A data-driven approach to continuous improvement in reverse logistics

Abstract

Verizon may rely on third-party logistics providers (3PLs) to manage some aspects of the reverse supply chain of Fios equipment. As a result, it depends on the 3PL to continually strive for increased quality, reliability, capacity, and speed. Above all, in order to have a successful partnership, the process must be economical for the 3PL. As several sources of variation are detrimental to the 3PL's margins and cause operational problems, Verizon is investing in the supplier relationship to ensure that the 3PL is profitable and positioned for the future. Making sure there is a "win-win" relationship is beneficial for both parties and helps to ensure that the investments that have been made will continue to result in success, including operational improvements. To do this, a culture of continuous improvement and data-driven decisions needs to be cultivated and developed at the 3PL. The goal of this project is two-fold. First, there is a need to understand the variation that exists in the 3PL's process as well as the associated costs, which include overtime, ineffective labor and production planning, and high turnover. The secondary goal of the project is to empower the 3PL to make data-driven decisions in the future and start to shift their culture to one that aligns better with Verizon's. By showing the benefits of collaboration between the two companies, this project will help build trust. In this thesis, we discuss how process mining is used to understand the 3PL's current state and guide data-driven continuous improvement. We introduce several opportunities for handling variation, including creating visibility into return volumes, reducing defects caused by incorrect packaging, and creating feedback mechanisms for operators. This is done in close collaboration with the 3PL to ensure they will ultimately have ownership of implementation.