The Smart Forecaster
Pursuing best practices in demand planning,
forecasting and inventory optimization
The unique challenges of inventory planning for spare parts, large capital goods and other infrequently or irregularly moving items drives the importance of finding smarter methods to forecast this kind of intermittent demand. Robert Bowman, Editor of Supply Chain Brain Magazine, and I discussed this topic at the October APICS conference in Denver, and video of our conversation is available at Supply Chain Brain‘s website.
Why plan for intermittent demand? Well, why plan for any demand? If you can understand what the likely range of demand will be until you can get more, you will know how much stock to keep in reserve, so you have just enough. This is the heart of demand forecasting and inventory optimization. Intermittent demand is exceptionally difficult to forecast, but this same principle holds true.
Unlike other demand patterns, where historical data suggests regular trends, ebbs and flows, seasonality or other discernible patterns, intermittent demand appears to be random. There are many periods of zero demand interspersed with irregular, non-zero demand. This occurs frequently with service parts, where parts are replaced when they break, and you just don’t know when that will occur. Most service parts inventories (70% or more!) can experience intermittent demand. Demand for specialized or configured products is also likely to be intermittent.
Supply Chain Brain has made the more in-depth discussion of this topic Bowman and I shared available here. For new visitors to Supply Chain Brain, a quick account sign-up is required to access the video.
Jeff Scott serves as Vice President, Marketing & Alliances for Smart Software.
If you hear the phrase “regime change” on the news, you immediately think of some fraught geopolitical event. Statisticians use the phrase differently, in a way that has high relevance for demand planning and inventory optimization. This blog is about “regime change” in the statistical sense, meaning a major change in the character of the demand for an inventory item.
Our customers are great teachers who have always helped us bridge the gap between textbook theory and practical application. A prime example happened over twenty years ago, when we were introduced to the phenomenon of intermittent demand, which is common among spare parts but rare among the finished goods managed by our original customers working in sales and marketing. This revelation soon led to our preeminent position as vendors of software for managing inventories of spare parts. Our latest bit of schooling concerns “blanket orders.”
The New Forecasting Technology derives from Probabilistic Forecasting, a statistical method that accurately forecasts both average product demand per period and customer service level inventory requirements.