The impact of information transparency on the dynamic behaviour of a hybrid manufacturing/remanufacturing system

Abstract

A combined manufacturing/remanufacturing system is modelled and its dynamic behaviour is investigated using a control engineering approach. The model is an extension of the automated pipeline, inventory and order based production control system (APIOBPCS), which is well understood from a dynamic perspective. It is assumed that the remanufacturing process is based on a PUSH policy. Utilising different levels of information transparency from the remanufacturing process, three system types are developed and compared. Mathematical and simulation analyses of the system types were undertaken and their robustness to remanufacturing process uncertainties were tested. Our analysis indicates that the greater the degree of information transparency, the greater the robustness of the hybrid system.

Closed-loop supply chains: What reverse logistics factors influence performance?

“…While Zhou et al (2005) and Zhou and Disney (2006) show that inventory stability improves as the percentage of recollected product increases, Qingli et al (2008) note an opposite trend. A peculiar exception is presented in the work of Tang and Naim (2004), in which, depending on the type of replenishment rules, inventory may have very different performances.…”

“…Furthermore, System Dynamics together with Control Theory represent the most adopted methodologies for the analysis of CLSC dynamics. To verify our model we reproduce the same experiments of Zhou and Disney (2006) and Tang and Naim (2004), which surely can be considered among the most cited studies in the stream of CLSC dynamics.…”

“…1 the results provided by Tang and Naim (2004) in terms of Inventory, WIP and Throughput (COMRATE) and our output, under the same boundary condition, are compared. Si4).milarly to the previous test, by reproducing the experiment of Tang and Naim (2004), we obtain an identical SC behaviour. This represents a relevant insight, as it allows us to conduct our analyses by using a simulation model which is validated with two well-known CLSC models in this field.…”

“…Secondly, through a differential equation systems approach we model different configurations of CLSC and by adopting a rigorous design of experiment we analyse the effect of four logistics' factors on the dynamics response. More specifically, we test the effect of (1) the return rate of recycled products, (2) the remanufacturing lead-time performance, (3) the number of echelons and (4) and two different order policies properly designed for inventory management in CLSCs, characterised by an increasing level of information visibility of the reverse logistics process, namely the Tang and Naim (2004) order policy and the Zhou and Disney (2006) order policy. We assess the performance in terms of bullwhip effect and inventory stability by adopting two well-known non-financial performance metrics (i.e., Order Rate Variance Ratio and Inventory Variance Ratio).…”

Optimal Control Approaches to the Aggregate Production Planning Problem

“…In this way high customer service levels can be achieved, which can only be accomplished when the dynamic behavior of its constituent parts (i.e., materials/information flow, operations performed, resources/decision, rules/performance measures used) has been taken into account [33].…”