Forecasting Over the Product Life Cycle
Demand forecasting is tough, and getting it right over the entire product lifecycle during the course of launch, maturation, and end-of-life is tougher still. This is true for any business with sizable turnover in the product portfolio and especially in industries like retail, electronics, and aftermarket parts.
Forecasting new product introductions is difficult because you don’t have sales history, and you don’t have a good idea of how quickly the product will take off. Early sales data can be notoriously deceptive due to channel packing—you ship a bunch of new products to distribution centers or stores to fill bins and shelves, creating an initial surge that is not necessarily a good indicator of sustainable end-user demand.
Substitution and replacement part demand can be somewhat easier to predict, but even they can be tricky if channel packing creates unexpected demand spikes or if it is hard to determine which previous histories are most applicable to how demand for the new product or part will behave.
End-of-life planning and campaign planning require balancing the need to satisfy demand with not getting stuck with inventory that has to be marked down or obsoleted. The goal is to deliver high in-season customer service levels, yet with a graceful exit at end of life or end of season.
Many firms manage this demand variability with manually intensive processes. They often take a different approach for each life cycle phase, with distinct algorithms for each one. They have to figure out at what point to switch from one phase to another, and make this decision according to the plan, not actual demand. This jolts the supply chain, sending a signal through the chain that is not based on anything that happened to product demand, but rather on the planning algorithm.
The reality is that the product life cycle is fluid; a product doesn’t suddenly “decide” that it is now in one phase or the other. Products typically transition smoothly, and the system must similarly smoothly transition with that demand pattern, as it reveals itself.
Product Life Cycle
Demand sensing software—and the subtle or larger changes in the demand pattern—can suggest whether the product is in or transitioning to maturation or an end-of-life demand profile. A demand modeling solution should monitor and sense demand automatically, and alert the user on an exception-only basis if something is out of the ordinary. It then becomes a more hands-off view—as well as a calmer, quieter, more smoothly operating supply chain, in service of the entire product life cycle.
Often the answer lies downstream—the further downstream you go, the purer the demand signal. Whether product introductions, midcourse sales, or end-of-life downslope, if you can get closer to the point of sale, you have an early signal of demand at each life cycle phase.
New data sources and machine learning automation are also making improvements to forecasting the product life cycle. For instance, if the new product is on your website, you can track indicators such as the number of clicks or page views, how many times someone downloads a product specification page, or how much time they spend on a page with detailed product descriptions. If you’re announcing a replacement upgrade on Twitter, you can see how many tweets or retweets you get. If you publish product news on Facebook or Instagram, you can count shares and likes as early indicators of an end-of-life slowdown.
Forecasting over the entire product lifecycle is still a challenge–but new methods, data and machine learning for supply chain forecasting are creating opportunities to make it more manageable.