Improving Reverse Logistic Repairs
Mu Sigma collaborated with a consumer electronics giant, leading to a comprehensive transformation of operations, supply chain, and quality control, resulting in a substantial cost decrease of over $330 million through improved reverse logistics forecasting precision.
The Challenge
A Fortune 500 consumer electronics company faced challenges with its in-warranty repairs forecast process, leading to frequent fluctuations and inaccuracies. The existing heuristic-driven approach required extensive manual intervention, leading to over-predictions and impacting cash flow adversely.
The Mu Sigma Approach
Our approach aimed to enhance forecast accuracy by accounting for variations, leading to the development of a unified forecasting framework tailored for critical functions such as workforce management, inventory optimization, and quality control adherence. By consolidating efforts across various business verticals, we established a single source of truth framework, providing unified forecasts at multiple levels.
To streamline the management of a vast array of models, we employed muFlow, our proprietary orchestration and automation workbench. This approach ensured adherence to key constraints for business process development. Furthermore, an automated anomaly detection system employing six detection algorithms was implemented, enabling early identification of fraud and manufacturing defects, resulting in improved system responsiveness and efficient consumption of the reverse logistics forecasting framework.
Business Impact
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98%
Improved Forecast Accuracy
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$330M
Cost Savings
Mu Sigma is the world's largest pure-play big data analytics and decision sciences company. Through our Art of Problem-Solving, we strive to scale and systematize better decision-making through business intelligence services. Mu Sigma works across the entire decision support ecosystem (data sciences, data engineering, and decision sciences) with Fortune 1000 companies across the world, including 140+ Fortune 500 companies.
The firm's is derived from the statistical terms "Mu" and "Sigma," which symbolize a
probability distribution's mean and standard deviation, respectively.
