Big Data in Green Supply Chain Management: Future Opportunities and Recommendations

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Introduction & Problem Motivation

According to academic research in the field of supply chain management (SCM), the industry is quickly changing because of the advent of digital instruments that are utilized to speed up supply chain processes and reduce the involvement of human workers. According to Hartley and Sawaya (2019), artificial intelligence (AI) is one of the most vivid establishments in the area because it encompasses a large number of initiatives that can be utilized to transform the whole notion of SCM completely. The Fourth Industrial Revolution mildly forces businesses to lean toward digital solutions and dehumanize their SCMs in order to prevent human error and ride the wave of technological advancements masterfully (Alzoubi, 2018). As the lines between different spheres of human lives are blurring, different biological, physical, and digital factors might have to be reconsidered in the face of an AI-based revolution where lots of data will be processed by machines.

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Problem Statement

The problem that the author reviewed within the framework of the current research project was the growing pool of green SCM knowledge that got interconnected with technology, such as Big Data. Nevertheless, there are also practical and theoretical implications that become evident after looking at the subject of green SCM from several unique perspectives. The problem that the author investigated within this research project was the presence of solutions based on Big Data that were implemented to improve green SCM operations and benefit employees at the same time.

Background & Literature Review

The growing level of interconnectedness between devices that are included in supply chains shows that humans have to investigate new ways of transmitting and analyzing information which might also be utilized to perform predictions and automate some of the operations. The generation of data leads to a situation where there is an increased need to meet the growing demand for digitalization and respond to it by investing in AI, Machine Learning, Big Data, or any other technology that is of interest to the respective organization (Min et al., 2019). This context makes it safe to say that machine-generated content could be much more carefully curated through the prism of a framework that includes “green” initiatives.

The importance of Big Data for green SCM cannot be either underestimated or ignored because accurate decision-making, even if computerized, depends on the amount of evidence available to the actor reaching the verdict (Baryannis et al., 2019). The future SCM trends are going to be associated with an even broader extent of computerization. It should be rational to assume that a simultaneous adoption of several automated tools for green SCM could become both a blessing and a curse for the organization (Alzoubi, 2018; Chehbi-Gamoura et al., 2020). Supply chain performance, therefore, has to be observed from several viewpoints in order not to focus on just one technology, such as the Internet of Things, for example (Hartley and Sawaya, 2019; Min et al., 2019). Practical and theoretical perspectives on the digitalization of supply chains suggest that possible drawbacks should be addressed first.

Data

The capabilities of green SCM were viewed through the prism of a systematic literature review on the subject of the use of Big Data and the collection of survey responses from supply chain employees. The application of a systematic literature review also responds to the purpose of the current research because it gave the researcher the opportunity to (a) summarize evidence without compressing it or leaving out certain details, (b) explore the question from several perspectives, and (c) compile a reliable knowledge base from the existing evidence and newly collected survey responses. All the relevant information collected from articles was analyzed in accordance with the Critical Appraisal Skills Program (CASP) worksheet so as to pick studies of the highest quality. The data was analyzed with the help of the SPSS software package in order to identify the key trends in both survey respondents’ answers and relevant literature on the subject. The parallels between data from the literature and the survey results were necessary to pinpoint the essential inclinations across the industry that should be either fortified or abandoned.

Model and Analysis

A total of 15 articles were picked for the systematic review of literature in order to provide the researcher with additional information on key methods of Big Data analytics and how those could add to green SCM. The author focused on the importance of data mining, machine learning, and statistics to highlight the value of Big Data and reinforce the idea that multiple inferences and conclusions might be made from a detailed review of the literature. The key three areas of green SCM were picked to generate deeper insight into the potential advantages of Big Data in SCM and its future applications: internal management practices, green purchasing, customer support for green initiatives, and general green SCM (Hartley and Sawaya, 2019; Min et al., 2019). These topics were also scrutinized in order for the researcher to compile a data set that would respond to the question of whether Big Data contributes to SCM in a positive manner.

A range of interview questions was also designed to analyze the relationship between big Data and sustainability practices in modern management. Representatives from small, mid- and largescale businesses with the implemented green supply chains were picked as a target audience for the interview. The respondents were asked to comment on the programmes utilized in their green supply chain management and the technology’s impact on it overall.

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From the existing evidence, it was found that internal management practices were crucial because they could pave the way for improvements related to the intraorganizational environment (Tseng et al., 2019). Green purchasing could be defined as the second most important topic because it could be associated with policies related to procurement and raw materials usage (Doolun et al., 2018). Customer support for green initiatives was linked to Zhao et al.’s (2017) idea that environmental performance could be improved under the strict guidance of individuals that were interested in direct participation and not just providing feedback. The author also considered the role of Just-in-Time and reverse logistics to gain insight into how green SCM could be improved with the help of data collection and analysis.

Results and Recommendations

Internal Management Practices

Based on the evidence obtained within the framework of the current paper, it may be stated that internal management practices depend on Big Data nowadays because of numerous opportunities for green innovation and eco-friendly design of supply chains (Gupta et al., 2019). This means that clean production is much easier to achieve as well, paving the way for more instances of correlational and causative analyses of what could be the essential contributors to efficient SCM (Doolun et al., 2018; Zhao et al., 2017). The potential of green SCM powered by Big Data may be deemed limitless because regular operation patterns would be extended with the help of huge data sets containing all kinds of information regarding customers, materials, logistics, and many more (Liu & Yi, 2017). According to surveys conducted by Abdel-Baset et al. (2019) and Singh and El-Kassar (2019), many respondents are looking forward to more instances of all-inclusive implementation of Big Data in green SCM because of powerful environment management. One of the potential venues of future research was the exploration of energy consumption patterns, as many respondents worried about the probability of saving that resource.

The key recommendation that stems from these findings is that manufacturing practices could become significantly more sustainable under the influence of Big Data and its derivatives, such as data mining, for example. The rationale behind this is that many organizations that choose to innovate digitally expect their decision-making actions to improve and enhance the degree of visibility within their supply chains. On the other hand, internal management practices based on green SCM could become one of the few means of strengthening contemporary competitive advantages through innovation and research. Green product design and advocacy for environment-friendly operations are required for organizations to cope with environmental challenges. These may include practically anything from interior temperature to air quality information that could be automatically transferred between different nodes of the existing supply chain.

Green Purchasing

Another essential concept that has to be considered by businesses expecting to benefit from green SCM is green purchasing. It means that the organization would constantly seek green suppliers and only partner with third-party organizations that are environment-friendly (Rajabion et al., 2019). In this case, Big Data would be rather helpful because it might provide manufacturers with insights into historical information and the potential trends in procurement (Choi, 2018). Digital analytics would provide the basis for a full-fledged examination of existing resources and their potential value for the organization. One of the examples from the literature is the possibility of utilizing machine learning and optimization to collect data on suppliers who follow low carbon emission regulations (Ilyas et al., 2020; Song et al., 2019). According to survey respondents, on the other hand, Big Data could serve as a support system for green purchasing through the interface of cloud computing and real-time data updates. Therefore, supplier selection criteria represent a dynamic variable that would have to be monitored closely with the help of green SCM and massive data sets.

In terms of green purchasing, the recommendation would be to utilize Big Data to achieve a tradeoff between the quality of the final product and the possible carbon footprint. High-quality raw materials from green suppliers would be turned into environment-friendly products intended to appeal to a larger target customer base. Optimization would be required to make prices affordable and help more consumers gain access to green products. Nevertheless, in order to accomplish this, organizations would have to engage in collaboration activities first to be able to motivate suppliers to partner with them. This hypothesis also reflects the idea that sustainable performance might be required to meet environmental criteria and attract more vendors to corporate operations. Big Data analytics would bypass the synchronization step and allow the administration to share required insights with green suppliers right away.

It is important to indicate, that with the impact of the COVID-19 pandemic customers’ optimism towards the future of the environment has increased. This lead to the general increase of interest in green purchasing, as it is now seen as more impactful. The chart below illustrates the recent statistics on the matter, and was built in accordance with GWI Core 2020 – 2021 report (Morris, 2021). It highlights the evolution of trends and the surge in general environmental optimism.

Environmetal Expextations

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Environmetal Expextations

Customer Support for Green Initiatives

The third crucial element of discussion is the presence of customer support for green initiatives established with the help of Big Data. For any organization that expects to achieve positive results, it may revolve around reverse logistics, constant two-way feedback, and smart transportation (Rahman et al., 2020). The existing capabilities of Big Data analytics allow for proper customer involvement in the processes of purchasing, production, and recycling (El-Kassar & Singh, 2019; Tiwari et al., 2018). Supply chain employees also pointed out the need to study eco-design trends and assess their viability for the organization. Green logistics is key to developing fruitful cooperation between companies and customers that expect to reduce the carbon footprint while maintaining the high quality of products delivered to consumers (Gawankar et al., 2020; Tseng et al., 2019). At the end of the day, customer support might assist organizations in terms of reducing resource consumption and making sure that environmental pollution is either absent or minimal.

The core recommendation for organizations planning to exploit Big Data analytics and develop green SCM would be to focus on the deployment of an optimization model that would connect consumers and employees via two-way feedback. The standard of environmental sustainability will be required to pave the way for smarter logistics activities that can be launched automatically and reduce the occurrence of human error. Also, more optimization algorithms might be required to ensure the proper incorporation of customer feedback into the system. Complex decision-making and green logistics are intertwined in a number of ways that require consumers to participate in the development of green supply chains.

Just-in-Time and Reverse Logistics

The concept of reverse logistics plays a crucial role in the development of green SCM due to how companies tend to fulfill their purpose while also adhering to the interests of stakeholders. One of the most common means of appealing to external actors is to encourage recycling initiatives and come up with novel disposal methods that might cover the limitations of outdated approaches to reutilization (Tseng et al., 2019). Based on the information presented by Min et al. (2019), it may be concluded that such closed-loop systems represent a significant benefit for organizations that are looking to deploy more green supply chain elements and recycle products without any obstacles. Such repurposing affects the industry in a positive manner and creates additional opportunities for companies that value their SCM practices (Tseng et al., 2019). Instead of considering some of the products to go to waste, organizations could divert pollution while also saving most of the resources and reusing them. With an increased level of sustainability, companies actually get a chance to re-establish themselves in the market and develop an improved image via green SCM techniques.

Just-in-Time (JIT) is another crucial element of proper green SCM that requires the organization to utilize resources only when it is required. The possibility to evade purchasing an unnecessary amount of business possessions prevents businesses from coping with excessive inventory (Doolun et al., 2018). As long as the warehouses are not flooded with spare resources, the company does not have to spend more money on keeping the warehouse intact as well. According to Zhao et al. (2017), the strategy for companies implementing JIT is to streamline production and seek new opportunities for reducing waste. Correct execution of the JIT agenda presupposes that the company is going to pay attention to consumer purchasing trends and develop the forecasting capability into one of the pillars of green SCM (Min et al., 2019). Without data analysis, modern companies are most likely to struggle since digitalization represents one of the shortest paths toward meeting customer demand successfully. Constant adherence to strict guidelines makes JIT practices a perfect companion for organizations that expect the best products to leave the facility and support green initiatives pushed by the administration.

Real-Life Examples of Green SCM

The most prominent example of a company that follows the guidelines of green SCM and appeals to most of its stakeholders and customers is Toyota. The Japanese car manufacturer recurrently communicates with crucial actors and resolves social issues. The company strives to achieve zero emissions and lead the campaign intended to attract other car manufacturers to focus on the amount of CO2 being released (Khan & Qianli, 2017). The Environmental Challenge 2050 program was developed by Toyota’s management to optimize resource utilization and reduce the adverse impact of car manufacturing on nature. One of the reasons why green SCM became so successful for Toyota is the incredible amount of sustainability across all production areas (Rajabian Tabesh et al., 2016). The Japanese car producer developed an invention environment where green SCM practices align against the remaining operations perfectly and do not cause any conflicts based on inconsistency or the lack of resources. The application of JIT methodology allows Toyota to make the best use of available possessions and create products that are both eco-friendly and appealing.

The second real-life example of a company that benefits from a green supply chain is UPS. Their approach to package delivery became a revelation for other organizations across the globe due to the company’s equal efforts to reduce emissions and care about customer demand (Pourhejazy & Kwon, 2016). This positive impact is also reinforced by additional methods, such as alternative fuel options and altered logistics. For the last two decades, UPS is on a mission to extend the green fleet and create the best future for its consumers. The growing demand does not scare away the delivery mogul since UPS advances its technologies recurrently and tries to reduce emissions wherever possible (Franchetti et al., 2017). The costs of shipping and transportation decrease continually, creating room for UPS to cover the most damaged areas in the supply chain and capitalize on the strongest assets. The exceptional nature of the company’s initiatives makes it possible for them to maintain one of the largest shipping networks in the world without losing any assets (Pourhejazy & Kwon, 2016). UPS constantly reinforces its green direction by partnering with related actors from the industry that are also focused on green SCM.

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The last organization that may serve as an example of a strong green supply chain is Johnson and Johnson. In their pursuit of sustainability, the organization tends to implement the most efficient solutions in order to allow for green methods and efforts (James et al., 2019). The key peculiarity of Johnson and Johnson is that the administration seeks green solutions while manufacturing medical and health products, unlike most of their rivals from the automotive and tech sectors. The factor that differentiates Johnson and Johnson from its direct competition is the strict adherence to practices and standards that have been developed and deployed in recent years (James et al., 2019). In a sense, the organization mirrors Toyota’s approaches since its supply chain program is also focused on reduced carbon emissions and a generally eco-friendly approach to manufacturing. Public disclosure of reduction initiatives makes it easier for Johnson and Johnson to benefit from its green SCM and create a sustainable future for stakeholders, customers, and executives (Porter & Kramer, 2019). On a long-term scale, lean operations developed by Johnson and Johnson represent a positive trend that has to be nurtured and advanced further.

Practical Implications

The first crucial implication of the current findings is that many modern organizations do not pay enough attention to the significance of environmental and social considerations. When a company focuses solely on its profits, it creates a conundrum where delivery and technology costs overpower the quality of the final product (El-Kassar & Singh, 2019). Knowing that the planet and the people are just as important as a company’s profitability, more organizations worldwide should partner with suppliers that do not violate sustainability requirements. In the words of Baryannis et al. (2019), all the harm given to the environment turns into less profitable initiatives over time. The presence of an extensive list of suppliers does not protect companies from making mistakes related to SCM, especially when consumers expect eco-friendly outcomes of manufacturing and delivery (Hartley & Sawaya, 2019). The role of technology, in this case, can be described as directly linked to the organization’s level of credibility. Therefore, most of the objectives related to SCM should be convergent in order to reinforce the impact of sustainability and attract more suppliers to the bigger picture.

Another implication that organizations would have to consider when switching to green SCM is the lack of training and smart procurements. The impact of Big Data becomes exceptionally immersive and omnipresent, causing companies to partner with tech-savvy organizations and individuals to improve procurement and manufacturing in a meaningful manner (Rahman et al., 2020). Green supply chain initiatives depend on locating resources of the highest quality and rewarding the team for paying enough attention to environmental and social predicaments in addition to direct profitability. The cost of the product and its delivery should be reviewed carefully in order to reprioritize and see how on-time data processing could predict certain events across the supply chain (Gupta et al., 2019). It may be safe to say that cost savings should not be placed at the top of SCM practices since the supply network has to be led with the help of eco-friendly decisions and detailed ‘green’ training for employees and stakeholders.

Finally, more organizations should start paying attention to how their relationships with suppliers could predict their decision-making and deploy improved sustainability through a better choice of resources. In a sense, more direct contact is required in order to create an environment where green SCM is the central focus of business initiatives proposed by the company (Choi, 2018). Procurement personnel should become better informed in order to communicate their concerns to executives and provide exceptional insights into how the current supply chain could be improved. In line with Abdel-Baset et al. (2019), such requirements should be rightfully enforced across most of the modern businesses that overlook their impact on the environment and choose to focus solely on profits. Even though the notions of health and safety are much more often included in the corporate agenda nowadays, there is still room for improvement. For instance, sustainability requirements could be disseminated among all suppliers to motivate them to follow suit and contribute to green SCM and a digitalized approach to it.

To implement the findings of interviews and literature reviews in business practice a concrete demonstration of links between examples of Big Data technology and green SCM is required. The table below outlines several examples of the Big Data tools, linked to the precise ways in which it can be utilized for the improvements in firms’ sustainability practices.

Tool Impact for sustainability
SAP Regular operation patterns simplified, implemented to increase efficiency in data management
HANA Accesses sensory data and incorporates it into regular production practices to reduce waste
Aqueduct Data-based real time tool that assesses the water risk in different parts of the world; is becoming increasingly popular in modern production-oriented firms
Lumify Advanced visualization technology widely utilized for the green product design implementation. Provides automatic layouts, 2D and 3D graphic tools, and a multimedia editor.

Conclusions

The current research project shows that the accuracy of decision-making could be significantly improved with the help of Big Data and its proper connection to the given green supply chain. One of the possible ways of exploiting that knowledge would be to predict future trends using modern technology and investigate all the possible patterns in order to reduce business uncertainty and environmental footprint at the same time. The essential implication of the existing research project is that there is a need for green SCM initiatives that would be powered by Big Data and its derivatives. The descriptions obtained from employee surveys showed that many individuals are looking forward to a better understanding of how technology could be included in SCM agendas without putting a strain on the company’s budget and human resources. Another crucial implication of the findings presented above is that investment decision-making represents a struggle for organizations that do not have enough experience in green SCM.

While taking into consideration all of the information above, the researcher also believes that future studies should pay more attention to how Big Data could influence the interconnectedness between different operations within the given supply chain. This means that none of the three factors discussed in this paper can be taken separately. A holistic framework might be necessary to answer all of the new questions that appear because of the large-scale implementation of Big Data analytics. The quick progress that organizations make in terms of deploying instruments based on Big Data is the most evident reason to acknowledge the future value of green SCM and its association with emerging innovations.

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