Different Approaches to Dynamic Replenishment

Extract from LogisticsViewpoint.com, by Steve Banker

I’ve been writing about the Demand Signal Repository (DSR) market a fair bit recently (see, for example, “How to Best Describe Demand Signal Repositories”). DSR solutions leverage a variety of different types of downstream data, including POS data, to power more robust and dynamic forms of replenishment.

As the saying goes, “everyone talks about the weather, but nobody does anything about it.” Well, when it comes to supply chain management, most companies know about the bullwhip effect, but very few do anything about it. Dynamic replenishment, based on downstream data, finally offers a way for companies to “do something about it.”

A second approach taken by some software vendors is to mimic store-level ordering policies. These solutions contain store order policy logic that anticipates what a store will order from the consumer goods manufacturer. One Network Enterprises (a Logistics Viewpoint sponsor) is the primary practitioner of this style of responsive replenishment. One Network provides both the core data repository and supply chain applications in a software-as-a-service (SaaS) model. The data repository contains retailer and supplier master data, transactional data, and the stores’ replenishment policy information. Del Monte is a One Network client that has publicly discussed its use of this framework. The company is leveraging Walmart and others’ downstream data to make better replenishment and transportation decisions.

In many ways, the DSR market is still immature. However, when it comes to using downstream data for dynamic replenishment, good solutions already exist.

Case Study: Del Monte’s Demand Driven Replenishment