Towards Enhancement of the Economy of a Thermal Power Generating System through Prediction of Plant Efficiency

The plant ‘Heat Rate’ (HR) is a measure of overall efficiency of a thermal power generating system. It depends on a large number of factors, some of which are non-measurable, while data relating to others are seldom available and recorded. However, coal quality (expressed in terms of ‘effective heat value’ (EHV) as kcal/kg) transpires to be one of the important factors that influences HR values and data on EHV are available in any thermal power generating system. In the present work, we propose a prediction interval of the HR values on the basis of only EHV, keeping in mind that coal quality is one of the important (but not the only) factors that have a pronounced effect on the combustion process and hence on HR. The underlying theory borrows the idea of providing simultaneous confidence interval (SCI) to the coefficients of a p-th p(≥1) order autoregressive model (AR(p)). The theory has been substantiated with the help of real life data from a power utility (after suitable base and scale transformation of the data to maintain the confidentiality of the classified document). Scope for formulating strategies to enhance the economy of a thermal power generating system has also been explored.     

Correlated random effects regression analysis for a log-normally distributed variable

In regression analysis, it is assumed that the response (or dependent variable) distribution is Normal, and errors are homoscedastic and uncorrelated. However, in practice, these assumptions are rarely satisfied by a real data set. To stabilize the heteroscedastic response variance, generally, log-transformation is suggested. Consequently, the response variable distribution approaches nearer to the Normal distribution. As a result, the model fit of the data is improved. Practically, a proper (seems to be suitable) transformation may not always stabilize the variance, and the response distribution may not reduce to Normal distribution. The present article assumes that the response distribution is log-normal with compound autocorrelated errors. Under these situations, estimation and testing of hypotheses regarding regression parameters have been derived. From a set of reduced data, we have derived the best linear unbiased estimators of all the regression coefficients, except the intercept which is often unimportant in practice. Unknown correlation parameters have been estimated. In this connection, we have derived a test rule for testing any set of linear hypotheses of the unknown regression coefficients. In addition, we have developed the confidence ellipsoids of a set of estimable functions of regression coefficients. For the fitted regression equation, an index of fit has been proposed. A simulated study illustrates the results derived in this report.     

Sequential estimation problems for negative exponential populations

The literature on sequential estimation problems for negative exponential populations has been reviewed here, We attempt to bring in all the published and unpublished materials known to us in a fairly coherent fashion. Both the concepts and theoretical findings are discussed.     

Measuring to Improve Versus Measuring to Prove: Understanding the Adoption of Social Performance Measurement Practices in Nascent Social Enterprises

Social enterprises are described as organizations with dual objectives—social and commercial. While the measurement of commercial performance is relatively straightforward and well understood, our understanding of the factors related to measuring social performance is more ambiguous. Is the adoption of social performance measurement (SPM) practices more related to external pressures, such as the need to demonstrate legitimacy to funders and peers, or is it more closely related to the growing rationalization within the social sector? We examine the relationship between external and internal factors and the adoption of SPM using a novel dataset of 1864 nascent social enterprises from around the world. Our findings suggest support for the argument that the adoption of SPM in social enterprise is related to the growing rationalization of the social sector, which challenges some of the past research on this topic, and provides a more nuanced perspective of SPM in social enterprise.     

Managing Demand Uncertainty through Core Acquisition in Remanufacturing

We discuss the optimal raw material acquisition strategy for a third party remanufacturer (3PR). We specifically investigate whether a 3PR should acquire used products or cores in bulk with uncertain quality levels, or in sorted grades with known quality levels; and whether to acquire and remanufacture cores before the demand is realized (planned acquisition), or after the demand is realized (reactive acquisition), or on both occasions (sequential acquisition). When only sorted cores are acquired, we find that, (i) it is optimal to acquire cores in multiple grades to balance acquisition and remanufacturing costs; (ii) if reactive acquisition is possible, it reduces the assortment size (number of grades in which cores are acquired) and the total inventory acquired in the planned acquisition; and (iii) the optimal portfolio of grades to acquire and the optimal acquisition and remanufacturing quantities of these grades can be determined analytically. When bulk cores are acquired in addition to sorted cores, the property of reduction in assortment size of the planned acquisition is preserved. We also show that the 3PR should acquire only a fraction of the demand in planned acquisition, and leave the rest for reactive acquisition. This fraction changes during the lifecycle of a remanufactured product. Using a combination of empirical and realistic data from a smartphone remanufacturer we show that sequential acquisition increases expected profit by up to 8% and 27% over only planned and only reactive acquisitions respectively, and reduces the inventory acquired by up to 21% over only planned acquisition.     

Design and implementation of an integrated production planning system for a pharmaceutical manufacturing concern in India

SmithKline Beecham (SBPI) is a major player in the Indian pharmaceutical industry. Though relatively small in terms of turnover, it has performed Pharmaceuticals (India) exceptionally well in terms ofprofitability and growth rate. The firm has substantial cash reserves. However, the firm operates in 'mass production' mode at present, with the established finished goods inventory norms being in the range of 1 to 1.5 months. Competitive pressure on the firm is now forcing it to move towards world class manufacturing and the application of concepts from MRP II, JIT and OPT is under consideration. This paper describes the considerations that went into the justification, design and implementation of an integrated production planning and control system in this situaion.     

Quantile dispersion graphs to compare the efficiencies of cluster randomized designs

The purpose of this article is to compare efficiencies of several cluster randomized designs using the method of quantile dispersion graphs (QDGs). A cluster randomized design is considered whenever subjects are randomized at a group level but analyzed at the individual level. A prior knowledge of the correlation existing between subjects within the same cluster is necessary to design these cluster randomized trials. Using the QDG approach, we are able to compare several cluster randomized designs without requiring any information on the intracluster correlation. For a given design, several quantiles of the power function, which are directly related to the effect size, are obtained for several effect sizes. The quantiles depend on the intracluster correlation present in the model. The dispersion of these quantiles over the space of the unknown intracluster correlation is determined, and then depicted by the QDGs. Two applications of the proposed methodology are presented.     

Optimality in the presence of two factor interactions among the nuisance factors

Here we consider an m way heterogeneity settingin the presence of two factor interactions among the heterogeneity directions . The set of experimental units considered do not exhaust all possible level combinations of the heterogeneity directions ; but the set is such that all heterogeneity effects assumed i n the model are orthogonally estmable.In such a setting , calleda doubly balanced m-way setting , the C-matrix of the reduced normal equations for the treatment effects is derived . Universally optimal designs are obtained in the cases where the settingis (i) Completely regular or (ii) partly regular of a special type . An interesting observation is that there are situations where the universally optimal designsin the present setting are totally different from the designs known t o be universally optimal when there is no interaction effect among the heterogeneity directions. This indicates that the usual optimality criteria are sensitive to validity or otherwise of the usual assumptions of lack of interactions among heterogeneity directions.