SAP APO Demand Planning – usability versus functionality
by Mark Chockalingam, Ph.D.

I have heard from Consultants and business managers across many companies: Very rarely there is user adoption of standard APO forecasting techniques. Users typically use an off-line forecasting process and enter the forecast into the planning book or even upload it with IT help. Why is it?

Are the standard models and algorithms not sufficient for the business models? Or is it becuase the users and IT staff do not understand the statistics and functionality behind APO?

Let me address some possible reasons for this unfortunate situation.

The complexity and scale of the data and functionality actually drowns out the requirement for business process mapping and leveraging the functionality to meet the business needs. So the key ingredient of building the bridge between what the users want versus what the tool can do is perhaps left out in the cold – either due to the hectic project management schedules or due to the lack of understanding by IT staff/Integrators etc... Continue reading!

Forecast Alerts
by Mark Chockalingam, Ph.D.

Popular software packages have forecast alert monitors. Each software may term it differently:

1. Manugistics calls them lists
2. APO and Dematra call them as alerts

An overworked demand planner typically plans in the order of 1000+ skus each month. That is where these alerts come in handy. You set a pre-determined alert based on the condition meeting a threshold……. Voila! As soon as the month-end job runs and re-creates the models, the alerts kick in and do their job.

You get a report, perhaps emailed to you at night comprised of all those items that exceeded your threshold. Then your job becomes a tad easier. You walk in to the office with your day planned out thinking to yourself:

“I am going to address those 45 SKUs that show a MAPE above 30% first, then address the next 100 that are above 20%”.

Typically the alerts are set using a comparison of new model values to the in-sample historical values. In essence, they address the model fit issue and alert you to specific situations where the models are insufficient or just plain wrong. Quite possibly, the history may have an anomaly as well... Continue reading!