Optimization for a three-stage production system in the Internet of Things: procurement, production and product recovery, and acquisition

Abstract

Recovery of the end-of-use products has become a topic of considerable interest in the advanced manufacturing industry due in part to uncertainties in the quality and volume of product returns. The Internet of Things (IoT) that enables the tracing, detecting, storing, and analyzing the product life cycle data for each individual item can mitigate or eliminate these uncertainties. In this paper, an integrated three-stage model is presented based on IoT technology for the optimization of procurement, production and product recovery, pricing and strategy of return acquisition. The remaining value is used to measure the return condition. The model considers three recovery options related to refurbishing, component reuse and disposal, and the value deterioration for satisfying the product demand in each stage of product life cycle (PLC). A novel particle swarm optimization (PSO) algorithm based on two heuristic methods is proposed to solve the problem. A numerical example and sensitivity analysis are used to illustrate the performance of both algorithm and applicability of the model.

Internet of things and supply chain management: a literature review

“…IoT technology is used to keep product lifecycle integrity. Fang et al (2016) proposed an integrated three-stage model based on IoT technology for the optimisation of procurement, production and product recovery, pricing and strategy of return acquisition. Thürer et al (2016) proposed the architecture of an IoT-driven Kanban system for solid waste collection.…”

Examining potential benefits and challenges associated with the Internet of Things integration in supply chains

“…Although prior works (e.g. Ondemir and Gupta, 2014;Fang et al, 2016) strongly suggested the IoT adoption significantly optimizes procurement, production and product recovery, and acquisition; recent research reveals that enterprises and procurement practitioners across Europe, UK and North America are still not strategically adopting IoT as a catalyst for deployment of innovative procurement solutions (Avery, 2015). The fifth least supported potential benefit was IoT as a disruptive technology that will reshape models of SCM considering autonomous parts from a digitalized value chain with electronic network of activities (mean = 3.85).…”

Forecasting for remanufacturing: The effects of serialization

“…This information can be of great benefit for the management of the dual‐source uncertainty faced by remanufacturers. Such technologies are integral to the shifts toward “Industrial IoT” in the United States and Industry 4.0 in Germany (Fang, Liu, Pardalos, & Pei, ). As a result of such initiatives, the serialized data we are concerned with in this work are becoming increasingly available, and the need for an empirical evaluation of their utility is now pressing.…”