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The purpose of this paper is to examine the implementation of IT portfolio management (IT PoM) and develop a framework guided by adaptive structuration theory to describe the key structures, features, and appropriation steps needed to effectively manage IT investments and assets.

Using a longitudinal case study approach, data were collected over an eight-month period from a US Fortune 500 company during its IT PoM implementation effort.

The case analysis highlights three major IT PoM features appropriated by the organization: creating the portfolio; assessing and analyzing the portfolio characteristics based on risk, benefits, alignment, criticality, and cost; and balancing decisions to start projects or terminate under-performing IT assets such as servers and applications. The spirit of IT PoM was interpreted differently by different stakeholders (data providers, business units, and IT PoM team) leading to resistance to implementation. The case data underscores the importance of establishing a governance steering committee and new internal structures to help push the balancing decisions across the organization.

The results are useful in developing guidelines and strategies to achieve successful implementation of IT PoM and to highlight critical factors that practitioners need to pay close attention to during an IT PoM implementation.

This study represents one of the first attempts to describe a detailed IT PoM implementation process and how IT PoM appropriation process can lead to improved decision making within the organization.

Malnutrition is a serious public health problem, which occurs because of an inadequate supply of nutrients and therefore affects many children and women (especially pregnant and lactating women of low economic class) worldwide. There is a great need to develop cost-effective fortified food supplements to overcome this problem. Therefore, this study aims to examine the factors that should be considered in the development of low-cost supplementary foods for breastfeeding women.

A wide variety of relevant publications were identified through search in electronic databases (ScienceDirect, PubMed, SciELO, Google Scholar, SpringerLink and ResearchGate) based on different keywords such as malnutrition, specific considerations, supplementary foods and breastfeeding women, which were published before 2020. After looking for adequate literature, 71 articles were examined, which provided an overview of these aspects.

Various food supplements and food products already available in the market are generally expensive and go beyond the purchasing power of lower- or middle-class families. Traditionally, efforts have been made to prepare such supplements at home, but the composition of these products varies based on the economic status of consumers. Therefore, the nutritional status is clearly compromised because of the low standard of living, which requires a great need to develop low-cost nutritious food products, accessible to all and that meet the appropriate nutritional, sensory and economic attributes without ignoring the regulatory guidelines.

This review is majorly focused on the specific considerations, alternative sources, ensuring nutritional and sensorial attributes and economic aspects for the development of supplementary low-cost food products for breastfeeding women with the goal of eliminating malnutrition.

The study aims to perform an extensive literature review in the area of the maintenance decision analysis (MDA), especially in power generation systems. In the basis of this review, the paper proposes a new model for the MDA which involves a combination of reliability, availability, maintainability and safety (RAMS) performance with energy efficiency performance (EEP).

Starting from the opportunity in Sustainable Development Scenario (SDS) by improving the energy efficiency (EE) and using renewable energy for the power generation system, also concerning to the major challenge of maintenance optimization in order to implement maintenance strategy, the maintenance decision-making and energetic efficiency management system (EEMS) have been reviewed. In the context of power generation system's performance, the measurement is also analyzed and identified. Then, the extensive literature review has been performed to compare between RAMS and EEP. And finally, the limitation and gap, where EEP is not yet a complementary consideration during MDA being identified and a new model for the performance-based MDA is proposed.

The new model proposed for the performance-based MDA is able to be used to conduct maintenance decision by utilizing the combination of RAMS and EEP depending on the type of decision required.

There is an opportunity for a maintenance organization of power generation plant to apply this new model proposed for the MDA in order to optimize the maintenance scope and schedule.

The result of work in this paper forms the basis for combining RAMS with EEP as performance-based MDA tools in the context of maintenance of the power generation system.

The purpose of this paper is to provide an overview of RAMS engineering in industry and research.

A range of research articles (1988‐2005) covering RAMS engineering is discussed to provide a practical and theoretical overview of RAMS engineering in industry and research.

The paper provides information about the current scenario and also about the past scenario of RAMS engineering in research and industry. Research limitations/implications – This paper reviews much of the literature on RAMS engineering. The literature is collected from major journals and conference proceedings, the period covered is from 1988 to 2005. The authors have tried to make it reasonably comprehensive, but those papers which are not included were either inadvertently overlooked or considered peripheral to this survey. In the present work the authors have only considered those articles which have included two or more aspects of RAMS. Practical implications – The paper is a very useful source of information for researchers working in the area of RAMS engineering. Originality/value – This paper offers help to researchers in understanding the current and past status of RAMS.

The purpose of this paper is to develop a mathematical model for the network design of a reverse supply chain in a multi-product, multi-period environment. The emission cost due to transportation activities is incorporated into the model to reduce the total cost of emission and study the significance of inclusion of emission cost on the network design decisions.

Mixed integer linear programming formulation is used to model the network. The developed model is solved and analysed using the commercial solver LINGO.

The mathematical model provides a unified design of the network for the entire planning horizon comprising of different periods. A reduction in the total cost of emission is achieved. The analysis of the problem environment shows that the network design decisions significantly vary with the consideration of emission cost.

A single mode of transportation is considered in this study. Also, a single type of vehicle is considered for the transportation purpose.

The developed model can aid the decision makers in making better decisions while reducing the total emission cost. The quantification of the emission cost due to transportation activities is presented in an Indian context and can be used for future studies.

An all-encompassing approach for the design of reverse logistics networks with explicit consideration of product structure and emission cost.

This study aims to analyze the dynamic performance of aerostatic thrust bearing for different geometries of recess. Different geometries of recess of equal recess area, i.e. circular, elliptical, rectangular and annular, have been considered in analysis. The work also analyzes the influence of tilt angle on the performance of thrust bearing. To compute the unknown pressure field, the Reynolds equation governing the flow of compressible lubricant (air) has been solved using finite element formulation. Further, separate finite element formulations have been carried out to compute fluid film stiffness and damping coefficients directly. This method provides quick computation of stiffness and damping coefficients of aerostatic thrust bearing than the usual approach.

As the Reynolds equation governing the flow of compressible lubricant is nonlinear partial differential equation, the computation of the stiffness and damping coefficient follows an iterative procedure. It requires a lot of computational energy. Therefore, in the present work, a novel technique based on finite element formulation is suggested to compute air film stiffness and damping coefficient in aerostatic thrust bearing.

A novel technique based on finite element formulation is illustrated to simulate the performance of tilted pad aerostatic thrust bearing. On the basis of simulated results, following key conclusions may be drawn. The static and dynamic performance of a circular aerostatic tilted thrust pad bearing is significantly affected with a change in the value of tilt parameter and the shape of the recess.

Implications are as follows: direct computation of air film damping coefficient is performed without perturbation method in finite element method (FEM); influence of tilt on aerostatic thrust bearing is studied; influence of recess shape on aerostatic thrust bearing is observed; and finite element formulation of aerostatic thrust bearing is performed.

The present work will be quite useful for bearing designer and academicians.

The purpose of this paper is to examine how changes in creditors' rights affect the dividend policy behavior of corporate firms.

The authors use the implementation of the bankruptcy and insolvency code (IBC) in India in 2016 as a quasi-natural experiment setup. Differential application of this law allows them to use the Difference in Differences approach to extract the marginal impact of change in creditors' rights on the dividend policy.

The authors show that firms responded to strengthening creditors' rights by decreasing their dividend payout. Further, the authors observe that this negative response is conditioned on firm leverage and the nature of the creditor, i.e. public or private. The firms with a greater leverage ratio and a greater proportion of private debt in the total debt in the pre-event period have shown greater response to the change in the law. Lastly, the authors show that stock markets positively respond to the observed decrease in dividends only when a corresponding decrease in the leverage accompanies such a decrease.

The authors contribute to the finance and law literature from several aspects. First, the authors extend this stream by bringing to light the dividend policy response of firms when they are subjected to a change in creditors' rights. Second, the authors also show how firm-level factors like financial policy and the nature of the creditor condition their response to IBC. Lastly, the authors also examine the market reaction to the dividend policy response of firms to the change in bankruptcy law.

Reliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of the best-fitted probability distribution are also contributing significantly to reliability estimation. In this work, a case study of load haul dump (LHD) machines is illustrated that consider the optimization of failure and repair rate parameters using two well established metaheuristic approaches, namely, genetic algorithm (GA) and particle swarm optimization (PSO). This paper aims to analyze the aforementioned points.

The data on time between failures (TBF) and time to repairs (TTR) are collected for a LHD machine. The descriptive statistical analysis of TBF & TTR data is performed, trend and serial correlation tested and using Anderson–Darling (AD) value best-fitted distributions are identified for repair and failure times of various subsystems. The traditional methods of estimation like maximum likelihood estimation, method of moments, least-square estimation method help only in finding the local solution. Here, for finding the global solution two well-known metaheuristic approaches are applied.

The reliability of the LHD machine after 60 days on the real data set is 28.55%, using GA on 250 generations is 17.64%, and using PSO on 100 generations and 100 iterations is 30.25%. The PSO technique gives the global best value of reliability.

The present work will be very convenient for reliability engineers, researchers and maintenance managers to understand the failure and repair pattern of LHD machines. The same methodology can be applied in other process industries also.

In this case study, initially likelihood function of the best-fitted distribution is optimized by GA and PSO. Reliability and maintainability of LHD machines evaluated by the traditional approach, GA and PSO are compared. These results will be very helpful for maintenance engineers to plan new maintenance strategies for better functioning of LHD machines.

The quality of production is an essential factor for the performance measure of a system; a casting process is the same section. It is a type of metal-forming practice in which the required shape of metal is acquired by pouring molten metal into the mold cavity and allowing it to solidify. Casting is done to provide strength and rigidity to the parts of a system for bearing mechanical impacts. The purpose of this paper is to investigate the various aspects which affect the casting process in the foundry industry, in order to optimize the quality of casting, with the assumption that sufficient repair facility is always available.

The considered casting system can have many defects such as the mold shift defect, blowhole defect, defect of shrinkage and porosity, defect of inclusion, defect of cold shut and much more. The studied system can be in three states during the process, namely, good state, failed state and degraded state. The system can repair after minor failures as well as a major failure. The average failure rates of various defects of the system considered as constant and repairs follow the general time distribution. The system is analyzed with the help of the supplementary variable technique and the Laplace transformation for evaluating its various performance measures in order to improve its performance/production.

This work provides a strong understanding of the casting industry, that which failure affects the production of casting and how much. For better understanding, the results have been demonstrated with the help of graphs.

In the present paper, a mathematical model based on the casting process in manufacturing industry has been developed.

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.

As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.

The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.

This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.