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Case Study

Image by Ashkan Forouzani

RETURNS (RMA) PROCESS

THE CHALLENGE

Our client’s products are found at the heart of many computing devices. It is a competitive industry where the boundaries of speed and performance are constantly being pushed. All companies that sell a product have to deal with the consequences when products are returned. Each return represents an unsatisfied customer. There is an obvious impact to brand reputation that erodes customer loyalty and impacts revenue. Furthermore, the processing of returns consumes revenues earned by selling the product. This cost was especially significant for our client given their recent integration of preexisting processes from two previously separate companies.

 

RESULTS

There were four primary contributors to lead time and variation in lead time. Targeted solutions were put in place for each of these contributors.

Overall, processing losses were reduced by 42% saving $17M.

✓ Issue: Inaccurate information

Solution: Released an "error-proofed" web portal for all customer groups to use to return product

✓ Issue: False calls at testing

Solution: Provided a free or subsidized test platform for all key customer groups to use

✓ Issue: Uncorrected design flaws

Solution: Created a knowledge management database to highlight faults based on analysis of defects

✓ Issue: Ad-hoc re-configuration of products by customers

Solution: Identify and include customer configuration requirements in the product design process

 

INGREDIENTS

Processing returns efficiently, while also gaining insights into why customers were making returns, would enable cost improvements now and down the road.

➢ Process Mapping Workshop: A core team was first assembled to see the project through. Lead time was identified as the most valuable metric. A team goal was set to reduce both lead time and variation in lead time. Workshops and interviews were conducted to map out the process and quantify areas of opportunity.

➢ Data Analysis:  Key pieces of process data were collected, aggregated, and analyzed to highlight specific improvement activities.

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