An approach to quantify the financial benefit of embedding sensors into products for end-of-life management: a case study

Abstract

Reverse logistics (RL) involves all the activities required for the collection and recovery or disposal of end-of-life (EOL) products. Various models have been developed for the design and management of RL networks. In all these models, materials flow according to the principles of traditional push system which results in excessive inventories. While implementation of just-in-time (JIT) would normally be a viable alternative, it requires the arrival of on-time and high-quality materials. However, in RL networks, arrival times and quality as well as quantity of EOL products are highly stochastic. The conflicting characteristics of JIT and RL make it challenging to integrate these two initiatives in their current forms. To overcome the integration difficulties, we propose and analyze an RL system controlled by a multi-kanban system (MKS), a novel JIT-based methodology originally developed for disassembly lines. MKS is a superior system that tames and controls the chaos created by the stochastic behavior of RL networks. To further exploit the efficiency of MKS, we explore the benefit of sensors embedded in products for detecting non-functional and/or missing components before disassembling the products. Separate experimental design studies based on orthogonal arrays are carried out for the disassembly of conventional products and sensor-embedded products (SEPs). Then, the results of pair-wise t-tests comparing the two cases based on different performance measures are presented. The results show that SEPs provide significant improvements in total revenue and profit while reducing holding, backorder, disassembly, transportation, testing and disposal costs.