Industry 4.0 Technology: A Cross-Industry View of Adoption, Usage and COVID-19 Effects

Industry 4.0 technology (I4.0) is inescapable. It transforms the way businesses and customers interact and revolutionizes how organizations produce goods and services (SAP Insights, 2020). It requires a level of agility that many organizations do not possess. Defending against disruptive business models is no longer enough. Organizations must be nimble to optimize assets and resources in response to adversity. In March 2020, the coronavirus (COVID-19) pandemic ushered a devastating blow to the U.S. economy and job market with pervasive shocks that continue to be a business threat. In response, many organizations are accelerating automation, digitization, and communication capabilities to close the gap and connect with customers. This dissertation examined the cross-industry adoption of the nine most common Industry 4.0 technologies: big data, artificial intelligence, cloud computing, the internet of things, cybersecurity, 3-D printing, autonomous technology, augmented reality, and blockchain. This descriptive study explored factors of I4.0 adoption across industries and organizational sizes during a national pandemic. The study sought to reveal “what” factors contributed to the adoption of Industry 4.0, “what” industry patterns exist, “what” effect COVID-19 had on these concepts. A quantitative method was used to examine the relationship between factors. An online survey was administered to a Qualtrics panel of 520 business owners and executives to capture perceptions, knowledge, and insights. A binary logistic regression analysis was performed. The results of this study inform a cross-industry framework of I4.0 technology adoption, which includes contributing factors. The findings also showed COVID-19 was less an accelerant of adoption but rather, the industry sector was a greater influencer

The Boost 4.0 Experience

In the last few years, the potential impact of big data on the manufacturing industry has received enormous attention. This chapter details two large-scale trials that have been implemented in the context of the lighthouse project Boost 4.0. The chapter introduces the Boost 4.0 Reference Model, which adapts the more generic BDVA big data reference architectures to the needs of Industry 4.0. The Boost 4.0 reference model includes a reference architecture for the design and implementation of advanced big data pipelines and the digital factory service development reference architecture. The engineering and management of business network track and trace processes in high-end textile supply are explored with a focus on the assurance of Preferential Certification of Origin (PCO). Finally, the main findings from these two large-scale piloting activities in the area of service engineering are discussed.publishersversionpublishe

Re-Think Insurance: A New Perspective of InsurTech

Technology improves performances of industries generally. While some applications impact insurance industry profoundly, however, some of the improvement is more of office automation and is better not classified as InsurTech. The article is to provide a practical perspective of InsurTech from the review of definitions and purposes of insurance, and the induction of risk information and risk financing, to silhouette insurance ecosystem and framework of InsurTech. Under risk information, the information layering is explored and the basic three elements of risk, contract and portfolio are identified in insurance ecosystem; under risk financing, transaction costs of insurance and law of large numbers are applied. Then, we propose a framework based on the three elements for InsurTech in regard of availability, affordability and assurability. Two approaches are also proposed for InsurTech development - evolutionary way to revise specific areas of the current insurance models and revolutionary way to revamp the insurance models as to redesign the arrangement of risk protection

A Comparative Analysis of Distributed Ledger Technology Platforms

Distributed Ledger Technology (DLT) has emerged as one of the most disruptive technologies in the last decade. It promises to change the way people do their business, track their products, and manage their personal data. Though the concept of DLT was first implemented in 2009 as Bitcoin, it has gained significant attention only in the past few years. During this time, different DLT enthusiasts and commercial companies have proposed and developed several DLT platforms. These platforms are usually categorized as public vs private, general-purpose vs application-specific and so on. As a growing number of people are interested to build DLT applications, it is important to understand their underlying architecture and capabilities in order to determine which DLT platform should be leveraged for a specific DLT application. In addition, the platforms need to be evaluated and critically analyzed to assess their applicability, resiliency and sustainability in the long run. In this paper, we have surveyed several leading DLT platforms and evaluated their capabilities based on a number of quantitative and qualitative criteria. The comparative analysis presented in this paper will help the DLT developers and architects to choose the best platform as per their requirement(s)

Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World

This report documents the program and the outcomes of GI-Dagstuhl Seminar 16394 "Software Performance Engineering in the DevOps World". The seminar addressed the problem of performance-aware DevOps. Both, DevOps and performance engineering have been growing trends over the past one to two years, in no small part due to the rise in importance of identifying performance anomalies in the operations (Ops) of cloud and big data systems and feeding these back to the development (Dev). However, so far, the research community has treated software engineering, performance engineering, and cloud computing mostly as individual research areas. We aimed to identify cross-community collaboration, and to set the path for long-lasting collaborations towards performance-aware DevOps. The main goal of the seminar was to bring together young researchers (PhD students in a later stage of their PhD, as well as PostDocs or Junior Professors) in the areas of (i) software engineering, (ii) performance engineering, and (iii) cloud computing and big data to present their current research projects, to exchange experience and expertise, to discuss research challenges, and to develop ideas for future collaborations

Deploying Blockchain Technology in the Supply Chain

In the rapidly evolving environment of the international supply chain, the traditional network of manufacturers and suppliers has grown into a vast ecosystem made of various products that move through multiple parties and require cooperation among stakeholders. Additionally, the demand for improved product visibility and source-to-store traceability has never been higher. However, traditional data sharing procedures in today’s supply chain are inefficient, costly, and unadaptable as compared to new and innovative technology. Blockchain technology has shown promising results for improving supply chain networks in recent applications and has already impacted our society and lifestyle by reshaping many business and industry processes. In an effort to understand the integration of blockchain technology in the supply chain, this paper systematically summarizes its current status, key characteristics, potential challenges, and pilot applications

The integrity of digital technologies in the evolving characteristics of real-time enterprise architecture

Advancements in interactive and responsive enterprises involve real-time access to the information and capabilities of emerging technologies. Digital technologies (DTs) are emerging technologies that provide end-to-end business processes (BPs), engage a diversified set of real-time enterprise (RTE) participants, and institutes interactive DT services. This thesis offers a selection of the author’s work over the last decade that addresses the real-time access to changing characteristics of information and integration of DTs. They are critical for RTEs to run a competitive business and respond to a dynamic marketplace. The primary contributions of this work are listed below. • Performed an intense investigation to illustrate the challenges of the RTE during the advancement of DTs and corresponding business operations. • Constituted a practical approach to continuously evolve the RTEs and measure the impact of DTs by developing, instrumenting, and inferring the standardized RTE architecture and DTs. • Established the RTE operational governance framework and instituted it to provide structure, oversight responsibilities, features, and interdependencies of business operations. • Formulated the incremental risk (IR) modeling framework to identify and correlate the evolving risks of the RTEs during the deployment of DT services. • DT service classifications scheme is derived based on BPs, BP activities, DT’s paradigms, RTE processes, and RTE policies. • Identified and assessed the evaluation paradigms of the RTEs to measure the progress of the RTE architecture based on the DT service classifications. The starting point was the author’s experience with evolving aspects of DTs that are disrupting industries and consequently impacting the sustainability of the RTE. The initial publications emphasized innovative characteristics of DTs and lack of standardization, indicating the impact and adaptation of DTs are questionable for the RTEs. The publications are focused on developing different elements of RTE architecture. Each published work concerns the creation of an RTE architecture framework fit to the purpose of business operations in association with the DT services and associated capabilities. The RTE operational governance framework and incremental risk methodology presented in subsequent publications ensure the continuous evolution of RTE in advancements of DTs. Eventually, each publication presents the evaluation paradigms based on the identified scheme of DT service classification to measure the success of RTE architecture or corresponding elements of the RTE architecture

Optimizing Product-Market Fit: A Digital Marketing Approach for Performance Measurement

In today's digital economy, digital marketing has grown in scope and now acts as a liaison connecting digital interactions and key start-up activities. The low investment and dynamic elements of digital marketing favor the start-up environment and enable a flexible match between digital interactions and start-up growth. However, start-ups are usually unfamiliar with all the possibilities digital marketing offers and connect its influence solely with marketing and sales. Digital marketing, powered by data analytics, can support start-ups in different key activities such as testing and experimentation, customer education, and others. This thesis first explores how digital marketing impacts start-up development within a set of growth drivers supporting different key activities and associated areas. Through a systematic literature review, the Macro- dynamic Framework was developed to identify and connect fifteen growth drivers and digital marketing impact for achieving start-up growth in four different areas. Secondly, as Product-Market fit is identified as one of the key drivers of start-up growth, this research focused on investigating how can product-market fit be influenced through digital marketing. One of the major challenges start-ups have in this perspective is measuring digital marketing performance and interpreting the collected data. As Product-Market fit and performance measurement both largely rely on the provided digital marketing analytics and received feedback, the DM_Optima framework is developed to connect the touchpoints of both areas. Through multiple case study research with nine start-ups, the thesis demonstrates this Framework application. The DM_Optima Framework is developed with a Balanced Scorecard and enables start-ups to test their ability to optimize product-market fit through digital marketing and performance measurement. The results of DM_Optima help start-up founders and managers to achieve or maintain their product-market fit and establish efficient practices of digital marketing performance measurement from an internal organizational perspective.Na economia digital de hoje, o marketing digital cresceu no seu âmbito e actua agora como um elo de ligação entre as interacções digitais e as principais actividades de arranque. O baixo investimento e os elementos dinâmicos do marketing digital favorecem o ambiente de arranque e permitem uma combinação flexível entre as interacções digitais e o crescimento de arranque. Contudo, as start-ups não estão normalmente familiarizadas com todas as possibilidades que o marketing digital oferece e ligam a sua influência apenas ao marketing e às vendas. O marketing digital, impulsionado pela análise de dados, pode apoiar start-ups em diferentes actividades chave como testes e experimentação, educação de clientes, e outras. Esta tese explora primeiro como o marketing digital tem impacto no desenvolvimento inicial dentro de um conjunto de factores de crescimento que apoiam diferentes actividades e áreas associadas. Através de uma revisão sistemática da literatura, a Estrutura Macrodinâmica foi desenvolvida para identificar e ligar quinze motores de crescimento e marketing digital para alcançar o crescimento inicial em quatro áreas diferentes. Em segundo lugar, uma vez que o ajuste Produto-Mercado é identificado como um dos principais motores do crescimento inicial, esta investigação centrou-se na investigação de como pode o ajuste produto-mercado ser influenciado através do marketing digital. Um dos maiores desafios que as novas empresas têm nesta perspectiva é medir o desempenho do marketing digital e interpretar os dados recolhidos. Uma vez que a adequação e a medição do desempenho do produto ao mercado dependem largamente da análise de marketing digital fornecida e do feedback recebido, a estrutura DM_Optima é desenvolvida para ligar os pontos de contacto de ambas as áreas. Através de múltiplos estudos de caso com nove start-ups, a tese demonstra esta aplicação da estrutura. A estrutura DM_Optima é desenvolvida com um Balanced Scorecard e permite às start-ups testar a sua capacidade de optimizar a adequação do produto ao mercado através do marketing digital e da medição do desempenho. Os resultados da DM_Optima ajudam os fundadores e gestores em fase de arranque a alcançar ou manter a sua adequação ao mercado de produtos e a estabelecer práticas eficientes de medição do desempenho do marketing digital de uma perspectiva organizacional interna

Digital Capability Dissected

There is a growing interest in digital innovation and transformation among the researchers and practitioners. It has been recognised that being “digital” is not all about digital data and information technologies. The notion of “digital capability” has been increasingly embraced, but definitions of this concept have remained vague and elusive. A salient research question remains: what is digital capability? This question is explored in this paper from theoretical and practical perspectives in the form of a conceptual construct: the Digital Capability Framework (D-CaF). The framework distinguishes six levels and seven dimensions of digital capability. It is intended to provide a foundation to plan and execute digital capability driven innovation and transformation initiatives. Further, it helps identify and prioritise the research areas of high impact for further studies

The Impact of Blockchain Technology on Financial Transactions

Blockchain technology could emerge as a disruptive innovation that streamlines financial transactions and attenuates their cost. Therefore, the financial industry must assess the opportunities and challenges presented by the technology. As a grand breakthrough, it could transform financial transactions and introduce new possibilities for established financial institutions as well as for new entrants. At the same time, incumbents and startups need to overcome technological, regulatory, and adoption challenges before blockchain technology can become a mainstream reality. Despite its potential, the literature on its impact on financial transactions is still fragmented, with weak empirical insights and limited theoretical explanations. Therefore, financial industry managers lack guidance on how to plan and prepare for the impact of blockchain technology on the operation of financial transactions. Against that backdrop, this dissertation explores the asserted and potential impacts on financial transactions with emphasis on asset verification, record keeping, data privacy, and transaction costs. The dissertation adopts a pluralist approach to examine the subject matter based on three approaches: analysis of the extant literature about blockchain technology concerning financial transactions; perception analysis based on interviews with financial executives, subject matter experts, and researchers; and a theoretical interpretation using transaction cost theory. Therefore, the dissertation synthesizes insights from the three approaches to offer managers of financial institutions guidance concerning the opportunities and challenges of blockchain technology