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An effective inventory management requires timely and accurate forecasting of demand for parts or items. In many realworld scenarios, however, the demand for rare, high-cost spare parts are scarce and erratic, making it highly challenging to perform a reliable forecast for intermittent demand. Studies in the past offered two approaches to such intermittent demand forecasting: a traditional approach to estimating demand parametrically, and a non-parametric approach that estimates the distribution of demands. The bootstrap method is considered to be one of the key non-parametric methods available. Despite its usefulness, however, application of conventional bootstrap methods in intermittent demand forecasting does not take into account any existing dependent structures in lead time demand, leading to inaccurate forecasting. In this paper, we suggest a new bootstrap method that takes into consideration the unique characteristics of intermittent demand to improve forecasting performance. We conclude by demonstrating the applicability of suggested new method through the results of a simple case experiment

An effective inventory management requires timely and accurate forecasting of demand for parts or items. In many realworld scenarios, however, the demand for rare, high-cost spare parts are scarce and erratic, making it highly challenging to perform a reliable forecast for intermittent demand. Studies in the past offered two approaches to such intermittent demand forecasting: a traditional approach to estimating demand parametrically, and a non-parametric approach that estimates the distribution of demands. The bootstrap method is considered to be one of the key non-parametric methods available. Despite its usefulness, however, application of conventional bootstrap methods in intermittent demand forecasting does not take into account any existing dependent structures in lead time demand, leading to inaccurate forecasting. In this paper, we suggest a new bootstrap method that takes into consideration the unique characteristics of intermittent demand to improve forecasting performance. We conclude by demonstrating the applicability of suggested new method through the results of a simple case experiment

An effective inventory management requires timely and accurate forecasting of demand for parts or items. In many realworld scenarios, however, the demand for rare, high-cost spare parts are scarce and erratic, making it highly challenging to perform a reliable forecast for intermittent demand. Studies in the past offered two approaches to such intermittent demand forecasting: a traditional approach to estimating demand parametrically, and a non-parametric approach that estimates the distribution of demands. The bootstrap method is considered to be one of the key non-parametric methods available. Despite its usefulness, however, application of conventional bootstrap methods in intermittent demand forecasting does not take into account any existing dependent structures in lead time demand, leading to inaccurate forecasting. In this paper, we suggest a new bootstrap method that takes into consideration the unique characteristics of intermittent demand to improve forecasting performance. We conclude by demonstrating the applicability of suggested new method through the results of a simple case experiment

© 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd. Histamine, a neurotransmitter/neuromodulator implicated in the control of arousal state, exerts a potent phase-shifting effect on the circadian clock in the rodent suprachiasmatic nucleus (SCN). In this study, the mechanisms by which histamine resets the circadian clock in the mouse SCN were investigated. As a first step, Ca2+-imaging techniques were used to demonstrate that histamine increases intracellular Ca2+ concentration ([Ca2+]i) in acutely dissociated SCN neurons and that this increase is blocked by the H1 histamine receptor (H1R) antagonist pyrilamine, the removal of extracellular Ca2+ and the L-type Ca2+ channel blocker nimodipine. The histamine-induced Ca2+ transient is reduced, but not blocked, by application of the ryanodine receptor (RyR) blocker dantrolene. Immunohistochemical techniques indicated that CaV1.3 L-type Ca2+ channels are expressed mainly in the somata of SCN cells along with the H1R, whereas CaV1.2 channels are located primarily in the processes. Finally, extracellular single-unit recordings demonstrated that the histamine-elicited phase delay of the circadian neural activity rhythm recorded from SCN slices is blocked by pyrilamine, nimodipine and the knockout of CaV1.3 channel. Again, application of dantrolene reduced but did not block the histamine-induced phase delays. Collectively, these results indicate that, to reset the circadian clock, histamine increases [Ca2+]i in SCN neurons by activating CaV1.3 channels through H1R, and secondarily by causing Ca2+-induced Ca2+ release from RyR-mediated internal stores.