T.L. Sankar, R.K. Mishra and A. Lateef Syed Mohammed
Development Banks (DBs) are specialized financial institutionscreated for the purpose of balanced industrialization. A developmentbank has to act more as a promotional agency than…
Abstract
Development Banks (DBs) are specialized financial institutions created for the purpose of balanced industrialization. A development bank has to act more as a promotional agency than a mere financial institution. Therefore separate institutions have been set up, namely State Industrial Development Corporations (SIDCs) in almost all the states in India for undertaking promotional activities. With the growing role of Development Banking in India, the SIDCs are facing financial hardships as they are wholly dependent on Government grants. The paucity of funds for SIDCs has prompted them to opt for divestment of their shareholdings from the existing units to recycle the funds for increasing industrial promotion. Divestment decisions are concerned with the quantum and timing of divestment and the determination of share prices for this purpose. SIDCs are different in that their divestment decisions need not be primarily guided by economic factors (capital appreciation). Highlights the divestment policy and share evaluation models adopted by a development bank, namely Andhra Pradesh Industrial Development Corporation Ltd, which is basically responsible for transforming an agrarian Indian state (Andhra Pradesh), into a moderate industrial organization.
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T.L. Sankar, R.K. Mishra and A. Lateef Syed Mohammed
Examines one of the most important reforms relating to publicenterprise (PE) policy in India, namely divestment of theirshare‐holdings. Discusses the philosophy, process…
Abstract
Examines one of the most important reforms relating to public enterprise (PE) policy in India, namely divestment of their share‐holdings. Discusses the philosophy, process, organizational mechanism, expectations and outcomes of divestment in PEs. Finally, points out the major weaknesses retarding the success of the newly introduced divestment policy and outlines some reformatory measures to overcome them. As a backdrop, presents the historical background, current scenario, and problems and performance of PEs in India, but has been restricted to the central PEs, i.e. enterprises owned and managed by the central government only.
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Priya Mandiratta and G.S. Bhalla
The purpose of this study is to represent an attempt to empirically capture the impact of disinvestment on the financial and operating performance of 26 Bombay Stock Exchange…
Abstract
Purpose
The purpose of this study is to represent an attempt to empirically capture the impact of disinvestment on the financial and operating performance of 26 Bombay Stock Exchange (BSE) listed central public sector enterprises (CPSEs) in India which got divested through stock market mechanism during the time period of 2000–2014.
Design/methodology/approach
Through ratio analysis different ratios such as return on assets, return on equity, net income efficiency, debt equity, dividend payout and employment levels have been computed. Pre- and post disinvestment performance of these firms is examined through Wilcoxon signed-rank test. The present research endeavors to examine the impact of disinvestment through random effect panel data models in order to control the effect of other firm specific variables.
Findings
The overall results of the study indicate statistically significant fall in profitability ratios. The empirical results have not witnessed positive effect of disinvestment on the profitability of the CPSEs; rather, this effect has found to be negative. The possible reasons behind these negative results could be poor pre disinvestment financial health of CPSEs, negative rate of return on capital employed by PSEs and inefficiency which need to be tested empirically by future researchers.
Originality/value
The fact that government-owned firms are typically less proficient or at least less gainful than private-owned firms is widely hypothesized. Therefore, the disinvestment policy aims at dropping the participation of the public sector in the economic actions of the country in order to support the private sector. The present study is a first of its kind to study the impact of disinvestment on the profitability of the firms, which got divested through stock market mechanism since the year 2000 by applying both univariate and multivariate analysis.
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Suggests that, with the severe and continuing resource problemsbesetting the Indian economy, and with the pressure on policy makers toincrease economic growth, there is a need to…
Abstract
Suggests that, with the severe and continuing resource problems besetting the Indian economy, and with the pressure on policy makers to increase economic growth, there is a need to privatize State Transport Undertakings (STUs). Points out that the debate as to whether STUs should undergo sweeping reforms, or be privatized, has assumed serious proportions. Makes an attempt to study the reasons for and against nationalization at the time of the takeover of the passenger road transport business in the public sector. Aims to contextualize this sector in the country′s planned economic development, outlines a profile of STUs, evaluates their operations, and indicates the scope and methods for reform and privatization. Sums up policy implications in the final section.
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Mosayeb Dashtpeyma and Reza Ghodsi
This research paper aims to identify and evaluate the enabling factors of agility capability in humanitarian relief chain network.
Abstract
Purpose
This research paper aims to identify and evaluate the enabling factors of agility capability in humanitarian relief chain network.
Design/methodology/approach
The research phases were implemented based on an integrated framework. First, a reference framework of the enablers has been constructed based on a literature review. Then, a hybrid evaluation approach is applied that combines fuzzy decision-making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP) to achieve reliable results. It provides a road map to identify and evaluate the interactions between the enabling factors and determines the weights correspond to their relative importance. This approach takes advantage of fuzzy set theory to deal with ambiguities, uncertainties and vagueness inherent in the evaluation process.
Findings
Relief chain agility is a vital determinant of the effectiveness to succeed humanitarian missions during and after natural and unnatural disasters such as earthquakes, epidemics and terrorist attacks. Results shed light on the essential enabling factors, relationships among them, and their importance for developing humanitarian relief chain agility enhancing the overall performance quality.
Originality/value
The integrated framework is implemented for the Red Crescent, a nongovernmental organization in Iran, which is trying to optimize the agility of their humanitarian relief chain network. In short, the findings are beneficial for identification and utilization of the essential prerequisites of agility in order to develop an agile humanitarian relief chain.
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Hasanuzzaman, Kaustov Chakraborty and Surajit Bag
Sustainability is a major challenge for India’s (Bharat’s) coal mining industry. The government has prioritized sustainable growth in the coal mining industry. It is putting forth…
Abstract
Purpose
Sustainability is a major challenge for India’s (Bharat’s) coal mining industry. The government has prioritized sustainable growth in the coal mining industry. It is putting forth multifaceted economic, environmental and social efforts to accomplish the Sustainable Development Goals (SDGs). This research aims to identify the factors for sustainable improvements in coal mining operations. Secondly, this study examines the intensity of causal relations among the factors. Thirdly, this study examines whether causal relations exist among the factors to be considered for sustainable improvement in coal mining operations. Lastly, the study aims to understand how the factors ensure sustainable improvement in coal mining operations.
Design/methodology/approach
An integrated three-phase methodology was applied to identify the critical factors related to coal mining and explore the contextual relationships among the identified factors. Fifteen critical factors were selected based on the Delphi technique. Subsequently, the fifteen factors were analyzed to determine the contextual and causal relationships using the total interpretive structural modelling (TISM) and DEMATEL methods.
Findings
The study identified “Extraction of Coal and Overburden” as the leading factor for sustainable improvement in coal mining operations, because it directly or indirectly influences the overall mining operation, environmental impact and resource utilization. Hence, strict control measures are necessary in “Extraction of Coal and Overburden” to ensure sustainable coal mining. Conversely, “Health Impact” is the lagging factor as it has very low or no impact on the system. Therefore, it requires fewer control mechanisms. Nevertheless, control measures for the remaining factors must be decided on a priority basis.
Practical implications
The proposed structural model can serve as a framework for enhancing sustainability in India’s (Bharat’s) coal mining operations. This framework can also be applied to other developing nations with similar sustainability concerns, providing valuable guidance for sustainable operations.
Originality/value
The current study highlights the significance of logical links and dependencies between several parameters essential to coal mining sustainability. Furthermore, it leads to the development of a well-defined control sequence that identifies the causal linkages between numerous components needed to achieve real progress towards sustainability.
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Sanjita Jaipuria and Siba Sankar Mahapatra
The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period…
Abstract
Purpose
The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period order-up-to level ((R, S)) inventory control policy and its different variants such as (R, βS) (R, γO) and (R, γO, βS) proposed by Jakšič and Rusjan, (2008) and Bandyopadhyay and Bhattacharya (2013).
Design/methodology/approach
A hybrid forecasting model has been developed by combining the feature of discrete wavelet transformation (DWT) and an intelligence technique, multi-gene genetic programming (MGGP), denoted as DWT-MGGP. Performance of DWT-MGGP model has been verified under (R, S) inventory control policy considering demand from three different manufacturing companies.
Findings
A comparison between DWT-MGGP model and autoregressive integrated moving average forecasting model has been done by estimating forecast error and BWE. Further, this study has been extended with analysing the behaviour of BWE considering different variants of (R, S) policy such as (R,βS) (R, γO) and (R,γO,βS) and found that BWE can be moderated by controlling the inventory smoothing (β) and order smoothing parameters (γ).
Research limitations/implications
This study is limited to different variants of (R, S) inventory control policy. However, this study can be further extended to continuous review policy.
Practical implications
The proposed DWT-MGGP model can be used as a suitable demand forecasting model to control the BWE when (R, S), (R,βS) (R,γO) and (R,γO,βS)inventory control policies are followed for replenishment.
Originality/value
This study analyses the behavior of BWE through controlling the inventory smoothing (β) and order smoothing parameters (γ) when demand is predicted using DWT-MGGP forecasting model and order is estimated using (R, S), (R,βS) (R,γO) and (R,γO,βS) inventory control policies.
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Prajakta Thakare and Ravi Sankar V.
Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating…
Abstract
Purpose
Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.
Design/methodology/approach
The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.
Findings
The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.
Originality/value
The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.
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Katherinne Salas-Navarro, Jaime Acevedo-Chedid, Gina Mora Árquez, Whady F. Florez, Holman Ospina-Mateus, Shib Sankar Sana and Leopoldo Eduardo Cárdenas-Barrón
The purpose of this paper is to propose an economic production quantity (EPQ) inventory model considering imperfect items and probabilistic demand for a two-echelon supply chain…
Abstract
Purpose
The purpose of this paper is to propose an economic production quantity (EPQ) inventory model considering imperfect items and probabilistic demand for a two-echelon supply chain. The production process is imperfect and the imperfect quality items are removed from the lot size. The demand rate of the inventory system is random and follows an exponential probability density function and the demand of the retailers is depending on the initiatives of the sales team.
Design/methodology/approach
Two approaches are examined. In the non-collaborative approach, any member of the supply chain can be the leader and takes decisions to optimize the profits, and in the collaborative system, all members make joint decisions about the production, supply, sales and inventory to optimize the profits of the supply chain members. The calculus approach is applied to find the maximum profit related to the members of the supply chain.
Findings
A numerical example is presented to illustrate the performance of the EPQ model. The results show that collaborative approach generates greater profits to the supply chain and the market’s demand represents the variable behavior and uncertainty that is generated in the replenishment of a supply chain.
Originality/value
The new and major contributions of this research are: the inventory model considers demand for products is random variable which follows an exponential probability distribution function and it also depends on the initiatives of sales teams, the imperfect production system generates defective items, different cycle time are considered in manufacturer and retailers and collaborative and non-collaborative approaches are also studied.
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S. Prathiba and Sharmila Sankar
The purpose of this paper is to provide energy-efficient task scheduling and resource allocation (RA) in cloud data centers (CDC).
Abstract
Purpose
The purpose of this paper is to provide energy-efficient task scheduling and resource allocation (RA) in cloud data centers (CDC).
Design/methodology/approach
Task scheduling and RA is proposed in this paper for cloud environment, which schedules the user’s seasonal requests and allocates resources in an optimized manner. The proposed study does the following operations: data collection, feature extraction, feature reduction and RA. Initially, the online streaming data of seasonal requests of multiple users were gathered. After that, the features are extracted based on user requests along with the cloud server, and the extracted features are lessened using modified principal component analysis. For RA, the split data of the user request is identified and that data is pre-processed by computing closed frequent itemset along with entropy values. After that, the user requests are scheduled using the normalized K-means algorithm (NKMA) centered on the entropy values. Finally, the apt resources are allotted to that scheduled task using the Cauchy mutation-genetic algorithm (CM-GA). The investigational outcomes exhibit that the proposed study outruns other existing algorithms in respect to response time, execution time, clustering accuracy, precision and recall.
Findings
The proposed NKMA and CM-GA technique’s performance is analyzed by comparing them with the existing techniques. The NKMA performance is analyzed with KMA and Fuzzy C-means regarding Prc (Precision), Rca (Recall), F ms (f measure), Acr (Accuracy)and Ct (Clustering Time). The performance is compared to about 500 numbers of tasks. For all tasks, the NKMA provides the highest values for Prc, Rca, Fms and Acr, takes the lowest time (Ct) for clustering the data. Then, the CM-GA optimization for optimally allocating the resource in the cloud is contrasted with the GA and particle swarm optimization with respect to Rt (Response Time), Pt (Process Time), Awt (Average Waiting Time), Atat (Average Turnaround Time), Lcy (Latency) and Tp (Throughput). For all number of tasks, the proposed CM-GA gives the lowest values for Rt, Pt, Awt, Atat and Lcy and also provides the highest values for Tp. So, from the results, it is known that the proposed technique for seasonal requests RA works well and the method optimally allocates the resources in the cloud.
Originality/value
The proposed approach provides energy-efficient task scheduling and RA and it paves the way for the development of effective CDC.