Enhancing Decision-Making In SCM: Investigating The Status Quo And Obstacles Of Advanced Analytics In Austrian Companies

Over the past few years, the stability and predictability of logistics and supply chain networks have significantly decreased. This has led to higher risks and increased uncertainty in decision-making within supply chain management (SCM). Fortunately, the abundance of available data presents a tremendous opportunity to alleviate this uncertainty. However, realizing the full potential of advanced analytics, such as predictive and prescriptive analytics, is hindered by a lack of knowledge regarding their practical applications and performance benefits, as well as a deficiency in implementation expertise. This research paper examines the current state of advanced analytics applications and the primary challenges faced by Austrian companies in this domain. The findings reveal a distinct pattern: although the literature highlights numerous performance advantages, the practical utilization of advanced analytics remains at a rudimentary stage and is primarily confined to isolated departments. While demand management, procurement, and transport planning have shown some initial success in their implementation, other areas like production planning and, particularly, warehouse management lag. The primary challenges observed in practice include a limited understanding of the potential of advanced analytics, lack of transparency and data quality issues, difficulties in internal marketing, and inadequate organizational integration. These challenges, along with potential courses of action, serve as a starting point for other companies aiming to address similar issues. The significance of this work lies not only in its theoretical contribution to existing research on advanced analytics in SCM but also as one of the few studies that delve into the practical implementation and specific application domains of advanced analytics in Austria

Improving resilience in Critical Infrastructures through learning from past events

Modern societies are increasingly dependent on the proper functioning of Critical Infrastructures (CIs). CIs produce and distribute essential goods or services, as for power transmission systems, water treatment and distribution infrastructures, transportation systems, communication networks, nuclear power plants, and information technologies. Being resilient, where resilience denotes the capacity of a system to recover from challenges or disruptive events, becomes a key property for CIs, which are constantly exposed to threats that can undermine safety, security, and business continuity. Nowadays, a variety of approaches exists in the context of CIs’ resilience research. This dissertation starts with a systematic review based on PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) on the approaches that have a complete qualitative dimension, or that can be used as entry points for semi-quantitative analyses. The review identifies four principal dimensions of resilience referred to CIs (i.e., techno-centric, organizational, community, and urban) and discusses the related qualitative or semi-quantitative methods. The scope of the thesis emphasizes the organizational dimension, as a socio-technical construct. Accordingly, the following research question has been posed: how can learning improve resilience in an organization? Firstly, the benefits of learning in a particular CI, i.e. the supply chain in reverse logistics related to the small arms utilized by Italian Armed Forces, have been studied. Following the theory of Learning From Incidents, the theoretical model helped to elaborate a centralized information management system for the Supply Chain Management of small arms within a Business Intelligence (BI) framework, which can be the basis for an effective decision-making process, capable of increasing the systemic resilience of the supply chain itself. Secondly, the research question has been extended to another extremely topical context, i.e. the Emergency Management (EM), exploring the crisis induced learning where single-loop and double-loop learning cycles can be established regarding the behavioral perspective. Specifically, the former refers to the correction of practices within organizational plans without changing core beliefs and fundamental rules of the organization, while the latter aims at resolving incompatible organizational behavior by restructuring the norms themselves together with the associated practices or assumptions. Consequently, with the aim of ensuring high EM systems resilience, and effective single-loop and double-loop crisis induced learning at organizational level, the study examined learning opportunities that emerge through the exploration of adaptive practices necessary to face the complexity of a socio-technical work domain as the EM of Covid-19 outbreaks on Oil & Gas platforms. Both qualitative and quantitative approaches have been adopted to analyze the resilience of this specific socio-technical system. On this consciousness, with the intention to explore systems theoretic possibilities to model the EM system, the Functional Resonance Analysis Method (FRAM) has been proposed as a qualitative method for developing a systematic understanding of adaptive practices, modelling planning and resilient behaviors and ultimately supporting crisis induced learning. After the FRAM analysis, the same EM system has also been studied adopting a Bayesian Network (BN) to quantify resilience potentials of an EM procedure resulting from the adaptive practices and lessons learned by an EM organization. While the study of CIs is still an open and challenging topic, this dissertation provides methodologies and running examples on how systemic approaches may support data-driven learning to ultimately improve organizational resilience. These results, possibly extended with future research drivers, are expected to support decision-makers in their tactical and operational endeavors

The impact of machine learning on the efficiency of the B2B sales service in pharmaceutical companies

The explanatory study examines the possible value of Machine Learning in the B2B sales process in pharmaceutical companies. Sales representatives accounting for a wide range of activities, suffering from time consuming and repetitive tasks. This study investigates the potential of Machine Learning applications for B2B sales in order to facilitate sales representative’s daily tasks and enhance the entire sales process. The results have been obtained through qualitative research based on 8 interviews with AI-experts, pharma consultants and sales representatives as well as secondary data in form of academic articles and reports. The findings reveal that, compared to other departments, ML-applications in B2B sales are less applied at the current stage, but mostly in the customer service process. The interviews have shown that the usage of ML-applications is possible within all steps of the sales process and enhances its overall efficiency and effectivity in terms of time, costs and quality. Furthermore, tasks which increase the efficiency of the sales department through ML applications are outlined. By applying ML within the B2B sales process, the daily work of sales representatives can be facilitated, which ultimately could not only have a positive impact on customer satisfaction, but also on employee commitment leading to competitive advantage in the price intense environment of the pharmaceutical industry.O presente estudo foca-se na possível importância da Aprendizagem Automática no serviço de vendas B2B em Empresas Farmacêuticas. Representantes de vendas responsáveis por uma grande variedade de actividades, afectados pelas demoradas e longas tarefas. Esta dissertação examina o potêncial da Aprendizagem Automática nas vendas B2B a fim de facilitar as tarefas diárias dos representantes de vendas, e de melhorar ainda todo o processo de vendas. Os resultados são obtidos através de uma pesquisa qualitativa baseada em 10 entrevistas com AI-experts, consultantes farmacêuticos e representantes de vendas, assim como fichas de dados provenientes de artigos e relatórios. Os resultados revelam que, em comparação com outros departamentos, a aplicação da Aprendizagem Automática em vendas B2B são actualmente menos aplicadas, sobretudo no que diz respeito ao atendimento ao cliente. As entrevistas mostraram que o uso da Aprendizagem Automática é possível em todas as fases do processo de vendas sendo que melhora toda a sua eficiência e efetividade em termos de tempo, custos e qualidade. Posteriormente, as tarefas de vendas mais eficientes dentro das farmácias estão estabelecidas; pelo que, a introdução da Aprendizagem Automática dentro do processo de vendas B2B poderá facilitar e, inclusive, melhorar o trabalho dos representantes de vendas, sendo que esta otimazação poderá, por conseguinte, não só ter um impacto positivo na satisfação do cliente como também no compromisso dos empregados originando, desta forma, uma vantagem competitiva face ao intenso ambiente de preços na industria farmacêutica

Mine the right process – towards a method for selecting a suitable use case for process mining adoption

Process mining (PM) is a big data analytics technology assisting organizations in process optimization by creating insights from event log data available in existing information systems. Although research on PM utilization exists, literature on the adoption phase is scarce. Hence, organizations lack an understanding of how to determine suitable use cases. Accordingly, we followed a design science-based approach and systematically identified twenty criteria, e.g., process variants, processual weaknesses, and analytical skills, to select suitable use cases for PM adoption. The criteria were evaluated with Celonis and Munich Airport and guide PM vendors, organizations, and consultancies through the evaluation process. Hence, we contribute to the early steps of PM diffusion by assisting in determining its consequences and founding the adoption decision. Future research may consider the criteria as a research framework to investigate their effects on the adoption decision

RFID in Retailing and Customer Relationship Management

Radio Frequency Identification (RFID) is a hot topic in retail supply chain management [Behrenbeck, Küpper et al. 2004]. Yet, a recent study predicts “that the true benefits of RFID for retailers will be in enhanced marketing opportunities” [Sharpless 2005]. Research on RFID for marketing purposes is still rare giving the opportunity for more specific research on how RFID will influence business to consumer (B2C) marketing and services [Curtin, Kauffman et al. 2005]. Apparel retailing will most likely be one of the first industries to adopt item level tagging and thus benefit from those new marketing opportunities [Chappell, Durdan et al. 2003; Kurt Salmon Associates 2005]. This paper investigates the opportunities of RFID to enhance B2C marketing of apparel retailers. The paper presents six out of 17 developed RFID applications that support relationship marketing of apparel retailers to better recruit, retain, and recover customers. The RFID applications are classified by the marketing goals they fulfill and the marketing phase they support. The authors describe the use of each RFID application exemplified, and evaluate the additional value for the customer company relationship as well as the feasibility for apparel retailers to implement the application into practice

A framework for the implementation of drones in German automotive OEM logistics operations

Intralogistics operations in automotive OEMs increasingly confront problems of overcomplexity caused by a customer-centred production that requires customisation and, thus, high product variability, short-notice changes in orders and the handling of an overwhelming number of parts. To alleviate the pressure on intralogistics without sacrificing performance objectives, the speed and flexibility of logistical operations have to be increased. One approach to this is to utilise three-dimensional space through drone technology. This doctoral thesis aims at establishing a framework for implementing aerial drones in automotive OEM logistic operations. As of yet, there is no research on implementing drones in automotive OEM logistic operations. To contribute to filling this gap, this thesis develops a framework for Drone Implementation in Automotive Logistics Operations (DIALOOP) that allows for a close interaction between the strategic and the operative level and can lead automotive companies through a decision and selection process regarding drone technology. A preliminary version of the framework was developed on a theoretical basis and was then revised using qualitative-empirical data from semi-structured interviews with two groups of experts, i.e. drone experts and automotive experts. The drone expert interviews contributed a current overview of drone capabilities. The automotive experts interview were used to identify intralogistics operations in which drones can be implemented along with the performance measures that can be improved by drone usage. Furthermore, all interviews explored developments and changes with a foreseeable influence on drone implementation. The revised framework was then validated using participant validation interviews with automotive experts. The finalised framework defines a step-by-step process leading from strategic decisions and considerations over the identification of logistics processes suitable for drone implementation and the relevant performance measures to the choice of appropriate drone types based on a drone classification specifically developed in this thesis for an automotive context

The agency and geography of socio-technical transitions: the case of urban transport innovations

The objective of this cumulative thesis is to gain deeper insights into the interplay of agency and structure through the empirical example of emerging technologies in the context of Industry 4.0. To achieve this goal, it enriches the theoretical background from evolutionary economic geography with insights from transition studies and management studies. Empirically, the analysis focuses on novelty creation toward intelligent transport systems in an urban environment. This encompasses software solutions such as big data platforms for traffic management, the Internet of Things to create a network of various objects and subjects within the city, or the development of autonomous vehicles. This thesis formulates four overarching research purposes: (1) comprehending socio-technical transitions during Industry 4.0 from an agency-based perspective; (2) understanding how agency facilitates or hinders innovation development; (3) identifying the impact of multi-scalar and cross-sectoral relations; and (4) integrating different theoretical approaches to gain a holistic understanding of the empirical domain. The thesis adopts a qualitative research design with a philosophical grounding in critical realism, drawing on semi-structured expert interviews, literature reviews, and document and network analysis. The main contribution of this thesis rests on four distinct research papers. A systematic literature review sets the conceptual basis for the analysis, identifying future research avenues based on the existing research body. The first case study analyzes the development of an app-based solution for managing urban logistics in Barcelona from a multi-level perspective. The other two case studies investigate the evolution of advanced air mobility in Germany and the city of Hamburg