Sensing Demand at Point of Sale – Part 2: Demand Signal Repositories
Last week we discussed how grabbing hold of data at the point of consumption—essentially, taking continuous-frame snapshots of buyer behavior—can improve supply chain performance and enhance inventory optimization.
But many companies don’t have their own captive retail network and therefore don’t have direct access to point-of-sale (POS) data. And even if they could access data from multiple retail networks, their Enterprise Resource Planning (ERP) software typically couldn’t handle this data at the granular, highly disaggregated level required for demand analytics.
Lora Cecere explained why in Consumer Goods Technology (April 2014), in a piece entitled Why Can’t I Get to Data? “The reason that companies are drowning in data and short on analytics insights is because we did not design the data for cross-functional use,” she said. “Instead, data is locked in silos and distinct applications.”
There is a solution, however, known as a Demand Signal Repository (DSR). Gartner defines a DSR as a centralized database that collects, stores, harmonizes and normalizes data, and organizes large volumes of demand data (such as point-of-sale data, wholesaler data, warehouse data feeds, inventory movement and promotional data) for use in decision support.
Demand Signal Repositories by themselves can’t turn demand signals into data insights—for that they need to be coupled with demand sensing technology. Demand sensing works with the detailed downstream data from the DSR to provide the ground game for better account level forecast accuracy, a more reliable understanding of what inventory you need to achieve target service levels, and where that stock should be located. It reduces latency in the supply chain by getting closer to the actual end-consumer pull and shelf activity.
Demand sensing draws from the underpinning DSR to help you identify demand trends for advance warning of problems—removing the awareness lag between plan targets and what is actually happening in the supply chain. The quicker these deviations can be identified, the faster and more intelligently it can respond. By importing fresh, daily demand data for collation in the DSR, it can immediately sense the demand signal changes, and can react with the appropriate forecast and replenishment adjustments.
Combining a DSR with demand sensing software allows businesses to sense demand across multiple retail streams—and plan within a single model. The DSR cleanses and harmonizes retail POS, inventory, and distribution-center shipment data, and integrates the data within the demand sensing and planning system platform. They are particularly effective in an multi-channel environment, because they can enable “predictive commerce”— maximum connection to the demand signal with minimum latency.
So B2C and B2B manufacturers and distributors can acquire data from their retail distribution network, bringing demand connectivity to companies who were previously one step removed from this data. Retailers can also now use POS data to optimize Vendor Managed Inventories (VMI) across a wide range of their suppliers.
Gartner says, “Interest in better aligning near-term supply execution with what is actually happening downstream is growing,” a capability Gartner calls “respond planning”.