2013 I was part of the Team that worked on Traffic Forecasting for Retail Stores
- Multiple stores across geographies
- Multiple DB’s for each local store
The forecasting system used to run at Enterprise, Synchronize data to local stores with their own internal synchronization jobs.
- These jobs were configured to run according to time zones of stores
- The algorithms were mostly around a weighted moving average, trend + moving average
- The forecast job runs leveraging previous data and projects forecasts by the hour for next day, hourly basis patterns
- The actuals are captured the following day and measured against it
- In case of data not present sister stores (similar stores) data was leveraged for calculation
Whatever we say as of today measure model drift, missing data features, work at scale, coexist along with existing transaction system was built as server components, custom-built.
What we missed are
- Instead of Traffic forecast if we had done a sales forecast it would have helped to apply solutions for both eCommerce and retail giant
- We had inherent details of out of stock, replenishment alerts. The same could have been used for out of stock forecast per zone, replenishment forecast per zone
- These real-time reports from RFID could have served as effective forecast opportunities on the same
Sometimes we may have the right technology and architecture but not the right use cases. Now I see the same things ML attempts to do with #kubeflow, #pipelines, #scale but the same problem which was solved with models available at that point in time would take a different set of skills to solve today 😊
Keep Exploring!!!
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