title: Towards Data Analytics-Driven Supply Chain Performance Measurement Systems
title: Development of a conceptual framework and a supportive guideline for the adoption of Data Analytics in Supply Chain Performance Measurement Systems
titleType: Subtitle
Creators: name: Mello, Raquel
Publisher: name: University of Muenster, Institutional Repository MIAMI
Year: 2023
Subjects: subject: 330 Economics

description: In der heutigen, äußerst wettbewerbsintensiven und dynamischen Geschäftswelt sind effiziente und effektive Lieferketten unerlässlich. Data analytics liefern datenbasierte Erkenntnisse, die eine effiziente Entscheidungsfindung ermöglichen. Gleichzeitig spielen Supply Chain Performance Measurement Systems (SCPMSs) eine zentrale Rolle bei der Effektivitätsmessung und -erreichung. Diese Dissertation führt eine umfassende Studie zum Zusammenspiel von data analytics und SCPMSs durch, mit dem Fokus auf Einblicke aus Forschung und Praxis. Dabei werden unterstützende Instrumente entwickelt, die das volle Potenzial eines data analytics-driven SCPMS aufzeigen. Die Forschung resultiert in einem prägnanten konzeptionellen Modell und praktischen Richtlinien, um Forschende und Praktiker bei der Gestaltung, Implementierung und Nutzung von data analytics-driven SCPMSs in verschiedenen Bereichen der Lieferketten zu unterstützen.
description: In an intensely competitive and dynamic business landscape, supply chains are required to be efficient and effective. Data analytics is seen as an enabler in this pursuit, offering data-driven insights that steer efficient decision-making. Simultaneously, Supply Chain Performance Measurement Systems (SCPMSs) play a vital role in measuring and achieving effectiveness. This thesis reports a comprehensive study on the synergistic interplay between data analytics and SCPMSs, focusing on gathering insights from both research and practice and developing supportive artifacts that disclose the full potential of a data analytics-driven SCPMS. This research resulted in a concise conceptual framework and a practical guideline to support scholars and practitioners in navigating the research, design, implementation, and utilization of data analytics-driven SCPMSs across diverse supply chain areas.
description: Acknowledgement ..... I Table of Contents ..... II List of Figures ..... V List of Tables ..... VII List of Abbreviations ..... VIII 1 Introduction ..... 1 1.1 Motivation ..... 1 1.2 Research Goals ..... 3 1.3 Research Design ..... 8 1.4 Thesis Structure ..... 15 2 Theoretical Background ..... 17 2.1 Performance Measurement Systems ..... 17 2.2 Supply Chain Performance Measurement Systems ..... 20 2.2.1 SCPMS Overall Structure and Definition ..... 21 2.2.2 SCPMS Purposes ..... 24 2.2.3 Supply Chain Performance Measurement Approaches ..... 26 2.3 Data Analytics ..... 28 2.3.1 Definition and Fundamental Concepts ..... 28 2.3.2 Data Characteristics and Sources for Data Analytics ..... 30 2.3.3 Data Analytics Types and Functionalities ..... 34 3 Development of a Conceptual Framework for Data Analytics-Driven Supply Chain Performance Measurement Systems ..... 38 3.1 Research Approach ..... 38 3.1.1 Systematic Literature Review ..... 39 3.1.2 Content Analysis ..... 42 3.2 Overview of the Developed Conceptual Framework for Data Analytics-Driven Supply Chain Performance Measurement Systems ..... 47 3.2.1 Preconditions ..... 47 3.2.2 Supply Chain Application Areas ..... 49 3.2.3 Performance Measures ..... 54 3.2.4 Data Types ..... 56 3.2.5 Data Sources ..... 57 3.2.6 Types of Data Analytics ..... 58 3.2.7 Benefits of Data Analytics in SCPMSs ..... 60 3.3 Evaluation of the Conceptual Framework for Data Analytics-Driven SCPMSs ..... 62 3.3.1 Overview of the Evaluation Approach ..... 62 3.3.2 Results of Evaluation ..... 63 3.4 Key Findings and Discussion ..... 65 4 State of the practice of Data Analytics in Supply Chain Performance Measure-ment Systems ..... 67 4.1 Research Approach ..... 67 4.1.1 Criteria for Selecting Companies and Interviewees ..... 68 4.1.2 Collection of Empirical Data ..... 70 4.1.3 Data Analysis ..... 72 4.2 Interview Findings: SCPMSs and Data Analytics-Driven SCPMSs ..... 74 4.2.1 Company A ..... 74 4.2.2 Company B ..... 82 4.2.3 Company C ..... 89 4.2.4 Company D ..... 94 4.2.5 Company E ..... 96 4.2.6 Company F ..... 99 4.3 Analysis and Findings ..... 101 4.3.1 Cross-Case Analysis ..... 102 4.3.2 Challenges of Data Analytics-driven SCPMSs in Practice ..... 106 5 Development of a Data Analytics-Driven Supply Chain Performance Measure-ment Systems Guideline ..... 109 5.1 Research Approach ..... 110 5.2 Requirements for Data Analytics-Driven Supply Chain Performance Measurement Systems Guideline ..... 112 5.3 Supply Chain Performance Measurement Systems Life Cycles ..... 116 5.3.1 Literature Review ..... 116 5.3.2 Identified Life Cycles ..... 118 5.3.3 Requirement Analysis ..... 122 5.4 Data Analytics Processes ..... 124 5.4.1 Literature Review ..... 125 5.4.2 Identified Data Analytics Processes ..... 126 5.4.3 Requirement Analysis ..... 132 5.5 Overview of the Proposed Data Analytics-Driven Supply Chain Perfor-mance Measurement Systems Guideline ..... 135 5.5.1 Design Phase ..... 136 5.5.2 Implementation Phase ..... 138 5.5.3 Use and Review Phases ..... 139 5.5.4 Main Actors ..... 141 5.6 Evaluation of the Data Analytics-Driven Supply Chain Performance Measurement Systems Guideline ..... 143 5.6.1 Requirement Analysis ..... 144 5.6.2 Focus Group ..... 145 5.6.3 Application Case ..... 152 6 Conclusion ..... 157 6.1 Summary ..... 157 6.2 Research Limitations ..... 160 6.3 Outlook and Future Research Opportunities ..... 161 References ..... 164 Appendix ..... 184 A PRISMA flow diagram ..... 185 B Material Evaluation - Conceptual Framework ..... 186 C Collecting Empirical Data ..... 190 C.1 Interview Guideline ..... 190 C.2 Levels of Research Questions ..... 191 D Data Analytics-driven SCPMS Guideline ..... 192 D.1 Performance Measurement Systems Life Cycles ..... 192 D.2 Focus Group Participants ..... 196 D.3 Data Analytics Roles ..... 197 D.4 Fulfilled Requirements ..... 198