Previous experimental work showed that newsvendors tend to order closer to mean demand than prescribed by the normative critical fractile solution. A recently proposed explanation for this mean ordering behavior assumes that the decision maker commits random choice errors, and predicts the mean ordering pattern because there is more room to err toward mean demand than away from it. Do newsvendors exhibit mean ordering simply because they make random errors? We subject this hypothesis to an empirical test that rests on the fact that the random error explanation is insensitive to context. Our results strongly support the existence of context-sensitive decision strategies that rely directly on (biased) order-to-demand mappings, such as mean demand anchoring, demand chasing, and inventory error minimization.newsvendor model, task context, heuristics, random choice
Supply chain contracting and the use of information are undoubtedly two critical and influential areas in modern supply chain management. However, relatively little is known about supply chain contracting mechanisms with different information settings. To fill this gap, we review and classify the related supply chain contracting literature into three categories with respect to different kinds of information considerations, namely (i) demand information updating, (ii) supply information updating and (iii) information asymmetry. We report the publication trend and classify the commonly studied supply chain contracts with the use of information such as pricing contracts, commitment contracts and menu of contracts. We discuss how contracting and the use of information influence each other in the supply chain. Moreover, we review the major application areas of information usage and report the historical development of major related topics. Finally, we propose several important future research directions.
We propose a new approach to optimize operations of hydro storage systems with multiple connected reservoirs which participate in wholesale electricity markets. Our formulation integrates short-term intraday with long-term interday decisions. The intraday problem considers bidding decisions as well as storage operation during the day and is formulated as a stochastic program. The interday problem is modeled as a Markov decision process of managing storage operation over time, for which we propose integrating stochastic dual dynamic programming with approximate dynamic programming. We show that the approximate solution converges towards an upper bound of the optimal solution. To demonstrate the efficiency of the solution approach, we fit an econometric model to actual price and inflow data and apply the approach to a case study of an existing hydro storage system. Our results indicate that the approach is tractable for a real-world application and that the gap between theoretical upper and a simulated lower bound decreases sufficiently fast.
The phenomenon of copycats is common in a wide range of industries. Recently, to indicate product authenticity and combat copycats, many brand name companies (BNCs) have started selling products through retailers. These BNCs deploy a scalable protocol that is integrated into a permissioned blockchain technology (PBT) platform. We examine how PBT combats copycats in the supply chain and how it benefits BNCs. Although PBT implementation helps novice customers identify product authenticity and the real quality of products, that is, to take advantage of a quality disclosure effect, we show that, if and only if the number of novice customers is large enough, then selling through a PBT retailer can effectively combat copycats. Thus, PBT increases the profit of the BNC, consumer surplus, social welfare, and reduces the profit of a copycat. Moreover, conventional wisdom tells us that PBT ensures supply chain transparency and motivates a firm to improve its product quality. However, the BNC reduces the quality of its products when using PBT, because an improvement in product quality is not profitable if consumers can distinguish between genuine and imitation products. Furthermore, we extend the model by considering the case where the BNC itself implements PBT. Without the double marginalization effect, even if the number of novice customers is small, blockchain technology may exist in the market (the BNC self‐implements). In addition, if the unit production cost of a genuine product is large enough, social welfare increases when production cost increases.
A renewable power producer who trades on a day-ahead market sells electricity under supply and price uncertainty. Investments in energy storage mitigate the associated financial risks and allow for decoupling the timing of supply and delivery. This paper introduces a model of the optimal bidding strategy for a hybrid system of renewable power generation and energy storage. We formulate the problem as a continuous-state Markov decision process and present a solution based on approximate dynamic programming. We propose an algorithm that combines approximate policy iteration with Least Squares Policy Evaluation (LSPE) which is used to estimate the weights of a polynomial value function approximation. We find that the approximate policies produce significantly better results for the continuous state space than an optimal discrete policy obtained by linear programming. A numerical analysis of the response surface of rewards on model parameters reveals that supply uncertainty, imbalance costs and a negative correlation of market price and supplies are the main drivers for investments in energy storage. Supply and price autocorrelation, on the other hand, have a negative effect on the value of storage.
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