MITS Advanced Research Techniques Research Proposal Candidate: Sandeep Shrestha Higher Education Department Victorian Institute of Technology Proposed Title: “PREDICTIVE ANALYTICS AND BIG DATA IN SUPPLY CHAIN MANAGEMENT” Abstract The main purpose of this research is to develop a theoretical model explaining the impact of predictive analytics and big data on supply chain management outcomes in organizations. Evidence from different data sources is to be evaluated in this study using theoretical models such as contingency theory and resource-based view logic. Literature review including a wide range of previous studies is to be performed in this research for deriving existing concepts and identifying areas of implementation for predictive analytics in this industry. Predictive analytics solutions are quite frequently used in the modern supply chain management processes in decision-making activities, allocation and organization of resources and future prediction of organizational outcomes. Many alternative areas of implementation have also been identified in previous studies that are to be explored in this research. This research further identifies the fundamental research questions that need to be addressed in the actual research work related to the topic of “predictive analytics application and big data in supply chain management processes”. Predictive analytics in Different individual areas such as inventory management, organizational performance measurements, information sharing and planning as well as economic or environmental predictions are to be considered in this study that can provide effective guidance to supply chain managers, policymakers and practitioners. Sustainability factors in supply chain management and their relatability with the use of predictive analytics techniques have also been evaluated to some extent in this study. This paper attempts to make some original contributions to establish the primary research background for further learning in this topic and it also presents evidence-based on previous findings that big data and predictive analytics are being researched extensively for supply chain implementations nowadays. Outline of the Proposed Research Background Predictive analytics and big data are some of the most commonly used words nowadays in supply chain management and only a few large-scale investigations are available in this field within this specific industry. Many previous studies have focused on the use of these new innovative technologies in multiple areas and yet it can be observed that investigations related to applicability in supply chain management functions are quite limited. The historical impact of development in technology and its implementation in supply chain management processes are to be explored in the study along with the development of new technologies according to the evolving requirements in the supply chain management industry. In the 21st century, information technology has penetrated all aspects of life and predictive analysis is well-positioned within the domain of data science. This technology primarily includes evaluation and analysis of large scale previous historical data for making more accurate predictions about the future behavior of different variables in businesses. According to the studies of , predictive analytics and big data solutions use different quantitative and qualitative methods in combination with the existing supply chain management theories to resolve different types of supply chain management problems. Many different challenges also exist in the implementation of such technological solutions in real life such as data availability issues as well as validity and reliability issues . Recent research works have also explored and discovered the advantages of using big data solutions and predictive analytics such as increasing overall business sustainability in the long term. Multiple different implementation areas in supply chain management such as inventory management, decision-making processes, information communication processes, tracking and transportation within the standard supply chain activities are to be explored throughout this research. Purpose The main purpose of this research is to report on the present use of predictive analytics and big data technologies in supply chain management and its underlying drivers. The overall benefits and drawbacks of implementing these technological solutions in the supply chain management industry are also to be evaluated in this study highlighting some specific strategies for efficient utilization in solving real-time problems. Relevant skills required for professionals to implement these technologies in the supply management processes along with reliability and accuracy of predictive analytics implementation is also to be evaluated throughout the study with the help of theoretical research, review of the literature and an analysis of pedagogical advancements in this field. Rationale Instances of practical implementation of big data technologies and other predictive analytics solutions are becoming increasingly frequent nowadays. Many of the existing challenges of manual management procedures can be efficiently eliminated using new and innovative technologies such as increased error rates and lack of accuracy in predictions. Process automation can also not be achieved using traditional supply chain management practices an implementation of technology can provide multidimensional benefits and optimizing the processes effectively . A proper inclusive report regarding the large-scale use of predictive analytics and data science in supply chain management processes is not available in supply chain management literature. Manual processes also do not enable fast analysis of large-scale data which is much easier using technologies such as these . And hence it is important to understand the role of different types of technologies in improving the supply chain management processes potentially further in future. This paper will therefore attempt to contribute to the existing literature evaluating increased adoption of predictive analytics technologies in supply chain management processes and how they have improved the existing practices in this field. This study is expected to shed light on the current developments in research related to predictive analytics and how the evolving new technologies can be further integrated into supply chain management processes in future. Research Topic and central research question The main research topic is related to understanding the role of “predictive analytics or big data in supply chain management”. The primary objectives of the study will be: To identify the extent to which predictive analytics and big data solutions can be implemented in supply chain management processes optimizationTo understand the benefits and challenges in implementing predictive analytics and big data solutions in supply chain managementTo recommend ways of further integration of big data and other predictive analytics technologies in future to optimize is SCM processes Depending on the primary objectives of the study, the following research questions can be identified that are to be addressed in the study: What is the importance and relevance of predictive analytics solutions in Supply Chain Management?How can Predictive Analytics technologies be used in different areas of supply chain management such as resource allocation, inventory management, information sharing?What are the positive and negative impact of using predictive analytics in supply chain management? Methodological Approach Both primary and secondary data is to be collected and analyzed in this study and the overall approach of the research will incorporate a mixed-method approach. Secondary data is to be analyzed qualitatively gathering information from multiple different secondary sources such as published articles, industry reports, journals and organizational reports. Statistical methods are to be used for analyzing quantitative data gathered from large scale surveys in order to understand the different underlying interrelationships between various variables in supply chain management. This will be useful to us for identifying the variables that act as barriers or alternatively provides benefits in supply chain optimization processes. The study will derive effective conclusions based on the implications understood from both the primary and secondary analysis processes. Effective insights and recommendations can also be formulated based on the findings of this research. Research Design The research designs adopted in most studies can be classified into three different types’ namely explanatory research design exploratory research design and descriptive research design . Explanatory research designs involve conducting research work to explain relationships between two or more variables whereas exploratory research designs are used for studies that are attempting to explore new areas in research that has not been covered previously . The research approach in this study will be following the deductive research approach as initially existing theories are to be evaluated for generating hypotheses that are going to be evaluated based on available evidence in the later stages of confirmation. A deductive research approach has been selected in this study since it provides ample opportunities to explain underlying causal relationships between identified variables. The deductive approach also provides better possibilities of generalisation from the research findings and quantitative measurements can be effectively used to accept or reject hypotheses . Therefore, this approach has been selected to be applied in this research. Contribution This research will contribute effectively to the supply chain management literature as it will include a multidimensional analysis of the different factors affecting the technological implementation of big data and predictive analytics in supply chain management processes along with identifying the benefits and drawbacks of these technologies. The extent of adoption of these technologies in the current stage among different leading companies in their supply chain management processes will be identified through primary quantitative data analysis processes and the respondents will include supply chain management professionals across different countries. Existing literature primarily focuses on the different benefits that big data analytics providers, and other predictive analytics or intelligence technologies used in supply chain management. Potential drawbacks and challenges are much less discussed throughout literature which necessitates further investigation in identifying both positive and negative aspects of the technological evolution. This study will cover all of these dimensions unlike previous literature and hence attempt to fill the existing gap in the literature Proposed Time Schedule The proposed timeline for the conduct of the research: NameBegin dateEnd dateResearch Questions and Abstract17th July,20218th Aug,2021Research Proposal9th Aug,202120th Aug,2021Introduction and Extended Abstract23rd Aug,20215th Sep,2021Literature Review6th Sep,20213rd Oct,2021Methodology4th Oct,202117th Oct,2021Full Submission18th Oct,202124th Oct,2021 Figure 1: Estimated Timetable Figure 2: Gantt chart Literature References K. Govindan, T. E. Cheng, N. Mishra and N. Shukla, “Big data analytics and application for logistics and supply chain management,” 2018.S. F. Wamba, A. Gunasekaran, T. Papadopoulos and E. Ngai, ” Big data analytics in logistics and supply chain management,” The International Journal of Logistics Management, 2018.S. Jeble, R. Dubey, S. J. Childe, T. Papadopoulos, D. Roubaud and A. Prakash, “Impact of big data and predictive analytics capability on supply chain sustainability,” The International Journal of Logistics Management, 2018.A. Gunasekaran, T. Papadopoulos, R. Dubey, S. F. Wamba, S. J. Childe, B. Hazen and S. Akter, “Big data and predictive analytics for supply chain and organizational performance,” Journal of Business Research, 70, pp. 308-317, 2017.A. Queirós, D. Faria and F. Almeida, “Strengths and limitations of qualitative and quantitative research methods,” European Journal of Education Studies, 2017.C. Opie, “Research approaches,” Getting Started in Your Educational Research: Design, Data Production and Analysis, 137, 2019.
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