Reverse Logistics Repairs Forecasting for a Leading Electronic Manufacturer


Reverse Logistics Repairs Forecasting for a Leading Electronic Manufacturer
  • CASE STUDIES
  • January 14th, 2020
  •   7715 Views

Mu Sigma partnered with a trendsetting consumer electronics giant to dramatically improve their reverse logistics forecasting accuracy for their repairs department. This exercise quickly turned into a transformation journey spanning many vital functions such as Operations, Supply Chain Management and Quality control.

Overall, this reverse logistics repairs forecasting framework resulted in a cumulative cost saving of over $330M

The Problem


The finance team and reverse logistics forecast team of Fortune 500 consumer electronics firm wanted to attribute cost in their in-warranty repairs forecast process for financial accrual purposes. The main challenges were:

•   Existing forecasts were heuristic driven
•   The heuristic process required a lot of manual intervention
•   Forecast fluctuations were frequent and unexplained
•   Forecast inaccuracy led our partner to over-predict, adversely impacting their cash flow

The Mu Sigma Approach


While we designed an accurate, scalable, and accountable solution to address the issue of improving repairs forecast accuracy and, in the process, accounting for variations, we realized that it had the potential to do more.

After establishing these quick wins for the Finance team, we recognized the need for a broader, unified forecasting framework that would serve other crucial functions such as:

•   Operations for workforce management
•   Supply chain optimization for inventory management
•   Quality control for Product quality-level adherence

Now that we had visibility into the connected problem space, we realized that this framework couldn’t be consumed by other business verticals that also required repairs forecast. So, we decided to consolidate the effort across the verticals.

We built a single source of truth framework that could be consumed by various business functions. This provides unified forecasts at a country, channel, and product level, accounting for hurdles such as,

•   Loss in Accuracy as the number of models increase and scale across verticals
•   Inventory management required addition of sales forecasts along with repairs forecast to support inventory management
•   50 times more models to build and maintain

While scaling the forecast to all warranty types, we realized that we needed to ensure that almost 100k models worked in tandem. This required us to orchestrate the models on a platform that enables reusability, ease of maintenance, and enterprise-wide availability.

We made this possible through an approach that mandates 3 constraints for business process development:

•   Each process task be independent (thereby reusable)
•   Technology-agnostic (thus scalable)
•   Visually represented (thereby maintainable)

We leveraged muFlow, our in-house orchestration and automation workbench to develop these 100K models while harmonizing adaptability, scale, resilience, transparency, and maintenance costs.

An automated anomaly detection system was developed to identify multiple issues at a nascent stage. After extensive validation against known cases, 6 detection algorithms were leveraged to capture fraud anomalies and manufacturing defect anomalies.

Through this reverse logistics forecasting framework, we not only enabled consumption but also improved the responsiveness of the system

The Impact


Starting from reverse logistics repairs forecasting, this consumer electronics giant’s transformation journey with Mu Sigma left lasting impact across various functions,

•   Forecast accuracy improved to 95-98%, saving over $200M in costs
•   Unified reverse logistics forecasting framework aligned multiple business units to overall company goals and added momentum to progress towards these goals
•   Enabled better inventory management across the supply chain with estimated cost-reduction of $130M