If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to provide a generic forecasting approach for predicting product returns in closed-loop supply chains. Design/methodology/approach -The approach is based on Bayesian estimation techniques. It permits to forecast product returns on the basis of fewer restrictions than existing approaches in CLSC literature. A numerical example demonstrates the application of the proposed approach using return times drawn from a Poisson distribution. Findings -The Bayesian estimation approach provides at least 50 percent higher accuracy in terms of error measures compared to traditional methods in all scenarios examined in the empirical part.Hence, more precise results can be obtained when predicting product returns.Research limitations/implications -The flexibility of the proposed approach allows for numerous applications in the field of CLSC research. Areas that depend on the results from a forecasting system, such as inventory management, can embed our estimation procedure in order to reduce safety stocks. Further research should address the incorporation of the quality of returned products and its impact on the actual utilizable amount of product returns. Originality/value -The generic character of the proposed forecasting approach leaves degrees of freedom to the user when adapting it to a specific problem. This adaptability is enabled by the following features: first, an arbitrary function is allowed for capturing the customers' demand. Second, the stochastic timeframe between sale and product return may follow an arbitrary distribution. Third, by adjusting two parameters finite as well as infinite planning horizons can be incorporated. Fourth, no assumptions regarding the joint distribution of product returns are necessary.

The coordination of order policies constitutes a great challenge in supply chain inventory management as various stochastic factors increase its complexity. Therefore, analytical approaches to determine a policy that minimises overall inventory costs are only suitable to a limited extent. In contrast, we adopt a heuristic approach, from the domain of artificial intelligence (AI), namely, Monte Carlo tree search (MCTS). To the best of our knowledge, MCTS has neither been applied to supply chain inventory management before nor is it yet widely disseminated in other branches of operations research. We develop an offline model as well as an online model which bases decisions on real-time data. For demonstration purposes, we consider a supply chain structure similar to the classical beer game with four actors and both stochastic demand and lead times. We demonstrate that both the offline and the online MCTS models perform better than other previously adopted AI-based approaches. Furthermore, we provide evidence that a dynamic order policy determined by MCTS eliminates the bullwhip effect.

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Überblick ■ Der Beitrag zeigt auf, unter welchen Bedingungen ein risikobehafteter mehrperiodiger Zahlungsstrom (resultierend aus einem Investitionsprojekt, einem Unternehmenskauf o. Ä.) durch Auswertung des (stochastischen) Kapitalwerts per Einperioden-Risikonutzenfunktion bewertet werden darf beziehungsweise muss. ■ Ist ein perfekter Geldmarkt vorhanden, so kann der zu bewertende stochastische Zahlungsstrom in vielfältiger Weise transformiert werden, ohne dass sich die intertemporale Abhängigkeitsstruktur verändert. Eine Risikonutzenfunktion, die nicht sensitiv auf derartige Transformationen reagiert, bezeichnen wir als invariant bezüglich risikoloser Anlage-und Verschuldungsmöglichkeiten oder kurz als geldmarktinvariant. Es wird geklärt, welche multiattributiven Risikonutzenfunktionen geldmarktinvariant sind. ■ Bei Verwendung geldmarktinvarianter Nutzenfunktionen erweist sich das Sicherheitsäquivalent des stochastischen Kapitalwerts als eine zur Unternehmensbewertung geeignete Größe. Dessen Berechnung erfordert i. A. Faltungen. In Spezialfällen ist jedoch die Herleitung einfacher expliziter Bewertungsgleichungen möglich.