Department of Management

Permanent URI for this collectionhttp://localhost:4000/handle/123456789/1930

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Start date: 2010End date: 2019

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Now showing 1 - 10 of 193
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    A study of lifestyle changes in Xavier Institute of Development Action & Studies, India to mitigate global warming
    (IOP, 2018) Tiwary, Daitri
    The Sustainable Development Goals (SDGs) outlined by the United Nations as tools to transform the world by 2030, outline Goal 13 to “Take urgent action to combat climate change and its impacts by regulating emissions and promoting developments in renewable energy”. But the seventeen goals are all interrelated and it is our cumulative efforts that can make them achievable in the next 13 years.As a civilization we have thrived upon decades of denudation and degradation, gradually, but immensely, yet quite indifferently. This indifference might be debated upon as the recent times has seen ever-growing concerns to address the crisis at hand, but this is no more than burning the night oil for a saving grace. If we look back, then it will not be difficult to analyze that the exploitation of the resources and the processes of degradation accelerated from the era of Industrial Revolution, dating back to 1750, but it took us more than a century to feel the repercussions of our actions. The first time ever the terms “global warming” and “climate change” was addressed as an internal issue of concern was in the Rio Summit, held in Rio-de-Janeiro in Brazil in 1992. Since then, the world has witnessed a series of environmental protocols, treaties and summits, each one raising a doubt on the validity and effectiveness of the previous. Meanwhile, the ever-impending population surge has compounded the environmental stresses. To combat the changes we are introducing in our ecosystem, the research focuses on implementation of minor and doable actions in the mundane yet regular activities of our daily life, which have a direct impact on individual carbon footprint. The research establishes the success of these measures in Xavier Institute of Development Action and Studies (XIDAS) by comparing the scenarios before and after implementation of a “Green Lifestyle”, which will help us achieve sustainability beyond environment protocols and beyond international treaties.
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    Mining safety rules for derailments in a steel plant using correspondence analysis
    (Elsevier, 2014-10) Verma, Abhishek
    In this study, we have analyzed a steel plant’s derailment data using correspondence analysis. The primary purpose of this analysis is to find out associations of categories of factors contributing to the derailments which ultimately lead to the development of meaningful rules for preventing derailments. 348 derailment incidents collected over a period of 42 months were analyzed considering 4 factors namely, shift of working, location, cause of derailment and department responsible. Descriptive statistics show that by shift of working there is not much difference in the occurrence of derailments. But from location, cause of derailment and responsibility (departments) points of view, ‘raw material line’, ‘manual operations’ and ‘production (raw material)’ accounted for 50%, 60% and 48.28% of derailments, respectively. From correspondence analysis, it is found that ‘level of movements’, ‘level of human involvement’, ‘management of wagons’, and ‘criticality of movements’ are the hidden root causes of derailments in the plant studied. In order to improve the safety of in-plant rail transport of the plant studied, the plant management should (i) collect and analyze derailment data related to ‘level of movements’ and ‘human involvement’, (ii) adopt collaborative maintenance of wagons as external agencies are also involved in rail transport, and (iii) practice risk based maintenance of the in-plant rail transportation systems.
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    Identifying patterns of safety related incidents in a steel plant using association rule mining of incident investigation reports
    (Elsevier, 2014-12) Verma, Abhishek
    The aim of this paper is to find out the patterns of incidents in a steel plant in India. Occupational incidents occur in steel plant mainly in form of injury, near miss, and property damage or in combination. Different factors are responsible for such incidents to occur. An incident investigation scheme is proposed. Association rule mining approach is used to discover cause-and-affect patterns (rules) using 843 incidents. Thirty-five meaningful association rules are extracted using three criteria, support (S), confidence (C) and lift (L). For example, the results show that unsafe acts done by others are more frequent in injury cases (S = 4.86%, C = 78.8%, L = 2.3). Similarly, one of the SOP (standard operating procedures) related rule: ‘SOP required, available, adequate but not complied’ led to property damage (S = 11.03%, C = 49.2%, L = 1.525). Another useful rule ‘SOP required, available but inadequate, followed’ led to near miss (S = 1.66%, C = 38.89%, L = 1.163). It is also found that for slip, trip and fall incidents, workers working alone (S = 3.91%, C = 76.74%, L = 2.239) or in a group (S = 3.20%, C = 75.00%, L = 2.188) does not make much difference. The findings pinpoint the areas of improvement such as inadequate SOPs, non-compliance of SOPs, training, and slip, trip and fall prevention to minimize incidents.
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    Prioritization of near-miss incidents using text mining and Bayesian network
    (Springer, 2017-07) Verma, Abhishek
    Near-Miss incidents can be treated as events to signal the weakness of safety management system (SMS) at the workplace. Analyzing near-misses will provide relevant root causes behind such incidents so that effective safety related interventions can be developed beforehand. Despite having a huge potential towards workplace safety improvements, analysis of near-misses is scant in the literature owing to the fact that near-misses are often reported as text narratives. The aim of this study is therefore to explore text-mining for extraction of root causes of near-misses from the narrative text descriptions of such incidents and to measure their relationships probabilistically. Root causes were extracted by word cloud technique and causal model was constructed using a Bayesian network (BN). Finally, using BN’s inference mechanism, scenarios were evaluated and root causes were listed in a prioritized order. A case study in a steel plant validated the approach and raised concerns for variety of circumstances such as incidents related to collision, slip-trip-fall, and working at height.
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    Prediction of occupational incidents using proactive and reactive data: a data mining approach
    (Springer, 2017-10) Verma, Abhishek
    Prediction of occupational incidents is an important task for any industry. To do this, reactive data has been used by most of the previous studies in this domain. As an extension of the existing works, the present study has used the underused proactive data coupled with reactive data to establish the predictive models so that the information inherent in both data sets could be better utilized. The main aim of the study is to predict the incident outcomes using mixed data set comprising reactive and proactive data together. Two decision tree classifiers, i.e. classification and regression tree (CART) and C5.0, have been implemented with tenfold cross validation. Furthermore, the ensemble technique, namely adaptive boosting has been implemented to increase the classification accuracy. Results show that boosted C5.0 produces higher accuracy than others for the prediction task. Furthermore, the rules obtained produce the insight of the incidents. The limitation of the present study includes the use of less amount of data and the requirement of experts’ domain knowledge for a large span of time. Future scope of the study includes the proper feature selection for preparation of the mixed data set and building the better classification algorithm for better prediction of occurrence of accidents. The present work sets out the potential use of both types of data sources together.
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    Data-driven mapping between proactive and reactive measures of occupational safety performance
    (Springer, 2017-10) Verma, Abhishek
    This study aims to analyse the incident investigation reports logged after the occurrence of events from an integrated steel plant and map it with proactive safety data. From the narrative text describing the event, this study has attempted to unfold the hazards and safety factors present at the workplace. Text document clustering with expectation maximization algorithm (EM) has been used to group the different events and find key phrases from them. These key phrases are considered as the root causes of the reported events. This study shows how the mapping of the safety factors from both proactive safety data and incident reports can help in the improvement of safety performance as well as better allocation of resources. The study points out specific areas to the management where improvements are needed. The mapping also indicates the areas of improvement made by the constant effort of safety practitioners.
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    A preliminary analysis of incident investigation reports of an integrated steel plant: some reflection
    (Taylor & Francis, 2017-12) Verma, Abhishek
    Large integrated steel plants employ an effective safety management system and gather a significant amount of safety-related data. This research intends to explore and visualize the rich database to find out the key factors responsible for the occurrences of incidents. The study was carried out on the data in the form of investigation reports collected from a steel plant in India. The data were processed and analysed using some of the quality management tools like Pareto chart, control chart, Ishikawa diagram, etc. Analyses showed that causes of incidents differ depending on the activities performed in a department. For example, fire/explosion and process-related incidents are more common in the departments associated with coke-making and blast furnace. Similar kind of factors were obtained, and recommendations were provided for their mitigation. Finally, the limitations of the study were discussed, and the scope of the research works was identified.
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    Text-document clustering-based cause and effect analysis methodology for steel plant incident data
    (Taylor & Francis, 2018-03) Verma, Abhishek
    The purpose of this study is to develop a text clustering-based cause and effect analysis methodology for incident data to unfold the root causes behind the incidents. A cause–effect diagram is usually prepared by using experts’ knowledge which may fail to capture all the causes present at a workplace. On the other hand, the description of incidents provided by the workers in the form of incident reports is typically a rich data source and can be utilized to explore the causes and sub-causes of incidents. In this study, data were collected from an integrated steel plant. The text data were analysed using singular value decomposition (SVD) and expectation-maximization (EM) algorithm. Results suggest that text-document clustering can be used as a feasible method for exploring the hidden factors and trends from the description of incidents occurred at workplaces. The study also helped in finding out the anomaly in incident reporting.
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    Decision support system for safety improvement: an approach using multiple correspondence analysis, t-SNE algorithm and K-means clustering
    (Elsevier, 2019-02) Verma, Abhishek
    An attempt has been made to develop a decision support system (DSS) for safety improvement using a multi-step knowledge discovery process involving multiple correspondence analysis (MCA), t-SNE algorithm and K-means clustering. MCA is used for dimension reduction and perceptual mapping from categorical data. Usually, the first two dimensions are used for perceptual mapping if these two dimensions explain a significant percentage of variance. Otherwise, the traditional method of two dimensional mapping, leads to loss of important categorical information involved with other dimensions. Considering the above, a novel R2-profile approach, as an alternate to inertia based approach, is adopted to obtain the desired number of dimensions to be retained without loss of significant amount of information. t-SNE technique reduces the high dimensional data into two dimensional (2D) map, which provides the associations amongst different categories. K-means clustering grouped the 2D categories in homogenous clusters as per the similarities of the categories. A novel kernel category based chi-square distance method is proposed to identify sub-clusters within a cluster which subsequently provides useful rules for safety improvement. The methodology also provides a logical approach of dimension reduction in a form called ‘funnel diagram’. Finally, the DSS is applied to analysing near miss incidents occurred in electric overhead traveling (EOT) crane operations in a steel plant. Several safety rules are identified and safety interventions are proposed.
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    SentiCon: A Concept Based Feature Set for Sentiment Analysis
    (IEEE, 2018) Mitra, Satanik
    Selection and extraction of appropriate numerical features to do sentiment analysis on text data with greater accuracy remain an open problem. In supervised machine learning based sentiment analysis, Term Frequency- Inverse Document Frequency (TF-IDF) scores are used as a feature for classifying polarity of text data. TF-IDF features are a high dimensional representation of the importance of a word in the document. TF-IDF features are sparse and do not consider the correlation among the words which constructs the latent concepts in the document. Latent Semantic Analysis (LSA) removes sparseness of the TF-IDF features by representing it in a low dimensional matrix and extracts those hidden concepts. On the other hand, a natural property of text document is its information content. The quantitative estimation of Parts-of-Speech tags, negation words, sentiment lexicons etc. represent the quality of information shared in a text data. In this work, we propose an approach to generate a concept based domain specific feature set SentiCon by consolidating LSA with the quality of information of the corpus. We have applied Singular Value Decomposition (SVD) on TF-IDF features to find the LSA. We have tested SentiCon with two benchmark datasets IMDB movie review and Epinion Cars, Books datasets using four well-known classifiers - Decision Tree, Random Forrest, Support Vector Machine, and K-Nearest Neighbour classifiers. We have used standard performance measures precision, recall and F-measure to analyze the results.