Journal Browser

► Journal Browser

Special Issue Editors

Prof. Dr. Jao-Hong Cheng

E-Mail Website
Guest Editor

Department of Information Management, National Yunlin University of Science and Technology, Douliu City, Taiwan
Interests: supply chain management; e-business; collaborative commerce; data-driven innovation

Prof. Dr. Chung Hsing Yeh

E-Mail Website
Guest Editor

Department of Data Science and Artifical Intelligence, Faculty of Information Technology, Monash University, Clayton, VIC, Australia
Interests: multicriteria decision analysis; applied artificial intelligence; business intelligence; transportation; supply chain management

Dr. Anne Yenching Liu

E-Mail Website
Guest Editor

Department of Business Administration, National Yunlin University of Science and Technology, Douliu City, Taiwan
Interests: e-commerce; technology management; intellectual property management; service science; innovation management

Special Issue Information

Dear Colleagues,

New types of information and communications technologies (ICTs) such as disruptive internet technology, blockchain, Internet of Things (IoT), and big data technologies are widely used as technical support for data-driven supply chains. Data-driven supply chain management has become a core inter-organizational function involved in the collaborative behaviors and activities between parties. Rational decision-making would be assured if all parties involved have jointly held data or information. Effective management of data (such as blockchain, IoT, and big data technologies) could have a huge impact on operations, supply chain, and business models (Wamba et al., 2020), improve production processes and reduce costs (Kamble et al., 2020), facilitate knowledge exchange (Vaccario et al., 2018), enhance user experience (Saura et al., 2021), strengthen service content (Wagner et al., 2021), support critical decision-making (Dodman et al., 2021), contribute to sustainability (Akhtar et al., 2016), and facilitate the execution of smart contracts between supply chain stakeholders (Dolgui et al., 2019).

The aim of this Special Issue is to cover a broad spectrum of original research contributions on the topic of sustainable data-driven innovations in supply chains. To apply sustainable data-driven innovations, the response to environmental, economic, and social challenges needs to be investigated. Reviews and research can include the impact of data-driven on business operations, product and service quality, stakeholders’ experience and expectations, and performance in the supply chain. In this Special Issue, we invite submissions with the focus on sustainable data-driven innovation in supply chains. We welcome submissions that offer important conceptual and empirical insights into the nature and processes of data-driven technology, supply chain management, sustainable development, and sustainable innovation.

References

Akhtar, P., Tse, Y. K., Khan, Z., & Rao-Nicholson, R. Data-driven and adaptive leadership contributing to sustainability: Global agri-food supply chains connected with emerging markets. International Journal of Production Economics. 2016, 181, 392–401.
Dodman, S. L., Swalwell, K., DeMulder, E. K., & Stribling, S. M. Critical data-driven decision making: A conceptual model of data use for equity. Teaching and Teacher Education, 99, 103272.
Dolgui, A., Ivanov, D., Potryasaev, S., Sokolov, B., Ivanova, M., Werner, F., 2019. Blockchain-oriented dynamic modelling of smart contract design and execution in the supply chain. International Journal of Production Research. Vol.58 No.7 pp.1–16.
Kamble, S. S., Gunasekaran, A., Gawankar, S. A., 2020. Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics. Vol.219 pp.179–194.
Saura, J. R., Ribeiro-Soriano, D., & Palacios-Marqués, D. (2021). From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets. International Journal of Information Management, 102331.
Vaccario, G., Tomasello, M. V., Tessone, C. J., & Schweitzer, F. (2018). Quantifying knowledge exchange in R&D networks: a data-driven model. Journal of Evolutionary Economics, 28(3), 461-493.
Wagner, A., Wessels, N., Brakemeier, H., & Buxmann, P. (2021). Why free does not mean fair: Investigating users’ distributive equity perceptions of data-driven services. International Journal of Information Management, 59, 102333.
Wamba, S. F., Queiroz, M. M., Trinchera, L., 2020. Dynamics between blockchain adoption determinants and supply chain performance: An empirical investigation. International Journal of Production Economics. Vol.229 pp.1–15.

Prof. Dr. Jao-Hong Cheng
Prof. Dr. Chung Hsing Yeh
Dr. Anne Yenching Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

data-driven innovations
data-driven supply chains
data-driven technology
data-driven society
data-driven service
decision-making
user experience
sustainability

Published Papers (1 paper)

Research

17 pages, 1469 KiB

Open AccessArticle

Price Prediction and Classification of Used-Vehicles Using Supervised Machine Learning

by Lucija Bukvić, Jasmina Pašagić Škrinjar, Tomislav Fratrović and Borna Abramović

Sustainability 2022, 14(24), 17034; https://0-doi-org.brum.beds.ac.uk/10.3390/su142417034 - 19 Dec 2022

Cited by 5 | Viewed by 7875

Abstract

Due to the large growth in the number of cars being bought and sold, used-car price prediction creates a lot of interest in analysis and research. The availability of used cars in developing countries results in an increased choice of used vehicles, and people increasingly choose used vehicles over new ones, which causes shortages. There is an important need to explore the enormous amount of valuable data generated by vehicle sellers. All sellers usually have the imminent need of finding a better way to predict the future behavior of prices, which helps in determining the best time to buy or sell, in order to achieve the best profit. This paper provides an overview of data-driven models for estimating the price of used vehicles in the Croatian market using correlated attributes, in terms of production year and kilometers traveled. In order to achieve this, the technique of data mining from the online seller “Njuškalo” was used. Redundant and missing values were removed from the data set during data processing. Using the method of supervised machine learning, with the use of a linear regression algorithm for predicting the prices of used cars and comparing the accuracy with the classification algorithm, the purpose of this paper is to describe the state of the vehicle market and predict price trends based on available attributes. Prediction accuracy increases with training the model with the second data set, where price growth is predicted by linear regression with a prediction accuracy of 95%. The experimental analysis shows that the proposed model predicts increases in vehicle prices and decreases in the value of vehicles regarding kilometers traveled, regardless of the year of production. The average value of the first data set is a personal vehicle with 130,000 km traveled and a price of EUR 10,000. The second set of data was extracted 3 months after the previously analyzed set, and the average price of used vehicles increased by EUR 1391 per vehicle. On the other hand, average kilometers traveled decreased by 8060 km, which justifies the increase in prices and validates the training models. The price and vehicle type are features that play an important role in predicting the price in a second-hand market, which seems to be given less importance in the current literature of prediction models. Full article

(This article belongs to the Special Issue Sustainable Data-Driven Innovations in Supply Chains)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Sustainable Data-Driven Innovations in Supply Chains

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (1 paper)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI