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In the present paper we generalise the classical newsvendor problem for critical perishable commodities having more severe costs than its linear alternative. Piece wise polynomial cost functions are introduced to accommodate the excess severity. Stochastic demand is assumed to follow a completely unknown probability distribution. Non parametric estimator of the optimal order quantity has been developed from an estimating equation using a random sample. Strong consistency of the estimator is proved for unique optimal order quantity and the result is extended for multiple solutions. Simulation results indicate that non parametric estimator is efficient in terms of mean square error. Real life application of the proposed non-parametric estimator has been demonstrated with Avocado demand in the United States of America and Covid-19 test kit demand during second wave of SARS-COV2 pandemic across 86 countries. [ABSTRACT FROM AUTHOR]

The increasing vulnerability of the population from frequent disasters requires quick and effective responses to provide the required relief through effective humanitarian supply chain distribution networks. We develop scenario-robust optimization models for stocking multiple disaster relief items at strategic facility locations for disaster response. Our models improve the robustness of solutions by easing the difficult, and usually impossible, task of providing exact probability distributions for uncertain parameters in a stochastic programming model. Our models allow decision makers to specify uncertainty parameters (i.e., point and probability estimates) based on their degrees of knowledge, using distribution-free uncertainty sets in the form of ranges. The applicability of our generalized approach is illustrated via a case study of hurricane preparedness in the Southeastern United States. In addition, we conduct simulation studies to show the effectiveness of our approach when conditions deviate from the model assumptions. [ABSTRACT FROM AUTHOR]

We investigate how in the context of Corporate Venture Capital (CVC), the investment decisions affect the likelihood of their subsequent exit strategies. We use OLS and probit regression as well as Weibull distribution of residual values, given its reliability and validity for studying lifetime analysis. Based on a sample of 8722 VC-backed ventures with the first investment dates between 1999 and 2018 in United States (US) and United Kingdom (UK), the results show that the presence of CVCs positively affects the funding amounts and the duration of the investment. CVC funds are more generous and more patient than Independent Venture Capital (IVC) funds regarding their investments in ventures. Moreover, the findings provide evidence that the exit strategies are directly influenced by the funding amounts and the duration of the investment which are influenced, in turn, by the fund type. Greater funding increases the likelihood of IPO exit which is reduced by longer investment duration. Our results are robust to alternative estimation methods, namely two-stage treatment-effects regressions. These results help the various stakeholders (VC funds, investors, ventures) make crucial decisions regarding investment amounts and duration, and exit. [ABSTRACT FROM AUTHOR]