2 resultados para Forecasting and replenishment (CPFR)

em Universitätsbibliothek Kassel, Universität Kassel, Germany


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Unternehmen konkurrieren in einem globalen Wettbewerb um den Transfer neuer Technologien in erfolgreiche Geschäftsmodelle. Aus diesem Grund stehen sie zunehmend der Herausforderung gegenüber, technologische Potenziale frühzeitig zu identifizieren, zu bewerten und Strategien für das Erschließen dieser Potenziale zu entwickeln. Dies ist zentraler Gegenstand der Vorausschau und Planung neuer Technologiepfade. In der vorliegenden Arbeit wird gemeinsam mit vier Unternehmen ein Leitfaden für die Strategiefindung, Entwicklung und Kommerzialisierung neu aufkommender Technologien entwickelt und angewendet. Den Ausgangspunkt der Arbeit bildet eine systematische Aufarbeitung des Forschungsstandes der Vorausschau und Planung neuer Technologien. Anschließend wird ein Beschreibungsmodell der Entstehung neuer Technologiepfade in technologiebasierten Innovationssystemen entwickelt. Auf Basis dieses Modells werden unterschiedliche Kategorien von Einflussfaktoren definiert, die als Analyserahmen für die neu entstehende Technologie dienen. Auf Basis der in der Literatur dokumentierten Abläufe, Teamstrukturen und Methoden (z.B. Roadmaps, Szenarien, Datenbankanalysen) wird ein sechsstufiger Ansatz für die Durchführung der Vorausschau und Planung neuer Technologiepfade konzipiert. Dieser Ansatz wird in vier Firmen für die Vorausschau und Planung neuer Technologien angewendet. Die untersuchten Technologien lassen sich den Feldern Biotechnologie, Nanotechnologie, Umwelttechnologie und Sensorik zuordnen. Zentrales Ergebnis der Arbeit ist ein entsprechend der Erfahrungen in den Unternehmen angepasster Ansatz für die Vorausschau und Planung neuer Technologiepfade. Dieser Ansatz ist in Abhängigkeit von Unternehmens- und Technologiecharakteristika für die weitere Anwendung konkretisiert. Dabei finden die zu beteiligenden Organisationseinheiten, zu betrachtende Einflussfaktoren sowie anwendbare Methoden besondere Berücksichtigung. Die Arbeit richtet sich an Personen in Führungspositionen im Bereich des strategischen Technologiemanagements sowie der Forschung und Entwicklung in Unternehmen, die Strategien für neu aufkommende Technologien entwickeln. Weiterhin sind die Ergebnisse der Arbeit für Wissenschaftler auf dem Gebiet der Methoden zur Vorausschau und Strategieentwicklung für neue Technologien von Interesse.

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The research of this thesis dissertation covers developments and applications of short-and long-term climate predictions. The short-term prediction emphasizes monthly and seasonal climate, i.e. forecasting from up to the next month over a season to up to a year or so. The long-term predictions pertain to the analysis of inter-annual- and decadal climate variations over the whole 21st century. These two climate prediction methods are validated and applied in the study area, namely, Khlong Yai (KY) water basin located in the eastern seaboard of Thailand which is a major industrial zone of the country and which has been suffering from severe drought and water shortage in recent years. Since water resources are essential for the further industrial development in this region, a thorough analysis of the potential climate change with its subsequent impact on the water supply in the area is at the heart of this thesis research. The short-term forecast of the next-season climate, such as temperatures and rainfall, offers a potential general guideline for water management and reservoir operation. To that avail, statistical models based on autoregressive techniques, i.e., AR-, ARIMA- and ARIMAex-, which includes additional external regressors, and multiple linear regression- (MLR) models, are developed and applied in the study region. Teleconnections between ocean states and the local climate are investigated and used as extra external predictors in the ARIMAex- and the MLR-model and shown to enhance the accuracy of the short-term predictions significantly. However, as the ocean state – local climate teleconnective relationships provide only a one- to four-month ahead lead time, the ocean state indices can support only a one-season-ahead forecast. Hence, GCM- climate predictors are also suggested as an additional predictor-set for a more reliable and somewhat longer short-term forecast. For the preparation of “pre-warning” information for up-coming possible future climate change with potential adverse hydrological impacts in the study region, the long-term climate prediction methodology is applied. The latter is based on the downscaling of climate predictions from several single- and multi-domain GCMs, using the two well-known downscaling methods SDSM and LARS-WG and a newly developed MLR-downscaling technique that allows the incorporation of a multitude of monthly or daily climate predictors from one- or several (multi-domain) parent GCMs. The numerous downscaling experiments indicate that the MLR- method is more accurate than SDSM and LARS-WG in predicting the recent past 20th-century (1971-2000) long-term monthly climate in the region. The MLR-model is, consequently, then employed to downscale 21st-century GCM- climate predictions under SRES-scenarios A1B, A2 and B1. However, since the hydrological watershed model requires daily-scale climate input data, a new stochastic daily climate generator is developed to rescale monthly observed or predicted climate series to daily series, while adhering to the statistical and geospatial distributional attributes of observed (past) daily climate series in the calibration phase. Employing this daily climate generator, 30 realizations of future daily climate series from downscaled monthly GCM-climate predictor sets are produced and used as input in the SWAT- distributed watershed model, to simulate future streamflow and other hydrological water budget components in the study region in a multi-realization manner. In addition to a general examination of the future changes of the hydrological regime in the KY-basin, potential future changes of the water budgets of three main reservoirs in the basin are analysed, as these are a major source of water supply in the study region. The results of the long-term 21st-century downscaled climate predictions provide evidence that, compared with the past 20th-reference period, the future climate in the study area will be more extreme, particularly, for SRES A1B. Thus, the temperatures will be higher and exhibit larger fluctuations. Although the future intensity of the rainfall is nearly constant, its spatial distribution across the region is partially changing. There is further evidence that the sequential rainfall occurrence will be decreased, so that short periods of high intensities will be followed by longer dry spells. This change in the sequential rainfall pattern will also lead to seasonal reductions of the streamflow and seasonal changes (decreases) of the water storage in the reservoirs. In any case, these predicted future climate changes with their hydrological impacts should encourage water planner and policy makers to develop adaptation strategies to properly handle the future water supply in this area, following the guidelines suggested in this study.