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Forecaster ABCs - The 'Vital Few' for Forecasting

Lets travel back in time in the 19th century, to take a quick look at the very interesting observation made by the Italian economist Vilfredo Pareto - 20% of the population possessed 80% of the country's wealth and the same was observed for other countries and over different periods of time. This has been known widely as the 'Pareto's Principle' or the 80/20 rule or the 'Law of Vital Few'. This principle has been adopted in the ABC Classification, which also happens to be the topic of my blog, with the 'A' group items [the 'vital few' representing 20%] contributing to 80% of the phenomenon, the 'B' group [representing 30%] contributing to 10% of the phenomenon and the 'C' group [the 'trivial many' representing 50%] contributing to only 10% of the phenomenon.

This law asserts that the outputs are not always equal as the inputs; that a small set of inputs, contribute or influence significantly the outputs. The principle plays an important role in depicting the imbalance, which may be 70/30, 80/20, 95/5 or 80/10, 90/30 or any set of numbers in between. The key to note is that this relationship is between two different sets of data [input & output or cause & effect] and hence need not add up to 100.

Pareto Chart sample.jpgThis phenomenon has been observed in many areas of business and management, some of them being - 20% of customers are responsible for 80% of revenue; 20% of steps in a typical process add 80% of the value. In the context of forecasting, we generally see this principle being analyzed for the contribution of Products to the overall revenue of the firm or business unit. There are these 'vital few' [Group 'A'] Products or Product Families that contribute to a significant extent of the revenue of the Business Unit or the firm and then there are the 'trivial many' [Group 'C'] Products that result in only a marginal contribution to the revenue, nevertheless are important for the business to continue. Any forecast error (in terms of %) would have a significant impact on the revenues for Group 'A' products compared to that for Group 'C' products. The subsequent fallout of this would be to concentrate the energies of the Forecasting organization to develop the forecasts for these Group 'A' products and ensure that these products are forecasted more often. On the other hand, we would want to setup the forecast process for the Group 'C' items on autopilot with minimal manual intervention - an example of that being using the Automodel Forecast model provided by SAP APO which will choose systematically the forecast model with the least error. We could decide the approach to manage the Group 'B' products to be in between the above two approaches. In a more general sense, we can say that the products are classified based on their importance to the business.

While this approach gives consideration to the key factor - the contribution of the product to the overall revenue, helping us to allocate our limited resources on the right priorities, there is one more key factor to further enhance this classification  - the forecastability of the product. In my earlier blog, I had discussed that the measure of dispersion can be used to assess the uncertainty related to a part, which directly impacts the forecastability of the part. The perspective here is to analyze the products for their individual forecastability and how these measure up against the collective whole, with the assumption that the more difficult a part is to forecast, the more time should be spent in generating the forecast. So similar to the classification done based on the contribution to the revenue, we must check for the pattern of imbalance for the products for their forecastability. In event we see a similar pattern, then we must classify the products as Group 'A' that contribute to the 80% of the overall uncertainty and hence need more energy and time to forecast these products compared to the Group 'C' products that would contribute to around the remaining 10%.

So this now brings us to a critical juncture - We have so far talked about classifying the population of products by two factors -

1. Importance of the product to the business and

2. Ability to forecast the product

ABC Classification Options.jpg

With these two being considered as 2-axes, we develop the following grid:

2-axes grid.jpg

With this approach, the parts that will be classified as Group 'A' will be the products that are important to business [and hence forecast error would have a bigger impact] and as well are difficult to forecast. Herein lies the paradox - these products are important to business and hence important to be forecasted with maximum Forecast Accuracy, but at the same time are difficult to forecast. This mandates the forecasting organization to plan for these parts more rigorously at a higher frequency with very narrow band of permissible forecast error. The forecast numbers generated can be buffered for these products to avoid any OOS for these business critical products.

On the other hand, the products that are of low importance to Business and are easy to forecast will be classified as Group 'C' items. These parts can be planned with minimal manual intervention and the forecast numbers reviewed at a lesser frequency.

Since we classify the products by charting them against two axes, the definition of the products in the diagonal to be classified as Group 'B' would be dependent on the Business discretion. Depending on the Product or Product Family, the thresholds could be set to classify these products as either Group 'B' or Group 'A' or 'C'.


I believe that there is a lot of value in not just classifying the products on the basis of business importance [or contribution to revenue], but also by considering the ability to forecast the products accurately. The exercise to review the thresholds for classification of products in groups 'A', 'B' and 'C' needs to be done on an annual basis for every product or product family. The ABC Classification such determined must be used as a key input to develop specific strategies for forecast generation and review process and also for reporting exceptions.

I will be happy to hear your thoughts and point of view on this simple yet effective approach.


Nice article, what if we add location into the mix? Some of group A products can become group B or C based on location, so we may want to add a third dimension. The 80/20 rule is also applied in inventory allocation when you want to reduce stock outs to min or nil for most imp location/product combinations.


Many thanks for your comments and you indeed bring up an interesting point.

The decision for the level [or dimensions to be considered] at which the ABC Classifications needs to be done would typically depend on the following:
1. The level at which the action is undertaken (in our case forecasting)
2. The level at which the exceptions/results are measured to be acted upon further
To further elucidate, if the forecasting is done at a Part and Region level and the KPIs are also measured at the Part and Region level, then I would want to determine the ABC Classification at the Part and Region combination. It is at this level, that I would want to determine the contribution of the Part-Region combination compared to all the parts in that Region and as well the forecastability of the Part and Region compared to all parts at that Region.
The point to note is that depending on the planning process one has, the level at which the computations are done would differ.

I believe it is important to realize that the ABC Classification is done with a purpose in mind – to highlight the disparity in the quantum of effort spent and the results observed there in for a particular process or phenomenon. What that means is that, we can have the products classified in different ways when the classification is going to be used as an input in different processes.
And again, location is a very important dimension to consider while classifying the parts from a Supply Planning perspective. I have seen many of my clients generate ABC Classification for product-location combination, which is then used to differentiate the Safety Stock and Procurement decisions. But this does not need to be the same one as the one you would have computed as part of the Demand Planning Process.

From a Supply Planning standpoint when deciding on the Safety Stock policies, you would probably want to classify the parts on the following axes:
1. The Demand [in dollarized terms] for a product at a particular location
2. The combination of the overall lead time required and the reliability of the suppliers to deliver on time
A classification on these lines could help different strategies for Safety Stocks to be maintained for products at each location, considering not only the contribution of the Product at a location, but also the requirement to keep buffers against lead time variations.

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