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Using Exploratory Data Analysis to Improve the Fresh Foods Ordering Process in Retail Stores

Summary: With the advent of the information era and abundance of availability of data, the approaches used by quality professionals can be significantly complemented by the art of data science. This case study demonstrates how explorative data analysis and basic statistics helped reduce the inefficiencies in the retail inventory and ordering process of fresh foods within grocery chains. Low shelf life and fluctuating demand had led to the need to hold clearance sales with zero or negative margin or to write off the inventory as shrinkage in some articles. The impact on the top line was up to 20 percent annually. Although the ordering process was automated, the algorithm was largely suitable for articles with higher shelf life (>90 days); hence, department managers would often overwrite the effectiveness of the automatic ordering system. There were no defined tolerance limits to manage stock and shrinkage across various stores due to thousands of stock keeping units (SKUs). This project was a classic case of establishing department-wide control charts and raibow SPC charts, combined with root cause analysis and an escalation matrix to help monitor day-to-day progress. The project achieved significant control of the process through the application of lean tools and simplified statistical process control charts. The reduced waste in shrinkage and stock levels has enabled the retailer to generate higher margins and concentrate more on availability and customer interaction.

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