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Sustainable Purchasing and Supply Management during and after the Pandemic...
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Sustainable Purchasing and Supply Management during and after the Pandemic Era: Effects, Operations, and Innovations

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 16040

Special Issue Editors

Prof. Dr. Guowei Hua

E-Mail Website
Guest Editor
School of Economics and Management, Beijing Jiaotong University, Shangyuancun 3, Beijing 100044, China
Interests: operations and supply chain management; sustainability; dual supply chain; inventory management; facility location
Special Issues, Collections and Topics in MDPI journals
Dr. Yi Zhang

E-Mail Website
Guest Editor
Logistics School, Beijing Wuzi University, Beijing 101149, China
Interests: operations management
Prof. Dr. T. C. Edwin Cheng

E-Mail Website
Guest Editor
PolyU Business School, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: e-business and e-commerce; information systems management; innovation and technology management; operations management; quality management; scheduling science; supply chain management
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Weihua Liu

E-Mail Website
Guest Editor
Department of Logistics and Supply Chain Management, College of Management and Economics, Tianjin University, Tianjin 300072, China
Interests: smart logistics and supply chain management; sustainable logistics operations management; service supply chain management; service operations management; supply chain financial management and innovation

Special Issue Information

Dear Colleagues,

The COVID-19 crisis shocked supply chains and provides additional evidence that the three sustainability dimensions are inextricably linked (Sarkis, 2020). As one of the significant operations of the companies in the pandemic era, sustainable purchasing and supply management has been met with big pressures and challenges. On one hand, how does COVID-19 affect the operation and sustainability outcomes of purchasing and supply management? How many traditional purchasing and supply strategies and policies will survive the COVID-19 outbreak after life returns to normal? On the other hand, how do companies seek to better models and tools to effectively deal with the purchasing and supply management problems to survive post-COVID-19, and how well prepared are they for similar risks? The crisis also helped us to identify new technologies and innovation opportunities to achieve a more sustainable and innovative purchasing and supply management in the post-pandemic era.

The overall purpose of this Special Issue is to focus on the operations and innovations of purchasing and supply management to better cope with the pandemic to contribute to sustainability. In this context, the Special Issue welcomes research papers addressing one or more of the following research topics:

  • Customer purchasing behaviors (e.g., purchasing pattern, purchasing preference);
  • Retailers’ purchasing behaviors;
  • Supplier selection;
  • Supplier management;
  • Supply chain contract management;
  • Risk management;
  • Innovative sourcing strategy or tools;
  • Data motivated sourcing management;
  • Innovative technologies (e.g., big data, blockchain, digital economy, 3D, virtual Technology, 5G) and sustainable purchasing;
  • Logistics management and purchasing management;
  • E-commerce innovations and purchasing management;
  • Consumer food purchasing;
  • Circular economy;
  • The sharing economy.

We look forward to receiving your contributions.

References:

Sarkis, J. (2020). Supply chain sustainability: learning from the COVID-19 pandemic. International Journal of Operations & Production Management.

Prof. Dr. Guowei Hua
Dr. Yi Zhang
Prof. Dr. T.C. Edwin Cheng
Prof. Dr. Weihua 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

  • purchasing and supply management
  • pandemic
  • sustainability

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Published Papers (5 papers)

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Research

17 pages, 1583 KiB  
Article
Intelligent Vehicle Sales Prediction Based on Online Public Opinion and Online Search Index
by Mingyang Zhang, Heyan Xu, Ning Ma and Xinglin Pan
Sustainability 2022, 14(16), 10344; https://doi.org/10.3390/su141610344 - 19 Aug 2022
Cited by 7 | Viewed by 2605
Abstract
Intelligent vehicles refer to a new generation of vehicles with automatic driving functions that is gradually becoming an intelligent mobile space and application terminal by carrying advanced sensors and other devices and using new technologies, such as artificial intelligence. Firstly, the traditional autoregressive [...] Read more.
Intelligent vehicles refer to a new generation of vehicles with automatic driving functions that is gradually becoming an intelligent mobile space and application terminal by carrying advanced sensors and other devices and using new technologies, such as artificial intelligence. Firstly, the traditional autoregressive intelligent vehicle sales prediction model based on historical sales is established. Secondly, the public opinion data and online search index data are selected to establish a sales prediction model based on online public opinion and online search index. Then, we consider the influence of KOL (Key Opinion Leader), a sales prediction model based on KOL online public opinion andonline search index is established. Finally, the model is further optimized by using the deep learning algorithm LSTM (Long Short-Term Memory network), and the LSTM sales prediction model based on KOL online public opinion and online search index is established. The results show that the consideration of the online public opinion and search index can improve the prediction accuracy of intelligent vehicle sales, and the public opinion of KOL plays a greater role in improving the prediction accuracy of sales than that of the general public. Deep learning algorithms can further improve the prediction accuracy of intelligent vehicle sales. Full article
(This article belongs to the Special Issue Sustainable Purchasing and Supply Management during and after the Pandemic Era: Effects, Operations, and Innovations)
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10 pages, 262 KiB  
Article
Loss Aversion Order Strategy in Emergency Procurement during the COVID-19 Pandemic
by Haozhe Huang, Xiaowei Li and Shuai Liu
Sustainability 2022, 14(15), 9119; https://doi.org/10.3390/su14159119 - 25 Jul 2022
Cited by 2 | Viewed by 1853
Abstract
The COVID-19 pandemic has had a serious impact on firms’ sourcing strategies. Since COVID-19 disrupted the supply chain, firms have had to make emergency purchases from other suppliers. In addition, emergency ordering is one of the most effective strategies to achieve sustainable operations [...] Read more.
The COVID-19 pandemic has had a serious impact on firms’ sourcing strategies. Since COVID-19 disrupted the supply chain, firms have had to make emergency purchases from other suppliers. In addition, emergency ordering is one of the most effective strategies to achieve sustainable operations because such a strategy can save inventory costs. We aim to address a retailer’s emergency procurement strategies during the COVID-19 pandemic. We use prospect theory and the newsvendor model to uncover the retailer’s inventory decisions. In our study, we find that retailers have the choice to order items before the selling period at the normal purchase price, and, if available, they can order them before the end of the selling period at the urgent purchase price. We perform a comparison of the optimal ordering policy and margins in this case with the conventional and loss aversion models. The influence of emergency procurement on the optimal order policy and margins is investigated as well. This paper contributes in theory that we innovatively capture the uncertainty of emergency sourcing, which is a feature that has never been considered in current research. Full article
(This article belongs to the Special Issue Sustainable Purchasing and Supply Management during and after the Pandemic Era: Effects, Operations, and Innovations)
14 pages, 264 KiB  
Article
A Sustainable Innovation Strategy Oriented toward Complex Product Servitization
by Zhiqiang Zhang, Ling Li and Huiying Zhang
Sustainability 2022, 14(7), 4290; https://doi.org/10.3390/su14074290 - 4 Apr 2022
Cited by 2 | Viewed by 1822
Abstract
Enterprises performing complex product servitization are more vulnerable to the 2019 coronavirus disease (COVID-19) pandemic because of their large number of suppliers and wide coverage, among other things. The present research focuses on how to promote the sustainable innovation of complex product servitization. [...] Read more.
Enterprises performing complex product servitization are more vulnerable to the 2019 coronavirus disease (COVID-19) pandemic because of their large number of suppliers and wide coverage, among other things. The present research focuses on how to promote the sustainable innovation of complex product servitization. We investigate complex products and sustainable innovation—factors influencing the sustainable innovation of complex product servitization—based on the characteristics of product servitization and by combining the definitions of product servitization. We find that inadequate innovation ability and poor technical research and development (R&D) competence are the primary concerns in the sustainable innovation of complex product servitization. Specific to innovation ability improvement, the sustainable innovation of complex product servitization must follow an innovation-driven development strategy, a hard power cultivation strategy, and a soft power cultivation strategy. In terms of technical R&D competence enhancement, technological innovation strategies, integrated outsourcing of technical R&D competence, and independent improvement of technical R&D competence must be implemented to facilitate the sustainable innovation of complex product servitization. Full article
(This article belongs to the Special Issue Sustainable Purchasing and Supply Management during and after the Pandemic Era: Effects, Operations, and Innovations)
20 pages, 4375 KiB  
Article
A New Container Throughput Forecasting Paradigm under COVID-19
by Anqiang Huang, Xinjun Liu, Changrui Rao, Yi Zhang and Yifan He
Sustainability 2022, 14(5), 2990; https://doi.org/10.3390/su14052990 - 3 Mar 2022
Cited by 5 | Viewed by 2849
Abstract
COVID-19 has imposed tremendously complex impacts on the container throughput of ports, which poses big challenges for traditional forecasting methods. This paper proposes a novel decomposition–ensemble forecasting method to forecast container throughput under the impact of major events. Combining this with change-point analysis [...] Read more.
COVID-19 has imposed tremendously complex impacts on the container throughput of ports, which poses big challenges for traditional forecasting methods. This paper proposes a novel decomposition–ensemble forecasting method to forecast container throughput under the impact of major events. Combining this with change-point analysis and empirical mode decomposition (EMD), this paper uses the decomposition–ensemble methodology to build a throughput forecasting model. Firstly, EMD is used to decompose the sample data of port container throughput into multiple components. Secondly, fluctuation scale analysis is carried out to accurately capture the characteristics of the components. Subsequently, we tailor the forecasting model for every component based on the mode analysis. Finally, the forecasting results of all the components are combined into one aggregated output. To validate the proposed method, we apply it to a forecast of the container throughput of Shanghai port. The results show that the proposed forecasting model significantly outperforms its rivals, including EMD-SVR, SVR, and ARIMA. Full article
(This article belongs to the Special Issue Sustainable Purchasing and Supply Management during and after the Pandemic Era: Effects, Operations, and Innovations)
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29 pages, 2044 KiB  
Article
Demand Forecasting of E-Commerce Enterprises Based on Horizontal Federated Learning from the Perspective of Sustainable Development
by Juntao Li, Tianxu Cui, Kaiwen Yang, Ruiping Yuan, Liyan He and Mengtao Li
Sustainability 2021, 13(23), 13050; https://doi.org/10.3390/su132313050 - 25 Nov 2021
Cited by 16 | Viewed by 4981
Abstract
Public health emergencies have brought great challenges to the stability of the e-commerce supply chain. Demand forecasting is a key driver for the sound development of e-commerce enterprises. To prevent the potential privacy leakage of e-commerce enterprises in the process of demand forecasting [...] Read more.
Public health emergencies have brought great challenges to the stability of the e-commerce supply chain. Demand forecasting is a key driver for the sound development of e-commerce enterprises. To prevent the potential privacy leakage of e-commerce enterprises in the process of demand forecasting using multi-party data, and to improve the accuracy of demand forecasting models, we propose an e-commerce enterprise demand forecasting method based on Horizontal Federated Learning and ConvLSTM, from the perspective of sustainable development. First, in view of the shortcomings of traditional RNN and LSTM demand forecasting models, which cannot handle multi-dimensional time-series problems, we propose a demand forecasting model based on ConvLSTM. Secondly, to address the problem that data cannot be directly shared and exchanged between e-commerce enterprises of the same type, the goal of demand information sharing modeling is realized indirectly through Horizontal Federated Learning. Experimental results on a large number of real data sets show that, compared with benchmark experiments, our proposed method can improve the accuracy of e-commerce enterprise demand forecasting models while avoiding privacy data leakage, and the bullwhip effect value is closer to 1. Therefore, we effectively alleviate the bullwhip effect of the entire supply chain system in demand forecasting, and promote the sustainable development of e-commerce companies. Full article
(This article belongs to the Special Issue Sustainable Purchasing and Supply Management during and after the Pandemic Era: Effects, Operations, and Innovations)
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