Hong Kong Shopping

Demand Signal Applications: the Basics

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The Landmark Shopping Mall in Hong Kong.  Photo by See-ming Lee, CC license.

It’s now possible to string together data points collected from the shelf (POS) and upstream from there (your supply chain) to optimize your sourcing, production and distribution decisions.  Application vendors are calling this Demand Signal Management (DSM).  It’s not new; the same concept a few years ago was called shelf-driven demand management.

Is this something you should invest in?

My experience is that these tools used even on a small scale are insightful and provide real returns, but that it’s easy to over-invest and get lost in what you’re trying to do with the mountains of data now available.

The vendor landscape is predictably unclear.  Before you consider vendors, though, it’s helpful to understand the different segments within DSM:

  • Shelf or Point-of-Sale data is just one component and by itself in its raw form it’s not very useful without formatting and organizing it and normalizing it to your definitions. This is where POS data providers add value.  Many retailers will only supply data for your products, not selected competing products or your product category as a whole. This to me is a big gap in understanding true demand.  From POS data one can calculate product performance indicators such as days of supply, out of stocks, and item velocity.
  • Customer warehouse data is one step upstream and is usually available from most retailers and included in the data package offered by POS vendors.  This data shows the quantities into and out of the warehouse, quantities on order, and current inventory. Again, usually only data for your products.
  • Upstream from the warehouse is your enterprise and any data you might want to incorporate into the analysis: forecast, orders, inventory, past or planned promotions, production plans, supplier orders, marketing events, advertising, etc.
  • Then there’s everything else: environmental data such as market size and segment trend data, geographic/demographic data, social media, the weather, the time of year, and cultural trends.

Putting all these pieces together for a coherent and insightful view of your demand is the promise of DSM.

Application vendors in this area fall into two main categories: 1) “point” applications that offer one solution for one part of the supply chain, such as POS data vendors; and 2) fuller end-to-solutions that offer the ability to incorporate many different data points from many parts of the enterprise and to relate them in a logical way to one another.  My advice:

  1. Try a point solution approach on a particularly difficult or troublesome part of your supply chain; it’s not expensive and many application vendors can enable a solution quickly on a cloud platform;
  2. Keep the financial commitment small and the option to exit from the solution easy.  This should not be difficult, as vendors often will agree to month-to-month contracts;
  3. Do steps 1) and 2) before launching any large end-to-end DSM initiative.  An end-to-end project can involve lots of data management (conversion, translation, normalization) and if its not done right the result could be a confusing and unreliable addition to your demand planning efforts.


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