Suchergebnisse

Collaborative Forecasting and Replenishment (CFAR) is a new interorganizational system that enables retailers and manufacturers to forecast demand and schedule production jointly. The capabilities of CFAR exceed those of the traditional EDI because CFAR allows exchange of complex decision support models and manufacturer/retailer strategies. The proponents of CFAR claim that the fastest way for retailers and manufacturers to slash inventory costs and product shortages is by coordinating their decisions. The impact of CFAR on the supply chain using a model consisting of a manufacturer selling its product through two independent identical retailers is analyzed. The model assumes that the manufacturer doesn't face capacity constraints, the demand is stationary, and retailer prices are constant over time; the shortages are backordered at the manufacturer and retailers to isolate the effects of information sharing in CFAR. The most significant findings relate to the increase in the cost incurred by the nonparticipant in CFAR, the increase in the manufacturer's cost of serving the nonparticipant if the participant can obtain delivery guarantees in return for demand information sharing, and the incentives of the players to move towards universal retailer participation in CFAR. The findings are explained using the risk structure faced by the players in the supply chain. The managerial implications of the impact of CFAR on the supply chain structure are also discussed.

AbstractAdvances in mobile analytics have enabled sellers to target mobile consumers using geolocation and the more granular mobile trajectory information. Using a game‐theoretical model of a context in which two competing sellers choose to target mobile consumers using location or trajectory, we examine the long‐term implications for sellers, consumers, and the social welfare. We show that mobile targeting, either location‐based or trajectory‐based, by both sellers indeed emerges as the equilibrium; however, trajectory‐based targeting need not always emerge as the equilibrium. For instance, location‐based targeting emerges as the equilibrium if the consumers' disutility from a consumption delay (i.e., time cost) is low relative to their transportation cost. The primary driver of our findings relates to the role of location and travel direction information in exposing (or masking) the degree of consumer heterogeneity and the competitive advantage a seller has over the other in attracting a consumer.

Firms are increasingly relying on software to detect fraud in domains such as security, financial services, tax, and auditing. A fundamental problem in using detection software for fraud detection is achieving the optimal balance between the detection and false-positive rates. Many firms use decision theory to address the configuration problem. Decision theory is based on the presumption that the firm's actions do not influence the behavior of fraudsters. Game theory recognizes the fact that fraudsters do modify their strategies in response to firms' actions. In this paper, we compare decision and game theory approaches to the detection software configuration problem when firms are faced with strategic users. We find that under most circumstances firms incur lower costs when they use the game theory as opposed to the decision theory because the decision theory approach frequently either over- or underconfigures the detection software. However, firms incur the same or lower cost under the decision theory approach compared with the game theory approach in a simultaneous-move game if configurations under decision theory and game theory are sufficiently close. A limitation of the game theory approach is that it requires user-specific utility parameters, which are difficult to estimate. Decision theory, in contrast to game theory, requires the fraud probability estimate, which is more easily obtained.

ABSTRACTKnowledge‐based systems support the decision‐making process with the help of domain specific knowledge bases. The knowledge bases almost always have uncertainty associated with them. A variety of approaches have been proposed in the artificial intelligence (AI) literature for the construction of and reasoning with uncertain knowledge bases. Building on this stream of research, we focus on how stochastic simulation can be used to construct and reason with knowledge bases that have uncertainties. An advantage of the simulation methodology is that it may not have to make many of the assumptions made by other approaches. It also allows the designer of the knowledge‐based system to control the methodology based on accuracy and time requirements. The simulation approach to knowledge base construction is a modified version of the concept induction procedure used in AI. However, it incorporates, as does simulation modeling, statistical tests to identify the best rule that describes the relationship among the variables. We show that when simulation is used to reason with uncertain knowledge bases, under certain conditions, the number of simulation trials needed to achieve a given level of accuracy is independent of the characteristics, such as the size, of the knowledge base. Empirical results obtained from an experiment confirm our theoretical results and provide evidence that simulation methodology is practical for real life knowledge‐based systems.

The correlated nature of security breach risks, the imperfect ability to prove loss from a breach to an insurer, and the inability of insurers and external agents to observe firms' self‐protection efforts have posed significant challenges to cyber security risk management. Our analysis finds that a firm invests less than the social optimal levels in self‐protection and in insurance when risks are correlated and the ability to prove loss is imperfect. We find that the appropriate social intervention policy to induce a firm to invest at socially optimal levels depends on whether insurers can verify a firm's self‐protection levels. If self‐protection of a firm is observable to an insurer so that it can design a contract that is contingent on the self‐protection level, then self‐protection and insurance behave as complements. In this case, a social planner can induce a firm to choose the socially optimal self‐protection and insurance levels by offering a subsidy on self‐protection. We also find that providing a subsidy on insurance does not provide a similar inducement to a firm. If self‐protection of a firm is not observable to an insurer, then self‐protection and insurance behave as substitutes. In this case, a social planner should tax the insurance premium to achieve socially optimal results. The results of our analysis hold regardless of whether the insurance market is perfectly competitive or not, implying that solely reforming the currently imperfect insurance market is insufficient to achieve the efficient outcome in cyber security risk management.