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Retail Planning and Forecasting Tips and Resources During COVID-19

Rory Borghesi, and Guiming Miao

We understand that many customers are experiencing challenges during this unprecedented era in retail.

Below is a set of scenarios that can help Oracle Retail Demand Forecasting (RDF) users review and assess the current situation to better forecast for current and future demand, along with helpful planning and optimization white papers on RDF, What-If Scenarios and the Merchandise Financial Planning (MFP) solution.

How Retailers Can Manage These Common Forecasting Scenarios

1. Changing and erratic sales demand in specific product categories

Your demand has aggressively risen over the past several weeks for certain categories that has led to unfortunate out of stocks resulting in a higher or upward-trending forecast baseline. 

There are two means to address this temporary behavior by either: 

(a) adjusting the erratic sales history patterns using RDF’s automated history preprocessing method or
(b) allowing RDF to switch to a more reasonable forecast method for the immediate term that is less reactional to these trends until sales return to a more normal trading pattern.

2. Locations forced to close due to regulations

In the unfortunate case of locations having to close during this crisis, shifting demand to other channels within your business, you will need to set forecasts to zero (0) for those locations during the planned period of closure.  

There are various ways to achieve this in RDF quickly depending on the locations, groups of locations, and the expected timing when a staggered reopening is planned and forecast generation will be required again.

3. Managing alerts and exceptions

During periods of disruption, as we are currently experiencing, it not uncommon to see an increase in alert “hits” in RDF, notifying users where attention is needed. Remember the system is designed to do so.  

During this time, alerts are still the best way to manage your forecasts and the most effective way for you to locate where there are areas that require attention.

If you feel that alert conditions have changed and need to be temporarily adjusted to lower the number of “hits,” an option to consider is an adjustment to the Alert Threshold Settings. 

Managing Forecasts Post-Crisis

When the time comes, you will need to consider reverting any temporary adjustments recommended above that you may have made as well as monitor RDF carefully as it self-adjusts to trading and business activities moving forward. 

Oracle Retail Demand Forecasting is a highly automated tool that during periods of significant market disruption will react and adjust quickly as it is intended to do. For any assistance regarding the above and other forecasting changes that you may be experiencing please set up a call for assistance or email Guiming Miao, Oracle Retail Director of Science, for more tips.

Helpful Planning and Optimization White Papers

How to Manage Your Oracle Retail Demand Forecast

This white paper provides recommendations on how to manage RDF forecasts with the unusual demand patterns caused by the current health crisis. It includes suggestions on managing forecast settings and history adjustments and is intended to help you achieve the best forecast today, and in the months to come.

How to Utilize Oracle RPAS Cloud Edition What-If Scenarios

This white paper provides recommendations on how to utilize the inherent What-If Scenario functionality available in the Retail Predictive Application Server Cloud Edition (RPAS CE). It includes suggestions on managing multiple forecasting scenarios to enable you to plan for whatever is to come with the help of your RPAS CE solution.


How to Modify Oracle MFP OP In-Season

This white paper provides recommendations on how the effects of modifying an Original Plan in the Merchandise Financial Planning (MFP) solution. It is designed to help you understand possible outcomes and asses your options during a rapidly evolving retail market.




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Comments ( 1 )
  • Andreas Kolodzey Thursday, April 30, 2020
    For changing the OP for elapsed periods, you will also need modify the RPAS_TODAY environment variable to trick the solution into behaving as if it were several weeks (or months) ago.
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