Tackle intermittent demand problem

18 May 2012

Silver bullets to tackle intermittent demand problem

Intermittent demand or sporadic demand is a fact of life.  In many cases, demand occurs in one lump followed by periods of no demand.

What drives intermittency in demand patterns?

  • Fixed Ordering Costs
  • Transportation Costs
  • Purchase Quantity discounts
  • Shoe Leather Costs….

In any case, it is important to come up with strategies to plan for intermittent demand.  We typically advise clients to follow a set of strategies in sequence to address the intermittent demand problem.  Rushing to find a magical statistical model to forecast intermittent demand is not the best first method.  The software industry has created a huge business trying to advocate solutions to solve sporadic demand, pounding the tables on their method being the best in the industry……….

SKU segmentation is an important first step before tackling the problem of intermittent demand.   This helps you to prioritize only those SKUs where intermittent demand needs to be actively dealt with from a planning point of view.

Not all non-zero demand patterns need to be considered intermittent.

  • Some could be seasonal and
  • Reasonably predictable with standard models.

If you have a good software package, expert selection will determine if a particular demand pattern is a truly sporadic demand. Typically the timing of the non-zero demand should be unpredictable.  More often than not, you should have more zeroes than positive demand points.

A typical scenario would look something like this:

What are the steps that effectively solve this challenge?

There are some business strategies you could adopt before looking at algorithmic options.  Although Segmentation is quantitative and requires some data mining, Segmentation allows you to prioritize and highlight the real problems.

Disciplined demand planning for intermittent demand should look at integrated demand-supply strategy and Sales and Marketing intelligence first.

First Step:  Come up with a holistic demand-supply strategy to address items with sporadic demand!

  • Work with your supply chain and perform a Cost benefit analysis of carrying more safety stock to handle the anticipated intermittent demand
  • In general, sporadically ordered MTS items could be also of low value.  Carrying more inventory may not break your bank.
  • Correlating the deployment strategy to the Lead time and reviewing of aggregation of demand will enable a better supply and safety stock strategy.  If your lead times are longer, then the aggregation of the demand buckets may result in lower noise and hence may result in lower inventories.

Second Step:  Get Sales and Marketing intelligence on the timing of the demand – they may be privy to customer ordering patterns.  They may also be able to give you the magnitude of the demand.

  • Check if demand gets splintered because of product migrations such as bonus packs that are made available only during certain periods.
  • A better forecast can be delivered by combining the split SKUs into one family.
  • The model that family and route the forecasts to the available packs  – Aggregating across products
  • Ask the question if there are promotions that are driving this intermittent demand.  If we offer price promotions, customers may stock up on the item resulting in no orders for the following few months.

If product migrations cause the intermittent demand problem, then we should apply the principle of demand chaining – aggregate the related SKUs together to create a forecast model.  Then use proportional forecasting methodologies to split the forecast down to the lower level SKUs.  Once you aggregate to a certain level, you may see a smooth pattern, and the intermittency may disappear.

The last resort will be to use the Statistical Models:

1.  Croston’s Models – Simply this averages the timing difference and the quantities to arrive at a constant forecast.  What is important is to look at the confidence limits to plan for inventory.

2.  Discrete Distribution models – You can use the Poisson model and the negative binomial models in certain cases.

Leave a Reply