Does sticky inventory management improve productivity?

PurposeThe purpose of this study is to demonstrate the nonlinear relationship between inventory stickiness and productivity, with investment efficiency being a mediator and environmental dynamism being a moderator.Design/methodology/approachUsing a large panel data collected from 1,479 Chinese listed manufacturing enterprises over the period from 2010 to 2020, this research employs the instrumental variable method combined with two-stage least squares estimators to explore the inverted-U-shaped relationship between inventory stickiness and productivity. Furthermore, the mediating role of investment efficiency and the moderating role of environmental dynamism are demonstrated via two three-model systems.FindingsAs its core, productivity initially increases with inventory stickiness until a turning point at the end of the sample, beyond which the incremental effect of inventory stickiness on productivity become negative. That is, an inverted U-shaped relationship between inventory stickiness and productivity is found to exist. Moreover, further mediated moderation analysis highlights that investment efficiency is a key mediator of this relationship, whereas environmental dynamism is a key moderator.Practical implicationsManagers ought to gauge carefully against the tradeoffs between inventory stickiness and productivity. In general, over 90% of manufacturing enterprises have great potential to increase productivity by implementing sticky inventory management. In addition, managers are suggested to place emphasis on investment management and environmental strategy.Originality/valueThis paper contributes to the current understanding about productivity by illustrating and verifying the nonlinear effect of sticky inventory management. It may be the first study to empirically demonstrate the mediating effect of investment efficiency and the moderating effect of environmental dynamism on the relationship between inventory stickiness and productivity.

The Poisson Nadarajah-Haghighi Distribution: Different Methods of Estimation

Estimation of parameters of Poisson Nadarajah-Haghighi (PNH) distribution from the frequentist and Bayesian point of view is discussed in this article. To this end, we briefly described ten different frequentist approaches, namely, the maximum likelihood estimators, percentile based estimators, least squares estimators, weighted least squares estimators, maximum product of spacings estimators, minimum spacing absolute distance estimators, minimum spacing absolute-log distance estimators, Cramér-von Mises estimators, Anderson-Darling estimators and right-tail Anderson-Darling estimators. To assess the performance of different estimators, Monte Carlo simulations are done for small and large samples. The performance of the estimators is compared in terms of their bias, root mean squares error, average absolute difference between the true and estimated distribution functions, and the maximum absolute difference between the true and estimated distribution functions of the estimates using simulated data. For the Bayesian inference of the unknown parameters, we use Metropolis–Hastings (MH) algorithm to calculate the Bayes estimates and the corresponding credible intervals. Results from the simulation study suggests that among the considered classical methods of estimation, weighted least squares and the maximum product spacing estimators uniformly produces the least biases of the estimates with least root mean square errors. However, Bayes estimates perform better than all other estimates. Finally, we discuss a practical data set to show the application of the distribution.

A New Generalized Odd Gamma Uniform Distribution: Mathematical Properties, Application and Simulation

n many applied areas there is a clear need for the extended forms of the well-known distributions.The new distributions are more flexible to model real data that present a high degree of skewness and kurtosis, such that each one solves a particular part of the classical distribution problems. In this paper, a new two-parameter Generalized Odd Gamma distribution, called the (GOGaU) distribution, is introduced and the fitness capability of this model are investigated. Some structural properties of the new distribution are obtained. The different methods including: Maximum likelihood estimators, Bayesian estimators (posterior mean and maximum a posterior), least squares estimators, weighted least squares estimators, Cramér-von-Mises estimators, Anderson-Darling and right tailed Anderson-Darling estimators are discussed to estimate the model parameters. In order to perform the applications, the importance and flexibility of the new model are also illustrated empirically by means of two real data sets. For simulation Stan and JAGS software were utilized in which we have built the GOGaU JAGS module