Transforming Repairs Forecast for Improved Reverse Logistics


supply chain, supply chain solutions, forecasting, repair forecast
  • CASE STUDIES
  • January 14th, 2020
  •   864 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
A trendsetting consumer electronics giant was relying on heuristic repairs forecast for financial accrual purposes. However, the effect of unexplained variations in these inaccurate forecasts masked crucial opportunities in other business functions as well.

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 Supply Chain, Products, Operations, and other teams. Moreover, the solution is created 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.

THE IMPACT
Enabling cost-cuts amounting to over $130M in their supply chain alone and $330M overall, this solution has helped streamline and synchronize decision-making across various business functions in their ecosystem through a common stream – data.

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