Markus Hammer

Eine strategische und operative Perspektive zur Anwendung von Big Data in der Industrie

Kurzfassung Durch die Digitalisierung sowie durch den Fortschritt in Technologien zur Datenspeicherung und -verarbeitung eröffnen sich für Industrieunternehmen vielfältige Anwendungsmöglichkeiten von Big Data. Vor diesem Hintergrund stellt sich für Unternehmen die Herausforderung, wie sie Big Data am besten für sich nutzen können. Im vorliegenden Beitrag wird eine Methodik zur zielgerichteten Anwendung von Big Data in der Industrie vorgestellt. Sie basiert auf einer Unterteilung in eine Identifikation von strategischen Einsatzfeldern von Big Data in Bezug auf externe Herausforderungen und auf einem Ansatz zur operativen Optimierung aus interner Sicht.

Digitization Perspective: Impact of Digital Technologies in Manufacturing

This chapter provides a perspective on digitization and the impact on manufacturing. Section 3.1 introduces digitization and the subsequent chapters cover Industry 4.0 (section 3.2), Industrial Internet of Things (section 3.3), Big Data (section 3.4), advanced analytics (section 3.5) and a summary (section 3.6).

Operations Perspective: Achieving Resource-Productive Operations

This chapter gives a perspective on resource-productive operations (section 5.1), operations improvement methods (section 5.2), advanced process control (section 5.3) and a summary of how to achieve resource-productive operations in section 5.4.

Industry Perspective: Challenges in Manufacturing in Process Industries

This chapter frames the setting for this thesis. Section 2.1 looks at mega trends such as the increasingly dynamic environment industrial companies are exposed to. Section 2.2 gives an overview of manufacturing industries. Section 2.3 focuses specifically on process industries. Section 2.4 provides a summary of the challenges faced by manufacturers in process industries.

Interim Conclusion: Scope of Work

In this chapter the research need is summarized and the scope of work is defined through the delimitation and requirements from the previous chapters dealing with theory, related work and observations from practical cases.

Methodology: Implementing an Analytics, Time and Six Sigma Based Operations Management Approach

In this chapter the previously conceived methodology will be explained in detail. The framework, Figure 72, follows the five phases of the Six-Sigma approach with 17 specific activities and tools for each phase stated by the author. This chapter gives the answer to RQ3 on how to implement a profit per hour management approach. As with all approaches, a customization, i.e. an extension of activities or adaptation of tools, to specific circumstances is required as part of a broader rollout – this need for adaptation and continuous improvement is also pointed out by Schwaninger 2013, p. 56.

Methodology Conception: Framing a Time-Based and Analytics Supported Operations Management Approach

In this chapter the methodology to answer the research questions from chapter 1 will be outlined. The overall objective is to provide industrial companies with a step-by-step process methodology to implement a time-based and analytics-supported operations management approach. In section 8.1 the basic concept and characteristics of the approach are introduced. Section 8.2 covers the theoretical classification of the methodology. Section 8.3 deals with criteria when the approach is meaningful. Section 8.4 looks at pre-conditions to enable the approach.

Methodology Validation: Prototypical Application in the Pulp Manufacturing Industry

In this chapter the conceived methodology is validated by taking a case study approach. The research is based again on process industries and deals with pulp manufacturing. It includes insights on the process, examples, and sanitized results.

Management Perspective: Performance Opportunities with Decision Support Systems

In this chapter a perspective of management is given. Section 4.1 starts with decision making followed by performance measures (section 4.2) as basis for fact-based decisions. Section 4.3 covers performance measurement and management systems, section 4.4 decision support systems and a summary of performance opportunities with decision-support systems is covered in section 4.5.

Practice Perspective: Learning from Case Research in the Cement and Ammonia Industry

After the review of related literature, this chapter provides a practical perspective based on sanitized examples from cement clinker production (section 6.1) and ammonia production (section 6.2). Section 6.3 summarizes the learning and requirements for the application of analytics in process industries.