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. Then I explored how forecasting techniques will need to change. Today I want to discuss the importance of a demand signal repository.
Given the potential benefits of shelf-connected systems (on-shelf availability, increased sales, reduced days of supply, and an overall reduction in supply chain costs), early adopters in the market have taken on the demand-driven value network challenge and moved in the direction of driving responsive replenishment through short-term forecasts. Many of the methods being used depend on what is called a demand signal repository or DSR, which should be core to any retail/CPG solution. DSRs are designed to leverage different types of downstream
data, including POS data, to power more robust and dynamic forms of replenishment. Some of these more advanced forecasting approaches require a level of analytics that involve both prediction and prescription.
Three popular examples of these short-term forecasting techniques are as follows:
- Generating a store-level forecast for each SKU which is then propagated upstream in the supply chain and aggregated into replenishment requirements. This is typically referred to as Flowcasting.
- Mirroring store-level ordering policies by writing algorithms that estimate store order policy logic at the DC so that CPG companies can optimize both replenishment and transportation.
- Taking demand data and applying pattern recognition algorithms to fine tune demand for different short term periods such as week-to-week across various supply chain nodes. Data to be leveraged includes store level days of supply, store level forecasts, DC shipments, DC inventory positions, DC inbound shipments, and DC open orders.
Which is best? Given all the data that is available to develop both short and long term forecasts, a weighted approach considering a combination of order information, DC shipments, POS data, geo-demographic considerations etc. makes the most sense. The key to improved performance is to reduce variability and forecast error, which would greatly minimize the classic “bullwhip” effect throughout the supply network. While this has always been a goal, today we can actually solve the problem. DSRs, along with advanced algorithms for continuous forecast management, or CFM, supports multiple methods of short-term forecasting, including those mentioned above, as well as various types of predictive and prescriptive analytics.
In future posts I”ll 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?
Latest posts by Greg Brady (see all)
- Why a Network Model Makes Sense for Automotive Suppliers - July 30, 2019
- The Financial Impact of Master Data Management - February 5, 2016
- Sharing the “Best Version of the Truth” with Trading Partners - January 18, 2016