Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains. Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin. Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed. Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations

While supply chain analytics shows promise regarding value, benefits, and increase in performance for logistics and supply chain management (LSCM) organizations, those organizations are often either reluctant to invest or unable to achieve the returns they aspire to. This article systematically explores the barriers LSCM organizations experience in employing supply chain analytics that contribute to such reluctance and unachieved returns and measures to overcome these barriers. This article therefore aims to systemize the barriers and measures and allocate measures to barriers in order to provide organizations with directions on how to cope with their individual barriers. By using Grounded Theory through 12 in-depth interviews and Q-Methodology to synthesize the intended results, this article derives core categories for the barriers and measures, and their impacts and relationships are mapped based on empirical evidence from various actors along the supply chain. Resultingly, the article presents the core categories of barriers and measures, including their effect on different phases of the analytics solutions life cycle, the explanation of these effects, and accompanying examples. Finally, to address the intended aim of providing directions to organizations, the article provides recommendations for overcoming the identified barriers in organizations

Explaining the competitive advantage generated from Analytics with the knowledge-based view: the example of Logistics and Supply Chain Management

The purpose of this paper is to provide a theory-based explanation for the generation of competitive advantage from Analytics and to examine this explanation with evidence from confirmatory case studies. A theoretical argumentation for achieving sustainable competitive advantage from knowledge unfolding in the knowledge-based view forms the foundation for this explanation. Literature about the process of Analytics initiatives, surrounding factors, and conditions, and benefits from Analytics are mapped onto the knowledge-based view to derive propositions. Eight confirmatory case studies of organizations mature in Analytics were collected, focused on Logistics and Supply Chain Management. A theoretical framework explaining the creation of competitive advantage from Analytics is derived and presented with an extensive description and rationale. This highlights various aspects outside of the analytical methods contributing to impactful and successful Analytics initiatives. Thereby, the relevance of a problem focus and iterative solving of the problem, especially with incorporation of user feedback, is justified and compared to other approaches. Regarding expertise, the advantage of cross-functional teams over data scientist centric initiatives is discussed, as well as modes and reasons of incorporating external expertise. Regarding the deployment of Analytics solutions, the importance of consumability, users assuming responsibility of incorporating solutions into their processes, and an innovation promoting culture (as opposed to a data-driven culture) are described and rationalized. Further, this study presents a practical manifestation of the knowledge-based view

Management von Supply Chain Analytics - Untersuchungen zur Durchführung von Analytics-Initiativen durch Organisationen in Logistik und Supply Chain Management

This doctoral thesis seeks to contribute to research on the managerial aspects of Analytics in the field of Logistics and Supply Chain Management by conducting four individual research studies and an extensive synopsis of their results. The goal of this thesis is to provide managers in Logistics and Supply Chain Management with means and insights to manage Analytics initiatives in their organizations successfully and impactfully. The four research studies investigate separate components of the management process of Analytics relevant to the research objective including contributing factors outside of Analytics initiatives influencing them, archetypes of common Analytics initiatives in Logistics and Supply Chain Management, the value contribution of Analytics initiatives to the competitive advantage of organizations as well as barriers against impactful Analytics initiatives and measures against these barriers. Combined with an additional investigation of 15 process models of conducting Analytics projects and initiatives, the insights of the individual studies are mapped to process phases of Analytics initiatives.Diese Doktorarbeit leistet einen Beitrag zur Forschung über die Managementaspekte von Analytics im Bereich Logistik und Supply Chain Management, indem vier individuelle Forschungsstudien und eine umfassende Zusammenfassung ihrer Ergebnisse durchgeführt werden. Das Ziel dieser Arbeit ist es, Managern im Bereich Logistik und Supply Chain Management Mittel und Erkenntnisse zur Verfügung zu stellen, um Analytics-Initiativen in ihren Organisationen erfolgreich und wirkungsvoll umzusetzen. Die vier Forschungsstudien untersuchen separate Komponenten des Managementprozesses von Analytics, die für das Forschungsziel relevant sind, einschließlich der sie beeinflussenden Faktoren außerhalb der Analytics-Initiativen, Archetypen gängiger Analytics-Initiativen in Logistik und Supply Chain Management, den Wertbeitrag von Analytics-Initiativen zum Wettbewerbsvorteil von Organisationen sowie Barrieren gegen wirkungsvolle Analytics-Initiativen und Maßnahmen gegen diese Barrieren. Verknüpft mit einer angestellten Untersuchung von 15 Prozessmodellen zur Durchführung von Analytics-Projekten und -Initiativen werden die Erkenntnisse der einzelnen Studien zu Prozessphasen von Analytics-Initiativen zugeordnet

Archetypes of Supply Chain Analytics Initiatives—An Exploratory Study

While Big Data and Analytics are arguably rising stars of competitive advantage, their application is often presented and investigated as an overall approach. A plethora of methods and technologies combined with a variety of objectives creates a barrier for managers to decide how to act, while researchers investigating the impact of Analytics oftentimes neglect this complexity when generalizing their results. Based on a cluster analysis applied to 46 case studies of Supply Chain Analytics (SCA) we propose 6 archetypes of initiatives in SCA to provide orientation for managers as means to overcome barriers and build competitive advantage. Further, the derived archetypes present a distinction of SCA for researchers seeking to investigate the effects of SCA on organizational performance

Examples from Blockchain Implementations in Logistics and Supply Chain Management: Exploring the Mindful Use of a New Technology

In the context of logistics, blockchain can help to increase end-to-end visibility along global supply chains. Thus, it can lead to improved tracking of goods and offer tamper-proof data to build trust among parties. Although a variety of blockchain use cases already exists, not all of them seem to rely on blockchain-specific features, but could rather be solved with traditional technologies. The purpose of this paper is, therefore, to identify characteristic use cases described for blockchain in the field of LSCM and to analyze them regarding their mindful technology use based on five mindful technology adoption principles: engagement with the technology; Technological novelty seeking; awareness of local context; cognizance of alternative technologies; and anticipation of technology alteration. The authors identified five blockchain case clusters and chose one case for each category to be analyzed in detail. Most cases demonstrate high engagement with the technology, but there are significant differences when it comes to the other mindful use principles. This paper highlights the need to understand the problem and to apply the right technology in order to solve it. When solving a problem, care should be taken to address a technology’s unique features to ensure effectiveness and cost-efficiency