Supply chain analytics connects inventory, demand forecasting, supplier performance, and logistics data to support the decisions that keep product moving without either stockouts or excessive working capital tied up in inventory. The data architecture is complex because the domains are distinct and the systems were never designed to talk to each other.
Supply chain analytics connects data from inventory systems, demand planning tools, supplier performance records, and logistics platforms to support the decisions that keep product moving: how much to order, when to order it, which suppliers to source from, and how to route logistics efficiently. The data is operationally critical — poor inventory decisions produce either stockouts (lost revenue, customer dissatisfaction) or excess inventory (working capital consumed, potential write-offs).
The architecture challenge is that supply chain data lives in systems that were not designed to work together: inventory in an ERP, demand signals in a planning tool, supplier data in a procurement system, logistics data in a WMS or TMS, and customer demand data in the e-commerce platform or order management system. Integrating these into a coherent analytical foundation requires explicit design decisions.
Core Data Domains
**Inventory data**: The current stock position — quantity on hand, quantity on order, quantity reserved for committed orders, safety stock levels, and reorder points. Inventory data typically comes from the ERP (SAP, Oracle, NetSuite, Dynamics 365) and is updated at each inventory movement (receipt, shipment, transfer, adjustment).
For analytics, inventory data requires point-in-time snapshots — not just current stock, but historical stock levels that enable analysis of stockout days, inventory turns, and seasonal patterns. Most ERP systems track inventory movements but not stock-level history; generating historical snapshots requires either: querying movement records and reconstructing balances at each time point, or taking daily snapshots from the current inventory view.
**Demand data**: Orders, order lines, shipment confirmations, and returns. Demand data enables: fill rate analysis (what proportion of orders were shipped complete on the requested date), sales velocity by SKU (how many units of each SKU sell per week), demand variability (how much does weekly demand fluctuate around the average). These metrics drive safety stock calculations and reorder frequency.
**Supplier performance data**: Lead time (from purchase order creation to receipt at the warehouse), lead time variability (how consistent is the supplier's lead time), fill rate (what proportion of the order quantity was actually delivered), and quality data (return rates for supplier quality failures). Supplier performance metrics directly affect safety stock requirements — a supplier with high lead time variability requires more safety stock than one with consistent delivery.
**Logistics data**: Shipment records from the WMS or TMS — carrier, route, transit time, cost, and delivery status. Logistics analytics supports: carrier performance comparison, route cost optimisation, on-time delivery rate, and cost-per-unit-shipped by origin-destination pair.
Key Supply Chain Metrics
**Inventory turns**: Annual units sold / average inventory. A measure of how efficiently inventory is being utilised. Higher turns indicate that inventory is moving quickly rather than sitting. Industry benchmarks vary widely by product category; the relevant comparison is against your own historical trend and against comparable competitors.
**Days of inventory on hand**: Current inventory quantity / average daily demand. How many days of demand the current inventory can satisfy. Below safety stock minimums triggers reorder; excessive days of inventory ties up working capital.
**Stockout rate**: Percentage of days (or percentage of order lines) where the requested product was not in stock. A stockout rate above 1-2% for core SKUs is typically material to revenue and customer experience. Stockout analysis by SKU, by location, and by season reveals where inventory planning is failing.
**Supplier on-time in-full (OTIF)**: Percentage of purchase orders delivered on time and in full. A combined metric that penalises both late delivery and short delivery. OTIF below 90% for critical suppliers is a procurement risk signal.
**Fill rate**: Percentage of order lines or order value shipped complete on the requested ship date. A 95% fill rate means 5% of order lines had at least partial stockout. Fill rate by SKU identifies chronic availability problems.
**Forecast accuracy**: For organisations using demand forecasting, the Mean Absolute Percentage Error (MAPE) of the forecast vs actual demand. Forecast accuracy by SKU and by planning horizon (7-day, 30-day, 90-day) identifies where the forecasting model is least reliable and where safety stock buffers are most needed.
Demand Forecasting Architecture
Statistical demand forecasting requires time series data at SKU level — weekly or daily demand by SKU over at least 1-2 years of history (more for seasonal products). The forecasting architecture:
**Demand history at transaction grain**: All sales orders at line-item grain (SKU, quantity, requested date, shipped date, location) provide the base data. Aggregate to weekly SKU-location demand for forecasting.
**Outlier handling**: Promotional spikes, one-time bulk orders, and stockout periods where demand was suppressed (actual orders were less than demand because product was not available) should be identified and handled. Forecasting models trained on suppressed demand produce systematically low forecasts; promotional spikes inflate baselines.
**Seasonal decomposition**: Time series decomposition separates a demand signal into trend, seasonality, and residual components. The seasonal component enables calendar-aware forecasting; the trend component enables growth adjustment.
**Forecast storage and comparison**: Both the forecasts and the actuals should be stored in the analytics layer to enable post-hoc forecast accuracy analysis. Forecast at the time it was made — not retrospectively updated — alongside subsequent actual demand enables honest accuracy evaluation.
ERP Integration Considerations
Supply chain analytics depends on accurate ERP data. ERP integration for analytics requires:
**Inventory movement tables**: The ERP typically stores inventory changes as movement records (goods receipt, goods issue, transfer). These movement tables are the source for reconstructing inventory positions over time. Identify the specific tables in the ERP that store movement records for your ERP system; they vary significantly between SAP, Oracle, and others.
**Master data synchronisation**: Product master data (SKUs, descriptions, units of measure, product hierarchy) and location master data (warehouses, locations within warehouses) must be synchronised between the ERP and the analytics layer. Changes in master data affect how historical analysis is interpreted.
**Near-real-time vs batch**: Inventory analytics for operational decisions (what do we have available to promise for today's orders?) may require near-real-time data. Inventory analytics for strategic decisions (what are our inventory turns by category?) can tolerate daily batch updates. Define the freshness requirement before the integration architecture.
Our data architecture practice designs supply chain analytics architectures from ERP integration to demand forecasting data models — contact us to discuss your supply chain analytics requirements.
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