Omnichannel Inventory

Overview: The Omnichannel Inventory explore provides a consolidated weekly snapshot of inventory levels across all e-commerce and retail channels【26†Description】. It brings together inventory data from online stores and retail accounts to help your operations team track product availability, in-stock rates, and inventory turnover across the entire business. In short, this explore answers: “How much stock do we have, and where, on a weekly basis across all channels?”

Key Content in Omnichannel Inventory

• Total On-Hand Inventory: A primary measure is the total units on hand for each product (or in aggregate) at the end of each week, combining e-commerce warehouses and retail store inventories【26†Description】.

• Channels Included: This explore spans both:

  • E-commerce inventory (e.g., warehouse stock for DTC channels, fulfillment center quantities).

  • Retail inventory (e.g., stock levels at retailer distribution centers or stores if available, from retail portals or EDI feeds).

  • Because it’s omnichannel, you get one view of a product’s total inventory across all selling channels.

• By Location or Channel: Likely dimensions exist to break inventory by location type:

  • You might have a dimension for “Channel” (Ecom vs Retail) or even specific retailer vs warehouse.

  • If data is available by location (like individual stores or warehouses), the explore might allow drilling into that. However, given the high level, it might aggregate unless you bring in a dimension like Location Name or Type.

• In-Stock Rate / Stockout Flags: While not explicitly stated, some inventory explores incorporate metrics like “In Stock (Yes/No)” per item, or count of SKUs in stock vs out of stock, especially for e-commerce. If this explore has such fields, you could filter for SKUs out of stock in at least one channel, etc.

• Inventory Turnover/Weeks of Supply: Perhaps not directly in the base explore, but you might calculate “weeks of cover” (inventory / average weekly sales) if sales info is joined in. Typically, though, this explore focuses on raw inventory counts; turnover analysis might be a separate derived analysis.

Use Cases for Omnichannel Inventory

• Operations Monitoring: Operations and supply chain teams use this explore to ensure inventory is at healthy levels across channels:

  • Identify if certain products are running low in any channel (e.g., SKU X might be fine in DTC warehouse but out of stock at a key retailer).

  • Track total inventory value (if there’s a cost field or price).

• Allocation & Rebalancing: If you see inventory skewed – e.g., a lot of stock in e-commerce warehouse but low at retail – you might decide to reallocate or run promotions accordingly.

• Trend Analysis: View how inventory levels trend week over week. Are we building up inventory (overstock) or consistently depleting (potential stockouts)? This helps in demand planning.

• Turnover Calculation: By comparing against sales (maybe via a separate explore or by blending in sales data), you can determine how quickly inventory is turning. For example, if a product has 10 weeks of supply on hand and lead time is 4 weeks, you’re in a good position; if it has 1 week of supply, that’s a fire drill.

Data Sources and Integration

• Data Sources: This explore pulls from all systems that track inventory:

  • E-commerce platform stock (Shopify, Magento etc. via their inventory endpoints).

  • 3PL or Warehouse management system data if integrated.

  • Retailer portals or reports (like Amazon FBA inventory reports, big-box retailer inventory data, etc.)【26†Sources (likely retail sources, though Sources was NaN meaning context assumed all relevant sources)】.

• Unified View: Because each source might report inventory differently (some give store-level, some give total by SKU), Daasity’s model likely normalizes this in a unified inventory table. Each row might be product × location × week with on-hand units (similar to how URS inventory is structured).

• Granularity: Weekly snapshots are mentioned【26†Description】. So probably the data is aggregated to last day of the week or weekly average. Usually, end-of-week on-hand quantity is used.

Important Dimensions & Measures

• Week: (Date dimension marking the week-ending date).

• Product Details: SKU, Product Name, etc., to filter or group inventory by product.

• Channel or Location: Possibly a high-level channel (ecom vs retail) or specific retailer name, to see where inventory resides.

• On Hand Units: The main measure – inventory count.

• Inventory Value: If cost per unit is integrated or retail value per unit, you might have a measure for inventory value (units * cost or MSRP).

• Inbound/On-order: Advanced inventory tracking might include units on order or in transit. Unlikely in this high-level explore unless explicitly integrated. Usually, that’s more detailed.

• Last Stock Date: Maybe fields like last received date or last sold date could appear to indicate activity. But again, not sure if in this explore.

How to Use Omnichannel Inventory Effectively

• Filter by Product or Category: If you manage by category or brand segments, filter to those to see inventory positions.

• Zero Stock Filter: Filter where On Hand = 0 to get a list of out-of-stock products (across all channels or per channel).

• Compare Channels: Pivot or use channel dimension to compare inventory across channels for the same product. For instance, you might notice product Y has 500 units DTC, 0 at Retailer A – maybe Retailer A needs restock.

• Inventory Heatmap: In a dashboard, use conditional formatting or charts to highlight low inventory items. The data from this explore would feed that.

Relationship with Sales Data:

It’s common to use this explore along with sales (like Company Performance or channel sales) to calculate metrics like Sell-through Rate (units sold / units available) and to project stockout dates. But those calculations might be outside the basic explore (in analysis or a combined dashboard).

Example Scenario: “We have 8 weeks of declining inventory for our top seller and the sales trend is steady – this explore shows the decline, alerting us to reorder.” Conversely, “We have rising inventory for a slow-moving SKU, indicates overstock – maybe mark it down.”

References:

• Daasity CSV Metadata: “Weekly inventory snapshot across all retail and e-commerce sources…”【26†Description】 – confirms scope and purpose.

• Unified Schema Documentation (URS and others): The Unified Retail Sales (URS) schema and others likely mention an Inventory table structure , giving context to how inventory is stored by week, product, location in the model.

• Use-case Knowledge Base: Retail Operations dashboards might be built on this explore, showing how it’s applied (for instance, if there’s a standard “Inventory” dashboard template, it would draw from here).