Using data to help a leading grocer’s merchandise operations team identify challenges and risks during a major transformation.

The Challenge

A leading UK grocer implemented a process to migrate inventory data from a legacy system to a new system. The two systems tracked the movement of inventory at outlets across the grocer’s estate. It was noted that there was a wide discrepancy between the two systems but this was often noticed when problems arose at an outlet, when it was too late to resolve, rather than through alerts or through any trend analysis. The challenge was to collate the data from the two systems into a common datasource where the metrics could be compared for analysis in a dashboard.

A stated aim of the project was to address the following: ‘This dashboard should provide a view of any issues regarding variance of inventory on a location or item level. It would be ideal to be able to drill down or filter to specific locations and then even further to identify products that have high levels of variance.’


We created a streamlined data model in Snowflake (SQL) which combined the data from both systems which reduced the loading and processing time from 8-10 hours to 10 minutes. An interactive dashboard was developed which allowed users to compare inventory between the two systems and to drill down through the hierarchy to investigate what products and/or locations are driving the discrepancy.

The Benefits

This was the first time that data could be compared between the two systems which allowed planners to identify any discrepancies that could lead to incorrect decisions to raise or lower stock levels at specific locations. Automated extract refreshes allowed the data to update automatically without any user intervention. The data model and dashboard views have provided a foundation for new datasets to be included to further enhance the insights and analysis that can help drive improved decision making.

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