Advantages and Disadvantages of DP

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Question Posed to Me on SAP DP

I was recently asked a question regarding the advantages and disadvantages of DP. I thought I would take part of the email exchange and post it here.

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Questioner:

I would be interested in knowing how DP is different from demand planning in Manugistics.

Especially:
  1. Weaknesses
  2. Strengths
  3. Level of integration with supply planning (eg ability to effectively measure forecast consumption)
  4. APO suitability for products with short life cycle (eg movie/music DVD)

Shaun:

Well, ok here is a more technical reply. I will think on it, I have articles you can read already to get a general background. Here are the DP related posts on www.scmfocus.com/sapplanning

In terms of Manugistics demand planning, I constantly hear how easy Manugistics is/was (now JDA) to use. Unfortunately I have not exposure to Manugistics, so its hard for me to comment.

However, I am not sure I get the short life cycle issue. As you are well aware, there is nothing new under the sun in forecasting, DP uses the same forecasting approaches that you have seen a million times, and has an auto select feature like everybody else. However, the auto select or best fit functionality has major issues and has not worked at 4 clients that I have worked at. The main thing about DP is that it is difficult to configure because it uses the Data Warehouse Workbench and requires the learning of an entirelynew vocabulary (InfoObject, InfoProvider), and a strong tolerance for pain to administer. It can forecast short or long term, like any other forecasting system. So what do they mean by applicability to short term forecasting, is there some trick or method companies who do short term forecasting use? SAP F&R, which is replenishment focused handles short term forecasting by allowing store managers to input their order amounts, and decentralizes the ordering and short term forecasting.

I can also say, I have never seen DP used for short term forecasting. DP typically has the longest time horizon, SNP shorter, PP/DS shorter still and so on.

As for DP to SNP, this is the most commonly implemented integration in APO-land. In terms of measuring forecast consumption, here is how it works.. and I quote from Supply Chain Management with APO by Jorg Thomas Dickersback.

“The horizons for the forecast consumption (number of days forwards or backwards from the confirmed date of the sales order) and the consumptive mode (backwards only, forwards only or both) are maintained in the product master.”

“The forecast is calculated each time a sales order is created, changed or deleted and each time the forecast is released from DP.”

“the consumption of the forecast is performed on the location product level, although it is possible to restrict the forecast consumption to a more detailed level using descriptive characteristics” (furthermore, forecast consumption can be set as backwards, (sales orders consume forecast quantities that lie before the requirements date), backward/foreward (sales orders consumer forecasted quantities that lie before the requirements date. If the actual demand is no satisfied, the sales orders then consume forecast quantities that lie after the requirements date in order to satisfy the remaining demand,forwardconsumption (sales orders consume forecasted quantities that lie after the requirements date). – The recommendation is that the for fast moving products, the consumption can be set backwards/forwards for a shorter period of time (say a week),and for slow moving products, the consumption can be set for a month or longer.

Descriptive characteristics are a different conversation, but essentially, consumption is inherent each time the forecast is released to SNP. The question of how inherent it is if it is released to SAP ERP and not SNP (an alternative implementation), is another question. I think it probably is, but that would become a question for the integration mechanism, called the CIF, and is another line of inquiry. – Dickersback

Strengths:

The following:

  1. Integration with SCM
  2. Integration with SAP

Weaknesses

  • Data Warehouse Workbench
  • The necessity to learn a new vocabulary to use
  • Statistical forecasting
  • Collaborative forecasting
  • User interface
  • Return on investment
I describe SAP DP as a worst of breed application. It combines not being very good at forecasting with high complexity of implementation. However, the fact is, it is widely installed.
Questioner:

What type of exception reporting (eg actual greater than forecast) are available in APO? How difficult is to create your own report (does this require coding or is it similar to combining fields like in access DB)?

Short term forecasting (the way you described it) is managed a lot better via retail inventory management solutions or VMI.

Does APO support:

  1. Easy elimination of certain data points (to exclude from forecast)?
  2. Good graphical depiction of interpolation (past data) and extrapolation (future forecast)?
  3. Causal analysis (or factor-based regression)
  4. Fair share logic (when forecasting is done at a sub-assembly level and then allocated to final SKUs using %%)
  5. Profile-based forecasting (with some factors impacting profile characteristics – eg slope)
  6. Calculation of statistical parameters (which helps to determine the quality of forecast)
  7. If a client outsourced all manufacturing and only drops orders to be fulfilled using APO DP for demand planning and its own tool for VMI (on the customer side) does it make sense to use CTM (with VMI generating replenishment orders)?

Shaun:

Exceptions are handled by the alert monitor. See this paper for the details.

It is very flexible and access can be given to whoever the project sees fit. We are trying to figure this out now on my current project.

  1. DP does not support the elimination of outliers.
  2. The graphical depiction is weak. The charting in DP is nonexistent. I am a big fan of the charts in the forecasting in the SPP module, but that is not DP so does not apply. (DP and SPP development teams don’t seem to talk)
  3. DP can do some causal based forecasting, is basic but functional. SPP has a stronger functionality called “Leading Indicator” forecasting, which is causal, but since it’s not implemented more than a few places, it’s not very well tested. My take is it’s not that important because very very few clients do causal forecasting. But I would like to hear your take on it.
  4. Yes, the forecast can be disaggregated this way, and a number of other ways as well. SAP makes a big thing about this functionality. However, in fact it is quite limited. Advanced forecasting solutions like DemandWorks Smoothie have moved beyond the hierarchical forecasting aggregation used in SAP and can perform pivot forecasting, which is far more flexible.
  5. I am not sure what profile forecasting is. I think this is just forecast parameters (i.e. a field for the trend component and so on), if that is what you mean then yes.
  6. Well DP has best fit, but the problem is no client I have worked with can get it to work. Forecast parameters are not optimized and must be manually set.
  7. No, but I don’t agree with VMI as a model, so I am not a good person to ask this quesiton of.