Case Study: a leading consumer electronics company improves repair forecasting with Mu Sigma

Optimizing decision supply chain of the repair business for a leading consumer electronics giant

Mu Sigma partnered with a leading consumer electronics company that was struggling with inaccurate forecasts for its product repair business. The high volatility in repair times made their existing manual and heuristic models unreliable, creating a poor customer experience and causing difficulties for financial planning.

Using their proprietary platform and the muPDNA™ approach, Mu Sigma developed a new algorithmic and automated forecasting model in just six weeks. This solution replaced the use of manufacturing batches with market-sold products as a cohort, significantly improving accuracy and scalability. The results included reducing the forecast generation time from one month to a single day for all product lines and helping the finance function set more accurate reserves. The client also appreciated the new problem-solving methodology, which included cross-industry learnings and led to an annual roadmap for tackling additional business challenges.