Diese Arbeit analysiert die zentrale Herausforderung moderner Automobilzulieferer im Zeitalter von Industrie 4.0: die Wahl der optimalen IT-Infrastruktur für ein prädiktives Wartungsprojekt. Am Praxisbeispiel der AutoParts Innovations GmbH werden On-Premise- und Public-Cloud-Lösungen anhand von Kriterien wie Skalierbarkeit, Sicherheit, TISAX-Compliance, Latenz und Kosten verglichen. Die Untersuchung zeigt, dass eine Public-Cloud-Strategie aufgrund ihrer flexiblen Skalierbarkeit und zertifizierten Compliance-Vorteile besonders für mittelständische Unternehmen geeignet ist, während On-Premise-Modelle nur noch in Ausnahmefällen rentabel erscheinen. Die Arbeit bietet fundierte Entscheidungsgrundlagen und konkrete Handlungsempfehlungen für die digitale Transformation in der Automobilindustrie.
Table of Contents
1. Introduction
2. Fundamentals and Key Concepts
2.1 On-premises Infrastructure
2.2 Cloud Computing
2.3 Predictive Maintenance and IoT Data
2.4 Compliance Frameworks
2.5 Edge Computing
2.6 Evaluation Criteria
3. Analysis of the Scenario: AutoParts Innovation GmbH
3.1 Scenario Overview
3.2 Technical Requirements Analysis
3.2.1 Data Volume and Velocity
3.2.2 Latency Requirements
3.2.3 Scalability for ML Workloads
3.3 Security and Compliance Analysis
3.3.1 TISAX Compliance
3.3.2 Data Encryption
3.4 Qualitative Discussion
4. Evaluation of Approaches
4.1 On-premises Solution
4.2 Public Cloud Solution
4.3 Hybrid Approach (Edge-Cloud)
4.4 Comparative Summary
4.5 Recommendation for API GmbH
5. Conclusion
Research Objectives and Core Themes
This paper investigates the optimal infrastructure strategy for implementing a machine learning-driven predictive maintenance system for a mid-sized German automotive supplier, API GmbH. The primary research question addresses whether a public cloud solution or an on-premises infrastructure is more suitable when considering data security, scalability, and cost efficiency in the context of Industry 4.0.
- Strategic comparison of on-premises versus public cloud infrastructure models.
- Evaluation of Industry 4.0 technical requirements, including data volume, latency, and ML scalability.
- Analysis of compliance and security standards, specifically TISAX Level 3 and ISO 27001.
- Economic assessment balancing capital expenditure (CapEx) and operational expenditure (OpEx).
- Actionable recommendations for mid-market organizations facing infrastructure transformation.
Excerpt from the Book
3.2.1 Data Volume and Velocity
As part of the infrastructure evaluation, API GmbH's legacy on-premises servers, which have been in operation for over eight years, lack the scalability and capacity to handle the projected 2.5 terabytes of IoT sensor data per day. Overcoming this limitation would require a major upgrade that includes high-performance storage systems, low-latency networking, and significant hardware upgrades. Industry analysis indicates that such overhauls are often associated with high capital expenditures for small and midsize manufacturing companies (Varma, 2023, p. 50; Madaan et al., 2023, p. 147). This static infrastructure model also limits the company's ability to quickly adapt to growing data volumes and evolving operational requirements.
In contrast, cloud platforms such as AWS S3 and Azure Blob Storage offer a scalable, flexible alternative that eliminates the need for up-front hardware investments. These services provide automatic scaling capabilities, allowing the infrastructure to dynamically expand in response to data growth (Velte et al., 2009, p. 16). For API GmbH, this means the ability to match infrastructure resources to workload demands - an essential advantage in predictive maintenance scenarios driven by fluctuating IoT data streams. The benefits of this approach are evident in real-world applications: companies like BMW have successfully migrated industrial analytics workloads to the cloud, leveraging its elasticity to accelerate implementation timelines and reduce operational overhead compared to on-premises systems (Amazon Web Services, 2020).
Chapter Summaries
1. Introduction: Outlines the challenges of implementing predictive maintenance in the automotive sector and defines the research question regarding infrastructure choices.
2. Fundamentals and Key Concepts: Defines essential IT infrastructure models, predictive maintenance, compliance frameworks like TISAX, and edge computing.
3. Analysis of the Scenario: AutoParts Innovation GmbH: Analyzes the specific technical requirements of API GmbH regarding data handling, latency, security, and scalability.
4. Evaluation of Approaches: Provides a comparative evaluation of on-premises, public cloud, and hybrid infrastructure strategies based on the predefined criteria.
5. Conclusion: Summarizes the findings and recommends a public cloud-first strategy to support long-term Industry 4.0 transformation.
Key Concepts
Cloud Computing, On-premises Infrastructure, Predictive Maintenance, IoT, Industry 4.0, TISAX, ISO 27001, Data Security, Scalability, CapEx, OpEx, Machine Learning, Edge Computing, Automotive Manufacturing, Data Privacy.
Frequently Asked Questions
What is the core focus of this research paper?
The paper focuses on evaluating the suitability of cloud versus on-premises infrastructure for implementing predictive maintenance systems within the mid-sized German automotive manufacturing sector.
What are the primary themes analyzed in this work?
The central themes include the trade-offs between data control and scalability, the impact of compliance standards like TISAX, the financial transition from CapEx to OpEx models, and the technical requirements for IoT-driven analytics.
What is the main research question?
The research asks whether a public cloud solution or an on-premises infrastructure is more suitable for API GmbH, considering data security, scalability, and cost constraints.
What scientific methods are utilized for this analysis?
The study employs a case study approach, utilizing industry benchmarks, literature-based analysis of infrastructure models, and a qualitative evaluation based on performance and regulatory criteria.
What topics are covered in the main body of the paper?
The main body covers technical requirements (data volume/latency), security/compliance analysis (TISAX/Encryption), and a comparative evaluation of cloud, on-premises, and hybrid approaches.
Which keywords best characterize this research?
Key terms include Predictive Maintenance, Cloud Computing, Industry 4.0, TISAX, Scalability, and Digital Transformation.
Why is TISAX compliance a critical factor for API GmbH?
TISAX Level 3 is a mandatory standard for automotive suppliers handling sensitive OEM data, making it a critical constraint for any chosen IT infrastructure.
How does the paper propose handling latency issues in the cloud?
The paper suggests using targeted edge computing deployments, such as Azure Stack Edge or AWS Outposts, to ensure near real-time data processing while maintaining cloud connectivity.
What is the final recommendation provided for API GmbH?
The paper recommends a public cloud-first strategy to leverage scalability and cost flexibility, while reserving on-premises infrastructure for specific, highly sensitive use cases.
- Quote paper
- Ron Delhees (Author), 2025, Analytical Software and Frameworks. On Premise vs Cloud Computing using the example of a German automotive company, Munich, GRIN Verlag, https://www.grin.com/document/1601345
