A Study on the Opportunities and Threats of Data Science, Predictive Analytics & Big Data (DPB) on the Services Supply Chain on the example of a key player in the german leasing/financial services market.
Working for a financial service provider raises the awareness for certain issues within this sector and how those could be solved by means of new trends such as big data.
This undergraduate thesis presents an exploration of the effects of big data on the supply chain and on decision making. A topic of significant relevance, especially since the literature review discovered that there had been insufficient research conducted on the supply chains of service providers. Furthermore, this investigation of existing literature created a frame for the content of the dissertation by displaying the roots and development of big data and its farreaching impact in and beyond the business context.
The research method that was used for this dissertation consists of qualitative data collection by interviewing employees of a key player in the German leasing and asset finance market, providing insight to the business from the industry leaders viewpoint.
After conducting and analysing the input, the key findings on one hand corresponded with parts of the reviewed literature in terms of the application of big data and on the other hand filled the identified gap regarding services supply chains. More specifically, as a tangible outcome an exemplary supply chain framework was created, based on the identified opportunities and threats regarding an implementation of big data as well as their critical evaluation.
The results ranged from chances for increased efficiency of background processes and improved effectivity of sales processes, leading to a greater profitability on one side, to being confronted with issues of privacy, economic viability and the adaptions caused by the better decision making stemming from enhanced insights through data science, predictive analytics and big data, on the other side.
In order to fully exploit these identified opportunities, further research within this field is recommenced, especially in regard to the increasing relevance of the sector and the megatrend itself as well as the actual technical implementation.
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
Services Supply Chain
In order to answer the research question unmitigatedly, it needs to be deconstructed and its components identified. This leads to the realization that the approach must start at a delineation of the SC before trying to measure the effects possible applications of big data might have. Based on its definition in the literature review, the SC is product specific, therefore, to be able to describe this process for a certain product and create a certain degree of generalizability, one needs to identify the product itself in beforehand.
What most participants agreed on was the product of the financial service provider to be a bundle consisting of asset, financing and services, which can be seen in the visualization above. The asset is at the core even though it is not being produced by the examined company, but the financing and the services are the tailor-made solution that perfectly matches each asset and that the customer is provided with. To be more precise, the deliverables however are the contracts for each component. The bundle stems from the fact that the customers do not only demand being lent money to purchase (im-)mobile assets, but convenience, the reason why the services are added, explains P2. P3 agrees, however sees this as the internal perspective. The customer would view the product in the final place to be more of a timely limited purchase of additional capacity.
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.
- Quote paper
- Lukas Ebert (Author), 2017, Effects of Data Science, Predictive Analytics & Big Data (DPB) on the Supply Chain, Munich, GRIN Verlag, https://www.grin.com/document/998029
