Chapter

Digitaler Schatten der Kundeninteraktionen produzierender Unternehmen

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

Article
Full-text available
CRISP-DM is the de-facto standard and an industry-independent process model for applying data mining projects. Twenty years after its release in 2000, we would like to provide a systematic literature review of recent studies published in IEEE, ScienceDirect and ACM about data mining use cases applying CRISP-DM. We give an overview of the research focus, current methodologies, best practices and possible gaps in conducting the six phases of CRISP-DM. The main findings are that CRISP-DM is still a de-factor standard in data mining, but there are challenges since the most studies do not foresee a deployment phase. The contribution of our paper is to identify best practices and process phases in which data mining analysts can be better supported. Further contribution is a template for structuring and releasing CRISP-DM studies.
Article
Full-text available
During the last few decades, the PSS literature has documented industrial firms’ transformation from the product dominant logic of business to product-service bundles constituted by machines and related services. This transformation has had several dramatic implications on firms’ profitability, strategy, operations, organizational setting, sales and marketing approaches, and R&D practices. However, more recently, industrial firms have started to adopt various smart technologies that are embedded within the PSS. The use of smart technologies in PSS gives rise to the new types of PSS that are referred to in this paper as Smart PSS. Based on a literature review of 43 papers from relevant academic fields, this paper seeks answer to the following research question: what are the value creating features of smart product service systems (Smart PSS) in industrial firms? We synthesize the knowledge on Smart PSS to provide a definition and show the distinctive features of Smart PSS and propose an agenda for future research.
Article
Full-text available
Today, machine manufacturers generate a significant share of their revenues with the provision of services. At the same time, they are confronted with the challenge of adopting of Industrie 4.0. One of the most important Industrie 4.0 concepts is the idea of the digital shadow, which contributes to the comprehensive structuring of different kinds of data from different data sources. It can be defined as the sufficiently precise, digital representation of reality in real-time. Thus, it also functions as a database of the considered area of a company that can be used for numerous applications. It serves as a central platform for the aggregation and distribution of data. Thereby, it helps to open isolated data silos. A system architecture that enables extraction of data from various sources and the aggregation of that data is an important prerequisite for the digital shadow. In addition, the merger of data from different sources requires a model of the part of the company to be mapped digitally. In this paper, we focus on maintenance, repair and overhaul (MRO) services of machine manufacturers. The scope comprises the whole order processing of a service including the utilized resources and the obtained results. MRO services and their single elements are mapped and structured using a case study research in a first step. Those elements provide a basis for designing the digital shadow. A second contribution of this paper is a data model for the digital shadow of MRO services that entails a comprehensive representation of that department.
Article
Full-text available
As more firms adopt big data analytics to better understand their customers and differentiate their offerings from competitors, it becomes increasingly difficult to generate strategic value from isolated and unfocused ad hoc initiatives. To attain sustainable competitive advantage from big data, firms must achieve agility in combining rich data across the organization to deploy analytics that sense and respond to customers in a dynamic environment. A key challenge in achieving this agility lies in the identification, collection, and integration of data across functional silos both within and outside the organization. Because it is infeasible to systematically integrate all available data, managers need guidance in finding which data can provide valuable and actionable insights about customers. Leveraging relationship marketing theory, we develop a framework for identifying and evaluating various sources of big data in order to create a value-justified data infrastructure that enables focused and agile deployment of advanced customer analytics. Such analytics move beyond siloed transactional customer analytics approaches of the past and incorporate a variety of rich, relationship-oriented constructs to provide actionable and valuable insights. We develop a customized kernel-based learning method to take advantage of these rich constructs and instantiate the framework in a novel prototype system that accurately predicts a variety of customer behaviors in a challenging environment, demonstrating the framework’s ability to drive significant value.
Chapter
Full-text available
Welchen Beitrag leistet das Prozessmanagement für die Maximierung des Kundennutzens? Wie lassen Unternehmen die Kundenperspektive in die Prozessgestaltung einfliessen? In welcher Ausprägung setzen Unternehmen Kunden‐ und Prozessdaten ein, um Kundenerlebnisse zu individualisieren? Ist dabei Transparenz und Datenherrschaft für Kunden sichergestellt? Erheben Unternehmen systematisch Digitalisierungspotenzial in ihren Prozessen? Wie steht es um die Durchgängigkeit der Prozesse und welche Formen der Prozessdigitalisierung kommen dabei zum Einsatz? Diese und weitere Fragen standen im Mittelpunkt der Online‐Befragung, die im Rahmen der vorliegenden Studie im Mai 2016 durchgeführt wurde.
Conference Paper
Full-text available
The digital transformation forces organisations to increasingly embed technology to catch up with customer demands. The omni-channel approach is one recent trend that requires taking the customer’s perspective and offering a consistent experience across channels and touchpoints. While this development clearly necessitates IT for implementation, past research primarily stems from the marketing domain. In this article, we present an entity-relationship-model and a linkage model that takes an IS perspective and thereby enables communication between marketing and IT.
Article
Full-text available
By providing access to data from numerous systems in one database and supporting the systems that can produce an appropriate customer experience, a customer data platform overcomes the limitations imposed by fragmented point solutions and presents a holistic approach to customer interactions.
Article
Full-text available
Most businesses agree that high customer satisfaction is important. Many are aware that it is a prerequisite for success, but few measure and monitor it in a structured way, thus failing to improve it. The aim of this article is to develop a customer touchpoint management tool that allows small and medium-sized, B2B mass customization companies to measure, monitor, and improve customer satisfaction. This case study identifies 48 customer touchpoints – classified in human, product, service, communication, spatial, and electronic interaction – in a three-step approach: Employees and existing customers were involved in identifying customer touchpoints, weighting them in terms of their general importance, and assessing some specific customer touchpoint’s importance for customer satisfaction. The results presented in this article suggest that not all existing customer touchpoints are perceived to be important or relevant, and that employees and customers largely agree regarding which customer touchpoints are most important. Customer touchpoints classified as human interaction were found to be most important and have the highest importance for customer satisfaction.
Article
Full-text available
In any knowledge discovery process the value of extracted knowledge is directly related to the quality of the data used. Big Data problems, generated by massive growth in the scale of data observed in recent years, also follow the same dictate. A common problem affecting data quality is the presence of noise, particularly in classification problems, where label noise refers to the incorrect labeling of training instances, and is known to be a very disruptive feature of data. However, in this Big Data era, the massive growth in the scale of the data poses a challenge to traditional proposals created to tackle noise, as they have difficulties coping with such a large amount of data. New algorithms need to be proposed to treat the noise in Big Data problems, providing high quality and clean data, also known as Smart Data. In this paper, two Big Data preprocessing approaches to remove noisy examples are proposed: an homogeneous ensemble and an heterogeneous ensemble filter, with special emphasis in their scalability and performance traits. The obtained results show that these proposals enable the practitioner to efficiently obtain a Smart Dataset from any Big Data classification problem.
Article
Full-text available
Nowadays, along with the application of new-generation information technologies in industry and manufacturing, the big data-driven manufacturing era is coming. However, although various big data in the entire product lifecycle, including product design, manufacturing, and service, can be obtained, it can be found that the current research on product lifecycle data mainly focuses on physical products rather than virtual models. Besides, due to the lack of convergence between product physical and virtual space, the data in product lifecycle is isolated, fragmented, and stagnant, which is useless for manufacturing enterprises. These problems lead to low level of efficiency, intelligence, sustainability in product design, manufacturing, and service phases. However, physical product data, virtual product data, and connected data that tie physical and virtual product are needed to support product design, manufacturing, and service. Therefore, how to generate and use converged cyber-physical data to better serve product lifecycle, so as to drive product design, manufacturing, and service to be more efficient, smart, and sustainable, is emphasized and investigated based on our previous study on big data in product lifecycle management. In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed. The detailed application methods and frameworks of digital twin-driven product design, manufacturing, and service are investigated. Furthermore, three cases are given to illustrate the future applications of digital twin in the three phases of a product respectively.
Article
Full-text available
Purpose The purpose of this paper is to propose a framework based on customer journeys for a structured portrayal of service delivery from the customer’s point of view. The paper also introduces customer journey analysis (CJA) for empirical investigation of individual service experiences in a multichannel environment. Design/methodology/approach The paper presents case studies for onboarding new customers on broadband services. CJA starts with modeling of the service process in terms of touchpoints. The individual customer journeys are reconstructed through methodological triangulation of interviews, diary studies, and process tracking. Findings The paper provides empirical insights into individual customer journeys. Four types of deviations during service delivery are identified: occurrence of ad hoc touchpoints, irregularities in the sequence of logically connected touchpoints, occurrence of failures in touchpoints, and missing touchpoints. CJA seems effective in revealing problematic and incoherent service delivery that may result in unfavorable customer experiences. Practical implications For a service company, the proposed framework may serve as a unifying language to ease cross-departmental communication and approach service quality in a systematic way. CJA discloses the gap between the planned and actual service delivery and can be used as a tool for service improvement. Originality/value The framework provides concepts, definitions, and a visual notation to structure and manage services in terms of customer journeys. CJA is a novel method for empirical studies of the service delivery process and the associated customer experience.
Article
Full-text available
Purpose – This study aims to gain a clearer understanding of digital channel design. The emergence of new technologies has revolutionised the way companies interact and engage with customers. The driver for this research was the suggestion that practitioners feel they do not possess the skills to understand and exploit new digital channel opportunities. To gain a clearer understanding of digital channel design, this paper addresses the research question: What digital channels do companies from a wide range of industries and sectors use? Design/methodology/approach – A content analysis of 100 international companies was conducted with multiple data sources to form a typology of digital “touchpoints”. The appropriateness of a digital channel typology for this study was for developing rigorous and useful concepts for clarifying and refining the meaning of digital channels. Findings – This study identifies what digital channels companies globally currently employ and explores the related needs across industries. A total of 34 digital touchpoints and 4 typologies of digital channels were identified across 16 industries. This research helps to identify the relationship between digital channels and enabling the connections with industry. Research limitations/implications – The findings contribute to the growing research area of digital channels. The typology of digital channels is a useful starting point for developing a systematic, theory-based study for enabling the development of broader, comprehensive theories of digital channels. Practical implications – Typologies and touchpoints are outlined in relation to industry, company objectives and customer needs to allow businesses to seize opportunities and optimise performance through individual touchpoints. A digital channel model as a key outcome of this research guides practitioners on what touchpoint to implement through an interrelated understanding of industry, company and customer needs. Originality/value – This is the first paper to explore a range of industries in relation to their use of digital channels using a unique content analysis. Contributions include clarifying and refining digital channel meaning; identifying and refining the hierarchical relations among digital channels (typologies); and establishing typology and industry relationship model.
Conference Paper
Full-text available
In this paper we propose a deterministic method to obtain subsets from big data which are a good representative of the inherent structure in the data. We first convert the large scale dataset into a sparse undirected k-NN graph using a distributed network generation framework that we propose in this paper. After obtaining the k-NN graph we exploit the fast and unique representative subset (FURS) selection method [1], [2] to deterministically obtain a subset for this big data network. The FURS selection technique selects nodes from different dense regions in the graph retaining the natural community structure. We then locate the points in the original big data corresponding to the selected nodes and compare the obtained subset with subsets acquired from state-of-the-art subset selection techniques. We evaluate the quality of the selected subset on several synthetic and real-life datasets for different learning tasks including big data classification and big data clustering.
Article
Full-text available
Design science research (DSR) has staked its rightful ground as an important and legitimate Information Systems (IS) research paradigm. We contend that DSR has yet to attain its full potential impact on the development and use of information systems due to gaps in the understanding and application of DSR concepts and methods. This essay aims to help researchers (1) appreciate the levels of artifact abstractions that may be DSR contributions, (2) identify appropriate ways of consuming and producing knowledge when they are preparing journal articles or other scholarly works, (3) understand and position the knowledge contributions of their research projects, and (4) structure a DSR article so that it emphasizes significant contributions to the knowledge base. Our focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, we propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. We evaluate the DSR contribution framework and the DSR communication schema via examinations of DSR exemplar publications.
Article
Full-text available
As a commentary to Juhani Iivari's insightful essay, I briefly analyze design science research as an embodiment of three closely related cycles of activities. The Relevance Cycle inputs requirements from the contextual envi- ronment into the research and introduces the research artifacts into environ- mental field testing. The Rigor Cycle provides grounding theories and methods along with domain experience and expertise from the foundations knowledge base into the research and adds the new knowledge generated by the research to the growing knowledge base. The central Design Cycle sup- ports a tighter loop of research activity for the construction and evaluation of design artifacts and processes. The recognition of these three cycles in a research project clearly positions and differentiates design science from other research paradigms. The commentary concludes with a claim to the pragmatic nature of design science.
Article
Full-text available
Purpose The purpose of this paper is to report the state‐of‐the‐art of servitization by presenting a clinical review of literature currently available on the topic. The paper aims to define the servitization concept, report on its origin, features and drivers and give examples of its adoption along with future research challenges. Design/methodology/approach In determining the scope of this study, the focus is on articles that are central and relevant to servitization within a wider manufacturing context. The methodology consists of identifying relevant publication databases, searching these using a wide range of key words and phrases associated with servitization, and then fully reviewing each article in turn. The key findings and their implications for research are all described. Findings Servitization is the innovation of an organisation's capabilities and processes to shift from selling products to selling integrated products and services that deliver value in use. There are a diverse range of servitization examples in the literature. These tend to emphasize the potential to maintain revenue streams and improve profitability. Practical implications Servitization does not represent a panacea for manufactures. However, it is a concept of significant potential value, providing routes for companies to move up the value chain and exploit higher value business activities. There is little work to date that can be used to help practitioners. Originality/value This paper provides a useful review of servitization and a platform on which to base more in‐depth research into the broader topic of service‐led competitive strategy by drawing on the work from other related research communities.
Chapter
Smart products, Social Media and innovative market research lead to an abundance of customer data, yet due to their heterogeneous sources and structures, they are scattered throughout the company. Joining these different types of data can lead to a large gain in customer insights that would not have been possible by analyzing the data individually. It is a necessary step for the transition of the current mostly hypothesis-based product design process towards a data-driven one and enables accelerated product development with truly innovative products tailored to the customer. This paper explains the holistic approach to identifying customer needs and requirements: the digital shadow of the customer. It is a concept transferred from the Internet of Production and its digital shadows of products and processes. The paper first gives an overview of customer data that form the customer data lake and reviews current data analysis methods using an explorative literature review. We then explain the concepts of the digital shadow and data lake, their main principles and benefits of using digital shadows for product development.
Article
CRISP-DM (CRoss-Industry Standard Process for Data Mining) has its origins in the second half of the nineties and is thus about two decades old. According to many surveys and user polls it is still thede factostandard for developing data mining and knowledge discovery projects. However, undoubtedly the field has moved on considerably in twenty years, with data science now the leading term being favoured over data mining. In this paper we investigate whether, and in what contexts, CRISP-DM is still fit for purpose for data science projects. We argue that if the project is goal-directed and process-driven the process model view still largely holds. On the other hand, when data science projects become more exploratory the paths that the project can take become more varied, and a more flexible model is called for. We suggest what the outlines of such a trajectory-based model might look like and how it can be used to categorise data science projects (goal-directed, exploratory or data management). We examine seven real-life exemplars where exploratory activities play an important role and compare them against 51 use cases extracted from the NIST Big Data Public Working Group. We anticipate this categorisation can help project planning in terms of time and cost characteristics.
Article
Der ARIS Architect 10 der Software AG ist eines der weltweit führenden Prozessmodellierungstools. Dieses Buches vermittelt das ARIS-Konzept und das praktische Arbeiten mit der Software. Ziel ist es, das tägliche Arbeiten und einfache Projekte mit ARIS selbstständig erledigen zu können. Im Vordergrund stehen die Modellierung und Modellauswertung. Für die vorliegende fünfte Auflage, ausgelöst durch den ARIS-Versionswechsel von 9 auf 10, wurde die komplette Funktionalität überprüft und angepasst. Das neue Kapitel 2 beschreibt in kurzen Zügen die innovative „ARIS Cloud“ für das orts- und zeitunabhängige Teamwork. Zahlreiche Aufgaben und zwei umfangreiche Fallstudien (jeweils mit Lösungen) bieten eigenständige Übungsmöglichkeiten. Das Buch ist auch für das Selbststudium bestens geeignet. Abbildungen und Modelle stehen zum Download zur Verfügung. Der Inhalt Kurzeinführung Prozessmanagement – ARIS Cloud – Prozessmodellierung und -optimierung – Systemverwaltung – Modellgestützte Prozessanalyse – BPMN 2.0 mit ARIS – Aufgaben und Fallstudien Die Zielgruppen Studierende sowie Praktiker in Beruf und Weiterbildung Der Autor Prof. Dr. Heinrich Seidlmeier ist Professor für Organisation und Wirtschaftsinformatik an der Technischen Hochschule Rosenheim mit jahrelanger Erfahrung in der Lehre sowie in anwendungsorientierten Forschungs- und Praxisprojekten.
Article
Purpose The proliferation of customer-related data provides companies with numerous service opportunities to create customer value. The purpose of this study is to develop a framework to use this data to provide services. Design/methodology/approach This study conducted four action research projects on the use of customer-related data for service design with industry and government. Based on these projects, a practical framework was designed, applied, and validated, and was further refined by analyzing relevant service cases and incorporating the service and operations management literature. Findings The proposed customer process management (CPM) framework suggests steps a service provider can take when providing information to its customers to improve their processes and create more value-in-use by using data related to their processes. The applicability of this framework is illustrated using real examples from the action research projects and relevant literature. Originality/value “Using data to advance service” is a critical and timely research topic in the service literature. This study develops an original, specific framework for a company’s use of customer-related data to advance its services and create customer value. Moreover, the four projects with industry and government are early CPM case studies with real data.
Chapter
Die digitale Transformation unserer Welt ist in aller Munde. Politik, Unternehmen und allen voran die Gesellschaft werden von der Dynamik dieses bisher in seiner Geschwindigkeit einmaligen Wandels aktuell erfasst und mitgerissen. Wo durch neue digitale Anbieter wie Airbnb, OpenTable, WhatsApp usw. die Gesellschaft sehr schnell in großen Wellen transformiert wird, stehen traditionelle Unternehmen mit einem häufig sehr komplexen Kunden- und Lieferantenumfeld einer völlig anderen Herausforderung gegenüber. Wie in den digitalen Wandel einsteigen, ohne die Basis des Unternehmens zu riskieren?
Chapter
Innovative und technologisch führende Produkte sind für viele mittelständische Unternehmen bislang ein Absatzgarant im internationalen Markt. Allerdings reichen diese ausgeprägte Produktfokussierung und der Einsatz von Produktinnovationen als alleiniges Differenzierungsmerkmal im globalen Wettbewerb auch in mittelständischen Unternehmen heute nicht mehr aus. Stattdessen gilt es, die Kunden, mit denen sich langfristig profitable Geschäftsbeziehungen aufbauen lassen, zu identifizieren und prioritär – möglichst individuell – zu bedienen. Die Differenzierung beginnt mit einer umfassenden Analyse von Kundenbedürfnissen und deren Verhalten entlang der Customer Journey. Die Segmentierung der Kunden liefert die Basis für die Planung personalisierter Value Propositions entlang des Kaufentscheidungsprozesses der Kunden. Mit einem verlässlichen Vertriebsforecast lässt sich der zukünftige ökonomische Erfolg des Vertriebs planen. Die zunehmende Digitalisierung eröffnet der Kundenanalyse und -planung zahlreiche Chancen, die Effizienz und Effektivität der Kundenbearbeitung zu steigern.
Article
The Digital Twin (DT) is commonly known as a key enabler for the digital transformation, however, in literature is no common understanding concerning this term. It is used slightly different over the disparate disciplines. The aim of this paper is to provide a categorical literature review of the DT in manufacturing and to classify existing publication according to their level of integration of the DT. Therefore, it is distinct between Digital Model (DM), Digital Shadow (DS) and Digital Twin. The results are showing, that literature concerning the highest development stage, the DT, is scarce, whilst there is more literature about DM and DS.
Article
With the rising interest in design science research (DSR), it is crucial to engage in the ongoing debate on what constitutes an acceptable contribution for publishing DSR—the design artifact, the design theory, or both. In this editorial, we provide some constructive guidance across different positioning statements with actionable recommendations for DSR authors and reviewers. We expect this editorial to serve as a foundational step toward clarifying misconceptions about DSR contributions and to pave the way for the acceptance of more DSR papers to top IS journals.
Chapter
This chapter outlines the key principles of machine learning and predictive analytics. It explains the new fundamentals of big data and the evolving technology. The chapter follows by the practical advice on how organizations can establish a new culture in order to truly transform their business in the new era. The wave of data frenzy did not happen overnight. Rather, it is a crescendo of events happening since the early 1980s where the fields of business intelligence and predictive analytics were known as 'data mining', a preexisting discipline with another closely related term known as knowledge discovery in databases (KDD), which is the aim of performing data mining. Analytics has a spectrum of methodologies, techniques, and approaches from descriptive, diagnostic, predictive and prescriptive analytics. Most data mining projects today follow the cross industry standard process for data mining (CRISP-DM) which was conceived in 1996.
Article
Kurzfassung Der Begriff “Digitaler Schatten“ steht für ein hinreichend genaues, digitales Abbild der Prozesse, Informationen und Daten eines Unternehmens. Dieses Abbild wird benötigt, um eine echtzeitfähige Auswertebasis aller relevanten Daten zu schaffen, um hieraus letztendlich Handlungsempfehlungen abzuleiten. Die Bildung des Digitalen Schattens ist damit ein zentrales Handlungsfeld von Industrie 4.0 und stellt die Grundlage für alle weitergehenden Aktivitäten dar.
Article
Understanding customer experience and the customer journey over time is critical for firms. Customers now interact with firms through myriad touch points in multiple channels and media, and customer experiences are more social in nature. These changes require firms to integrate multiple business functions, and even external partners, in creating and delivering positive customer experiences. In this article, the authors aim to develop a stronger understanding of customer experience and the customer journey in this era of increasingly complex customer behavior. To achieve this goal, they examine existing definitions and conceptualizations of customer experience as a construct and provide a historical perspective of the roots of customer experience within marketing. Next, they attempt to bring together what is currently known about customer experience, customer journeys, and customer experience management. Finally, they identify critical areas for future research on this important topic.
Chapter
Die Digitalisierung erfordert neue Spielregeln im Vertrieb – und setzt alte außer Kraft. Durch das Hinzukommen zahlreicher neuer Touchpoints wird es für Unternehmen unabdingbar, das ganzheitliche Markenerleben im Rahmen von Omni-Channel-Ansätzen zu orchestrieren. Um den wirksamen Vertrieb sicherzustellen, muss das Customer-Touchpoint-Management systematisch und ganzheitlich erfolgen. Der Beitrag gibt eine Übersicht, wie Touchpoint Management für den Vertriebserfolg genutzt werden kann und stellt dabei einzelne Module des Customer-Touchpoint Managementprozesses dar. Neben der Betrachtung wie Customer Touchpoints auf Basis eines Assessments systematisch gesteuert werden können, zeigt der Beitrag auf, wie daraufhin durch das Verständnis der Customer Journey die Erkenntnisse im Rahmen einer Customer-Touchpoint Strategie wirkungsvoll in Maßnahmen überführt werden können.
Chapter
Im Werkzeugmaschinen- und Anlagenbau sehen sich Unterlieferanten im Servicegeschäft oftmals schwierigen strategischen Entscheidungen gegenübergestellt, da die zu bedienenden Kundengruppen sehr unterschiedliche Ziele verfolgen. Üblicherweise lassen sich mit den Original Equipment Manufacturer (OEM) und den Endkunden zwei Kundengruppen klassifizieren. Es spannt sich eine sogenannte Service-Chain zwischen dem Endkunden, dem OEM und dem Zulieferer auf, in der die Rollen und die zu pflegenden Kontakte einer gewissen Chronologie der Sachgutlieferung unterworfen sind. Für den Unterlieferanten stellt sich die Frage, wie er sich am besten mit seinem Service positionieren kann, ohne Zielkonflikte auszulösen. Das Bestreben muss es sein, stetig für die Erweiterung des Kundenstammes zu sorgen. Die Aussicht, beim Endkunden Dienstleistungen lukrativ abzusetzen, birgt allerdings die Gefahr, das Verhältnis zum OEM zu belasten, da direkt in dessen Geschäftsfeld eingedrungen wird. Die auftretende Konkurrenzsituation kann dazu führen, dass sich dieser bei der zukünftigen Auswahl seiner Anlagenkomponenten gegen die bestehende Partnerschaft stellt. Dies bedeutet, dass u.U. bei einer Erstausrüstung der Anlagen ein anderer Lieferant bevorzugt wird—der Absatz der Kernleistung würde folglich negativ beeinflusst.
Conference Paper
Data exploration is about efficiently extracting knowledge from data even if we do not know exactly what we are looking for. In this tutorial, we survey recent developments in the emerging area of database systems tailored for data exploration. We discuss new ideas on how to store and access data as well as new ideas on how to interact with a data system to enable users and applications to quickly figure out which data parts are of interest. In addition, we discuss how to exploit lessons-learned from past research, the new challenges data exploration crafts, emerging applications and future research directions.
Article
Article
Although research continues to debate the future of the marketing concept, practitioners have taken the lead, appraising customer experience management (CEM) as one of the most promising marketing approaches in consumer industries. In research, however, the notion of CEM is not well understood, is fragmented across a variety of contexts, and is insufficiently demarcated from other marketing management concepts. By integrating field-based insights of 52 managers engaging in CEM with supplementary literature, this study provides an empirically and theoretically solid conceptualization. Specifically, it introduces CEM as a higher-order resource of cultural mindsets toward customer experiences (CEs), strategic directions for designing CEs, and firm capabilities for continually renewing CEs, with the goals of achieving and sustaining long-term customer loyalty. We disclose a typology of four distinct CEM patterns, with firm size and exchange continuity delineating the pertinent contingency factors of this generalized understanding. Finally, we discuss the findings in relation to recent theoretical research, proposing that CEM can comprehensively systemize and serve the implementation of an evolving marketing concept.
Conference Paper
Future generations of NASA and U.S. Air Force vehicles will require lighter mass while being subjected to higher loads and more extreme service conditions over longer time periods than the present generation. Current approaches for certification, fleet management and sustainment are largely based on statistical distributions of material properties, heuristic design philosophies, physical testing and assumed similitude between testing and operational conditions and will likely be unable to address these extreme requirements. To address the shortcomings of conventional approaches, a fundamental paradigm shift is needed. This paradigm shift, the Digital Twin, integrates ultra-high fidelity simulation with the vehicle's on-board integrated vehicle health management system, maintenance history and all available historical and fleet data to mirror the life of its flying twin and enable unprecedented levels of safety and reliability.
Book
Engineering design must be carefully planned and systematically executed. In particular, engineering design methods must integrate the many different aspects of designing and the priorities of the end-user. Engineering Design (3rd edition) describes a systematic approach to engineering design. The authors argue that such an approach, applied flexibly and adapted to a particular task, is essential for successful product development. The design process is first broken down into phases and then into distinct steps, each with its own working methods. The third edition of this internationally-recognised text is enhanced with new perspectives and the latest thinking. These include extended treatment of product planning; new sections on organisation structures, simultaneous engineering, leadership and team behaviour; and updated chapters on quality methods and estimating costs. New examples have been added and existing ones extended, with additions on design to minimise wear, design for recycling, mechanical connections, mechatronics, and adaptronics. Engineering Design (3rd edition) is translated and edited from the sixth German edition by Ken Wallace, Professor of Engineering Design at the University of Cambridge, and Luciënne Blessing, Professor of Engineering Design and Methodology at the Technical University of Berlin. Topics covered include: Fundamentals; product planning and product development; task clarification and conceptual design; embodiment design rules, principles and guidelines; mechanical connections, mechatronics and adaptronics; size ranges and modular products; quality methods; and cost estimation methods. The book provides a comprehensive guide to successful product development for practising designers, students, and design educators. Fundamentals are emphasised throughout and short-term trends avoided; so the approach described provides a sound basis for design courses that help students move quickly and effectively into design practice. Engineering Design is widely acknowledged to be the most complete available treatise on systematic design methods. In it, each step of the engineering design process and associated best practices are documented. The book has particularly strong sections on design from the functional perspective and on the phase of the process between conceptual and detail design in which most key design decisions are made. The 3rd edition includes new material on project planning and scheduling. Anyone committed to understanding the design process should be familiar with the contents of this book. Warren Seering, Weber-Shaughness Professor of Mechanical Engineering, Massachusetts Institute of Technology.
Data
Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science. The behavioral-science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior. The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts. Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology. Our objective is to describe the performance of design-science research in Information Systems via a concise conceptual framework and clear guidelines for understanding, executing, and evaluating the research. In the design-science paradigm, knowledge and understanding of a problem domain and its solution are achieved in the building and application of the designed artifact. Three recent exemplars in the research literature are used to demonstrate the application of these guidelines. We conclude with an analysis of the challenges of performing high-quality design-science research in the context of the broader IS community.
Article
Every day, every hour, every minute, every second trillion of bytes of data is being generated by enterprises especially in telecom sector. To achieve level best decisions for business profits, access to that data in a well-situated and interactive way is always a dream of business executives and managers. Data warehouse is the only viable solution that can bring that dream into a reality. The enhancement of future endeavors to make decisions depends on the availability of correct information that based on quality of data underlying. The quality data can only be produced by cleaning data prior to loading into data warehouse. So correctness of data is essential for well-informed and reliable decision making. The framework proposed in this paper implements robust data quality to ensure consistent and correct loading of data into data warehouse that necessary to disciplined, accurate and reliable data analysis, data mining and knowledge discovery.
Article
Zusammenfassung Marken hinterlassen an allen Kontaktpunkten mit Kunden Spuren und Fingerabdrücke. Dessen sind sich Manager und Mitarbeiter oft nicht bewusst. Das Management sowie die marken- und kundenspezifische Abstimmung aller Kontaktpunkte bieten somit großes Potenzial für Unternehmen zur wirksameren Vermarktung ihrer Marken. Unternehmen, die Kunden ganzheitlich positive und zufriedenstellende Markenerlebnisse an allen Kontaktpunkten vermitteln, sind erfolgreicher im Wettbewerb.
Article
The CRISP-DM (CRoss Industry Standard Process for Data Mining) project proposed a comprehensive process model for carrying out data mining projects. The process model is independent of both the industry sector and the technology used. In this paper we argue in favor of a standard process model for data mining and report some experiences with the CRISP-DM process model in practice.
Article
The skyrocketing trend for social media on the Internet greatly alters analytical Customer Relationship Management (CRM). Against this backdrop, the purpose of this paper is to advance the conceptual design of Business Intelligence (BI) systems with data identified from social networks. We develop an integrated social network data model, based on an in-depth analysis of Facebook. The data model can inform the design of data warehouses in order to offer new opportunities for CRM analyses, leading to a more consistent and richer picture of customers characteristics, needs, wants, and demands. Four major contributions are offered. First, Social CRM and Social BI are introduced as emerging fields of research. Second, we develop a conceptual data model to identify and systematize the data available on online social networks. Third, based on the identified data, we design a multidimensional data model as an early contribution to the conceptual design of Social BI systems and demonstrate its application by developing management reports in a retail scenario. Fourth, intellectual challenges for advancing Social CRM and Social BI are discussed.
Article
This work presents a novel method for estimating missing values in daily precipitation series. It is aimed at identifying the event time location with good accuracy and reconstructing the correct amount of daily rainfall. In addition, the statistical properties of the time series, i.e. both probability distribution and long-term statistics, are preserved. The completion method is based on a two-step algorithm that uses information from a cluster of neighboring stations. First, wet and dry days are tagged, and subsequently, the full precipitation amount for wet-classified days is estimated by a modified multi-linear regression approach. This method avoids overestimation of the number of wet days and underestimation of intense precipitation events, which are typical side effects of common regression-based approaches. Copyright © 2009 Royal Meteorological Society
Chapter
Dem Dienstleistungsmanagement in der Unternehmung kommt eine zunehmend hohe Bedeutung zu. Reine Dienstleistungsunternehmungen wie Banken, Versicherungen oder öffentliche Verwaltungen müssen in der Lage sein, rasch auf Marktänderungen reagieren zu können und ihr Produktangebot zu ändern, d. h. es an neue Gegebenheiten oder Konkurrenzangebote anzupassen oder neue Produkte systematisch zu entwickeln. Dasselbe gilt auch für Unternehmungen, die überwiegend Sachgüter produzieren, da Sachgüter zunehmend in Verbindung mit Dienstleistungen angeboten werden, und der entsprechende Anteil der Dienstleistung oft ein entscheidendes Qualitäts- und Wettbewerbskriterium darstellt.
Article
This paper describes the process of inducting theory using case studies-from specifying the research questions to reaching closure. Some features of the process, such as problem definition and construct validation, are similar to hypothesis-testing research. Others, such as within-case analysis and replication logic, are unique to the inductive, case-oriented process. Overall, the process described here is highly iterative and tightly linked to data. This research approach is especially appropriate in new topic areas. The resultant theory is often novel, testable, and empirically valid. Finally, framebreaking insights, the tests of good theory (e.g., parsimony, logical coherence), and convincing grounding in the evidence are the key criteria for evaluating this type of research.