Ansätze einer Algorithmischen Anwendung Quantititiver Verfahren zur Effizienten Bedarfsprognose von Vorprodukten. Erste Ergebnisse Einer Empirischen Untersuchung

Zufällig schwankende Nachfragen nach Vorprodukten bzw. Teilen und Komponenten machen die Verwendung von stochastischen Modellen der Lagerhaltung notwendig. Das vorliegende Papier beschreibt einen standardisierten algorithmischen Ansatz, mit dem der Verbrauch von Vorprodukten für die Zeiträume von drei, sechs oder zwölf Monaten mit Hilfe zeitreihenökonometrischer Verfahren prognostiziert werden kann. Im Rahmen dieses Ansatzes werden für jede Vorproduktgruppe die unterschiedlichsten quantitativen Prognosetechniken angewendet. Zu den Techniken zählen unter anderem AR-, MA-, ARMA-, ARIMA- und strukturelle Regressionsmodelle. Durch algorithmisches Vorgehen wird aufgrund von Gütekriterien (z. B. die Prognosefähigkeit in einem Testdatensatz) ein optimales Prognosemodell ermittelt, das für die Prognose des Bedarfs verwendet wird. Für alle gewählten Prognosezeiträume erwies sich das ARMA-Modell der d-differenzierten Zeitreihe als bestes Prognosemodell, gefolgt von einfachen Moving Average und ARIMA-Modellen. Die Bedeutung autoregressiver Verfahren nimmt aber mit der Länge des Prognosezeitraumes ab. Strukturelle Ansätze erweisen sich allerdings fast nie als beste Prognosemodelle, auch wenn deren Bedeutung mit der Länge des Prognosezeitraumes zunimmt. Der algorithmische Ansatz ermöglicht für einen erheblichen Teil (rund 60 Prozent) der Vorprodukte eine gute Prognosequalität. Die Güte der Prognose verbesserte sich, je seltener Zeiträume mit fehlender Nachfrage auftreten. Bei Beachtung ausgearbeiteter Voraussetzungen, dürfte diese algorithmische – und daher einfach durch den Computer zu ermittelnde – Vorgehensweise, die praktische Aufgabe der Prognose von Lagerabflüssen für einen erheblichen Teil von Vorprodukten bzw. Teilen und Komponenten vereinfachen.Inventory Management, Forecasting, Material Requirement Planning, Time Series

Just a game? How continuous time modelling can ameliorate corporate strategy games

Strategy games are a popular part of today's management training in education and business. Complex economic interrelationships can be illustrated and interdependencies of economic decisions can be more easily understood. Most business games work with a discrete time model, i.e., decisions are made once for an entire time period; usually one fiscal year. Possible drawbacks of the discrete time modelling on the planning and control processes, on the presentation of effect timelines and on the learner's comprehension of interrelationships in companies are discussed. The authors hypothesise that a continually-controlled strategy game would greatly increase the simulation's applicability to real-world business. Among other advantages of a continually-controlled strategy game, errors in the planning process could be corrected and an early warning system could be implemented. The well-known problem of myopic business decisions at the end of the game could be resolved by linking success in the game with the sustainable development of the simulated company. This article's results are backed by experience as well as the results of a survey at Pforzheim University.strategy games; continuous time modelling; planning processes; effect timelines; corporate strategies; management training; complex interrelationships; economic interrelationships; interdependencies; economic decisions; business games; discrete time models; time periods; fiscal years; control processes; learner comprehension; continually-controlled games; simulation applicability; real-world businesses; error correction; early warnings; myopic decisions; business decisions; simulated companies; Pforzheim University; Germany; higher education; universities; management; sustainable development; sustainability.

Jellyfish risk communications: The effect on risk perception, travel intentions and behaviour, and beach tourism destinations

Jellyfish hazards at 3S destinations are underrepresented in tourism research. Using a novel conceptual model based on risk perception and destination image theories, we used an experimental setting to examine whether different types of jellyfish risk messages influenced people's travel intentions and behaviours. In addition, the study tested the influence of worry and culture. We sampled 415 prospective visitors to two of the world's most successful beach tourism destinations, the Costa Brava coastline of Spain and the Great Barrier Reef region of northern Australia, both adversely affected by the presence of jellyfish. At these unique destinations, contact with jellyfish can be painful and deadly. Early in the Covid-19 pandemic, fictitious vignettes were posted on an internet Travel Forum containing two different jellyfish risk messages, one informal and the other official. Participants' responses to these communications were tested. We found that risk messages influenced destination image but not travel intention. People from risk-averse Germanic European countries were more inclined to alter their behaviour by avoiding the water than other cultures. These findings add to the body of knowledge about the relationship between risk communications, risk perceptions and destination image. This study suggests that wildlife-associated risk communications can influence people's risk perceptions, but not sufficiently to change their travel plans. This knowledge is important in policy-making and managing responses to risk at tourism destinations. It is also important in building visitor trust and confidence, whereby tourists know that their safety and enjoyment are valued and are paramount to the destination