It was a pleasure to attend the 5th IMA and OR Society Conference at Aston University, Birmingham, and to present my research with Anna Sroginis on model-based demand classification. A great crowd of people from universities across the UK, along with several esteemed international colleagues. The event was very well organised – thanks to Aris […]
For the last year, Anna Sroginis and I have been working on a paper, trying to modernise demand classification schemes and make them useful in the brave new era of machine learning. We have finally wrapped it up and submitted it to a peer-reviewed journal. But the temptation to share was too strong, so we […]
I presented our current work with Anna Sroginis during my visit of IÉSEG School of Management, Lille, France last week. It was great to see my colleague and friend Sarah Van der Auweraer, and I enjoyed the discussion we had with people in her group related to forecasting and intermittent demand. You can see details […]
I’m currently doing a literature review for one of my papers on intermittent demand forecasting with machine learning, and I’ve noticed a recurring fundamental mistake in several recently published papers, even in respectable peer-reviewed journals. The mistake? Using error measures based on the Mean Absolute Error (MAE). This is a crime against the humanity when […]
I’ve seen several times ML experts applying principles of classification for intermittent demand forecasting. For example, they try predicting, WHEN the demand will happen. This is not a very sensible thing to do. The featured image in this post shows two forecasting approaches: one that tries to predict when demand happens (the yellow line), and […]
While Naive is considered a standard benchmark in forecasting, there is a case where it might not be a good one: intermittent demand. And here is why I think so. Naive is a forecasting method that uses the last available observation as a forecast for the next ones. It does not have any parameters to […]
Anna Sroginis and I have been working on a new approach for intermittent demand classification over the past year. We’ve taken a fresh look at the problem, starting by asking: why do zeroes happen? Let’s discuss why indeed. First, a quick note: it’s a mistake to define intermittent demand simply as “demand with zeroes”. That […]
When you start working with your data and suddenly realise that there are zeroes there, i.e. it is intermittent demand, what should you do first? Some people use SBC classification, but is that what you need? Let’s discuss! Intermittent demand comes in different flavours: sometimes zeroes occur frequently with low demand volumes, while other times […]
Sometimes, when you need to forecast demand, you may notice that the recorded data contains zeroes. There are several possible reasons for this, but today we’ll briefly discuss one of them. Welcome to the world of “intermittent demand”! Intermittent demand is the demand that happens at irregular frequency (Svetunkov & Boylan, 2023). This means you […]
Authors: Ivan Svetunkov, John E. Boylan Journal: International Journal of Production Economics Abstract: Inventory decisions relating to items that are demanded intermittently are particularly challenging. Decisions relating to termination of sales of product often rely on point estimates of the mean demand, whereas replenishment decisions depend on quantiles from interval estimates. It is in this […]