Dataset for Modelling of Sharing Networks in the Circular Economy

Published: 3 May 2019| Version 2 | DOI: 10.17632/924hb6dsps.2
Contributors:
Jaivignesh Jayakumar,
Jayakrishna Kandasamy

Description

This research study attempts to mathematically model the integration of a sharing network and a circular production system in the supply chain of a leading Indian manufacturer of laptops. The projected outcomes of this study are to effectively quantify the economic benefits and the environmental impacts associated with this integration. Seeing as there is an inherent trade-off between the two objectives, we utilize an optimization package that deploys a Multi-Objective Mixed-Integer Linear-Programming (MOMILP) model to select an appropriate configuration. The mathematical model, data-set, and the code utilised are attached below.

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Steps to reproduce

The following information was collected through an integrated approach in which the first step involved the acquisition of production figures, sales data, market research, and logistical data. The underlying data was then cleaned and analysed using analytical toolboxes such as R-Studio and Tableau. Certain fields were populated through information collected from surveys and case studies of key stakeholders that were involved in the process. Data related to pollution was computed through a theoretical method as this study didn’t possess the capabilities to retrofit the logistical carriers with data-acquisition devices such as sensors. The amount of fuel consumed by the logistical carrier was estimated using mileage associated with various legs of the network and the average fuel economy exhibited by the logistical carrier. However, this data was correlated with the Carbon Calculator provided by DHL Express, as shown in Fig. 3. This calculator allows for the easy calculation and analysis of carbon footprint through interactive and dynamic form inputs that include route visualizations and shipment parameters. The emissions calculations follow an activity-based methodology to estimate the carbon footprint in accordance with multiple international protocols and standards. This data was then standardized and segregated on a per-product and/ or per-part level according to the parameters that this study required.