MITS Advanced Research Techniques Research Report Candidate: Sandeep Shrestha Higher Education Department Victorian Institute of Technology Report Title: “PREDICTIVE ANALYTICS AND BIG DATA IN SUPPLY CHAIN MANAGEMENT” Extended 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 research 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. Both primary and secondary data have been included in this study to understand the practical implications as well as theoretical underpinnings in predictive analytics implementation reading supply chain management sector. Some of the limitations and ethical considerations of this research have been outlined in this paper along with the definition of the primary hypothesis that are to be tested. Table of Contents Introduction. 4 1.1 Statement of the problem.. 4 1.2 Research questions and objectives. 4 1.3 Significance of the problem.. 5 1.4 Background. 5 1.5 Purpose of the study. 6 1.6 Statement of hypothesis. 6 1.7 Assumptions and limitations. 6 1.8 Ethical considerations. 7 1.9 Overview of the methodology. 7 References. 8 Introduction 1.1 Statement of the problem Multiple different types of problems are encountered by supply chain managers in monitoring and optimizing the supply chain management processes that have been argued to be potentially resolved by technological implementations. This study primarily aims to evaluate the ways in which modern technology such as big data and other predictive analytics solutions can help in resolving existing supply chain management issues. In the opinion of , some of the most important issues in supply chain management include management of changing customer expectations, transparency issues in supply chains, communication barriers, transportation problems as well as information management issues. Technological solutions such as big data and predictive analytics can provide integrated platforms for resolving many of these issues and automate certain processes that can minimize inaccuracy problems in operations . Management of supplier networks, maintaining quality as well as sustainability and risk mitigation can also be significant challenges that can be addressable through technology. Therefore, this study attempts to research and develop theories about the practical utility of modern predictive analytics technologies in supply chain management improvement. 1.2 Research questions and objectives Research aim The main aim of this study is to evaluate and understand the role of big data and predictive analytics technologies in supply chain management. Research objectives 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 Research questions 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 is the positive and negative impact of using predictive analytics in supply chain management? 1.3 Significance of the problem This research will provide a significant contribution to the supply chain management literature guiding managers in this field to effectively understand the ways in which innovative technologies such as predictive analytics and big data can resolve the existing problems in supply chain management. Analysis of secondary literature related to supply chain management operations and the issues faced by managers has been included in this study that helps in identifying the potential areas of implementation for these technologies. This study will also help in understanding the existing gaps in the literature regarding the application of innovative technologies in supply chain management. Moreover, the benefits of big data analytics as well as potential problems that can be faced in the implementation of new technologies in supply chain management operations are also to be revealed through this study. 1.4 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. Innovation in supply chain management has always been a well-visited topic of research and many previous studies have identified that currently, organizations are investigating the applicability of predictive analytics technologies in the sector . It has been argued that existing supply chain management problems such as lack of traceability, disruptions in the process resulting in delays as well as high dependency on single supplier networks can be resolved effectively using technological solutions . It is, therefore, important to understand the ways in which these technologies can be implemented to resolve the most prominent challenges in supply chain management. Every dimension of supply chain management operation such as communication systems, inventory management systems, transportation and decision-making activities can utilize predictive analytics technologies in different ways that have been evaluated in detail throughout this study. 1.5 Purpose of the Research 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. Predictive analytics and big data technologies are quite often used across industries in the 21st century and in this study, the main purpose of research has been to understand the impact of these technologies in optimizing supply chain management processes. This research incorporates the identification of both positive and negative influences of predictive analytics and big data technologies in supply chain management processes. The analysis in this study has also aimed at evaluating some of the underlying drivers of supply chain management along with the determination of potential strategies and relevant skills required for the successful implementation of predictive analytics technologies in the supply chain management sector. 1.6 Statement of hypothesis The main hypotheses that are to be tested in this research include: H0: Predictive analytics and big data technologies do not play significant positive roles in improving supply chain management processes H1: Predictive analytics and big data technologies play significant positive roles in improving supply chain management processes 1.7 Assumptions and limitations Due to the limited availability of time and other resources like funds in the research, only a certain amount of data can be incorporated in the study for analysis. Approximately 30 different secondary sources related to big data and predictive analytics implementation in supply chain management can be included for secondary analysis and in the primary data collection, around 10 different supply chain managers from real-life organizations can be included for interviews. Attempts of including supply chain managers and executives can exceed the total number of inclusions in the study since in many cases there are limitations such as disagreement from interviewees or unwillingness to participate. 1.8 Ethical considerations The most basic ethical considerations that are required to be incorporated in this research include disclosure and privacy factors. It is important that all the data collected from the secondary sources are not in any way manipulated or changed to maintain the authenticity and validity of the study . Personal information and identification-related details of the subjects in the primary data collection process have also been protected from unauthorized disclosure. It has been ensured in this study that any type of commercial usage of information is avoided at all costs throughout the research process. 1.9 Overview of the methodology A combination of both primary and secondary qualitative research methods has been used in this research and therefore data collection procedures included a collection of data from many different sources . Previous studies, reports, published articles, and available details about organizational supply chain management processes using predictive analytics technologies have been included in the study as secondary sources and this data is to be analyzed qualitatively for understanding the benefits and drawbacks of implementation effectively . Direct conversation through interviews with supply chain management executives and managers in different companies across countries constitute the data for primary qualitative analysis in this study. Both primary and secondary data collected in the study provide effective insights into the benefits and drawbacks of utilizing predictive analytics or big data technologies in supply chain management processes. A thematic analysis based on the deductions from data collection and analysis processes in this study potentially provides valid implications regarding the research hypotheses identified. References K. Govindan, T. E. Cheng, N. Mishra and N. Shukla, “Big data analytics and application for logistics and supply chain 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, pp. 308-317, 2017.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.P. Pinto, “10 Global supply chain management challenges and how to approach them,” neat, 2021. [Online]. Available: https://www.neatcommerce.com/blog/global-supply-chain-management-challenges/.S. Ketefian, “Ethical considerations in research. Focus on vulnerable groups,” Investigación y Educación en Enfermería, vol. 33, no. 1, pp. 164-172, 2015.B. Temple, R. Edwards and C. Alexander, “Grasping at context: Cross language qualitative research as secondary qualitative data analysis,” Forum Qualitative Sozialforschung/Forum: Qualitative Social Research (Vol. 7, No. 4).V. Sherif, “Evaluating preexisting qualitative research data for secondary analysis,” Forum: qualitative social research (Vol. 19, No. 2, pp. 26-42), vol. Freie Universität Berlin, 2018.
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