Predictive analytics and big data

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Table of Contents
1. Introduction. 3
2. Research Onion. 3
3. Research methods. 4
3.1 Research philosophy. 4
3.2 Research design. 4
3.3 Research approach. 5
4. Research Framework or Strategy for this Research. 5
5. Secondary research. 6
6.  Primary research. 6
7. Data collection and analysis. 7
8. Sampling method. 7
9. Key issues. 7
References. 9

            Predictive analytics and big data have been identified from previous literature to Bihar commonly used in supply chain management operations and in this chapter, the specific methodologies used for investigating different associated attributes of the research problem have been described. The methodological approaches selected for the study and justifications behind selecting such methodologies have been provided in the following sections.

            The research on the model was first proposed by [1], which outlines the basic steps of research strategy delineating research that should be undertaken based on the particular research problem selected. According to the system,  research methodology in any research study must start with the outermost layer of the research onion model moving on to the in layers gradually in the later stages.
Source: [2]
             Recently different other opinions about the developed contrasting the traditional views of utilising research onion model in different studies. Contradicting opinions argue that this model can also be utilised by starting from the innermost layer and proceeding with the outer layers in the later stages [1].

            Various philosophical theories from the basic techniques of research based on classical research methodology and the two classical mainstream distinguishing categories of research philosophy include the positivist and interpretivist philosophies. Recently other philosophers have also been adopted frequently in scientific research studies such as the pragmatist and critical realist philosophies [3]. In this case,  positivist research philosophy can be considered to be most suitable based on the research objectives and research question.  According to the positivism philosophy,  ontology is primarily based on objectivist assumptions and entities can be considered to be atomic events that are external to social activities [4]. According to this philosophy,  only empirical data and observational data can be considered to be credible.

             Research Design in Scientific research works mostly indicate categories of the research process such as explanatory, exploratory or descriptive research designs.  explanatory research designs are ideal for studies aimed at explaining in detail the variability between different factors related to the research problem area [2].  On the other hand,  exploratory research studies are used in exploring new areas that have not been visited by empirical studies. An explanatory Research Design has been pursued throughout this study since different factors influencing the implementation of predictive analytics in supply chain management as well as various implementation areas can be identified from previous studies.  The impacts of different factors and challenges on the implementation process of predictive Analytics has been explained based on analysis of the results in this study [4]. Therefore,  explanatory Research Design is the most suitable approach for addressing the present research problem.

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            Choosing a research approach specifies the progress in each step of the research study and the most common research approaches for reasoning types that are used in scientific research works include Inductive and deductive reasoning. Inductive reasoning initiates with making observations and in the gradual steps, tentative hypotheses and theories are developed based on the observations [4]. On the other hand, the deductive reasoning process starts with the development of theory and hypothesis in the initial stages based on certain assumptions or evidence gathered from previous research works. The latest steps include a collection of observatory data and confirmation of the assumed theories or hypotheses in the previous steps.
Source: [4]
In this study,  deductive reasoning processes and used since the research questions and hypothesis have been previously formed in the initial stages from literature review and in the results and analysis phase evidence in the results and analysis phase evidence to be collected has been collected to verify and test the hypotheses.

            The research strategy used in this study includes both primary Research and secondary research methods. Empirical evidence from previous studies, as well as qualitative and quantitative evidence, have been analysed in the secondary research process in this study that sheds light on the different challenges and opportunities faced by practitioners in implementing predictive analytics and Big Data Analytics solutions within the supply chain management context.  The secondary qualitative research strategy that has been included in this study are previously published research papers that are not older than 10 years,  newspaper articles,  published books,  published conference papers,  peer-reviewed journals,  and verified online data from government websites and database [5]. The primary qualitative research method has also been implemented for evaluating real-time opinions of individuals working in the supply chain management sector about the implementation areas and effects of Technology implementation on supply chain management operations [6].

            Secondary data is to be collected in this study from different sources such as published journals and articles which are peer-reviewed, newspaper articles,  textbooks,  validator bases and other types of online data that are accessible. secondary data is to be analysed qualitatively based on different themes identified in the previous literature [9].  Approximately, more than 30 different online sources have been analysed in the qualitative secondary research method that can identify the different implementation areas and challenges in the integration of Technology within supply chain management operations. The secondary study can be helpful in explaining underlying relationships between challenges and implementation frequency off predictive and Big Data Analytics by supply chain managers in different industries.

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            The primary research method indicates interaction with the primary subjects in a research study which has also been implemented how it is helpful in understanding real-life problem frequency and solutions. In the present case,  approximately 10 different supply chain managers from different industries are to be introduced administration of an open-ended questionnaire about the problems faced by them in implementing predictive analytics of the big data analytics in supply chain management. Which of the potential solutions seem to resolve such problems are also evaluated in the research study. A closed-ended questionnaire has also been prepared that is to be administered to approximately 150 supply chain workers to understand the different types of predictive analytics and big data technologies that are used frequently in supply chain management.

            Secondary data collection sources have been explained in the previous section which includes secondary databases,  public data from valid websites, published journals and articles. For primary data collection, the close-ended questionnaire for the supply chain workers is to be distributed through email and the open-ended questionnaire for the interviews of the supply chain manager is also to be communicated through email or conducted online through  Google meet. A systematic and thematic review of secondary sources has been performed throughout the study for identifying the in-depth concept about predictive analytics and big data Technologies often integrated into supply chain management. statistical analysis methods are to be used for analysing the responses to a close-ended questionnaire from the supply chain management workers

 The purposive sampling method has been selected in this study for selecting the interview respondents and only supply chain management operations managers have been selected for the interview from different companies.  Contrarily, for statistical analysis,  supply chain management workers have been selected using a random sampling method. for selecting the secondary sources of data, only relevant articles have been selected related to the topic of supply chain management and implementation of predictive analytics or Big Data Analytics.

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            Various types of issues have been faced during collecting data and implementing the overall methodological approach in this study since in many cases the respondents that are chosen for interviews are not willing to participate and there are concerns of disclosure of personal data while giving responses among the respondents. This increases the chances of Biased responses leading to inaccurate results in research studies.  Moreover,  other problems such as limited time availability  related problems and data security management problems have also been encountered in this study that has been effectively managed in time.

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