Closed loop supply chain waste reduction through predictive modelling and process analysis

Abstract

Verizon distributes Customer Premises Equipment (CPE) such as set top boxes, broadband routers, and WiFi extenders to Fios customers via a variety of paths; for example: direct ship to customer (either for self-install or for later installation by a field technician), delivery via field technicians, or retail store pickup (primarily for self-install). Each method has its own benefits and shortcomings due to impacts on metrics such as inventory levels, shipping costs, on-time delivery, and system complexity. Although the majority of shipments are successfully activated in the customer's home, a non-trivial percentage results in unused returns or inventory shrinkage. These undesirable results represent a significant amount of wasted resources. This thesis is focused on identifying and realizing cost savings in the Fios supply chain through reduction in waste associated with unsuccessful shipments.

In order to effectively analyze the closed-loop supply chain, accurate and reliable process mapping is critical. Interviews with key stakeholders, together with order and shipment data analysis yielded a complete picture of the ecosystem's processes and infrastructure. Process mining techniques augmented this understanding, using event log data to identify and map equipment and information flows across the supply chain. All together this analysis is used to identify order cancellations as a key source of waste. To limit waste, it is necessary to conduct analysis both internal to Verizon's processes and externally, to determine if there are customer trends leading to order termination. Process mining was used for the internal analysis and, while it helped identify singular cases in which process abnormalities were associated with undesirable outcomes, its current form proved unsuited for root cause analysis.

Internal analysis did, however, illuminate opportunities for improvement in radio-frequency identification (RFID) usage and protocols across the supply chain. Current systems can result in poor visibility of equipment as it moves within some segments of the supply chain. The actual monetary impact is difficult to determine but likely to increase as the importance of RFID increases. External analysis is conducted through predictive modelling. Using a variety of data sources, a model with over 80% sensitivity and a low false positive rate is achieved. Operationalizing this model through real time incorporation with sales was explored but found to be overly complex. Instead, the random forest model yielded policy changes guided by the features with the highest importance. A pilot is currently in development to test the efficacy of suggested changes, as the model implies significant savings opportunity.