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

• Published in: Decision analytics journal Vol. 9; p. 100347
• Main Authors: Barth, Linard, Schweiger, Lukas, Benedech, Rodolfo, Ehrat, Matthias
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.12.2023
• Subjects: Digital process twin; Digital twin; Internet of Things; Smart cities; Waste management; Waste management; Digital process twin; Smart cities; Digital twin; Internet of Things
• ISSN: 2772-6622; 2772-6622
• DOI: 10.1016/j.dajour.2023.100347

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. [Display omitted] •Technological innovations to drive the development of smart cities, including municipal waste management systems.•Enhanced waste management service quality using key performance indicators with two fill level thresholds.•Optimized availability, performance, and quality with smart waste management digital twin.•Waste management optimization via sensors using the mixed-method model and process twin.•Benchmarks for implementing similar systems in other municipalities without creating their simulations.