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Demand Planning by Segmentation-based Exceptions

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The average number of planning combinations managed by demand planners is at an all-time high and steadily increasing. This creates unprecedented pressure on demand planners. Software programs, such as supply chain planning (SCP) solutions, can help if they are used properly. However, no software solution can automate the entire demand planning process. As we say in the industry, the future is inherently uncertain, and it is not possible to codify uncertainty.

Given these uncertainties and the underlying heavy data volume, how can a demand planner effectively use the planning solution at hand to make better decisions? How to focus the time and energy on the parts of the data which can create the most value for the company? How to automate what can be automated so that the limited time and energy can be focused appropriately?

A typical answer to this question is segmentation. Data can be divided into smaller segments based on certain business criteria. These segments can then be planned differently. For example, the people involved might be different, or one might use a more complex process for certain segments. In extreme cases, different software might be used for a particular segment. Specific to forecasting, different forecasting methods may apply to different segments. This is an effective technique and we have written about it here.

What is an exception?

An exception can be defined as something outside a general statement or does not follow a rule. For example, in demand planning, a general desire might be to forecast using a statistical forecasting engine to achieve an accuracy of 75%. Well, then any combination that needs to be forecasted differently can be an exception. Or one that shows exceptionally poor accuracy. Or one that requires the most human intervention.

When driving the planning process via exceptions, it is important to choose as few as possible. Applying every single rule imaginable to generate exceptions could result in too much work interpreting why something is an exception, let alone the work needed in dealing with the exceptions. A progressive system where one aims to identify the most significant exceptions as opposed to all the exceptions works better. The general idea would be to identify those exceptions that allow a user to review them a bit more diligently while letting the planning software update the non-exceptions automatically (i.e. follow the rule).

A separate way to think of the progressive definition of exceptions is this: A company might determine that they want to cover the top 95% of items via planning, be it automatic or manual, and leave the rest to be managed via make-to-order (MTO). Or they might have an upper limit on the number of combinations that a planner can deal with as exceptions. Assuming this is the case, it does not make sense to try and find combinations based on all rules imaginable only to ignore a lot of them. Most planners apply this type of progressive logic intuitively.

Listed below are a few of the segmentation rules that are used to create exception lists:

  • Pareto
  • Variability
  • Forecast Accuracy
  • Popularity
  • Next Day Orders
  • Stockout frequency

Often exceptions are created when two or more of these segments are juxtaposed.

A good exception system is also supported by a way of creating alerts and notifications. This way the planner is flagged for the presence of exceptions. The other important piece is the process to deal with exceptions. In demand planning, this could range from using human forecasting input to calling a customer.

Join us for a live webinar Wednesday, September 28, 2022, at 11 am ET to learn more. Register here.

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  • Source: https://blog.arkieva.com/demand-planning-by-segmentation-based-exceptions/

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