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The main programming language was Visual Basic, as implemented in Visual Basic 6 and Visual Basic for Applications in Excel 2000 and higher.
We took an unconventional approach and created a program that uses Excel as a calculation and programming platform.
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We specifically aimed at students and researchers who are new to meta-analysis, with important parts of the development oriented towards creating internal interactive tutoring tools and designing features that would facilitate usage of the software as a companion to existing books on meta-analysis.
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We set out to create and validate an easy-to-use and comprehensive meta-analysis package that would be simple enough programming-wise to remain available as a free download. Unfortunately, it can take a substantial amount of time to get acquainted with some of these programs and most contain little or no interactive educational material. Consequently, the number of software packages that can perform meta-analysis has increased over the years. So far, meta-analysis has been particularly useful in evaluating and comparing therapies and in assessing causes of disease. Meta-analysis has become a well-known method for synthesis of quantitative data from previously conducted research in applied health sciences.
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The models are available for download as open public domain software allowing for modifications and improvements of the model. The paper also compares the deterministic and stochastic models and shows that the deterministic model may be suitable for most applications while the stochastic model should only be used if found necessary. This paper develops an Excel based deterministic and stochastic model for a WFI system with the latter allowing for the modeling of offtake volume and schedule uncertainty. Discrete event simulation may provide an answer where the typical engineering approach of utilizing a diversity factor fails. Water for injection (WFI) and other pharmaceutical water distribution systems are notoriously difficult to analyze analytically due to the highly dynamic variable demand that is drawn from these systems. The proposed models may also be utilized to determine size or analyze the performance of other utilities, such as heat transfer media, drinking water, etc. The models are programmed within Excel 2003 and are available for download as open public domain software (1), allowing for public modifications and improvements of the model. This paper compares the deterministic and stochastic models and shows that the deterministic model may be suitable for most applications and that the stochastic model should only be used if found necessary by the deterministic simulation. The Monte Carlo method is applied to solve the stochastic method. Whereas the deterministic model ignores uncertainties, the stochastic model allows for both volume and schedule uncertainties. The models utilize discrete event simulation to compute the demand profile from the distribution system they also use a continuous simulation to calculate the variation of the water level in the storage tank. The objective of the simulations is to determine if additional DI/WFI demand from future production processes can be supported by an existing DI/WFI system. This work presents a deterministic and a stochastic model for the simulation of industrial-size deionized water and water for injection (DI/WFI) systems. This evidence is useful to confirm once again the effectiveness of the Z'' Score in a non-American context but also, and above all, to provide suggestions to the Italian legislator so that it can refine the predictive model currently in force. but more effective in determining whether a firm is truly healthy. The Italian method proved to be less effective in predicting a crisis than the Z'' score. The results of this analysis produced two distinct findings. To this end, the two models were applied to the balance sheets of 789 Italian firms that went bankrupt in the period 2016-2018 and, at the same time, to a control sample, equal in number and composition, of non-bankrupt firms.
In order to find a justification for this choice, the present work intends to test the effectiveness of the warning indices that will be adopted in Italy by comparing them with the Altman predictive model in the Z'' Score version.
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However, in Italy, the legislator, in launching the new Business Crisis Code in 2019, in adherence to an important European recommendation, did not adopt the aforementioned model but approved a different one. The response of numerous studies confirms the substantial validity of this algorithm. Numerous studies have been conducted to verify whether, and under what conditions, Altman's Z-Score model can also be applied to unlisted, non-US companies.
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