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The purpose of this paper is to develop a competency dictionary and model for contractual workers of the steel manufacturing sector of India.

In-depth interviews with 30 supervisors and behavioral events interviews (BEIs) with 40 contractual workers were conducted to identify and validate the competencies of the contractual workers.

The competencies identified are arranged according to the similarity and dissimilarity between it under three broad categories of competencies, i.e., knowledge, skills and attitudes. These categories are used to develop a competency dictionary which has behavioral indicators, and a framework that can be used to map and evaluate the competencies.

The competency dictionary and the framework developed in the present study will assist human resource practitioners in implementing competency-based human resource processes such as recruitment and selection, training and development and performance management for the contractual workers in the manufacturing sector.

The present study is among the few empirical studies that provide a competency dictionary and a framework of contractual workers in the manufacturing sector of India.

In this paper, a mixture of exponential and Rayleigh distributions in the proportions α and 1 − α and all the parameters in the mixture distribution are estimated based on fuzzy data.

The methods such as maximum likelihood estimation (MLE) and method of moments (MOM) are applied for estimation. Fuzzy data of triangular fuzzy numbers and Gaussian fuzzy numbers for different sample sizes are considered to illustrate the resulting estimation and to compare these methods. In addition to this, the obtained results are compared with existing results for crisp data in the literature.

The application of fuzziness in the data will be very useful to obtain precise results in the presence of vagueness in data. Mean square errors (MSEs) of the resulting estimators are computed using crisp data and fuzzy data. On comparison, in terms of MSEs, it is observed that maximum likelihood estimators perform better than moment estimators.

Classical methods of obtaining estimators of unknown parameters fail to give realistic estimators since these methods assume the data collected to be crisp or exact. Normally, such case of precise data is not always feasible and realistic in practice. Most of them will be incomplete and sometimes expressed in linguistic variables. Such data can be handled by generalizing the classical inference methods using fuzzy set theory.

This paper considers a series system consisting of n different components having unknown and variable failure rates, where the lifetime of components follow exponential distribution having non-constant failure rates. Moreover, the failure rates are bounded by above and are dependent on environmental factors such as temperature, pressure, through linear relationship. The purpose of this paper is to design a component reliability test plan for such a series system with an unknown variable failure rate.

The reliability of the system is estimated with the help of the unbiased estimator of failure rate. The testing procedure is stopped when a fixed number of failures occur for each component.

An optimal reliability test plan is designed and the resultant non-linear integer optimization problem is formulated satisfying the constraints of producer’s and consumer’s risks. The obtained results are compared with the results available in the literature. Some examples are considered to illustrate the approach.

It is observed that use of prior information in the form of an upper bound and incorporation of environmental factors have the advantage of savings in the total testing cost.

The changes in tensile behavior of polycrystalline nanocopper lattice with changes in temperature, average grain size (AGS) and strain rate, have been explored. The existence of a critical AGS has also been observed which shows that the Hall–Petch relationship behaves inversely.

Nanoscale deformation of polycrystalline nanocopper has been done in this study with the help of an embedded atom method (EAM) potential. Voronoi construction method has been employed for creating four polycrystals of nanocopper with different sizes. Statistical analysis has been used to examine the observations with emphasis on the polycrystal size effect on melting point temperature.

The study has found that the key stress values (i.e. elastic modulus, yield stress and ultimate tensile stress) are significantly influenced by the considered parameters. The increase in strain rate is observed to have an increasing impact on mechanical properties, whereas the increase in temperature degrades the mechanical properties. In-depth analysis of the deformation mechanism has been studied to deliver real-time visualization of grain boundary motion.

This study provides the relationship between required grain size variations for consecutive possible variations in mechanical properties and may help to reduce the trial processes in the synthesis of polycrystalline copper based on different temperatures and strain rates.

The purpose of the paper is set to minimize the total cost of a manufacturing system when an acceptance sampling plan (ASP) is carried out in a fuzzy environment.

A fuzzy acceptance sampling plan (FASP) is employed for the inspection of the batch of products and a fuzzy cost optimization problem is formulated.

The extent of uncertainty determines an interval for the total cost function with upper and lower bounds. The effect of variation in the ambiguity of the proportion of defectives in the probability of acceptance is determined.

The proposed model is specifically designed for production and supply units with ASP for attributes. Still, the proportion of defectives in the inspection process is fuzzy.

Fuzzy probability distribution is used to model an optimal inspection plan for a general supply chain. Economic design of supply chain under fuzzy proportion of defectives is discussed for the first time.

This article presents the multi-state stress-strength reliability computation of a component having three states namely, working, deteriorating and failed state.

The probabilistic approach is used to obtain the reliability expression by considering the difference between the values of stress and strength of a component, say, for example, the stress (load) and strength of a power generating unit is in terms of megawatt. The range of values taken by the difference variable determines the various states of the component. The method of maximum likelihood and Bayesian estimation is used to obtain the estimators of the parameters and system reliability.

The maximum likelihood and Bayesian estimates of the reliability approach the actual reliability for increasing sample size.

Obtained a new expression for the multi-state stress-strength reliability of a component and the findings are positively supported by presenting the general trend of estimated values of reliability approaching the actual value of reliability.

This article considers a reliability model where the failure is due to cumulative damage exceeding a threshold level. The concept that the threshold level of cumulative damage at each arrival of shock can change based on whether the magnitude of each shock exceeds its defined threshold level is considered to compute the system reliability.

The stochastic process approach is used to obtain the cumulative damage based on Poisson arrival of shocks. The general expression for reliability is obtained using the conditional probability over each arrival of shock. The method of maximum likelihood estimation is used to obtain the estimators of the parameters and system reliability. A sensitivity analysis is performed to measure the effect of the parameter representing the rate of arrival of shock.

The maximum likelihood estimates of the reliability approach the actual reliability for increasing sample size. A sensitivity analysis study on the parameter representing the rate of arrival of shock shows that as the values of parameter increase (decrease), the reliability value decreases (increases).

Obtained a new expression for the cumulative damage–shock model and the findings are positively supported by presenting the general trend of estimated values of reliability approaching the actual value of reliability. The sensitivity analysis also genuinely supports our findings.

The purpose of this paper is to improve the understanding of the role of the bottlenecks in the dynamic software development supply chains. The paper examines the effects of the task priorities in the software development and investigates the possible strategies to manage them effectively.

In this paper, a software development supply chain has been simulated. This includes modeling of the various sizes of software requirement, different priorities, variations in development times, quality defects, etc. The model assumes a fixed set of resources of various skills. The model is studied for the bottlenecks, throughput, work in progress (WIP), etc. under various work preemption scenarios.

The results indicate that job priorities impact the bottleneck formulation, throughput and WIP of the software development. The work interruption policies to accommodate priority jobs adversely impact the throughput. Selective introduction of interruptions by leaving the bottlenecks from interruptions helps balancing the throughput and priorities.

The impact of the learning curve and knowledge acquisition time needed by the resources to restart the interrupted work has not been considered in this paper, which can be a future area of research.

The paper helps the practicing managers evaluate the dynamics of the bottlenecks with various task management approaches and comprehend the possible tradeoffs between priority and throughout.

The paper looks at software development from a perspective of workflow dynamics. This is a pioneer effort, as it utilizes simulation and modeling approach in understanding the software supply chains better.

Epilepsy is a chronic neurological disorder that can have profound physical, social and psychological consequences. We aimed to assess the clinical predictors of quality of life of people with epilepsy. We recruited 31 patients suffering from epilepsy in this cross-sectional study. Their clinical profile was recorded. Quality Of Life in Epilepsy (QOLIE-31) was used to assess quality of life of our patients. Depression was screened by Neurological Disorders Depression Inventory in Epilepsy (NDDI-E). Among all the clinical variables, only seizure frequency significantly correlated with seizure worry (P=0.002), emotional well-being (P=0.026) and social functions (P=0.013) subscales of QOLIE-31. NDDIE score showed a significant negative correlation with all the subscales of QOLIE-31 except medication effects (P=0.993). A significant positive correlation was also noted between seizure frequency and NDDI-E score (r=0.417, P=0.020). Seizure frequency and depression are the most important predictors of quality of life in epilepsy patients. The management of patients with epilepsy should not only be aimed at just preventing seizures but the treating clinicians should also be cognizant about depression which itself can significantly affect the quality of life of patients.

This purpose of this study is to develop a simple framework for designing a warehouse incorporating lean principles. Multiple objectives like resource planning, material handling, storage, inventory management, including internal and external logistics, are considered.

A design procedure to incorporate lean principles for designing a warehouse for a complex multi-model production line has been proposed. The preferred standards and factors affecting warehouse design, the inputs and outputs of process flow characteristics, are incorporated into the design. Current and future state value stream mappings are drawn to bring out the challenges in the value flow.

The framework for designing a lean warehouse have been implemented and validated in a heavy machinery manufacturer. This framework will ease the work of the future lean-based warehouse designers to apply simple step-by-step processes to achieve the goal with the nearest accuracy. The steps followed can be summarized as defining the lean processes, making the lean process as the design base, collecting inputs like stock-keeping unit master, inventory and space details, and building the lean warehouse design with the step-by-step processes.

Practical tips on warehouse design have been explained focusing on the part volume, quantity handled, inventory and throughput. This will assist the practitioners in designing a lean warehouse and leading to an improved operational performance.

A simplified design procedure for designing a lean warehouse, along with a real-time case study has been enumerated in detail. Effective use of space and resources with lean tools and techniques lead to better storage and picking efficiency resulting in an overall reduction in cost.