Next Article in Journal
The Impact of Corporate Social Responsibility Practices on Customer Value Co-Creation and Perception in the Digital Context: A Case Study of Taiwan Bank Industry
Previous Article in Journal
Experimental and Numerical Studies of Self-Expansible Polyurethane Slurry Diffusion Behavior in a Fracture Considering the Slurry Temperature
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 11
10.3390/su15118564
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

A Literature Review on the Application of Digital Technology in Achieving Green Supply Chain Management

by
Yi Wang
1,†,
Yafei Yang
2,†,
Zhaoxiang Qin
3,*,
Yefei Yang
4 and
Jun Li
1
1
Inner Mongolia Kunming Cigarette Limited Liability Company, Hohhot 010020, China
2
Faculty of Logistics, Beijing Wuzi University, Beijing 101100, China
3
School of Economic and Management, Guangxi Science & Technology Normal University, Laibin 546199, China
4
School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2023, 15(11), 8564; https://doi.org/10.3390/su15118564
Submission received: 3 March 2023 / Revised: 10 May 2023 / Accepted: 17 May 2023 / Published: 25 May 2023
(This article belongs to the Section Sustainable Management)
Browse Figures
Review Reports Versions Notes

Abstract

:
Digitization and greening have become the characteristics of social and economic development. Digital technology, as a critical enabler of green supply chain management, has been widely considered and recognized by academia and business circles. With the advent of the Industry 4.0 era and the rapid development of digital technology, this emerging field of technology is constantly being updated, and so the academic research in this field is increasing but has yet to reach saturation. We systematically reviewed 144 relevant papers published in the last 14 years. We used qualitative analysis to classify, summarize and analyze the literature in two dimensions (i.e., digital technologies and green supply chain practices). Then, we continued the classification from each dimension. According to the basic characteristics, there are five digital technologies: the Internet of Things, big data, cloud computing, blockchain, and artificial intelligence. The green supply chain is divided into green procurement, green production, green consumption, and green logistics according to the essential practices of the supply chain. This study explores which digital technologies are needed in a green supply chain. The study also discusses how these technologies can reduce the input of resources and energy and the emission of pollutants, finally improving the operating efficiency of the green supply chain, and achieving economic, social, and environmental benefits.

1. Introduction

Digital technology involves the acquisition and integration of data and information by computer, multimedia, and communication technology. Data and information are introduced into programs to establish corresponding data models for subsequent analysis and application [1]. Green supply chain management is a trend and an innovative management tool. Most companies take “green” as the development trend, purchase raw materials with high environmental performance, produce low-carbon and recyclable finished products, sell the products consumers need, and share transportation services, finally achieving overall green development [2,3].
This study divides digital technology into the Internet of Things, big data, cloud computing, blockchain, and artificial intelligence. Specifically, the Internet of Things connects all things to transmit information between things. Then, it tracks and predicts data and information through a public information management system. These systems can be used to make accurate decisions [4,5,6]. Big data technology obtains a large amount of data from the Internet, selects valuable data from this large quantity, uses a model to conduct data analysis, and finds the general operation law of things. Such technology obtains the correlations between data, which provides valuable help for operators to make decisions [7,8,9,10,11]. Cloud computing is a form of Internet-based computing. It decomposes vast data into a single data cluster through the network “cloud” and processes and analyzes the data cluster through a system composed of multiple servers. Finally, it integrates the calculation results [12,13,14,15,16,17]. Cloud computing has powerful computing capabilities that can process millions of data points in minutes and enable on-demand resource allocation. Blockchain technology uses the idea of decentralization and untampered data. It stores the data and information of every practice in the chain so that all stakeholders involved in the supply chain can check it, which solves the problem of mutual distrust in the supply chain and reduces the game cost. Finally, each node on the supply chain can cooperate to achieve maximum total revenue [18,19,20,21,22,23,24]. Artificial intelligence refers to the design of intelligent machines or algorithms with capabilities highly similar to or even far beyond the human brain. Such cutting-edge technology can realize the automation and intelligence of a particular practice [25,26,27,28].
Based on the literature, this research classifies green supply chain practices into four categories: procurement, production, consumption, and logistics [29,30,31,32]. Each practice is accompanied by capital flow and information flow. Green procurement should consider the quantity and quality of raw materials to avoid the waste of resources, which reduces costs and ensures the smooth progress of subsequent work to achieve environmental performance in procurement [33,34,35,36]. Green production refers to the practice of conducting green production processes or producing green products to meet the requirements of society and customers [37,38,39]. Consumers pay attention to health problems associated with food, and as a result strict checks are carried out on production practices [40,41,42]. Advanced production technologies could maximize the optimization of the operation process and reduce the waste of resources [43,44,45]. The essential thing in green consumption is on-demand production, which can accurately grasp consumers’ preferences and consumption habits, developing an excellent cooperative relationship with consumers [46,47,48]. In the distribution stage of supply chain logistics, rapid response has become a major problem in the supply chain, and so most companies implement logistics-sharing services as a mode of green logistics. This mode can aid the choice of urgent logistics distribution services on sharing platforms and scientifically plan transportation routes [49,50,51].
The application of digital technology in the green supply chain has been a hot topic in academia in recent years [52]. Many scholars have extensively researched the advantages of digital technology in the green supply chain [53,54,55]. Traditional supply chains include push-type and pull-type supply chains. However, digital technology perfectly combines the push- and pull-type supply chains to achieve efficient operation. It changes the previous push-type supply chain with an inventory squeeze phenomenon caused by uncertain demand and a pull-type supply chain with high customer response [56,57,58,59,60,61]. A great deal of research focuses on the performance evaluation of digital technology in green supply chains and the feasibility of investing in digital technology in green supply [62,63,64]. Indeed, the design cost of digital technology is relatively high, and the benefits brought by digital technology are still being considered by many enterprises [65,66,67]. However, the application of digital technologies in the green supply chain is still in its infancy, and most practices are still in their early stages [68,69]. Many scholars have carried out digital reform, but they needed help to put the concept of digital technology into the deep implementation of green supply chains [70,71]. Much work is limited to designing and applying a specific digital technology instead of integrating various digital technologies to achieve overall intelligence [72,73]. There needs to be more research on universal mathematical models of data that can be implanted. Specific models can only solve specific problems, which increases the difficulty of information processing [74,75,76]. Finally, these gaps and deficiencies encourage us to review the existing literature systematically, integrate the existing knowledge, understand the research progress, and explore the future research direction [77,78,79].
The literature review in this paper tries to answer the following questions: (1) What digital technologies are needed in a green supply chain? (2) How can these technologies affect the green supply chain? Based on 144 relevant papers published in the last 14 years, this study systematically draws conclusions regarding the key findings and proposes the direction of future study.
The research structure is as follows (see Figure 1): The first section introduces the background, motivation and research questions of this study. The second section describes the methods and process of searching the literature and of filtering and analyzing the key literature. The third section presents findings from the literature review and summarizes the main contents which are currently in focus in the literature. In the fourth section, the study concludes the key findings, proposes the direction of future research, and explains the significance and limitations of the research.

2. Literature and Methods

The following is roughly divided into two parts: (1) We provide an explanation of the method of searching the literature and the reasons for selecting the corresponding databases and journals. (2) We summarize and sort the data in the search process in the form of a graph, so that readers can intuitively see the development trend of the research topic; this provides specific data support for subsequent research on this topic.

2.1. Search and Read

In order to obtain the literature related to our research topic, we searched many literature databases and conducted summaries. The specific process of searching, reviewing, and screening was as follows: (1) We searched the relevant literature related to digital technologies and green supply chain in the three databases of Web of Science, Science Direct, and Wiley, which are large-scale and comprehensive databases with multi-disciplinary and core journal citation indexes. We then began to search the top-tier OM journals in the databases (e.g., Production and Operations Management, Journal of Marketing Research, European Journal of Operational Research) to guarantee the quality of this study. (2) Further, we applied the retrospective method to search for the relevant literature in the above top-tier journals. Specifically, we searched topics related to digital technologies and green supply chain management and excluded the literature that only focused on environmental problems and digital algorithms. We traced the original articles and then expanded the scope of literature information one by one from the reference lists listed in this highly relevant literature. The advantages of the method include time savings, the avoidance of blind and purposeless searches, as well as the ensured quality of references and accuracy of conclusions. However, the single search method needed to be revised because it was necessary that the literature searched reflected the subject’s development trend. Therefore, we searched for articles on related topics chronologically. We found literature related to the relevant topics published in a 14-year period, from January 2009 to December 2022. This process complements the cited literature and makes the present article more persuasive. (3) Finally, we deleted the literature published in the early years of the range as well as the literature that only focused on the improvement of a microscopic algorithm in digital technology and the simple empirical study of the environment. We excluded these studies to narrow the scope of the research to be close to the topic of interest. The main literature search process is shown in Figure 2.
The 144 selected research papers met the following criteria: The paper is written in English. The identified keywords (e.g., green supply chain, digital technology) appear in the title, keywords, and abstract. These kinds of study related to the application of digital technology in the green supply chain are more inclined to study the issue at the macro level—the management construction of information platforms, the layout and design of a specific digital technology in the green supply chain, and the analysis of the relevant organizational framework established by the application of digital technology in the green supply chain. It is convenient for us to find the obstacles to the implementation of digital technology in the application of green supply chains and explore the potential of this application to provide specific ideas for subsequent research.

2.2. Analysis and Synthesis

Figure 3 shows the number of relevant articles that the research initially searched for in the database using the keywords “Digital Technology” and “Green Supply Chain” in the three databases, finally yielding an initial 497 articles. Subsequently, 353 articles were read deeply and excluded from the study following consideration of their publication year and scope. We aimed to ensure that this study has a more general and complete understanding of the research topic and an overall high quantity. The application of digital technology in the green supply chain is a relatively new topic, and the number of papers related to this topic is small, which provides a quantitative basis for the feasibility of subsequent research. This figure also classifies and organizes these 497 articles according to five essential digital technologies to intuitively display which digital technologies are more prevalent in the existing research and, in contrast, which fields are currently less researched and need further development.
Figure 4 shows the final 144 articles retained following exclusion of the studies not meeting the necessary criteria, organized by journal. It clearly and intuitively shows that most of the literature from journals was related to the environment and manufacturing, and there were also papers published in journals in the field of operations management. However, they were less in number than those from the first two journals (i.e., Computers and Operations Research and Journal of International Business Studies). In the classification process, we found that the application of digital technology in the production and manufacturing process of the entire supply chain was the main research focus, indicating that the previous research on digital technology in the green supply chain has been largely concerned with how to save energy, reduce emissions, and protect the environment. Breakthrough research is needed on cost-saving and efficiency. This provides the basis for the ideas in our article’s third part.
A total of 144 articles were included in this paper, distributed from January 2009 to December 2022. Figure 5 shows the development trend of articles from 2015 to 2022 because the number of articles linking digital technology with green supply chains before 2015 was generally stable between 0 and 1, and these articles mainly focus on unilateral research on green logistics, reverse logistics, green supply chain, or digital technology. This study found that the number of papers in the past seven years has continued to increase. The growth ratio after 2019 is even faster, which indicates the continuous introduction of digital technologies in various fields due to the drive of Industry 4.0. During the pandemic, from 2020–2022, the uncertain environment forced many medium-sized businesses to adopt digital technology to achieve competitive advantages. Moreover, in line with the general trend, the concept of low-carbon green is deeply rooted in industrial development, and keeping a green supply chain is also a hot spot.

3. Results and Discussion

This section describes our use of qualitative analysis to summarize and analyze the reviewed literature. The analysis can be categorized based on two dimensions, namely digital technology and green supply chain. We continue to subdivide them separately, ensuring the completeness of the research questions [80,81]. The specific classification is as follows: (1) The content of the research study is the application of digital technology in green supply chain; first, we split the digital technology according to five main categories, namely the Internet of Things, big data, cloud computing, blockchain, and artificial intelligence. We then summarize the application of these technologies in the specific practice of green supply chain. (2) We classify green supply chain according to the links involved, divided into four categories: green procurement, green production, green logistics, and green sales, then summarize the digital technologies that should be used in each link and draw tables for sorting.

3.1. The Application of Internet of Things Technology in Green Supply Chain

As shown in Table 1, the price and sales of raw materials in logistics procurement can be remotely understood through Internet of Things technology, so that suppliers can choose the best partners [82]. It is possible to remotely judge the quantity of shortage and demand through radio-frequency technology to accurately procure raw materials [83]. Wireless location technology can accurately predict the arrival time of raw materials.
Internet of Things technology is used to track and monitor each step in the production practice. The semi-finished products are automatically identified from one process to the next according to the set procedures [67,84,90]. Therefore, the application of Internet of Things technology in procurement and production could avoid the quantity error of procurement and production caused by information asymmetry, thus preventing the waste of resources.
In consumption practices, there is the barcode information of the goods that people want to buy; people can easily enjoy door-to-door delivery by scanning the APP of the mobile terminal [3,64,85]. At this time, the customer’s demand will be immediately reflected to the client of the logistics information system. The supplier will rapidly respond according to the logistics information system and carry out on-demand stock and route planning.
In the transportation process, Internet of Things technology could help to make the most accurate planning of the transportation line, carry out real-time positioning in transportation, and update the logistics [86,91]. The Internet of Things, meanwhile, keeps a real-time record of energy consumption during transportation and alerts suppliers when the wrong route is chosen, prompting them to design a new one [81,87,92].
Finally, green remanufacturing represents a higher form of reuse. In reverse logistics, the randomness of product return, the imbalance of return and demand rate, and the unknown status of recovered products make remanufacturing enterprises’ production planning and control activities more complicated [88,93,94]. The Internet of Things is implemented at the beginning of the production practice, so that the original information can be obtained in the recycling process, and the recycling efficiency can be improved—the Internet of Things technology has utility for flexible application and the establishment of exclusive information systems [67,95]. Every practice, from green procurement to green production, green consumption, and logistics, is interlinked [89,96]. Information flows from one end to the other, avoiding information delay and asymmetry. Enterprises can make decisions through logistics information technology in each practice, then reflect them into the information system and then make decisions through the system’s feedback.

3.2. Application of Big Data in Green Supply Chain

As shown in Table 2, big data in the procurement process accurately push relevant raw material suppliers according to the procurement needs and support the selection of the final raw material suppliers through comparison and analysis [97,98].
In production practice, the research can accurately judge the quantity of demand based on big data. To date, research has covered the purchase of raw materials according to actual needs, saving procurement costs and avoiding resource waste [99,105]. When deciding whether to produce a specific product or operate a particular business, the supplier can evaluate the project’s performance, implemented and developed through extensive data analysis to make a feasibility investment. At the same time, more and more enterprises use extensive data analysis to evaluate whether a specific production practice is energy-saving and providing sufficient environmental protection in line with green development.
However, through the click rate of the Internet, consumer consumption records, and other data, it is possible to understand consumers’ preferences to accurately forecast and analyze their preferences to provide them with more timely and personalized services [69,100,106]. In line with the development of the times, people are increasingly indifferent to the ownership of commodities and more so enjoy the services brought by commodities. Therefore, the development of commodity leasing is very rapid, significantly improving the supply chain’s flexibility [88,107,108]. Only rapid data analysis and processing can improve the order response degree and give customers a better experience [101,109,110,111]. Finally, big data can continue to create consumer experiences by studying consumers’ purchasing patterns through long-term consumption habits to predict their preferences and produce products that satisfy them. Enterprises constantly adjust and change their business models according to such predictions, forming a virtuous circle between enterprises and customers [102,112]. This differs from the previous model of an enterprise guiding customers to choose their products following the enterprise’s ideas. Enterprises now follow the customers’ needs to produce the products needed by the customers and finally serving them. In this way, enterprises not only have a loyal buying group but can also avoid the phenomenon of supply without demand due to oversupply [103]. The most important thing is to avoid the production of unnecessary inventory as a result of not meeting customers’ needs, helping to realize green operation [104].

3.3. Application of Cloud Computing in Green Supply Chain

As shown in Table 3, cloud manufacturing in the production process connects manufacturing resources related to production to establish a resource-sharing platform [113,114,115]. In production, some equipment is expensive, with high purchasing costs and a low utilization rate. Enterprises can use the shared platform to rent production equipment in this case. With the help of a cloud platform, enterprises can reduce fixed costs and enjoy personalized, integrated services [116,117]. Due to the rapid development of the green supply chain, a new service has been added to the cloud platform that will record the energy consumption of every practice in the production process in real-time. Then, based on the cloud optimization and simulation software module ecosystem, practices with serious energy consumption in the production process will be optimized, and the whole process will be simulated. In this continuous optimization process, enterprises can achieve the lowest possible resource consumption, align with the current green trend, and reduce production costs. At the same time, the platform integrates various technologies to achieve a high degree of automation in multiple processes, avoiding losses caused by manual errors [118,119]. The development of green logistics promotes green cloud services.
In logistics distribution, many small and medium-sized enterprises need more equipment to respond quickly to customer needs. At this time, they can allocate resources and provide personalized services by understanding the needs of the sharing platform of cloud services. In the practice of logistics and transportation, speed meters and cameras are used to collect the essential information about the road, build the traffic cloud, store the relevant video data, facilitate the data analysis, and generate the corresponding dynamic shortest-path algorithm based on the transportation demand [107,120]. Under the cloud service platform, we can use various services as quickly as water, electricity, and gas. For the whole supply chain, cloud services can integrate related resources in the whole life cycle of procurement, production, logistics, and other related practice and provide a standardized and shareable supply chain service mode [121,122].

3.4. The Application of Blockchain in Green Supply Chain

As shown in Table 4, blockchain in green procurement can make suppliers’ data, raw materials, and costs transparent, select the most suitable options based on the comparison of multiple suppliers, and establish long-term cooperative relationships [123,124]. Often, in the procurement process, for their vested interests, procurement personnel cover up and collude with each other in some illegal situations in the process of procurement activities. They may conceal the actual situation or collude with the supplier to deliver benefits in the form of incorrect prices and obtain their own kickbacks [80,125,126]. Blockchain helps to construct what we call a “transparent veil” on the abuse of power in the procurement practice of the supply chain to control these behaviors and reduce enterprises’ procurement risks.
In green production, blockchain makes the entire production process transparent and visual. The entire production process can be traced back to when something goes wrong in a particular production practice [127,132]. In this way, specific unfair practices in production can be identified, and targeted improvements can be made. With the deepening of people’s safety concept, people are not very sensitive to the price of products, and are concerned with ensuring that product composition is environmentally friendly and healthy. Blockchain can ensure the traceability of food “from farm to table,” where consumers can see the whole process, from farm picking to reprocessing, to meet the needs of consumer safety and public health [105,128]. The whole process of traceability is collected in the cloud database, which can be directly viewed on a mobile APP through a mobile terminal. Blockchain technology can store all the data publicly for the client, and the emissions of polluting and waste gases in the production process will be displayed to the client to prevent enterprises from tampering with data by recklessly discharging pollutants for profit [112,129]. Blockchain technology provides convenience for the government in controlling environmental pollution in the production processes of enterprises. It can be used to carry out effective governance and promote the sustainable development of green supply chains.
In the consumption practice, blockchain technology in intelligent contracts makes all costs from procurement to production transparent, ensuring the transparency of product distribution structure, preventing companies from pursuing excessive profits and building trust between enterprises and customers [130,131]. In addition, consumers’ attitudes towards products, degree of psychological satisfaction, and consumption habits can also be reflected on the platform in real-time. These suggestions will continue to influence the production of enterprises’ products.

3.5. Application of Artificial Intelligence in Green Supply Chain

As shown in Table 5, artificial intelligence is widely used in production practice. Robots can replace human beings to carry out the most basic operations. As long as a robot is continuously charged, it will keep working. Robots operate with the most standard actions when working, while humans are more flexible, which will inevitably cause mistakes in production practice [115,133]. Due to the rapid development of machine production in artificial intelligence, replacing ordinary front-line workers with intelligent robots has become a trend, so many enterprises are constantly laying off workers [134]. However, manufacturing with robots is not necessarily the best choice for companies. Robots’ design and development require precise control of the entire production process.
Sometimes, when funds are invested in artificial intelligence, they cannot be used to the maximum effect and can only be fixed for a particular operation [135]. Once there is innovation in this practice, the machine will be eliminated. Therefore, it is an urgent problem to improve the universality of artificial intelligence machines [136,140]. Green environmental protection and energy saving have become new assessment factors to be added to production practices. Intelligent equipment can judge the relationship between energy consumption and product output through the input and output of parameters in operation practice, constantly adjust production processes, and eliminate unnecessary work practices, to achieve efficient production while reducing the energy consumption in the interest of environmental protection. Inventory practice is essential in the supply chain [137,141]. Lean inventory management can achieve a relative balance between supply and demand, reduce inventory costs, and improve production efficiency. The logic of all important warehouse design covers from site layout to the selection of storage and order-picking systems, batch processing, and distribution, all of which must be reconsidered in intelligent operation. Therefore, new models and methods are needed to solve these systems’ design and operation control challenges, especially the integration of subsystems, only by designing subsystems of each practice. Through mutual compatibility, integration forms a complete automation system designed into a complete intelligent warehouse [93,138].
Artificial intelligence can also be used to predict future product demand and logistics transport vehicles. Alibaba Group, for example, pioneered an integrated online-to-offline retail model that allows customers to order goods they need at home or in a restaurant, promising delivery within 20 min after the order is placed [139]. In order to meet these service commitments, Alibaba has developed software specifically for vehicle scheduling and the shortest route, which can quickly utilize surrounding resources to provide the fastest service. In logistics, robotic handling systems are increasingly used in distribution centers, which require little floor space and can work effectively.

4. Conclusions

We reviewed 144 articles on the impact of digital technologies on green supply chains. The research organizes the major digital technologies into five categories: the Internet of Things, big data, cloud computing, blockchain, and artificial intelligence. First, this study considers the application of these digital technologies in different supply chain practices, i.e., green procurement, green production, green consumption, and green logistics. The significance of this study is that digital technology connects with the green supply chain to form a multidisciplinary research topic. Second, environmental protection is the general trend in today’s society. Digital technology can be introduced into the entire supply chain, which can reduce energy consumption to the greatest extent, finally achieving a green and energy-saving supply chain.
Our literature review has three contributions: (1) We systematically reviewed 144 pieces of literature related to digital technology and green supply chain, summarized and classified them, and identified the current research progress in applying digital technology in the supply chain. (2) By reading the literature, this research has deepened our understanding of digital technology and green supply chain concepts, clarifying the current situation and development trend of digital technology and the specific environmental requirements of green supply chains. (3) By summarizing the existing literature related to digital technology and green supply chain, we found that there is a gap related to the application of digital technology in green supply chains, which needs to be further studied by experts and scholars. This paper provides four research directions for future research.

4.1. Direction I

This literature review found that digital technology has a significant impact on the development of the supply chain in terms of rapid response, personalized service, resource sharing, and cost reduction. Under the influence of the green supply chain, more and more physical goods are transferred from the previous purchase-based model to the lease model. Charges are based on frequency and time of use. The transformation of the service model makes establishing the sharing platform particularly important. Therefore, the current hot spot is research on the pricing of the services and the operation mode of the sharing platforms.

4.2. Direction II

The current research focuses on applying a single digital technology to a particular supply chain practice. Since each technology in digitization is interrelated, multiple digitization technologies can be applied to the same practice in the green supply chain. Therefore, the needs of each practice can be met by understanding the intrinsic function and future development trends of digital technology. Future research could design a complete digital system for the whole supply chain.

4.3. Direction III

Although digital thinking has been formed in most fields, most scholars have realized the importance of digital transformation in emerging fields. Many scholars study the establishment of relevant information platforms. However, the research on information platforms only exists to establish a basic framework without in-depth research on every practice—for example, there is a need for more relevant normative research on the integration and analysis of information obtained. Internet of Things platforms often lead to the loss of information, errors, and, eventually, information asymmetry. For the decentralized blockchain platforms, corresponding management research is needed. Due to the old game behavior between enterprises, there is still a major problem related to the degree of trust of all entrepreneurs, and the whole nature of data cannot be realized. In follow-up research, more attention should be paid to platform construction.

4.4. Direction IV

At present, state-of-the-art digital technologies and their role in the green supply chain may go far beyond what is mentioned in our academic literature. Research should continue to pay attention to the innovation of digital technology to bring more enlightenment to the development of green supply chains. The research field produces a great deal of data every day, which is efficient. In order to reduce the cost of data implantation, research is needed related to the design of a general mathematical model for a specific field, which can conduct data analysis at a low cost.

5. Limitation

This article has the following limitations: (1) This is not an opinion article, providing arguments and objectivity throughout the paper. However, there were inevitably subjective tendencies when analyzing data in the second part. (2) A total of 144 papers were included, but many recently published studies were omitted.

Author Contributions

Conceptualization, Y.W. and Y.Y. (Yefei Yang); methodology, Z.Q. and Y.Y. (Yafei Yang); formal analysis, Y.Y. (Yafei Yang) and Y.W.; writing—original draft preparation, J.L. and Y.W.; project administration and funding acquisition, Y.Y. (Yefei Yang). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Philosophy and Social Science Program of Guangxi grant number 21FGL047.

Institutional Review Board Statement

Not applicable for this study.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created in this study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Liao, Y.; Deschamps, F.; Loures, E.D.F.R.; Ramos, L.F.P. Past, present and future of Industry 4.0-a systematic literature review and research agenda proposal. Int. J. Prod. Res. 2017, 55, 3609–3629. [Google Scholar] [CrossRef]
  2. Ben-Daya, M.; Hassini, E.; Bahroun, Z. Internet of things and supply chain management: A literature re-view. Int. J. Prod. Res. 2019, 57, 4719–4742. [Google Scholar] [CrossRef]
  3. Chandra, S.; Verma, S. Big Data and Sustainable Consumption: A Review and Research Agenda. Vis. J. Bus. Perspect. 2021, 27, 11–23. [Google Scholar] [CrossRef]
  4. Qu, T.; Thürer, M.; Wang, J.; Wang, Z.; Fu, H.; Li, C.; Huang, G.Q. System dynamics analysis for an Inter-net-of-Things-enabled production logistics system. Int. J. Prod. Res. 2017, 55, 2622–2649. [Google Scholar] [CrossRef]
  5. Shrouf, F.; Miragliotta, G. Energy management based on Internet of Things: Practices and framework for adoption in production management. J. Clean. Prod. 2015, 100, 235–246. [Google Scholar] [CrossRef]
  6. Seng, K.P.; Ang, L.M.; Ngharamike, E. Artificial intelligence Internet of Things: A new paradigm of distributed sensor networks. Int. J. Distrib. Sens. Netw. 2022, 18, 34–45. [Google Scholar] [CrossRef]
  7. Cheng, T.C.E.; Kamble, S.S.; Belhadi, A.; Ndubisi, N.O.; Lai, K.-H.; Kharat, M.G. Linkages between big data analytics, circular economy, sustainable supply chain flexibility, and sustainable performance in manufacturing firms. Int. J. Prod. Res. 2021, 60, 6908–6922. [Google Scholar] [CrossRef]
  8. Kamble, S.S.; Gunasekaran, A. Big data-driven supply chain performance measurement system: A review and framework for implementation. Int. J. Prod. Res. 2019, 58, 65–86. [Google Scholar] [CrossRef]
  9. Ban, G.-Y.; Rudin, C. The Big Data Newsvendor: Practical Insights from Machine Learning. Oper. Res. 2019, 67, 90–108. [Google Scholar] [CrossRef]
  10. Camm, J.D.; Fry, M.J.; Shaffer, J. A Practitioner’s Guide to Best Practices in Data Visualization. INFORMS J. Appl. Anal. 2017, 47, 473–488. [Google Scholar] [CrossRef]
  11. Zhou, C.; Xia, W.; Feng, T.; Jiang, J. Enabling environmental innovation via green supply chain integration: A per-spective of information processing theory. Aust. J. Manag. 2022, 48, 90–102. [Google Scholar] [CrossRef]
  12. Li, D.-P.; Xie, L.; Cheng, P.-F.; Zhou, X.-H.; Fu, C.-X. Green Supplier Selection under Cloud Manufacturing Environment: A Hybrid MCDM Model. SAGE Open 2021, 11, 56–67. [Google Scholar] [CrossRef]
  13. Park, J.; Han, K.; Lee, B. Green Cloud? An Empirical Analysis of Cloud Computing and Energy Efficiency. Manag. Sci. 2023, 69, 1639–1664. [Google Scholar] [CrossRef]
  14. Agrawal, T.K.; Pal, R. Traceability in Textile and Clothing Supply Chains: Classifying Implementation Factors and Information Sets via Delphi Study. Sustainability 2019, 11, 1698. [Google Scholar] [CrossRef]
  15. Singh, A.; Kumari, S.; Malekpoor, H.; Mishra, N. Big data cloud computing framework for low carbon supplier selection in the beef supply chain. J. Clean. Prod. 2018, 202, 139–149. [Google Scholar] [CrossRef]
  16. Hsu, C.-Y.; Yang, C.-S.; Yu, L.-C.; Lin, C.-F.; Yao, H.-H.; Chen, D.-Y.; Lai, K.R.; Chang, P.-C. Development of a cloud-based service framework for energy conservation in a sustainable intelligent transportation system. Int. J. Prod. Econ. 2014, 164, 454–461. [Google Scholar] [CrossRef]
  17. Karakas, S.; Acar, A.Z.; Kucukaltan, B. Blockchain adoption in logistics and supply chain: A literature review and research agenda. Int. J. Prod. Res. 2021, 8, 1–24. [Google Scholar] [CrossRef]
  18. Saberi, S.; Kouhizadeh, M.; Sarkis, J.; Shen, L. Blockchain technology and its relationships to sustainable supply chain management. Int. J. Prod. Res. 2018, 57, 2117–2135. [Google Scholar] [CrossRef]
  19. Wang, B.; Lin, Z.; Wang, M.; Wang, F.; Xiangli, P.; Li, Z. Applying blockchain technology to ensure compliance with sustainability standards in the PPE multi-tier supply chain. Int. J. Prod. Res. 2022, 8, 1–17. [Google Scholar] [CrossRef]
  20. Li, Z.; Guo, H.; Barenji, A.V.; Wang, W.M.; Guan, Y.; Huang, G.Q. A sustainable production capability evaluation mechanism based on blockchain, LSTM, analytic hierarchy process for supply chain network. Int. J. Prod. Res. 2020, 58, 7399–7419. [Google Scholar] [CrossRef]
  21. Bernards, N.; Campbell-Verduyn, M.; Rodima-Taylor, D. The veil of transparency: Blockchain and sustainability governance in global supply chains. Environ. Plan. C Politi-Space 2022, 5, 89–95. [Google Scholar] [CrossRef]
  22. Yoo, M.; Won, Y. A study on the transparent price tracing system in supply chain management based on block-chain. Sustainability 2018, 10, 4037. [Google Scholar] [CrossRef]
  23. Di Vaio, A.; Boccia, F.; Landriani, L.; Palladino, R. Artificial intelligence in the agri-food system: Rethinking sus-tainable business models in the COVID-19 scenario. Sustainability 2020, 12, 4851. [Google Scholar] [CrossRef]
  24. Ren, S.; Zhang, Y.; Liu, Y.; Sakao, T.; Huisingh, D.; Almeida, C.M.V.B. A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions. J. Clean. Prod. 2018, 210, 1343–1365. [Google Scholar] [CrossRef]
  25. Zailani, S.; Govindan, K.; Shaharudin, M.R.; Kuan, E.E.L. Barriers to product return management in automotive manufacturing firms in Malaysia. J. Clean. Prod. 2017, 141, 22–40. [Google Scholar] [CrossRef]
  26. Lee, K.-H. Integrating carbon footprint into supply chain management: The case of Hyundai Motor Company (HMC) in the automobile industry. J. Clean. Prod. 2011, 19, 1216–1223. [Google Scholar] [CrossRef]
  27. Nahvi, B.; Habibi, J.; Mohammadi, K.; Shamshirband, S.; Al Razgan, O.S. Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature. Comput. Electron. Agric. 2016, 124, 150–160. [Google Scholar] [CrossRef]
  28. Osman, J.; Inglada, J.; Dejoux, J.F. Assessment of a Markov logic model of crop rotations for early crop map-ping. Comput. Electron. Agric. 2015, 113, 234–243. [Google Scholar] [CrossRef]
  29. Pantazi, X.; Moshou, D.; Tamouridou, A. Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers. Comput. Electron. Agric. 2018, 156, 96–104. [Google Scholar] [CrossRef]
  30. Wu, K.-J.; Liao, C.-J.; Tseng, M.-L.; Lim, M.K.; Hu, J.; Tan, K. Toward sustainability: Using big data to explore the decisive attributes of supply chain risks and uncertainties. J. Clean. Prod. 2017, 142, 663–676. [Google Scholar] [CrossRef]
  31. Zeng, H.; Chen, X.; Xiao, X.; Zhou, Z. Institutional pressures, sustainable supply chain management, and circular economy capability: Empirical evidence from Chinese eco-industrial park firms. J. Clean. Prod. 2016, 155, 54–65. [Google Scholar] [CrossRef]
  32. Zhao, R.; Liu, Y.; Zhang, N.; Huang, T. An optimization model for green supply chain management by using a big data analytic approach. J. Clean. Prod. 2017, 142, 1085–1097. [Google Scholar] [CrossRef]
  33. Cao, M.; Zhang, Q. Supply chain collaboration: Impact on collaborative advantage and firm performance. J. Oper. Manag. 2011, 29, 163–180. [Google Scholar] [CrossRef]
  34. Wang, J.-B.; Huang, L.-F. A Game-Theoretic Analytical Approach for Fostering Energy-Saving Innovation in the Electric Vehicle Supply Chain. SAGE Open 2021, 11, 21582440211021581. [Google Scholar] [CrossRef]
  35. Liou, J.J.; Chuang, Y.-C.; Zavadskas, E.K.; Tzeng, G.-H. Data-driven hybrid multiple attribute decision-making model for green supplier evaluation and performance improvement. J. Clean. Prod. 2019, 241, 118321. [Google Scholar] [CrossRef]
  36. Zhao, Q.; Tsai, P.-H.; Wang, J.-L. Improving Financial Service Innovation Strategies for Enhancing China’s Banking Industry Competitive Advantage during the Fintech Revolution: A Hybrid MCDM Model. Sustainability 2019, 11, 1419. [Google Scholar] [CrossRef]
  37. Linkov, I.; Trump, B.D.; Poinsatte-Jones, K.; Florin, M.-V. Governance Strategies for a Sustainable Digital World. Sustainability 2018, 10, 440. [Google Scholar] [CrossRef]
  38. Dubey, R.; Gunasekaran, A.; Childe, S.J.; Luo, Z.; Wamba, S.F.; Roubaud, D.; Foropon, C. Examining the role of big data and predictive analytics on collaborative performance in context to sustainable consumption and production behaviour. J. Clean. Prod. 2018, 196, 1508–1521. [Google Scholar] [CrossRef]
  39. Azadeh, K.; De Koster, R.; Roy, D. Robotized and automated warehouse systems: Review and recent developments. Transp. Sci. 2019, 53, 917–945. [Google Scholar] [CrossRef]
  40. Hu, H.; Zhang, Y.; Wei, J.; Zhan, Y.; Zhang, X.; Huang, S.; Ma, G.; Deng, Y.; Jiang, S. Alibaba Vehicle Routing Algorithms Enable Rapid Pick and Delivery. INFORMS J. Appl. Anal. 2022, 52, 27–41. [Google Scholar] [CrossRef]
  41. Liang, Y.; Lu, X.; Li, W.; Wang, S. Cyber Physical System and Big Data enabled energy efficient machining optimisation. J. Clean. Prod. 2018, 187, 46–62. [Google Scholar] [CrossRef]
  42. Lekkas, D.F.; Panagiotakis, I.; Dermatas, D. A digital circular bioeconomy—Opportunities and challenges for waste management in this new era. Waste Manag. Res. J. Sustain. Circ. Econ. 2021, 39, 407–408. [Google Scholar] [CrossRef]
  43. Wong, C.W.; Wong, C.Y.; Boonitt, S. Green service practices: Performance implications and the role of environ-mental management systems. Serv. Sci. 2013, 5, 69–84. [Google Scholar] [CrossRef]
  44. Li, J.; Zhang, Y.; Du, D.; Liu, Z. Improvements in the decision making for Cleaner Production by data mining: Case study of vanadium extraction industry using weak acid leaching process. J. Clean. Prod. 2017, 143, 582–597. [Google Scholar] [CrossRef]
  45. Lizot, M.; Júnior, P.P.A.; Trojan, F.; Magacho, C.S.; Thesari, S.S.; Goffi, A.S. Analysis of Evaluation Methods of Sustainable Supply Chain Management in Production Engineering Journals with High Impact. Sustainability 2019, 12, 270. [Google Scholar] [CrossRef]
  46. Abdollahnejadbarough, H.; Mupparaju, K.S.; Shah, S.; Golding, C.P.; Leites, A.C.; Popp, T.D.; Shroyer, E.; Golany, Y.S.; Robinson, A.G.; Akgun, V. Verizon Uses Advanced Analytics to Rationalize Its Tail Spend Suppliers. INFORMS J. Appl. Anal. 2020, 50, 197–211. [Google Scholar] [CrossRef]
  47. Liu, P. Pricing policies and coordination of low-carbon supply chain considering targeted advertisement and carbon emission reduction costs in the big data environment. J. Clean. Prod. 2018, 210, 343–357. [Google Scholar] [CrossRef]
  48. Mao, D.; Hao, Z.; Wang, F.; Li, H. Innovative Blockchain-Based Approach for Sustainable and Credible Environment in Food Trade: A Case Study in Shandong Province, China. Sustainability 2018, 10, 3149. [Google Scholar] [CrossRef]
  49. Li, G.; Li, N.; Sethi, S.P. Does CSR reduce idiosyncratic risk? Roles of operational efficiency and AI innovation. Prod. Oper. Manag. 2021, 30, 2027–2045. [Google Scholar] [CrossRef]
  50. Tseng, M.-L.; Wu, K.-J.; Lim, M.K.; Wong, W.-P. Data-driven sustainable supply chain management performance: A hierarchical structure assessment under uncertainties. J. Clean. Prod. 2019, 227, 760–771. [Google Scholar] [CrossRef]
  51. Raut, R.D.; Mangla, S.K.; Narwane, V.S.; Gardas, B.B.; Priyadarshinee, P.; Narkhede, B.E. Linking big data analytics and operational sustainability practices for sustainable business management. J. Clean. Prod. 2019, 224, 10–24. [Google Scholar] [CrossRef]
  52. Cloutier, C.; Oktaei, P.; Lehoux, N. Collaborative mechanisms for sustainability-oriented supply chain initiatives: State of the art, role assessment and research opportunities. Int. J. Prod. Res. 2019, 58, 5836–5850. [Google Scholar] [CrossRef]
  53. Theorin, A.; Bengtsson, K.; Provost, J.; Lieder, M.; Johnsson, C.; Lundholm, T.; Lennartson, B. An event-driven manufacturing information system architecture for Industry 4.0. Int. J. Prod. Res. 2016, 55, 1297–1311. [Google Scholar] [CrossRef]
  54. Caldarelli, G.; Rossignoli, C.; Zardini, A. Overcoming the Blockchain Oracle Problem in the Traceability of Non-Fungible Products. Sustainability 2020, 12, 2391. [Google Scholar] [CrossRef]
  55. Ko, T.; Lee, J.; Ryu, D. Blockchain technology and manufacturing industry: Real-time transparency and cost savings. Sustainability 2018, 10, 4274. [Google Scholar] [CrossRef]
  56. Tarafdar, M.; Qrunfleh, S. Agile supply chain strategy and supply chain performance: Complementary roles of supply chain practices and information systems capability for agility. Int. J. Prod. Res. 2016, 55, 925–938. [Google Scholar] [CrossRef]
  57. Ketter, W.; Schroer, K.; Valogianni, K. Information Systems Research for Smart Sustainable Mobility: A Framework and Call for Action. Inf. Syst. Res. 2022, 5, 780–790. [Google Scholar] [CrossRef]
  58. Hoen, K.M.R.; Tan, T.; Fransoo, J.C.; van Houtum, G.-J. Switching Transport Modes to Meet Voluntary Carbon Emission Targets. Transp. Sci. 2014, 48, 592–608. [Google Scholar] [CrossRef]
  59. He, L.; Liu, S.; Shen, Z.M. Smart urban transport and logistics: A business analytics perspective. Prod. Oper. Manag. 2022, 31, 3771–3787. [Google Scholar] [CrossRef]
  60. Bai, C.; Dhavale, D.; Sarkis, J. Complex investment decisions using rough set and fuzzy c-means: An example of investment in green supply chains. Eur. J. Oper. Res. 2015, 248, 507–521. [Google Scholar] [CrossRef]
  61. Van Wassenhove, L.N. Sustainable innovation: Pushing the boundaries of traditional operations management. Prod. Oper. Manag. 2019, 28, 2930–2945. [Google Scholar] [CrossRef]
  62. Vishnubhotla, A.K.; Pati, R.K.; Padhi, S.S. Can Projects on Blockchain Reduce Risks in Supply Chain Management?: An Oil Company Case Study. IIM Kozhikode Soc. Manag. Rev. 2020, 9, 189–201. [Google Scholar] [CrossRef]
  63. Zhao, J.; Lee, J.Y. Effect of Connected and Autonomous Vehicles on Supply Chain Performance. Transp. Res. Rec. J. Transp. Res. Board 2022, 2677, 402–424. [Google Scholar] [CrossRef]
  64. Etzion, D.; Aragon-Correa, J.A. Big data, management, and sustainability: Strategic opportunities ahead. Organ. Environ. 2016, 29, 147–155. [Google Scholar] [CrossRef]
  65. Bag, S. Role of Green Procurement in Driving Sustainable Innovation in Supplier Networks: Some Exploratory Empirical Results. Jindal J. Bus. Res. 2017, 6, 155–170. [Google Scholar] [CrossRef]
  66. Voola, R.; Bandyopadhyay, C.; Azmat, F.; Ray, S.; Nayak, L. How are consumer behavior and marketing strategy researchers incorporating the SDGs? A review and opportunities for future research. Australas. Mark. J. 2022, 30, 119–130. [Google Scholar] [CrossRef]
  67. Bai, C.; Li, H.A.; Xiao, Y. Industry 4.0 technologies: Empirical impacts and decision framework. Prod. Oper. Manag. 2022, 12, 56–70. [Google Scholar] [CrossRef]
  68. Lee, C.-C.; He, Z.-W.; Wen, H. The impact of digitalization on green economic efficiency: Empirical evidence from city-level panel data in China. Energy Environ. 2022, 34, 67–89. [Google Scholar] [CrossRef]
  69. Constantinides, P.; Henfridsson, O.; Parker, G.G. Introduction—Platforms and Infrastructures in the Digital Age. Inf. Syst. Res. 2018, 29, 381–400. [Google Scholar] [CrossRef]
  70. Bennett, E.E.; McWhorter, R.R. Virtual HRD’s Role in Crisis and the Post COVID-19 Professional Lifeworld: Accelerating Skills for Digital Transformation. Adv. Dev. Hum. Resour. 2021, 23, 5–25. [Google Scholar] [CrossRef]
  71. Manko, B. Elevating the customer shopping experience through digital technologies: How Wegmans Food Markets became a destination. J. Inf. Technol. Teach. Cases 2022, 34, 54–60. [Google Scholar] [CrossRef]
  72. Gausdal, A.H.; Czachorowski, K.V.; Solesvik, M.Z. Applying Blockchain Technology: Evidence from Norwegian Companies. Sustainability 2018, 10, 1985. [Google Scholar] [CrossRef]
  73. Franco, S.F.; Graña, J.M.; Flacher, D.; Rikap, C. Producing and using artificial intelligence: What can Europe learn from Siemens’s experience? Compet. Chang. 2022, 12, 34–42. [Google Scholar] [CrossRef]
  74. Bonilla, S.H.; Silva, H.R.O.; Terra da Silva, M.; Gonçalves, R.F.; Sacomano, J.B. Industry 4.0 and Sustainability Implications: A Scenario-Based Analysis of the Impacts and Challenges. Sustainability 2018, 10, 3740. [Google Scholar] [CrossRef]
  75. Narayanan, R.G.; Das, S. Sustainable and green manufacturing and materials design through computations. Part C J. Mech. Eng. Sci. 2014, 228, 1581–1605. [Google Scholar] [CrossRef]
  76. Madakam, S.; Uchiya, T.; Mark, S.; Lurie, Y. Artificial Intelligence, Machine Learning and Deep Learning (Literature: Review and Metrics). Asia-Pacific J. Manag. Res. Innov. 2022, 18, 7–23. [Google Scholar] [CrossRef]
  77. Giusti, R.; Iorfida, C.; Li, Y.; Manerba, D.; Musso, S.; Perboli, G.; Tadei, R.; Yuan, S. Sustainable and De-Stressed International Supply-Chains through the SYNCHRO-NET Approach. Sustainability 2019, 11, 1083. [Google Scholar] [CrossRef]
  78. Zekhnini, K.; Cherrafi, A.; Bouhaddou, I.; Benabdellah, A.C.; Bag, S. A model integrating lean and green practices for viable, sustainable, and digital supply chain performance. Int. J. Prod. Res. 2021, 60, 6529–6555. [Google Scholar] [CrossRef]
  79. Phillips, W.; Roehrich, J.K.; Kapletia, D.; Alexander, E. Global value chain reconfiguration and COVID-19: Investigating the case for more resilient redistributed models of production. Calif. Manag. Rev. 2022, 64, 71–96. [Google Scholar] [CrossRef]
  80. Núñez-Merino, M.; Maqueira-Marín, J.M.; Moyano-Fuentes, J.; Martínez-Jurado, P.J. Information and digital technologies of Industry 4.0 and Lean supply chain management: A systematic literature review. Int. J. Prod. Res. 2020, 58, 5034–5061. [Google Scholar] [CrossRef]
  81. Kalkanci, B.; Rahmani, M.; Toktay, L.B. The Role of Inclusive Innovation in Promoting Social Sustainability. Prod. Oper. Manag. 2019, 28, 2960–2982. [Google Scholar] [CrossRef]
  82. Bodrožić, Z.; Adler, P.S. Alternative futures for the digital transformation: A macro-level Schumpeterian perspective. Organ. Sci. 2022, 33, 105–125. [Google Scholar] [CrossRef]
  83. Giustiziero, G.; Kretschmer, T.; Somaya, D.; Wu, B. Hyperspecialization and hyperscaling: A resource-based theory of the digital firm. Strat. Manag. J. 2021, 56, 78–90. [Google Scholar] [CrossRef]
  84. Wang, R.; Wijen, F.; Heugens, P.P. Government’s green grip: Multifaceted state influence on corporate environmental actions in China. Strateg. Manag. J. 2018, 39, 403–428. [Google Scholar] [CrossRef]
  85. Petricevic, O.; Teece, D.J. The structural reshaping of globalization: Implications for strategic sectors, profiting from innovation, and the multinational enterprise. J. Int. Bus. Stud. 2019, 50, 1487–1512. [Google Scholar] [CrossRef]
  86. Ascui, F.; Haward, M.; Lovell, H. Salmon, sensors, and translation: The agency of Big Data in environmental governance. Environ. Plan. D Soc. Space 2018, 36, 905–925. [Google Scholar] [CrossRef]
  87. Husin, M.H.; Ibrahim, N.F.; Abdullah, N.A.; Syed-Mohamad, S.M.; Samsudin, N.H.; Tan, L. The Impact of Industrial Revolution 4.0 and the Future of the Workforce: A Study on Malaysian IT Professionals. Soc. Sci. Comput. Rev. 2022, 24, 67–75. [Google Scholar] [CrossRef]
  88. Pigini, D.; Conti, M. NFC-Based Traceability in the Food Chain. Sustainability 2017, 9, 1910. [Google Scholar] [CrossRef]
  89. Lähdeaho, O.; Hilmola, O.-P. Business Models Amid Changes in Regulation and Environment: The Case of Finland–Russia. Sustainability 2020, 12, 3393. [Google Scholar] [CrossRef]
  90. Alagarsamy, S.; Mehrolia, S.; Mathew, S. How Green Consumption Value Affects Green Consumer Behaviour: The Mediating Role of Consumer Attitudes towards Sustainable Food Logistics Practices. Vis. J. Bus. Perspect. 2021, 25, 65–76. [Google Scholar] [CrossRef]
  91. Davis, M.M.; Field, J.; Stavrulaki, E. Using Digital Service Inventories to Create Customer Value. Serv. Sci. 2015, 7, 83–99. [Google Scholar] [CrossRef]
  92. Kalagnanam, J.; Phan, D.T.; Murali, P.; Nguyen, L.M.; Zhou, N.; Subramanian, D.; Pavuluri, R.; Ma, X.; Lui, C.; da Silva, G.C. AI-Based Real-Time Site-Wide Optimization for Process Manufacturing. INFORMS J. Appl. Anal. 2022, 52, 363–378. [Google Scholar] [CrossRef]
  93. Mohammed, R.A.; Nadi, A.; Tavasszy, L.; de Bok, M. Data Fusion Approach to Identify Distribution Chain Segments in Freight Shipment Databases. Transp. Res. Rec. J. Transp. Res. Board 2023, 23, 578–580. [Google Scholar] [CrossRef]
  94. Maghazei, O.; Lewis, M.A.; Netland, T.H. Emerging technologies and the use case: A multi-year study of drone adoption. J. Oper. Manag. 2022, 68, 560–591. [Google Scholar] [CrossRef]
  95. Villena, V.H.; Dhanorkar, S. How institutional pressures and managerial incentives elicit carbon transparency in global supply chains. J. Oper. Manag. 2020, 66, 697–734. [Google Scholar] [CrossRef]
  96. Li, G.; Li, L.; Choi, T.; Sethi, S.P. Green supply chain management in Chinese firms: Innovative measures and the moderating role of quick response technology. J. Oper. Manag. 2019, 66, 958–988. [Google Scholar] [CrossRef]
  97. Shabanpour, H.; Yousefi, S.; Saen, R.F. Forecasting efficiency of green suppliers by dynamic data envelopment analysis and artificial neural networks. J. Clean. Prod. 2017, 142, 1098–1107. [Google Scholar] [CrossRef]
  98. Akkaş, A.; Gaur, V. OM Forum—Reducing Food Waste: An Operations Management Research Agenda. Manuf. Serv. Oper. Manag. 2022, 24, 1261–1275. [Google Scholar] [CrossRef]
  99. Seele, P. Predictive Sustainability Control: A review assessing the potential to transfer big data driven ‘predictive policing’ to corporate sustainability management. J. Clean. Prod. 2017, 153, 673–686. [Google Scholar] [CrossRef]
  100. Singh, S.K.; El-Kassar, A.-N. Role of big data analytics in developing sustainable capabilities. J. Clean. Prod. 2018, 213, 1264–1273. [Google Scholar] [CrossRef]
  101. Song, M.; Cen, L.; Zheng, Z.; Fisher, R.; Liang, X.; Wang, Y.; Huisingh, D. How would big data support societal development and environmental sustainability? Insights and practices. J. Clean. Prod. 2017, 142, 489–500. [Google Scholar] [CrossRef]
  102. Zhang, Y.; Ma, S.; Yang, H.; Lv, J.; Liu, Y. A big data driven analytical framework for energy-intensive manufacturing industries. J. Clean. Prod. 2018, 197, 57–72. [Google Scholar] [CrossRef]
  103. Zhang, Y.; Ren, S.; Liu, Y.; Si, S. A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. J. Clean. Prod. 2017, 142, 626–641. [Google Scholar] [CrossRef]
  104. Azadi, M.; Moghaddas, Z.; Cheng, T.; Saen, R.F. Assessing the sustainability of cloud computing service providers for Industry 4.0: A state-of-the-art analytical approach. Int. J. Prod. Res. 2021, 57, 1–18. [Google Scholar] [CrossRef]
  105. Lo, C.K.Y.; Tang, C.S.; Zhou, Y.; Yeung, A.C.L.; Fan, D. Environmental Incidents and the Market Value of Firms: An Empirical Investigation in the Chinese Context. Manuf. Serv. Oper. Manag. 2018, 20, 422–439. [Google Scholar] [CrossRef]
  106. Liu, S.; Hua, G.; Cheng, T.C.E.; Choi, T.M.; Dong, J.X. Pricing strategies for logistics robot sharing plat-forms. Int. J. Prod. Res. 2023, 61, 410–426. [Google Scholar] [CrossRef]
  107. Nguyen, A.; Lamouri, S.; Pellerin, R.; Tamayo, S.; Lekens, B. Data analytics in pharmaceutical supply chains: State of the art, opportunities, and challenges. Int. J. Prod. Res. 2021, 60, 6888–6907. [Google Scholar] [CrossRef]
  108. Dekker, R.; Bloemhof, J.; Mallidis, I. Operations Research for green logistics—An overview of aspects, issues, contributions and challenges. Eur. J. Oper. Res. 2012, 219, 671–679. [Google Scholar] [CrossRef]
  109. Batt, R.J.; Gallino, S. Finding a needle in a haystack: The effects of searching and learning on pick-worker performance. Manag. Sci. 2019, 65, 2624–2645. [Google Scholar] [CrossRef]
  110. Li, Q.; Yu, P.; Wu, X. Managing Perishable Inventories in Retailing: Replenishment, Clearance Sales, and Segregation. Oper. Res. 2016, 64, 1270–1284. [Google Scholar] [CrossRef]
  111. Borodin, V.; Bourtembourg, J.; Hnaien, F.; Labadie, N. Handling uncertainty in agricultural supply chain management: A state of the art. Eur. J. Oper. Res. 2016, 254, 348–359. [Google Scholar] [CrossRef]
  112. Dey, K.; Roy, S.; Saha, S. The impact of strategic inventory and procurement strategies on green product design in a two-period supply chain. Int. J. Prod. Res. 2018, 57, 1915–1948. [Google Scholar] [CrossRef]
  113. Singh, A.; Mishra, N.; Ali, S.I.; Shukla, N.; Shankar, R. Cloud computing technology: Reducing carbon footprint in beef supply chain. Int. J. Prod. Econ. 2015, 164, 462–471. [Google Scholar] [CrossRef]
  114. Nakano, K.; Hirao, M. Collaborative activity with business partners for improvement of product environmental performance using LCA. J. Clean. Prod. 2011, 19, 1189–1197. [Google Scholar] [CrossRef]
  115. Sharma, R.; Shishodia, A.; Gunasekaran, A.; Min, H.; Munim, Z.H. The role of artificial intelligence in supply chain management: Mapping the territory. Int. J. Prod. Res. 2022, 60, 7527–7550. [Google Scholar] [CrossRef]
  116. Bevilacqua, M.; Ciarapica, F.E.; Mazzuto, G.; Paciarotti, C. Environmental analysis of a cotton yarn supply chain. J. Clean. Prod. 2014, 82, 154–165. [Google Scholar] [CrossRef]
  117. Ahumada, O.; Villalobos, J.R. Application of planning models in the agri-food supply chain: A review. Eur. J. Oper. Res. 2009, 196, 1–20. [Google Scholar] [CrossRef]
  118. Polenzani, B.; Riganelli, C.; Marchini, A. Sustainability perception of local extra virgin olive oil and consumers’ attitude: A new Italian perspective. Sustainability 2020, 12, 920. [Google Scholar] [CrossRef]
  119. Ridier, A.; Chaib, K.; Roussy, C. A Dynamic Stochastic Programming model of crop rotation choice to test the adoption of long rotation under price and production risks. Eur. J. Oper. Res. 2016, 252, 270–279. [Google Scholar] [CrossRef]
  120. Oliveira, T.A.; Oliver, M.; Ramalhinho, H. Challenges for Connecting Citizens and Smart Cities: ICT, E-Governance and Blockchain. Sustainability 2020, 12, 2926. [Google Scholar] [CrossRef]
  121. Liu, L.; Li, F.; Qi, E. Research on Risk Avoidance and Coordination of Supply Chain Subject Based on Blockchain Technology. Sustainability 2019, 11, 2182. [Google Scholar] [CrossRef]
  122. Noshad, K.; Awasthi, A. Supplier quality development: A review of literature and industry practices. Int. J. Prod. Res. 2014, 53, 466–487. [Google Scholar] [CrossRef]
  123. Nikolakis, W.; John, L.; Krishnan, H. How Blockchain Can Shape Sustainable Global Value Chains: An Evidence, Verifiability, and Enforceability (EVE) Framework. Sustainability 2018, 10, 3926. [Google Scholar] [CrossRef]
  124. Yakovleva, N.; Sarkis, J.; Sloan, T. Sustainable benchmarking of supply chains: The case of the food industry. Int. J. Prod. Res. 2012, 50, 1297–1317. [Google Scholar] [CrossRef]
  125. Kim, J.S.; Shin, N. The impact of blockchain technology application on supply chain partnership and performance. Sustainability 2019, 11, 6181. [Google Scholar] [CrossRef]
  126. Gawankar, S.A.; Gunasekaran, A.; Kamble, S. A study on investments in the big data-driven supply chain, performance measures and organisational performance in Indian retail 4.0 context. Int. J. Prod. Res. 2020, 58, 1574–1593. [Google Scholar] [CrossRef]
  127. Lahkani, M.J.; Wang, S.; Urbański, M.; Egorova, M. Sustainable B2B E-Commerce and Blockchain-Based Supply Chain Finance. Sustainability 2020, 12, 3968. [Google Scholar] [CrossRef]
  128. Kouhizadeh, M.; Sarkis, J. Blockchain Practices, Potentials, and Perspectives in Greening Supply Chains. Sustainability 2018, 10, 3652. [Google Scholar] [CrossRef]
  129. Fu, B.; Shu, Z.; Liu, X. Blockchain enhanced emission trading framework in fashion apparel manufacturing industry. Sustainability 2018, 10, 1105. [Google Scholar] [CrossRef]
  130. Tan, B.Q.; Wang, F.; Liu, J.; Kang, K.; Costa, F. A Blockchain-Based Framework for Green Logistics in Supply Chains. Sustainability 2020, 12, 4656. [Google Scholar] [CrossRef]
  131. Chang, S.E.; Luo, H.L.; Chen, Y. Blockchain-Enabled Trade Finance Innovation: A Potential Paradigm Shift on Using Letter of Credit. Sustainability 2019, 12, 188. [Google Scholar] [CrossRef]
  132. Caro, F.; Corbett, C.J.; Tan, T.; Zuidwijk, R. Double counting in supply chain carbon foot printing. Manuf. Serv. Oper. Manag. 2013, 15, 545–558. [Google Scholar] [CrossRef]
  133. Tijan, E.; Aksentijević, S.; Ivanić, K.; Jardas, M. Blockchain technology implementation in logistics. Sustainability 2019, 11, 1185. [Google Scholar] [CrossRef]
  134. Pantazi, X.; Moshou, D.; Alexandridis, T.; Whetton, R.; Mouazen, A. Wheat yield prediction using machine learning and advanced sensing techniques. Comput. Electron. Agric. 2016, 121, 57–65. [Google Scholar] [CrossRef]
  135. Ramos, P.; Prieto, F.; Montoya, E.; Oliveros, C. Automatic fruit count on coffee branches using computer vision. Comput. Electron. Agric. 2017, 137, 9–22. [Google Scholar] [CrossRef]
  136. Mehdizadeh, E.; Niaki, S.T.A.; Hemati, M. A bi-objective aggregate production planning problem with learning effect and machine deterioration: Modeling and solution. Comput. Oper. Res. 2018, 91, 21–36. [Google Scholar] [CrossRef]
  137. Khanal, S.; Fulton, J.; Klopfenstein, A.; Douridas, N.; Shearer, S. Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield. Comput. Electron. Agric. 2018, 153, 213–225. [Google Scholar] [CrossRef]
  138. Bhakoo, V.; Choi, T. The iron cage exposed: Institutional pressures and heterogeneity across the healthcare supply chain. J. Oper. Manag. 2013, 31, 432–449. [Google Scholar] [CrossRef]
  139. Wang, C.; Wang, L.; Liu, X.; Du, C.; Ding, D.; Jia, J.; Yan, Y.; Wu, G. Carbon footprint of textile throughout its life cycle: A case study of Chinese cotton shirts. J. Clean. Prod. 2015, 108, 464–475. [Google Scholar] [CrossRef]
  140. Baryannis, G.; Validi, S.; Dani, S.; Antoniou, G. Supply chain risk management and artificial intelligence: State of the art and future research directions. Int. J. Prod. Res. 2018, 57, 2179–2202. [Google Scholar] [CrossRef]
  141. Bechtsis, D.; Tsolakis, N.; Iakovou, E.; Vlachos, D. Data-driven secure, resilient and sustainable supply chains: Gaps, opportunities, and a new generalised data sharing and data monetisation framework. Int. J. Prod. Res. 2021, 60, 4397–4417. [Google Scholar] [CrossRef]
Sustainability 15 08564 g001 550
Figure 1. The research structure of this study.
Figure 1. The research structure of this study.
Sustainability 15 08564 g001
Sustainability 15 08564 g002 550
Figure 2. The literature search process.
Figure 2. The literature search process.
Sustainability 15 08564 g002
Sustainability 15 08564 g003 550
Figure 3. Quantitative distribution according to digital technologies.
Figure 3. Quantitative distribution according to digital technologies.
Sustainability 15 08564 g003
Sustainability 15 08564 g004 550
Figure 4. The 144 papers included in the present study, organized by journal.
Figure 4. The 144 papers included in the present study, organized by journal.
Sustainability 15 08564 g004
Sustainability 15 08564 g005 550
Figure 5. Trends in the topic distribution of the included 144 papers on digital technologies from 2015 to 2022.
Figure 5. Trends in the topic distribution of the included 144 papers on digital technologies from 2015 to 2022.
Sustainability 15 08564 g005
Table
Table 1. The application of Internet of Things technology.
Table 1. The application of Internet of Things technology.
Digital TechnologyHow to Link in Green Supply ChainBenefitsKey References
Internet of Things technology
✧
Green procurement
✧
Green production
✧
Green consumption
✧
Green logistics
✧
The ability of the Internet of Things to obtain information in all practices of the supply chain is relatively mature, but the ability to integrate and analyze the acquired data and information needs to be improved.
✧
The construction of information platforms is extensive, but the standardization of the platforms and the subdivision of each role in the platforms need to be improved.
[82,83,84,85,86,87,88,89]
Table
Table 2. The application of big data.
Table 2. The application of big data.
Digital TechnologyHow to Link in Green Supply ChainBenefitsKey References
Big data
✧
Green procurement
✧
Green production
✧
Green consumption
✧
A large number of researchers have studied models for data analysis and feasibility suggestions for data analysis, but few scholars have studied models for data quality detection.
✧
A large number of studies use only big data independently and lack the support of corresponding sub-technologies.
[97,99,100,101,102,103,104]
Table
Table 3. The application of cloud computing.
Table 3. The application of cloud computing.
Digital TechnologyHow to Link in Green Supply ChainBenefitsKey References
Cloud computing
✧
Green production
✧
Green logistics
✧
There are many studies on the operating model of sharing platforms, but few studies on the pricing of services provided by sharing platforms.
[113,114,116,118,120,121]
Table
Table 4. The application of blockchain.
Table 4. The application of blockchain.
Digital TechnologyHow to Link in Green Supply ChainBenefitsKey References
Blockchain
✧
Green procurement
✧
Green production
✧
Green consumption
✧
The management systems of decentralized platforms are not perfect.
✧
The comprehensiveness and universality of the data of the whole chain are still not sufficient.
✧
The speed of data update is fast, but the cost of data maintenance is high.
✧
Most of the research on blockchain is in the financial field, and the research on environmental protection is not perfect.
[123,125,127,128,129,130,131]
Table
Table 5. The application of artificial intelligence.
Table 5. The application of artificial intelligence.
Digital TechnologyHow To Link In Green Supply ChainBenefitsKey References
Artificial intelligence
✧
Green production
✧
Green logistics
✧
Automation has been realized in some specific production practices, but the benefits may not be greater than the previous human labor. There are few studies that systematically weigh the advantages of artificial intelligence in various aspects.
[133,134,135,136,137,138,139]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, Y.; Yang, Y.; Qin, Z.; Yang, Y.; Li, J. A Literature Review on the Application of Digital Technology in Achieving Green Supply Chain Management. Sustainability 2023, 15, 8564. https://doi.org/10.3390/su15118564

AMA Style

Wang Y, Yang Y, Qin Z, Yang Y, Li J. A Literature Review on the Application of Digital Technology in Achieving Green Supply Chain Management. Sustainability. 2023; 15(11):8564. https://doi.org/10.3390/su15118564

Chicago/Turabian Style

Wang, Yi, Yafei Yang, Zhaoxiang Qin, Yefei Yang, and Jun Li. 2023. "A Literature Review on the Application of Digital Technology in Achieving Green Supply Chain Management" Sustainability 15, no. 11: 8564. https://doi.org/10.3390/su15118564

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop