Transforming Repairs Forecast for Improved Reverse Logistics


Transforming Repairs Forecast for Improved Reverse Logistics
  • CASE STUDIES
  • January 14th, 2020
  •   7007 Views

Problem spaces are deeply interconnected in today’s complex business landscape. A new Art of Problem Solving is required to shed light on these interactions and create lasting solutions.

THE PROBLEM
We helped a trendsetting consumer electronics giant dramatically improve their repairs forecast accuracy, resulting in $330M worth of cost savings across multiple verticals, particularly their supply chain.

THE MU SIGMA APPROACH
It started as a simplistic problem of attributing variations in repairs forecast for financial accrual purposes. But our methodic solutioning approach highlighted connected problems leading us to develop a larger unified forecast that now supports the Operations, Supply Chain, Products, and other teams. Moreover, the forecast models are developed to be scalable, reusable, and sustainable through our proprietary BPMN platform, muFLOW.

Additionally, we put in place an anomaly detection system to check and validate the input repair behavior and hence, maintain the high forecast accuracy and improve reverse logistics.

THE IMPACT
Enabling cost-cuts amounting to over $130M in their SCM operations alone, this solution has helped streamline and synchronize decision-making across various business functions in their ecosystem through a common stream – data.

Download the case study to know more.

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