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30 Jun Forecast Consumption Case Study, Part I: A Comedy of Errors

At Implementation

A previous client of mine worked themselves into a scenario that I think really demonstrates how important the forecast consumption settings in an SAP environment can really be. This all started because no one on the team really understood the potential implications of incorrect consumption, so the company implemented what would typically be considered “standard” consumption settings (backward/forward mode and +/- 30 days consumption period) and never looked at it again.

As can happen with high volume/high demand products, they came upon a situation where the forecast was over-consumed in the current month and then proceeded to consume most of the next month’s forecast as well (did I mention that no one is watching this…) As a result, they almost shorted their most important customer on that customer’s most important product, which resulted in an overreaction to the situation. Forward consumption and any sign that it had ever existed were wiped from the records and the memories of any employees and all materials were set to backward consumption only.

Also around this time, a couple of other decisions were made that contributed to the end problem:

  • They started to split their demand forecast that is generated and managed in monthly buckets into weekly buckets as it is transferred to the supply system. This is a pretty typical request by the supply team as it creates more even demand from a supply perspective, assuming that the production will be mostly executed in weekly production orders. This also allows for less order movement when executing capacity planning and could also potentially make the purchases of raw materials a little smoother as well.
  • They decided to “delete” the remaining forecast from the previous week once the current week had started. Again, this was a knee jerk reaction to previous scenarios where the forecast had allowed for consumption of unconsumed forecast in the previous month when the additional demand in the current month was really incremental demand. So, the decision was made to delete all unconsumed forecast from the previous week in a background job executed every Sunday night. Now recall from above that ALL materials were set to a consumption type of backwards only. The result of that decision, plus this decision to delete last week’s forecast effectively left them with sales orders only consuming the current week’s forecast.

Drowning in Inventory

Now this company’s SOP process is similar to many others in that the demand planners take the freshly minted statistical forecast at the beginning of the month and sit down with the sales personnel, allowing them to provide sales and marketing intelligence to the statistical forecast. (Whether or not this practice actually contributes to greater forecast accuracy could be another blog post).

At this point in the game, the supply planners are pretty much in a full revolt, claiming that the demand forecast accuracy is so abysmal that they can’t possibly produce a workable plan since the sales orders that actually come in are so vastly different than what the forecast said to produce. The demand planners are countering that argument saying that while their forecast accuracy is not perfect, (it is a forecast after all) it is absolutely obvious to them that the forecast is to blame for this atrocity… but is it really?

Keep in mind: a 100% accurate demand forecast can cause serious problems in supply planning when the forecast consumption settings are as detailed above to answer the question, “If a demand forecast doesn’t provide the supply planners what they need, is it still a good forecast?”

Join me in my next installment where I will show you the results of the scenario above, some lessons to be learned, and a possible fix.

And if you missed it, read the Top 5 Tips for Forecast Consumption here.

Forecast Consumption Case Study, Part II: A Comedy of Errors
The Top 5 Tips for Effective Forecast Consumption