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
- Introduction
- Fundamentals and Key Concepts
- On-premises Infrastructure
- Cloud Computing
- Predictive Maintenance and IoT Data
- Compliance Frameworks
- Edge Computing
- Evaluation Criteria
- Analysis of the Scenario: AutoParts Innovation GmbH
- Scenario Overview
- Technical Requirements Analysis
- Data Volume and Velocity
- Latency Requirements
- Scalability for ML Workloads
- Security and Compliance Analysis
- TISAX Compliance
- Data Encryption
- Qualitative Discussion
- Evaluation of Approaches
- On-premises Solution
- Public Cloud Solution
- Hybrid Approach (Edge-Cloud)
- Comparative Summary
- Recommendation for API GmbH
Objectives and Key Themes
This paper analyzes the optimal infrastructure solution (on-premises vs. cloud) for AutoParts Innovation GmbH (API GmbH), a German automotive supplier, implementing a machine learning-driven predictive maintenance system. The study aims to provide actionable insights for mid-sized companies navigating the trade-offs between cost, scalability, and compliance in adopting advanced analytics. * Comparison of on-premises and cloud computing infrastructure for predictive maintenance. * Analysis of the technical and security requirements for handling large volumes of IoT data. * Evaluation of cost-effectiveness and scalability considerations for SMEs. * Assessment of compliance with industry standards (e.g., TISAX). * Exploration of hybrid cloud approaches.Chapter Summaries
Introduction: This chapter introduces the context of predictive maintenance in the automotive industry, highlighting the significant costs associated with downtime. It presents the case study of API GmbH, a German automotive supplier facing substantial production losses due to CNC machine failures. The research question focuses on determining whether a cloud or on-premises solution is more suitable for API GmbH's predictive maintenance project, considering data security, scalability, and cost constraints. The introduction lays out the key criteria for evaluation and outlines the paper's organization. Fundamentals and Key Concepts: This section defines crucial terms and frameworks relevant to the infrastructure decision. It details the characteristics of on-premises infrastructure, emphasizing full data control but highlighting high capital expenditure and limited scalability. It then explores cloud computing, outlining public, private, and hybrid cloud models and their respective advantages and disadvantages in the context of manufacturing. The chapter also touches upon predictive maintenance architectures and compliance frameworks, establishing a foundational understanding for the subsequent analysis. Analysis of the Scenario: AutoParts Innovation GmbH: This chapter delves into the specifics of API GmbH's situation, providing a detailed overview of their technical requirements. It examines their data volume and velocity, latency requirements for real-time anomaly detection, and the need for scalability to handle machine learning workloads. The analysis also addresses security and compliance concerns, particularly focusing on TISAX compliance and data encryption strategies. This thorough examination of API GmbH's needs provides a robust basis for the subsequent evaluation of different infrastructure approaches. Evaluation of Approaches: This chapter presents a comparative evaluation of on-premises, public cloud, and hybrid cloud solutions based on the criteria established in the preceding sections. Each approach is analyzed in terms of its suitability for API GmbH's specific needs, considering the trade-offs between security, cost, scalability, and compliance. This section likely details the advantages and disadvantages of each approach with respect to API GmbH's specific circumstances. A comparative summary and a recommendation for API GmbH are expected to follow.Keywords
Predictive Maintenance, Cloud Computing, On-premises Infrastructure, IoT, Machine Learning, Data Security, TISAX Compliance, Scalability, Cost-effectiveness, Automotive Manufacturing, Industry 4.0, SMEs, Hybrid Cloud, Edge Computing.
Frequently asked questions about the Language Preview
What is the main topic of this document?
This document analyzes the optimal infrastructure solution (on-premises vs. cloud) for AutoParts Innovation GmbH (API GmbH), a German automotive supplier, to implement a machine learning-driven predictive maintenance system. It aims to provide insights for mid-sized companies navigating the trade-offs between cost, scalability, and compliance in adopting advanced analytics.
What are the objectives and key themes explored in the document?
The document focuses on several key themes:
- Comparison of on-premises and cloud computing infrastructure for predictive maintenance.
- Analysis of technical and security requirements for handling large volumes of IoT data.
- Evaluation of cost-effectiveness and scalability considerations for SMEs.
- Assessment of compliance with industry standards (e.g., TISAX).
- Exploration of hybrid cloud approaches.
What is the case study company and its context?
The case study involves AutoParts Innovation GmbH (API GmbH), a German automotive supplier. The company is facing substantial production losses due to CNC machine failures and is considering a predictive maintenance system to mitigate these issues.
What problem is API GmbH trying to solve?
API GmbH is aiming to reduce downtime and production losses caused by CNC machine failures through the implementation of a predictive maintenance system driven by machine learning.
What infrastructure options are being considered for the predictive maintenance system?
The document evaluates three main infrastructure options:
- On-premises solution
- Public cloud solution
- Hybrid approach (edge-cloud)
What factors are considered when evaluating the infrastructure options?
The evaluation considers several key factors including:
- Cost
- Scalability
- Data Security
- Compliance (specifically TISAX)
- Latency requirements
- Data volume and velocity
What is TISAX and why is it important in this context?
TISAX (Trusted Information Security Assessment Exchange) is a standard relevant to the automotive industry that focuses on information security. Compliance with TISAX is a crucial requirement for API GmbH due to the sensitive nature of their data and partnerships.
What are the key components analyzed in the "Analysis of the Scenario" chapter?
This chapter focuses on the specifics of API GmbH's situation, analyzing:
- Data volume and velocity requirements
- Latency requirements for real-time anomaly detection
- Scalability needs for machine learning workloads
- Security and compliance concerns, especially TISAX compliance and data encryption strategies
What are the key words associated with this document?
The keywords include Predictive Maintenance, Cloud Computing, On-premises Infrastructure, IoT, Machine Learning, Data Security, TISAX Compliance, Scalability, Cost-effectiveness, Automotive Manufacturing, Industry 4.0, SMEs, Hybrid Cloud, Edge Computing.
What is the purpose of the "Fundamentals and Key Concepts" chapter?
This chapter provides definitions and explanations of key terms and frameworks relevant to the infrastructure decision, including on-premises infrastructure, cloud computing models (public, private, hybrid), predictive maintenance architectures, and compliance frameworks.
- 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.hausarbeiten.de/document/1601345
