| Title |
Exploring recommendations for circular supply chain management through interactive visualisation |
| ID_Doc |
6292 |
| Authors |
van Capelleveen, G; van Wieren, J; Amrit, C; Yazan, DM; Zijm, H |
| Title |
Exploring recommendations for circular supply chain management through interactive visualisation |
| Year |
2021 |
| Published |
|
| DOI |
10.1016/j.dss.2020.113431 |
| Abstract |
The new era of circular supply chain management (CSCM) produces a new complex decision area for process managers. Part of it can be attributed to green procurement, in which a large number of potential ideas need to be reviewed that can sustain business. Such a large amount of data can quickly lead to information overload, especially without the presence of appropriate decision support tools. While there exists a range of visualisation methods that can aid the exploration of recommendations, there is a lack of studies that illustrate how these exploration techniques can facilitate the identification of CSCM activities. This paper showcases a study on how to ease the identification of new sustainable business opportunities through visual data exploration. Following the design science methodology, we have designed and evaluated a recommender system prototype (the IS Identification App) that supports sector-based identification of industrial symbiosis. The interactive visualisation enhances users with more control over recommendations and makes the recommendation process more transparent. Our case study results indicate that the interactive visualisation technique is a viable, fast and effective approach for exploring recommendations that increase the sustainability of the supply chain. |
| Author Keywords |
Circular supply chain management; Circular economy; Industrial symbiosis; Recommender systems; Exploration; Set visualisation |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000596871300007 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Operations Research & Management Science |
| Research Area |
Computer Science; Operations Research & Management Science |
| PDF |
https://doi.org/10.1016/j.dss.2020.113431
|