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Ein entscheidender Erfolgsfaktor jeder Supply Chain ist das adäquate Design ihrer Leistungserstellungsprozesse. Da diese Prozesse regelmäßig durch einen hohen Grad an Komplexität gekennzeichnet sind, stellt deren Gestaltung eine große Herausforderung dar. In dieser Forschungsarbeit wird deshalb die Frage behandelt, wie komplexe Leitungserstellungsprozesse einer Supply Chain effektiv und effizient verbessert werden können. Dazu wird eine systematische Vorgehensweise für die Analyse und Bewertung von Supply Chain Prozessen vorgestellt und im Rahmen einer Fallstudie auf eine reale Logistikkette in der Elektronikindustrie angewandt. Bei der Prozessanalyse wird eine Reduktion der Komplexität durch eine Kombination von analytischen Methoden und Simulation erzielt, sodass nicht nur die Validität der Ergebnisse, sondern auch die praktische Umsetzbarkeit gewährleistet ist. Die Bewertung von alternativen Prozessdesigns erfolgt multikriteriell und strategiebasierend, damit sowohl Zielkonflikte als auch die verfolgte Strategie der Logistikkette Berücksichtigung finden. Generell spielt die Wahl des marktbezogenen Produktionstyps (z.B. Make-to-Order, Make-to-Stock) bei der Verbesserung von Supply Chain Prozessen eine entscheidende Rolle. Daher wird auf diesen Aspekt besonders eingegangen, wobei der hybride Produktionstyp Make-to-Forecast in einem Supply Chain-Kontext in der Fallstudie implementiert wird.

Distribution & warehousing management

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Up to now, demand fulfillment in make-to-stock manufacturing is usually handled by advanced planning systems. Orders are fulfilled on the basis of simple rules or deterministic planning approaches not taking into account demand fluctuations. The consideration of different customer classes as it is often done today requires more sophisticated approaches explicitly considering stochastic influences. This book reviews current literature, presents a framework that addresses revenue management and demand fulfillment at once and introduces new stochastic approaches for demand fulfillment in make-to-stock manufacturing based on the ideas of the revenue management literature.

Economic theory & philosophy --- Purchasing & supply management --- Distribution & warehousing management --- Available-to-Promise --- Demand --- Demand Fulfillment --- Make --- Management --- Manufacturing --- Quante --- Revenue Management --- Stochastic --- Stochastic Models --- Stock --- Economics --- Purchasing --- Distribution (Economic theory) --- Philosophy. --- Management. --- Wealth

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This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one.

Information storage and retrieval. --- Information Storage and Retrieval. --- Information storage. --- Information retrieval. --- Data retrieval --- Data storage --- Discovery, Information --- Information discovery --- Information storage and retrieval --- Retrieval of information --- Documentation --- Information science --- Information storage and retrieval systems --- Information Storage and Retrieval --- Evaluation --- Information Retrieval --- Multilingual Information Access --- NTCIR --- Test Collections --- Information Search --- Information Storage --- Artificial Intelligence --- Open Acces --- Information retrieval --- Data warehousing

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This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation.

Information retrieval --- Business & management --- Research & development management --- Information technology industries --- Databases --- Information Storage and Retrieval --- Business and Management, general --- Innovation/Technology Management --- The Computer Industry --- Big Data --- Innovation and Technology Management --- Technology Commercialization --- Digital Transformation --- Innovation Spaces --- Data-Driven Innovation --- Data Analytics --- Technology Management --- Data Ecosystems --- Data Protection --- Big Data Business Models --- Open Access --- Data warehousing --- Business & Management --- Industrial applications of scientific research & technological innovation

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Business --- Commercial analysis --- Data marts --- Data mining --- Electronic data processing --- Time-series analysis --- Analysis of time series --- Autocorrelation (Statistics) --- Harmonic analysis --- Mathematical statistics --- Probabilities --- ADP (Data processing) --- Automatic data processing --- Data processing --- EDP (Data processing) --- IDP (Data processing) --- Integrated data processing --- Computers --- Office practice --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Marts, Data --- Data warehousing --- Automation --- Enterprise Miner. --- SAS (Computer file) --- Statistical analysis system --- SAS system --- SAS Enterprise miner --- Quantitative methods in social research --- Programming