Forecasting in a Shelf-Connected Supply Network

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Christian Schnettelker/Flickr
Christian Schnettelker/Flickr

Note: This is the next installment in an ongoing series that explores shelf-connected supply networks. In the first post, I asked whether a shelf-connected system was possible with traditional ERP systems. Today I want to discuss how new approaches to forecasting processes are required in a shelf-connected world. 

A “demand-driven value network” (DDVN) oriented architecture is similar in concept to a “Consumer-Driven” network, and is a good framework for building a shelf-connected system. DDVN’s seek to align and synchronize demand, supply, and product cycles, as well as enable the ability to sense and shape demand to generate a profitable demand response. Focusing on a single view of the consumer for all members of the extended supply chain requires an approach that not only includes retailers, manufacturers, and logistics providers but also requires that they be connected across a multiparty execution backbone. This backbone then orchestrates a coordinated response to the end-consumer’s demand signal.

Unfortunately today’s more popular forecasting and replenishment methods are based on a limited technology architecture that basically constitutes a push or ERP batch-oriented type environment, where an attempt is made to collect and react to exceptions. The problem is this architecture simply isn’t designed to resolve issues in a timely fashion (e.g. daily) given its long lead times and serially integrated nature. Using this method means a price must be paid in lost sales along with excess and obsolete inventory.

The vision of the demand-driven value network is simply to design a process to drive the upstream supply network based on actual end-customer demand, basically the sell-through to consumers, rather than what is typically used, which is shipments, or the sell-in to the Distribution Centers (DCs) and retail stores. By leveraging sell-through data rather than just sell-in, and minimizing the latency between actual consumer demand and its measurement, retailers and their suppliers can gain a more accurate understanding of market conditions. This will also enable them to optimize both their production and distribution plans.

Going a step further, by using a single version of the truth and calculating a sell-in forecast that is mathematically based on the sell-through forecast (taking into account channel inventory and actual lead times), retailers and their suppliers can obtain a more accurate forecast of sell-in versus the traditional practice of forecasting the sell-in directly. Visibility is greatly enhanced given that we are moving beyond the restriction of observing only shipments to one of a direct relationship between consumer behavior and sell-in shipments. With this more accurate forecast, retailers can improve on-shelf availability, increase sales, reduce days of supply, and decrease overall supply chain costs.

In future posts I”ll go a bit deeper into  forecasting, and also explore how the approach to replenishment, collaboration, and S&OP processes differ in a shelf-connected system. If you’re impatient, I suggest you download the new white paper: Is your supply network really shelf-connected?

Greg Brady