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This study aims to explore the structural aspects (roles, responsibilities and reporting) of dyad leadership in one health-care organization (HCO).

The perceptions of 32 leaders (17 physician leaders and 15 dyad co-leaders) in formal leadership positions (six first-level with formal authority limited to teams or divisions, 23 middle-level with wider departmental or program responsibility and three senior-level with institution-wide authority) were obtained through focus groups and surveys. In addition, five senior leaders were interviewed. Descriptive statistics was used for quantitative data, and qualitative data were analyzed for themes by coding and categorization.

There are a large number of shared responsibilities in the hybrid model, as most activities in HCOs bridge administrative and professional spheres. These span the leadership (e.g. global performance and quality improvement) and management (e.g. human resources, budgets and education delivery) domains. The individual responsibilities, except for staff and physician engagement are in the management domain (e.g. operations and patient care). Both partners are responsible for joint decision-making, projecting a united front and joint reporting through a quadrat format. The mutual relationship and joint accountability are key characteristics and are critical to addressing potential conflicts and contradictions and achieving coherence.

Clarity of role will assist development of standardized job descriptions and required competencies, recruitment and leadership development.

This is an original empirical study presenting an integrated view of dyad leaders and senior leadership, meaningful expansion of shared responsibilities including academic functions and developing mutual relationship and emphasizing the central role of stability generating management functions.

The operational aspects of supply chain, when handled correctly, results in diminishing the impact of the bullwhip effect. The purpose of this study is to analyze the impact of operational and financial variables on the bullwhip effect. Various operational factors that contribute to the bullwhip effect in a supply chain are identified and their impact on variability in production is measured at manufacturer’s end in the supply chain.

Ten different sectors of the Indian economy are identified and analyzed on the basis of bullwhip effect. The ratio of change in production with respect to change in demand is taken as a metric to measure the bullwhip effect. Initially, the impact of identified variables on bullwhip effect is analyzed using the linear regression analysis and then to gain more insights, the threshold regression model is applied according to the change in bullwhip ratio.

The study identifies four threshold regions in which bullwhip ratio is changing its slope considerably. The operational and financial variables impacting bullwhip effect differently in these four regions provide useful insights about how the variables are impacting the bullwhip effect.

Past 11 years of observations on identified operational and financial variables are studied for ten different sectors. The operational and financial variables are identified on basis of available literature but may not be exhaustive in nature.

The present study implies that the emphasis must be given to the magnitude of the bullwhip ratio. Strategies must be adopted that result in mitigation of bullwhip effect. Such mitigation strategies must not only be restricted on the basis of type of product or sector, perhaps they must be on the basis of threshold region of bullwhip ratio.

The study suggests a novel approach to study the bullwhip effect in supply chain management using the application of threshold regression considering the bullwhip ratio as a threshold variable.

Present study deals with the most discussed rather than addressed yet still an unsolved problem of supply chain known as the bullwhip effect. Operational variables affecting the bullwhip effect are identified and their role in causing the bullwhip effect has been explored using artificial neural networks. The purpose of this study is to analyze the impact of identified operational reasons that affect the bullwhip effect and to analyze the bunch of variables that are more prominent in explaining the phenomenon of the bullwhip effect.

Ten major sectors of the Indian economy are analyzed for the bullwhip effect in the present study, and the operational variables affecting the bullwhip effect in these sectors are identified. The bullwhip metric is developed as the ratio of variance in production to the variance in the demand. The impact of identified operation variables on the bullwhip effect has been discussed using the artificial neural network technique known as multilayer perceptron. The classification is also performed using neural network, logistic regression and discriminant analysis.

The operation variables are found to be varying with respect to sectors. The study emphasizes that analyzing the right set of operation variables with respect to the sector is required to deal with the complex problem, the bullwhip effect. The operational variables affecting the bullwhip effect are identified. The classification result of the neural network is compared with those of the logistic regression and discriminant analysis, and it is found that the dynamism present in the bullwhip effect is better classified by neural network.

The study used 11 years of observations to analyze the bullwhip effect on the basis of operational variables. The bullwhip effect is a complex phenomenon, and it is explained on the basis of an extensive set of operational variables which is not exhaustive. Further, the behavioral aspect (bullwhip because of decision-making) is not explored in the present study.

The operational aspect plays a gigantic role to explain and deal with the bullwhip effect. Strategies to mitigate the bullwhip effect must be in accordance with the operational variables impacting the sector.

The study suggests a novel approach to study the bullwhip effect in supply chain management using the application of neural networks in which operational variables are taken as predictor variables.

An experimental investigation for developing structure-property correlations of hot-rolled E410 steels with different carbon contents, i.e. 0.04wt.%C and 0.17wt.%C metal active gas (MAG) and cold metal transfer (CMT)-MAG weldments was undertaken.

Mechanical properties and microstructure of MAG and CMT-MAG weldments of two E410 steels with varying content of carbon were compared using standardized mechanical testing procedures, and conventional microscopy.

0.04wt.%C steel had strained ferritic and cementite sub-structures in blocky shape and large dislocation density, while 0.17wt.%C steel consisted of pearlite and polygonal ductile ferrite. This effected yield strength (YS), and microhardness being larger in 0.04wt.%C steel, %elongation being larger in 0.17wt.%C steel. Weldments of both E410 steels obtained with CMT-MAG performed better than MAG in terms of YS, ultimate tensile strength (UTS), %elongation, and toughness. It was due to low heat input of CMT-MAG that resulted in refinement of weld metal, and subzones of heat affected zone (HAZ).

A substantial improvement in YS (∼9%), %elongation (∼38%), and room temperature impact toughness (∼29%) of 0.04wt.%C E410 steel is achieved with CMT-MAG over MAG welding. Almost ∼10, ∼12.5, and ∼16% increment in YS, %elongation, and toughness of 0.17wt.%C E410 steel is observed with CMT-MAG. Relatively low heat input of CMT-MAG leads to development of fine Widmanstätten and acicular ferrite in weld metal and microstructural refinement in HAZ subzones with nearly similar characteristics of base metal.

The advancements in the cloud computing has gained the attention of several researchers to provide on-demand network access to users with shared resources. Cloud computing is important a research direction that can provide platforms and softwares to clients using internet. However, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing (LB) method for allocating tasks to virtual machines (VMs) without influencing system performance. This paper aims to develop a technique for LB in cloud using optimization algorithms.

This paper proposes a hybrid optimization technique, named elephant herding-based grey wolf optimizer (EHGWO), in the cloud computing model for LB by determining the optimal VMs for executing the reallocated tasks. The proposed EHGWO is derived by incorporating elephant herding optimization (EHO) in grey wolf optimizer (GWO) such that the tasks are allocated to the VM by eliminating the tasks from overloaded VM by maintaining the system performance. Here, the load of physical machine (PM), capacity and load of VM is computed for deciding whether the LB has to be done or not. Moreover, two pick factors, namely, task pick factor (TPF) and VM pick factor (VPF), are considered for choosing the tasks for reallocating them from overloaded VM to underloaded VM. The proposed EHGWO decides the task to be allocated in the VM based on the newly derived fitness functions.

The minimum load and makespan obtained in the existing methods, constraint measure based LB (CMLB), fractional dragonfly based LB algorithm (FDLA), EHO, GWO and proposed EHGWO for the maximum number of VMs is illustrated. The proposed EHGWO attained minimum makespan with value 814,264 ns and minimum load with value 0.0221, respectively. Meanwhile, the makespan values attained by existing CMLB, FDLA, EHO, GWO, are 318,6896 ns, 230,9140 ns, 1,804,851 ns and 1,073,863 ns, respectively. The minimum load values computed by existing methods, CMLB, FDLA, EHO, GWO, are 0.0587, 0.026, 0.0248 and 0.0234. On the other hand, the proposed EHGWO with minimum load value is 0.0221. Hence, the proposed EHGWO attains maximum performance as compared to the existing technique.

This paper illustrates the proposed LB algorithm using EHGWO in a cloud computing model using two pitch factors, named TPF and VPF. For initiating LB, the tasks assigned to the overloaded VM are reallocated to under loaded VMs. Here, the proposed LB algorithm adapts capacity and loads for the reallocation. Based on TPF and VPF, the tasks are reallocated from VMs using the proposed EHGWO. The proposed EHGWO is developed by integrating EHO and GWO algorithm using a new fitness function formulated by load of VM, migration cost, load of VM, capacity of VM and makespan. The proposed EHGWO is analyzed based on load and makespan.

The purpose of this study is to model the strategic thinking process, considering the different psychological traits of TMTs (top management teams) and how the technological dynamism affects the strategies framed together impacting the performance of the firm.

Modeling and simulation are done in this study using the system dynamics (SD) tool. The data are extracted using social media analytics, and the same is given as an input for the SDmodel, which is used for modeling and simulation of the interdependencies between the psychological factors, technological dynamism and firm performance. The analysis decodes how a change in the thinking process of a TMT has an impact on the performance of the company in an automobile market.

The study has explained how different psychological traits affect the thinking process of a TMT and how the strategies framed with this thinking behavior have an impact on firm performance.

The study is limited only to the automobile industry in India, and only partial psychological constructs were considered to examine their impact on firm performance. This study can be further extended by analyzing the same to various other industries along with many other psychological constructs.

The findings identify the change in behavior of the performance due to the thinking process and technological dynamism. This helps the top management to take into consideration different factors that affect the strategies framed for the company and what are the threshold points in the system that are to be focused on during the framing of a strategy.

The study fills the unattended gaps in the literature regarding how the psychological traits are interdependent and how their relationship is affecting the thinking process, which is going to have an impact on the behavior of the firm performance. It also adds to the literature of systems thinking.