Simulation to Enable a Data-Driven Circular Economy

Fiona Charnley,Mariale Moreno,Windo Hutabarat,Okechukwu Okorie,Divya Tiwari,Ashutosh Tiwari

doi:10.3390/su11123379

Fiona Charnley, Mariale Moreno + Show 4 more

Open Access

https://doi.org/10.3390/su11123379

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Abstract

This paper presents an investigation on how simulation informed by the latest advances in digital technologies such as the 4th Industrial Revolution (I4.0) and the Internet of Things (IoT) can provide digital intelligence to accelerate the implementation of more circular approaches in UK manufacturing. Through this research, a remanufacturing process was mapped and simulated using discrete event simulation (DES) to depict the decision-making process at the shop-floor level of a remanufacturing facility. To understand the challenge of using data in remanufacturing, a series of interviews were conducted finding that there was a significant variability in the condition of the returned product. To address this gap, the concept of certainty of product quality (CPQ) was developed and tested through a system dynamics (SD) and DES model to better understand the effects of CPQ on products awaiting remanufacture, including inspection, cleaning and disassembly times. The wider application of CPQ could be used to forecast remanufacturing and production processes, resulting in reduced costs by using an automatised process for inspection, thus allowing more detailed distinction between “go” or “no go” for remanufacture. Within the context of a circular economy, CPQ could be replicated to assess interventions in the product lifecycle, and therefore the identification of the optimal CE strategy and the time of intervention for the current life of a product—that is, when to upgrade, refurbish, remanufacture or recycle. The novelty of this research lies in investigating the application of simulation through the lens of a restorative circular economic model focusing on product life extension and its suitability at a particular point in a product’s life cycle.

Highlights

The circular economy model offers an alternative to the traditional linear economy, decoupling economic value creation from resource consumption by keeping resources in use for as long as possible, extracting the maximum value from them whilst in use and recovering and regenerating products at the end of each service life [1]
Data-driven intelligence is rapidly becoming a pervasive feature of our economy, where data generated through social, mobile, machine and product networks are being leveraged through data analytics to create new forms of value
To address the research gap in data-driven simulation for remanufacturing, this paper aims to investigate the value of simulation techniques to inform decision-making processes in remanufacturing, considering the stochastic nature of returned products

Summary

Introduction

The circular economy model offers an alternative to the traditional linear economy (take, make, use and dispose), decoupling economic value creation from resource consumption by keeping resources in use for as long as possible, extracting the maximum value from them whilst in use and recovering and regenerating products at the end of each service life [1]. The implementation of circular economy strategies in manufacturing environments is subject to several risks, including the mismatch between fluctuating demand, supply and value of used components, causing uncertainty regarding costs and return on investment [3]. Another issue is the lack of information concerning the condition, availability and location of in-service assets [4]. Pairing the digital revolution with the principles of a circular economy model has the potential to radically transform the industrial landscape and its relationship to materials and finite resources, unlocking additional value for the manufacturing sector [6]

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