From data to value in smart waste management : optimizing solid waste collection with a digital twin-based decision support system

Barth, Linard; Schweiger, Lukas; Benedech, Rodolfo Andres; Ehrat, Matthias

doi:10.1016/j.dajour.2023.100347

Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-30027

Publication type:	Article in scientific journal
Type of review:	Peer review (publication)
Title:	From data to value in smart waste management : optimizing solid waste collection with a digital twin-based decision support system
Authors:	Barth, Linard Schweiger, Lukas Benedech, Rodolfo Andres Ehrat, Matthias
et. al:	No
DOI:	10.1016/j.dajour.2023.100347 10.21256/zhaw-30027
Published in:	Decision Analytics Journal
Volume(Issue):	9
Issue:	100347
Issue Date:	28-Oct-2023
Publisher / Ed. Institution:	Elsevier
ISSN:	2772-6622
Language:	English
Subjects:	Waste management; Smart city; Internet of Things; Digital process twin; Digital twin
Subject (DDC):	363: Environmental and security problems
Abstract:	The importance of waste management, including collection, separation, recovery, and recycling, increases with the growing amount of waste. Technological innovations such as smart connected products, the Internet of Things, and digital twins are driving the development of smart management systems. Investments in necessary product-service systems are justified by cost savings and improved service quality, especially in affluent societies like Switzerland. However, there is a trade-off between cost savings and service quality that raises the question of optimal balance. Using a Swiss municipality as an example, this paper models the trade-off between cost savings and service quality using waste bin sensor modules. Simulation results demonstrate the impact of cost savings on service quality reduction and that substantial cost savings are possible without a service quality compromise. We also introduce a digital process twin as a decision support system that is able to leverage a growing database. These results contribute to research, firstly through the field study with 98 waste bins equipped with fill level sensor modules, secondly through the model-based analysis of the trade-off between cost savings and service quality, and thirdly by conceptualizing a digital twin-based decision support system. The results further contribute to practice, firstly by providing benchmarks for implementing similar systems in other municipalities without having to create their own simulations, secondly by presenting an innovative key performance indicator to measure service quality, and thirdly with a model that can be used for simulations to determine the individual optimum between costs and service quality.
URI:	https://digitalcollection.zhaw.ch/handle/11475/30027
Fulltext version:	Published version
License (according to publishing contract):	CC BY-NC-ND 4.0: Attribution - Non commercial - No derivatives 4.0 International
Departement:	School of Engineering School of Management and Law
Organisational Unit:	Institute of Data Analysis and Process Design (IDP) Institute of Marketing Management (IMM)
Appears in collections:	Publikationen School of Management and Law

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Barth, L., Schweiger, L., Benedech, R. A., & Ehrat, M. (2023). From data to value in smart waste management : optimizing solid waste collection with a digital twin-based decision support system. Decision Analytics Journal, 9(100347). https://doi.org/10.1016/j.dajour.2023.100347

Barth, L. et al. (2023) ‘From data to value in smart waste management : optimizing solid waste collection with a digital twin-based decision support system’, Decision Analytics Journal, 9(100347). Available at: https://doi.org/10.1016/j.dajour.2023.100347.

L. Barth, L. Schweiger, R. A. Benedech, and M. Ehrat, “From data to value in smart waste management : optimizing solid waste collection with a digital twin-based decision support system,” Decision Analytics Journal, vol. 9, no. 100347, Oct. 2023, doi: 10.1016/j.dajour.2023.100347.

BARTH, Linard, Lukas SCHWEIGER, Rodolfo Andres BENEDECH und Matthias EHRAT, 2023. From data to value in smart waste management : optimizing solid waste collection with a digital twin-based decision support system. Decision Analytics Journal. 28 Oktober 2023. Bd. 9, Nr. 100347. DOI 10.1016/j.dajour.2023.100347

Barth, Linard, Lukas Schweiger, Rodolfo Andres Benedech, and Matthias Ehrat. 2023. “From Data to Value in Smart Waste Management : Optimizing Solid Waste Collection with a Digital Twin-Based Decision Support System.” Decision Analytics Journal 9 (100347). https://doi.org/10.1016/j.dajour.2023.100347.

Barth, Linard, et al. “From Data to Value in Smart Waste Management : Optimizing Solid Waste Collection with a Digital Twin-Based Decision Support System.” Decision Analytics Journal, vol. 9, no. 100347, Oct. 2023, https://doi.org/10.1016/j.dajour.2023.100347.