Every time we come across a slow-moving item, we have to spend quite a bit of time analyzing the different factors to come up with a forecasting strategy that will handle the challenge.
In one of our recent projects, slow-moving items accounted for more than 25% of the SKU population. Short history for many items limited us from using many of the statistical models, simply estimates would not be robust given the limited history length. Now, this is not unique to slow-moving items.
We, at Demand Planning LLC, preach forecasting by exception. Here we will describe our approach that leverages the exception management methodology to come up with a forecasting strategy for the slow-moving items.
Let us first define a slow-moving item. Using some business rules that align with the business model, we created a rule to codify an item as a slow-moving if it has not been consumed in the last four months. This can change depending on the type of industry you operate in. Also, make sure you have sufficient flexibility when it comes to lifetime buys and critical parts required for infrequent break-downs. We have also recommended to clients that they look at it from months of inventory on hand based on projected usage to define slow-moving.
An objective way to do this will be to analyze the last POS or usage date. You may also want to use the last shipment date (although there may be inventory at retail or the shop floor in the case of service shops).
Next we imposed an intersection on the amount of available history. We sub-categorized all slow moving items based on history (long history and short history), volume (High volume and low volume) and intermittency (Intermittent and Non-intermittent).
Among these slow moving items, We forecasted most of the High volume-Long history items using the Forecast PRO TRAC software package. Analyzing the R-squared and MAPE, we were satisfied that the forecasted best fit model looked decent. We made some minor model tweaks but the expert engine did a great job at first cut without much extra effort. AS you would have conjectured, the expert selection was good enough for High volume-Long history items. These items accounted for only 1.5% of the total item numbers. If you notice in the graph below, 18% of the slow moving items were also characterized by short history and of very low volume.
Given the predominance of the low volume items, we find models suggesting consistently one approach if they are intermittent versus non-intermittent.
- How should one forecast low volume slow moving items?
- What are the best practices if we notice intermittency in slow moving products?
- How should we leverage the software tool to get the best out of for such items?
We came up with different forecasting strategies for different segments. The segmentation approach also threw a lot of light for the business on their SKU complexity. After a detailed business review with manufacturing and marketing, the business decided to shut off production and deployment for some of the SKUs.
The beauty of using exponential smoothing models using standard software packages is that these models learn with history and the passage of time. When there is no historical data for the last few months, the software best fit itself suggests a zero or a near-zero forecast. This is the first sign that should make you ask if this item is obsolete. You may also want to create a class called Non-moving which is in between Slow Moving and obsolete.
More difficult than forecasting a slow-moving item is to forecast and produce an obsolete item. Although technology and high-powered software are important, it is more important to use an exception management approach and gather appropriate business intelligence to come up with a forecasting strategy.
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Kudos to Rohan Asardohkar for excellent analysis and painstaking compilation of a large database of items!!