I’m answering your question above on behalf of Charles here.

We typically request 1 -2 years of actual demand. The more granular the better the results will be. However, weekly or monthly data works well for many companies. Feel free to contact me with additional questions at gregh@smartcorp.com

Thanks,
Greg Hartunian
President and CEO
Smart Software

Thanks for your comment and sorry for the delay in getting back to you.

Yes, I have heard of some people attempting to use a variant of the Poisson model to handle intermittent demand. Howvever, we feel that the empiricaly-based statistical bootstrapping solution that we have patented and incorporated in our SmartForecasts system gives consistently accurate estimates not only of the demand forecasts but also of the corresponding safety stock and inventory requirements for an intermittently demanded item.

There are different ways to define intermittent demand, but practically speaking, intermittently demanded items normally have many periods of zero demand interspersed with seemingly random-occuring spikes of non-zero demand. If your demand data has this pattern and the proportion of periods with zero demand is at least 25-30% (and in certain cases, like the demand for service/spare parts, often much greater than this), you probably have intermittently demanded items.

If you have any further questions about intermittent demand or our approach to forecasting it, please feel free to contact me directly at charless@smartcorp.com.

Regards,

Charles Smart
Smart Software, Inc.

Thanks

There are a variety of different demand patterns which can all be classified as intermittent to some extent. There is the classic Croston’s model, which corresponds to having a single customer who orders about the same quantity every few months or so. There are product life cycle models which correspond to cases where old products are superceded by new ones resulting in only intermittent demand for the old products. There is also seasonal intermittent demand for products like Halloween candy, and many other different cases. I don’t think there is a single model which handles all of these situations effectively – you just need to have a Swiss Army Knife in your stats toolbox to be able to model each situation appropriately.

Regards,

Rudi Pizzano
RoadMap Technologies
rudi@roadmap-tech.com

Thanks for your response to my blog.

Croston’s method and some of the other dedicated intermittent demand forecasting (IDF) techniques are OK at projecting a single number representing the average demand per period. Where they tend to have problems is in generating an accurate distribution of all possible values of total cumulative demand over a product’s lead time. Croston’e method, in particular, assumes that this distribution will be normal, i.e., look like a bell-shaped curved. In fact, lead time demand distributions for intermittently demanded items tend to be asymmetric with long tails, like the distribution shown in Figure 2 in the blog.

In our demand forecasting and planning system, SmartForecasts, we offer a patented IDF solution (based on NSF-funded research) that is empirically based and uses statistical bootstrapping technology to generate the lead time demand distribution. From this, we can calculate optimal safety stock and inventory requiremments for any desired service level, in addition to the average demand per period. If you would like to learn more about our approach, I would invite you to visit our web site at http://www.smartcorp.com/intermittent_demand_planning.asp.

Regards,

Charles

Charles N. Smart
President & CEO
Smart Software, Inc
Ph: (800) 762-7899 or (617) 489-2743
Email: CharlesS@smartcorp.com
Web site: http://www.smartcorp.com

Can’t speak for all of them, but Croston’s method (as well as others) tend to introduce some bias as a result of the nature of demand.
( http://eprints.lancs.ac.uk/7060/)

Smart’s is actually pretty good in the real world scenarios I’ve seen so far. Hope that helps.
Mike