible only in pieces. Each piece is managed by someone with a view of their segment, but nobody has a view of the whole — and the problems that matter most in supply chain management are usually the ones that span segments.
A supply chain dashboard consolidates the data from every system that touches the supply chain into the operational visibility that supply chain managers, logistics directors, and operations leadership actually need — not a static report produced at month end, but a live operational view that shows current inventory levels, in-transit shipments, supplier delivery performance, warehouse throughput, carrier performance, and the exception flags that tell the team where attention is needed right now.
We build custom supply chain dashboards for logistics businesses, manufacturers, distributors, retailers, and any organisation managing a supply chain complex enough that fragmented visibility is creating operational problems. The stack centres on Rust for the high-throughput data processing that consolidating supply chain data across many source systems requires, React and Next.js for dashboards that update in real time and present complex operational data in a form that supports fast decision-making, and direct integration with the ERPs, WMS platforms, carrier systems, and operational databases that supply chain data lives in.
What Supply Chain Dashboards Surface
Inventory visibility. Stock levels across every location — own warehouses, 3PL facilities, in-transit between locations, consignment stock at customer sites, stock on order with suppliers. Inventory visibility answers the questions that supply chain decisions depend on: how much do we have, where is it, when do we need to replenish, and what is at risk of stockout or overstock?
Multi-location inventory views aggregate stock across all locations to the SKU level, show the distribution of stock across locations and the transit stock that is moving between them, and surface the imbalances — locations that are overstocked while others are running low — that rebalancing decisions need to address.
Inventory ageing for businesses where stock deteriorates or becomes obsolete over time — the age profile of each SKU across each location, the proportion of stock approaching expiry or end-of-life, and the inventory value at risk from expiry or write-off.
Inbound supply visibility. Purchase orders placed with suppliers, the goods in transit from supplier to warehouse, the expected arrival dates and the variance against those dates. Inbound supply visibility answers the question that procurement and planning teams need answered continuously: what is coming, when is it arriving, and is it on track?
Supplier on-time delivery performance — the actual delivery date against the promised delivery date for every purchase order, aggregated into the supplier performance metrics that procurement reviews and supplier negotiations depend on. Early warning of inbound delays — purchase orders where the supplier has indicated a delay or where the expected arrival date is approaching without shipment confirmation — gives planning teams the lead time to adjust production or fulfilment plans before the delay becomes a shortage.
Outbound shipment tracking. The full pipeline of outbound orders — picked and packed, awaiting collection, in transit, out for delivery, delivered, exception — consolidated across all channels and all carriers into a single outbound visibility view. The daily delivery picture: how many shipments were despatched today, how many are in transit, how many are out for delivery, how many have been delivered, and how many have exceptions that require attention.
Carrier performance in the outbound view — on-time delivery rates by carrier, by service level, by destination region — surfaces the carrier performance data that ongoing carrier management and future carrier selection depend on.
Warehouse operations. Throughput metrics for warehouse operations — inbound receipts processed per day, outbound orders picked and packed, despatch volumes — compared against capacity and against operational targets. Queue depths — orders awaiting picking, orders awaiting packing, returns awaiting processing — that surface bottlenecks in the warehouse flow before they cause fulfilment delays.
For businesses operating multiple warehouse locations, the warehouse operations view shows the operational status of each location simultaneously — the location that is running behind, the location with a staffing gap, the location whose throughput is affecting overall fulfilment performance.
Supplier performance. Delivery performance — on-time rate, in-full rate, order accuracy — aggregated by supplier and by supplier category. Quality performance — defect rates, return rates — where quality data is available. Responsiveness metrics — the time between order placement and order confirmation, the time between order confirmation and despatch notification.
Supplier performance dashboards give procurement teams the data they need to manage supplier relationships proactively — identifying underperforming suppliers before their performance affects operations, providing the data for performance review conversations, and supporting the supplier selection decisions that category management requires.
Transportation and carrier analytics. Cost per shipment, cost per kilogram, and cost per stop by carrier and by service level. Transit time performance against service level commitments. Damage and loss rates by carrier. Failed delivery rates by carrier. The carrier analytics that freight procurement and carrier relationship management depend on.
Order fulfilment performance. The end-to-end fulfilment performance metrics that customer service and operations leadership need: order cycle time from order placement to delivery, on-time delivery rate against committed delivery dates, first-time delivery success rate, and the order-level exceptions — delays, partial fulfilment, returns — that track the quality of the fulfilment operation.
Cost and financial visibility. Logistics cost as a percentage of revenue, by channel, by customer segment, and by product category. Freight cost trends over time — tracking the effect of carrier rate changes, volume changes, and route mix changes on total freight spend. Warehousing cost per unit and per order. The cost data that logistics financial management and cost-to-serve analysis require.
Dashboard Architecture for Supply Chain Data
Supply chain dashboards face a data integration challenge that most operational dashboards do not: the data they need to present comes from many different source systems, often with different data models, different update frequencies, different data quality characteristics, and different access patterns. Building a dashboard that presents a coherent, accurate view of the supply chain from this data requires more than connecting a BI tool to the relevant databases.
Data consolidation layer. Source data from ERP systems, WMS platforms, TMS systems, carrier APIs, and supplier portals is extracted, validated, transformed, and consolidated into a unified supply chain data model. The consolidation layer handles the entity matching that multi-system supply chain data requires — matching the same SKU across systems that use different item codes, matching the same purchase order across the ERP and the supplier's system, matching the same shipment across the carrier tracking system and the TMS.
Refresh strategy. Different data elements require different refresh frequencies. Carrier tracking events need to be retrieved as they occur — webhook or high-frequency polling. Inventory levels from the WMS need to refresh frequently enough to support operational decisions — every few minutes for active warehouse operations. ERP financial data may be refreshed daily. The refresh strategy is designed per data element based on how frequently it changes and how quickly stale data would create an operational problem.
Historical data retention. Supply chain dashboards need both current operational data and historical data for trend analysis, performance benchmarking, and root cause investigation. Historical data retention — storing the time series of inventory levels, throughput metrics, carrier performance, and order fulfilment data — supports the analytical views that operational dashboards need alongside the operational views.
Calculated metrics. Many supply chain metrics are not stored anywhere — they are calculated from the underlying transactional data. Days of inventory on hand, calculated from current stock level and recent consumption rate. On-time delivery rate, calculated from the comparison of actual delivery dates to committed delivery dates. Order cycle time, calculated from the timestamps of each stage in the order fulfilment process. The dashboard calculation layer computes these metrics on the consolidated data and keeps them current as the underlying data changes.
Multi-Entity and Multi-Location Views
Supply chains often span multiple legal entities, multiple countries, and multiple operational locations. Supply chain dashboards for complex supply chains need both the entity-level views that each location's management uses and the consolidated group-level view that supply chain leadership requires.
Group-level consolidation. Inventory, throughput, and performance metrics aggregated across all entities and locations — the total supply chain position visible from a single view. Group-level consolidation handles the currency translation and the intercompany elimination that financial supply chain data requires.
Entity and location drill-down. From the group view, drill-down to the entity or location level — the inventory position at a specific warehouse, the throughput metrics for a specific facility, the carrier performance for a specific country operation. The drill-down path allows supply chain leadership to navigate from the aggregate exception to the specific operational context that drives it.
Comparative views. Location-to-location performance comparison — which warehouse is operating most efficiently, which carrier relationship is delivering the best performance, which supplier is most reliable — gives supply chain managers the benchmarking data that performance improvement decisions depend on.
Exception Management
A supply chain dashboard that presents data without surfacing the exceptions that require action is a reporting tool rather than an operational tool. Exception management in the supply chain dashboard surfaces the specific situations that need attention — the stockout risk, the delayed shipment, the supplier that has not confirmed a purchase order, the warehouse backlog that is building — with the context that allows the responsible person to act immediately.
Stock alert thresholds. Configurable minimum stock level alerts by SKU and by location — surfacing the SKUs that are approaching stockout before they reach zero, with the expected days of cover remaining and the open purchase orders that will replenish the stock. Overstock alerts for SKUs whose stock level exceeds the maximum that the operational plan requires — flagging the working capital that is tied up in excess inventory.
Inbound delay alerts. Purchase orders that have not been confirmed by the supplier within the expected timeframe, shipments from supplier that have not been despatched by the expected date, inbound shipments whose tracking has not progressed as expected.
Outbound exception alerts. Shipments that have experienced failed delivery attempts, shipments that have been held at a warehouse or hub for longer than expected, shipments approaching the delivery deadline without a confirmed delivery event.
Performance threshold breaches. Carrier on-time delivery rate below the contracted SLA threshold, warehouse throughput below the operational target, supplier delivery performance below the agreed performance level — surfaced as exceptions when thresholds are breached, rather than only visible in periodic performance reports.
Integration Points
ERP systems. Exact Online, AFAS, SAP — inventory data, purchase order data, sales order data, and the financial supply chain data that ERP systems hold. ERP integration provides the authoritative inventory and order data that the supply chain dashboard builds its operational view from.
Warehouse management systems. WMS platforms — inventory location data, inbound receipt status, outbound pick and pack status, returns processing status. WMS integration provides the real-time warehouse operational data that throughput and queue depth metrics require.
Transport management systems. TMS platforms — shipment data, carrier assignment, route planning data, freight cost data. TMS integration provides the transportation data that carrier performance and freight cost analytics require.
Carrier tracking systems. PostNL, DHL, FedEx, UPS, SendCloud, MyParcel — tracking event data from every carrier in the portfolio. Carrier tracking integration provides the outbound shipment visibility that customer service and delivery performance management require.
Supplier systems. EDI or API connectivity with supplier systems — purchase order confirmation, advance shipping notice, delivery documentation. Supplier system integration provides the inbound supply visibility that procurement and planning require.
Customs and compliance systems. Customs declaration status, import clearance status, duty payment status for international supply chains where customs clearance affects the supply chain timeline.
Technologies Used
- Rust / Axum — high-throughput supply chain data processing, real-time metric computation, multi-source data consolidation
- C# / ASP.NET Core — ERP and WMS integration, complex supply chain logic, EDI processing, financial data consolidation
- React / Next.js — supply chain dashboard frontend, exception management views, drill-down navigation, reporting interfaces
- TypeScript — type-safe frontend and API code throughout
- SQL (PostgreSQL, MySQL) — consolidated supply chain data store, historical metrics, exception records, performance analytics
- Redis — real-time metric cache, exception alert queuing, dashboard update coordination
- Exact Online / AFAS / SAP — ERP supply chain data integration
- PostNL / DHL / FedEx / SendCloud APIs — carrier tracking data integration
- EDI (EDIFACT / X12) — supplier and logistics partner data exchange
- REST / Webhooks — multi-source data ingestion from operational systems
- AWS S3 — historical supply chain data storage for trend analysis
- Slack / SMTP / SMS — exception alert delivery to supply chain teams
The Visibility Gap in Supply Chain Management
The supply chain visibility gap — the difference between what supply chain managers need to see and what they can actually see from the systems available to them — is one of the most persistent operational problems in logistics. It is not a technology shortage problem: ERP systems, WMS platforms, and carrier tracking systems all generate the data that supply chain visibility requires. It is an integration problem: the data exists in silos that each present a partial picture, and assembling the complete picture requires manual aggregation that is too slow, too labour-intensive, and too error-prone to support the operational decisions that supply chain management requires.
Custom supply chain dashboards close this gap — not by replacing the source systems that hold the data, but by connecting them through a consolidation layer that presents the unified supply chain view that source systems cannot provide individually.
One View Across the Whole Supply Chain
Supply chain performance is determined by the weakest link. Identifying the weak links requires seeing the whole chain simultaneously — inventory, inbound supply, warehouse operations, outbound shipments, carrier performance, and supplier reliability — in a single operational view that surfaces exceptions before they become failures.