Effects of Data Science, Predictive Analytics & Big Data (DPB) on the Supply Chain

Table of Contents

1 Introduction

1.1 Research Topic, Relevance and Focus

1.2 Research Objectives

1.3 Structure

2 Literature Review

2.1 Overview

2.2 The Terminology

2.3 Big Data

2.4 Data Science

2.5 Supply Chain and Supply Chain Management

2.6 Applied SCM Data Science

2.7 Implementation of SCM Data Science

2.8 Predictive Analytics

2.9 Financial Sector

2.10 Services Supply Chain

2.11 Summary

3 Methodology

3.1 Introduction

3.2 Approach

3.3 Design

3.4 Strategy & Role

3.5 Methods

3.6 Credibility & Limitations

3.7 Ethics

3.8 Data Collection

3.9 Interviews

4 Findings, Application and Discussion

4.1 Introduction

4.2 Services Supply Chain

4.3 Big Data

4.4 Decision Making

4.5 Summary

5 Conclusion

5.1 Results and Answers

5.2 Reflection and Outlook

6 Appendices

6.1 Definition: Bandwagon Effect

6.2 Applications of Analytics

6.3 Effectiveness vs. Domain Knowledge

6.4 Definition: Sexy

6.5 Implementation of Big Data

6.6 Maturity Map

6.7 Methodological Pyramid

6.8 Research Onion

6.9 Individual Consent

6.10 Organisational Consent

6.11 Interview Guide

6.12 Extract of transcription and translation

6.13 Extracts from the interview analysis process

6.14 Interview Participant Coding

6.15 Customer Journey

6.16 Definition: Buzzword

6.17 Definition: Opportunity Costs

6.18 Explanation: SWOT

6.19 Definition: Micro-Management

6.20 Definition: Telematics

6.21 Definition: Generation Y

6.22 Reflective Statement

Research Objectives and Key Topics

This undergraduate thesis explores the impact of Big Data, data science, and predictive analytics on supply chain management and corporate decision-making within the German financial services sector, using a key market participant as a case study.

• Examination of the effects of Big Data on supply chain efficiency and profitability.
• Analysis of decision-making processes in financial service providers enhanced by predictive analytics.
• Identification of opportunities and threats regarding the implementation of data-driven frameworks.
• Development of a specialized supply chain framework for services in the financial sector.

Excerpt from the Book

Summary of Chapters

Introduction: Outlines the research topic regarding Big Data in the financial services sector and defines the core research objectives and dissertation structure.

Literature Review: Provides a theoretical foundation covering Big Data, data science, supply chain management, and the specific dynamics of the financial services sector.

Methodology: Details the research philosophy, the inductive approach, the use of qualitative semi-structured interviews, and the ethical considerations taken.

Findings, Application and Discussion: Analyzes the gathered interview data, presenting the identified opportunities and threats of Big Data through the SWOT framework and its impact on decision-making.

Conclusion: Summarizes the key results, affirms the "game changer" status of Big Data for the sector, and provides a final reflection on the research process.

Keywords

Supply Chain Management, Big Data, Data Science, Predictive Analytics, Financial Services, Leasing, Digitalization, Decision Making, Business Intelligence, Service Supply Chain, Customer Journey, Process Automation, Risk Management, Innovation, Industry 4.0

Frequently Asked Questions

What is the core focus of this dissertation?

The research focuses on analyzing how Big Data, data science, and predictive analytics influence the supply chain and managerial decision-making processes within the financial services sector, specifically within a major leasing company.

What are the primary themes discussed in this work?

Key themes include the transformation of supply chains into data-driven models, the transition from conventional to predictive analytics, the challenges of Big Data in a services context, and the cultural and regulatory hurdles faced by financial institutions.

What is the central research question?

The study seeks to identify the effects of DPB (Data Science, Predictive Analytics, and Big Data) on the supply chain and the decision-making of companies in the financial service sector in Germany.

Which methodology was chosen for this research?

The author employed a qualitative research approach, conducting semi-structured interviews with eight experts from the investigated organization to identify patterns and build a generalizable theory.

What topics are covered in the main body of the work?

The work moves from establishing a theoretical framework for supply chain management and Big Data to conducting a thematic analysis of expert interviews, focusing on practical applications and future impacts.

How is the work characterized by its keywords?

The work is characterized by the intersection of traditional financial services, such as leasing, and the modern, disruptive potential of Big Data and automated decision-making processes.

How does the author define the "product" of a financial service provider?

Based on the interview analysis, the product is identified as a bundle consisting of three components: the underlying asset, the financing solution, and additional services.

What is the significance of the "leasing triangle"?

Proposed by interview participants, the "leasing triangle" is suggested as a more appropriate visualization method for the financial services supply chain compared to traditional manufacturing-based models.

Why are privacy regulations considered a major threat?

The research highlights that strict privacy regulations in Germany limit the extent to which customer data can be utilized, creating a potential barrier to the full realization of Big Data potentials.