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, 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 (711). 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 366). 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.
Problem Statement
The problem that the author is willing to review within the framework of the current research project is the growing pool of green SCM knowledge that gets interconnected with technology, such as Big Data. Nevertheless, there have to be practical and theoretical implications that would become evident after looking at the subject of green SCM from several unique perspectives. The problem that the author is going to investigate within this research project is the presence of solutions based on Big Data that are implemented to improve green SCM operations and benefit employees at the same time.
Background & Project Overview
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. 47). 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. 1000). 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 366; Chehbi-Gamoura et al. 373). 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 712; Min et al. 49). Practical and theoretical perspectives on the digitalization of supply chains suggest that possible drawbacks should be addressed first.
Methodology
The capabilities of green SCM will be viewed through the prism of a systematic literature review on the subject of the use of AI 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 they have 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 will be analyzed in accordance with the Critical Appraisal Skills Program (CASP) worksheet so as to pick studies of the highest quality. The data will be 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 will be necessary to pinpoint the essential inclinations across the industry that should be either fortified or abandoned.
Project Timeline
Workload
Approximately 15 hours will be spent on the analysis of literature related to green SCM and its relation to Big Data. The same amount of time is allocated to the development of a sound methodology that will serve as the backbone for the final results. Over the course of the next 40 hours, the author will conduct a systematic review of literature on the subject of the use of Big Data in the field of green SCM in order to establish the key benefits and drawbacks that have to be considered when investing in new technology. The next 20 hours will be required to perform a detailed data analysis and outline the key trends in green SCM that could be improved with the help of Big Data. The final ten hours are allocated to finalize the paper and draw relevant conclusions stemming from the systematic literature review.
References
Alzoubi, H. “The Role of Intelligent Information System in e-Supply Chain Management Performance.” International Journal of Multidisciplinary Thought, vol. 7, no. 2, 2018, pp. 363-370.
Baryannis, George, et al. “Predicting Supply Chain Risks Using Machine Learning: The Trade-Off between Performance and Interpretability.” Future Generation Computer Systems, 101, 2019, pp. 993-1004.
Chehbi-Gamoura, Samia, et al. “Insights from Big Data Analytics in Supply Chain Management: An All-Inclusive Literature Review Using the SCOR Model.” Production Planning & Control, vol. 31, no. 5, 2020, pp. 355-382.
Hartley, Janet L., and William J. Sawaya. “Tortoise, not the Hare: Digital Transformation of Supply Chain Business Processes.” Business Horizons, vol. 62, no. 6, 2019, pp. 707-715.
Min, Soonhong, et al. “Defining Supply Chain Management: In the Past, Present, and Future.” Journal of Business Logistics, vol. 40, no. 1, 2019, pp. 44-55.