MITS Advanced Research TechniquesResearch ProposalCandidate:Sandeep ShresthaHigher Education DepartmentVictorian Institute of TechnologyProposed Title:“PREDICTIVE ANALYTICS AND BIG DATA IN SUPPLY CHAIN MANAGEMENT”AbstractThe main purpose of this research is to develop a theoretical model explaining the impact ofpredictive analytics and big data on supply chain management outcomes in organizations. Evidencefrom different data sources is to be evaluated in this study using theoretical models such as contingencytheory and resource-based view logic. Literature review including a wide range of previous studies is tobe performed in this research for deriving existing concepts and identifying areas of implementation forpredictive analytics in this industry. Predictive analytics solutions are quite frequently used in themodern supply chain management processes in decision-making activities, allocation and organizationof resources and future prediction of organizational outcomes. Many alternative areas ofimplementation 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 theactual research work related to the topic of “predictive analytics application and big data in supplychain management processes”. Predictive analytics in Different individual areas such as inventorymanagement, organizational performance measurements, information sharing and planning as well aseconomic or environmental predictions are to be considered in this study that can provide effectiveguidance to supply chain managers, policymakers and practitioners. Sustainability factors in supplychain management and their relatability with the use of predictive analytics techniques have also beenevaluated to some extent in this study. This paper attempts to make some original contributions toestablish the primary research background for further learning in this topic and it also presentsevidence-based on previous findings that big data and predictive analytics are being researchedextensively for supply chain implementations nowadays.Outline of the Proposed ResearchBackgroundPredictive analytics and big data are some of the most commonly used words nowadays insupply chain management and only a few large-scale investigations are available in this field withinthis specific industry. Many previous studies have focused on the use of these new innovativetechnologies in multiple areas and yet it can be observed that investigations related to applicability insupply chain management functions are quite limited. The historical impact of development intechnology and its implementation in supply chain management processes are to be explored in thestudy along with the development of new technologies according to the evolving requirements in thesupply chain management industry. In the 21st century, information technology has penetrated allaspects of life and predictive analysis is well-positioned within the domain of data science. Thistechnology primarily includes evaluation and analysis of large scale previous historical data for makingmore accurate predictions about the future behavior of different variables in businesses. According tothe studies of , predictive analytics and big data solutions use different quantitative and qualitativemethods in combination with the existing supply chain management theories to resolve different typesof supply chain management problems. Many different challenges also exist in the implementation ofsuch technological solutions in real life such as data availability issues as well as validity and reliabilityissues . Recent research works have also explored and discovered the advantages of using big datasolutions 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 withinthe standard supply chain activities are to be explored throughout this research.PurposeThe main purpose of this research is to report on the present use of predictive analytics and bigdata technologies in supply chain management and its underlying drivers. The overall benefits anddrawbacks of implementing these technological solutions in the supply chain management industry arealso to be evaluated in this study highlighting some specific strategies for efficient utilization in solvingreal-time problems. Relevant skills required for professionals to implement these technologies in thesupply management processes along with reliability and accuracy of predictive analyticsimplementation 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.RationaleInstances of practical implementation of big data technologies and other predictive analyticssolutions are becoming increasingly frequent nowadays. Many of the existing challenges of manualmanagement procedures can be efficiently eliminated using new and innovative technologies such asincreased error rates and lack of accuracy in predictions. Process automation can also not be achievedusing traditional supply chain management practices an implementation of technology can providemultidimensional benefits and optimizing the processes effectively . A proper inclusive reportregarding the large-scale use of predictive analytics and data science in supply chain managementprocesses is not available in supply chain management literature. Manual processes also do not enablefast analysis of large-scale data which is much easier using technologies such as these . And hence itis important to understand the role of different types of technologies in improving the supply chainmanagement processes potentially further in future. This paper will therefore attempt to contribute tothe existing literature evaluating increased adoption of predictive analytics technologies in supply chainmanagement processes and how they have improved the existing practices in this field. This study isexpected to shed light on the current developments in research related to predictive analytics and howthe evolving new technologies can be further integrated into supply chain management processes infuture.Research Topic and central research questionThe main research topic is related to understanding the role of “predictive analytics or big datain 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 implementedin supply chain management processes optimization– To understand the benefits and challenges in implementing predictive analytics and big datasolutions in supply chain management– To recommend ways of further integration of big data and other predictive analyticstechnologies in future to optimize is SCM processesDepending on the primary objectives of the study, the following research questions can be identifiedthat are to be addressed in the study:1. What is the importance and relevance of predictive analytics solutions in Supply ChainManagement?2. How can Predictive Analytics technologies be used in different areas of supply chainmanagement such as resource allocation, inventory management, information sharing?3. What are the positive and negative impact of using predictive analytics in supply chainmanagement?Methodological ApproachBoth primary and secondary data is to be collected and analyzed in this study and the overallapproach of the research will incorporate a mixed-method approach. Secondary data is to be analyzedqualitatively gathering information from multiple different secondary sources such as publishedarticles, industry reports, journals and organizational reports. Statistical methods are to be used foranalyzing quantitative data gathered from large scale surveys in order to understand the differentunderlying interrelationships between various variables in supply chain management. This will beuseful to us for identifying the variables that act as barriers or alternatively provides benefits in supplychain optimization processes. The study will derive effective conclusions based on the implicationsunderstood from both the primary and secondary analysis processes. Effective insights andrecommendations can also be formulated based on the findings of this research.Research DesignThe research designs adopted in most studies can be classified into three different types’ namelyexplanatory research design exploratory research design and descriptive research design .Explanatory research designs involve conducting research work to explain relationships between two ormore variables whereas exploratory research designs are used for studies that are attempting to explorenew areas in research that has not been covered previously . The research approach in this study willbe following the deductive research approach as initially existing theories are to be evaluated forgenerating hypotheses that are going to be evaluated based on available evidence in the later stages ofconfirmation. A deductive research approach has been selected in this study since it provides ampleopportunities to explain underlying causal relationships between identified variables. The deductiveapproach also provides better possibilities of generalisation from the research findings and quantitativemeasurements can be effectively used to accept or reject hypotheses . Therefore, this approach hasbeen selected to be applied in this research.ContributionThis research will contribute effectively to the supply chain management literature as it will include amultidimensional analysis of the different factors affecting the technological implementation of bigdata and predictive analytics in supply chain management processes along with identifying the benefitsand drawbacks of these technologies. The extent of adoption of these technologies in the current stageamong different leading companies in their supply chain management processes will be identifiedthrough primary quantitative data analysis processes and the respondents will include supply chainmanagement professionals across different countries. Existing literature primarily focuses on thedifferent benefits that big data analytics providers, and other predictive analytics or intelligencetechnologies used in supply chain management. Potential drawbacks and challenges are much lessdiscussed throughout literature which necessitates further investigation in identifying both positive andnegative aspects of the technological evolution. This study will cover all of these dimensions unlikeprevious literature and hence attempt to fill the existing gap in the literatureProposed Time ScheduleThe 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 TimetableFigure 2: Gantt chartLiterature References K. Govindan, T. E. Cheng, N. Mishra and N. Shukla, “Big data analytics and application forlogistics and supply chain management,” 2018. S. F. Wamba, A. Gunasekaran, T. Papadopoulos and E. Ngai, ” Big data analytics in logistics andsupply 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 dataand predictive analytics capability on supply chain sustainability,” The International Journal ofLogistics 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 ofBusiness Research, 70, pp. 308-317, 2017. A. Queirós, D. Faria and F. Almeida, “Strengths and limitations of qualitative and quantitativeresearch methods,” European Journal of Education Studies, 2017. C. Opie, “Research approaches,” Getting Started in Your Educational Research: Design, DataProduction and Analysis, 137, 2019.
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