A study on key lean enablers of the coal mining sector using ISM, MICMAC and SEM

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sorokhaibam Khaba ◽  
Chandan Bhar ◽  
Ankita Ray
Keyword(s):  
Conceptual Model ◽  
Coal Mining ◽  
Questionnaire Survey ◽  
Coal Mines ◽  
Mining Industry ◽  
Mining Sector ◽  
Lean Implementation ◽  
Content Type ◽  
Coal Mining Industry ◽  
Indian Coal

PurposeThe purpose of this research is to identify and study the contextual relationships of the significant lean enablers in the Indian coal mining industry using the application of interpretive structural modeling (ISM), matrice d' impacts croisés-multiplication appliquée á un classement (MICMAC) and structural equation modeling (SEM).Design/methodology/approachIn this study, a conceptual model based on ISM was developed forming a hierarchy and contextual relationships of significant enablers for lean implementation in the Indian coal mining industry using a literature review and eliciting expert opinion, which is followed by MICMAC for grouping of enablers and questionnaire survey to validate the ISM based conceptual model using SEM.FindingsThe study modeled and analyzed ten significant enablers of lean implementation in the Indian coal mining industry. The findings suggest that the most important lean enablers in the Indian coal mining industry are employee empowerment, employee motivation and commitment, consistent financial performance measurement and management support.Research limitations/implicationsJudgmental sampling was used for selecting the respondents for conducting the questionnaire survey in this research work as there are a few numbers of coal mines implementing lean principles in India. Although the study was not restricted to a particular part of India with the sample representing the heterogeneous population, the study represents more data from the coal mines in eastern India.Practical implicationsThe model on lean enablers would help the researchers, decision-makers and practitioners to anticipate potential lean enablers in the Indian coal mines and rank the enablers for improved and efficient usage of the available resources creating value to customers with lean and to sustenance academic research on lean.Originality/valueStudies on lean enablers in the mining sector are scarce in the literature, and this study is a novel contribution of exploring lean enablers in the Indian coal mining industry using an integrated approach of ISM–MICMAC and SEM.

Quantifying SWOT analysis for the Indian coal mining industry using Fuzzy DEMATEL

2017 ◽  
Vol 24 (4) ◽  
pp. 882-902 ◽  
Author(s):  
Sorokhaibam Khaba ◽  
Chandan Bhar
Keyword(s):  
Coal Mining ◽  
Swot Analysis ◽  
Mining Industry ◽  
Coal Industry ◽  
Fuzzy Decision Making ◽  
Quality Improvement Initiative ◽  
Mining Sector ◽  
Content Type ◽  
Coal Mining Industry ◽  
Indian Coal

Purpose The purpose of this paper is to quantify the strengths, weaknesses, opportunities and threats (SWOT) analysis for the Indian coal mining industry using Fuzzy Decision-Making Trial and Evaluation Laboratory. Design/methodology/approach After obtaining 17 factors from literature and expert opinion, an interview questionnaire was designed and tested to assure the content validity of questionnaire. A group of 15 qualified experts consisting of 4 professors from academic institutions and 11 management professionals from mining sector with substantial experience were consulted. Findings The result from causal relationship implied that the decision makers should focus on improving the ability of exploitation and production using quality improvement initiative such as lean production, developing research and development units for clean coal technology and working with strong exporters. This study also finds that foreign investment in mining sector is also a main factor that highly influences other factors. Research limitations/implications The study is based on personal judgments and the shortage of respondents limits the study to ensure the validity. Practical implications The stated strategies both for the government and industry through SWOT analysis could facilitate improved productivity of the Indian coal industry if adopted. Originality/value This paper demonstrates a process for quantitative SWOT analysis for the Indian coal mining industry that can be performed even when there is dependence among factors.

Analysing the barriers of lean in Indian coal mining industry using integrated ISM-MICMAC and SEM

2018 ◽  
Vol 25 (7) ◽  
pp. 2145-2168 ◽  
Author(s):  
Sorokhaibam Khaba ◽  
Chandan Bhar
Keyword(s):  
Coal Mining ◽  
Structural Equation ◽  
Academic Research ◽  
Mining Industry ◽  
Equation Modeling ◽  
Interpretive Structural Modeling ◽  
Lean Implementation ◽  
Content Type ◽  
Coal Mining Industry ◽  
Indian Coal

Purpose The purpose of this paper is to develop and validate a model for key barriers to lean implementation in the Indian coal mining industry. Design/methodology/approach Interpretive structural modeling (ISM) has been used to develop a proper hierarchy and contextual relationship of key barriers to lean implementation in the Indian coal mining industry through literature review and expert opinion which is followed by the classification of barriers using Matrice d’ Impacts Croisés-Multiplication Appliquée á un Classement (MICMAC) and questionnaire-based survey to validate the ISM model using structural equation modeling (SEM). Findings In this study, 14 key barriers to lean implementation in the Indian coal mining industry have been identified, modeled and analyzed. The lack of top management commitment, financial constraints and lack of inter-departments co-ordination are found to be the most important barriers to lean implementation in the mining industry. The ISM-based model is validated using the SEM. Research limitations/implications The analysis of data represents that relatively more participants were from the mines located in eastern India and the maximum participants were managers and executives holding different levels (lower, middle or upper), although key participants in different mines were encouraged to distribute the survey to other employees also. Practical implications This model on lean barriers would help the decision makers, researchers and practitioners to anticipate potential barriers to lean implementation and support the existing academic research on lean. Accordingly, the focus on the lean barriers can be prioritized for the better utilization of the available resources for eliminating or minimizing the barriers. Originality/value This paper is an original contribution of analysis of the lean barriers in Indian mining industry using the integrated ISM–MICMAC and SEM approach.

Lean awareness and potential for lean implementation in the Indian coal mining industry

2018 ◽  
Vol 35 (6) ◽  
pp. 1215-1231 ◽  
Author(s):  
Sorokhaibam Khaba ◽  
Chandan Bhar
Keyword(s):  
Coal Mining ◽  
Academic Research ◽  
Mining Industry ◽  
New Paradigm ◽  
Lean Implementation ◽  
Content Type ◽  
Coal Mining Industry ◽  
Electronic Survey ◽  
Indian Coal ◽  
Pencil And Paper

Purpose The purpose of this paper is to determine the degree of lean awareness and implementation and to identify the perception on tools, enablers, barriers and potential benefits of lean in the Indian coal mining industry. Design/methodology/approach A systematic review of lean literature and expert opinion was used to design the survey instrument. Data were collected through electronic survey and traditional pencil and paper approach. In order to test the research hypotheses, independent sample t-tests were done. Findings The study reveals that there is a certain degree of lean awareness although the level of lean implementation is still at a nascent stage in the Indian coal mining sector. The main applicable tools, barriers, enablers and benefits have also been identified based on 54 respondents out of 109 suitable respondents. Research limitations /implications In this study, relatively the maximum participants were from the mines located in Eastern India. Thus, a certain level of bias can be anticipated. The findings would help the decision makers, researchers and practitioners to better utilize the available resources for lean implementation and support the existing academic research on lean. Originality/Value The concept of lean in mining industry is relatively a new paradigm and there is a lack of empirical study that explores applicable tools, barriers, enablers and benefits of lean in Indian mining industry. The study addresses this gap in the lean literature.

scholarly journals Fire Safety in Indian Coal Mines using Machine Learning Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 4003-4005
Keyword(s):  
Machine Learning ◽  
Coal Mining ◽  
Coal Mines ◽  
Mining Industry ◽  
Mining Operation ◽  
Machine Learning Techniques ◽  
Fire Hazards ◽  
Indian Coal ◽  
Learning Software ◽  
The Right

There are around 493 coal mines in India (300+ underground and around190 opencast mines) engaged in coal production for meeting energy and other requirements of our country. Coal and the process of mining itself creates an environment conducive for self-oxidation leading to build up of heat and subsequently break out of fire. This causes safety hazards, decrease in production, increased in de-settlement of colonies, fire related fatalities and risk to life and property. Occurrence of fires in coal mines has always been an undesirable proposition for the coal mining community worldwide due to its high hazard potential towards loss of human lives and property. However, with advent of AI/ML and deep learning, there emerges a vast scope of leveraging its application towards significantly reducing fire hazards in coal mining. Data capturing from such fiery mines, providing machine learning and predicting it beforehand for similar mining situations would significantly enhance safety standard in coal mining industry. This project proposes to develop an algorithm on getting input data from the past incidences/accidents of fire in coal mines and apply machine learning software to help it learn pattern/features vis a vis the fire outcomes. Once the learning is over and data trained, the programme would process the test data of other active projects and may predict for fire threat during forthcoming mining operation. The algorithm aims to enable mining personnel to assess and evaluate the risk of fire in their workplace and take informed decisions based on the predictions based on Machine learning outputs. Also, active fires can as well be studied and predicted in a similar way. This will help the mining team to decide about the right approach of continuing mining operation in such an affected area.

Designing a framework for integrating environment management with drivers of economic performance

2015 ◽  
Vol 9 (3) ◽  
pp. 376-392 ◽  
Author(s):  
Rashmi Ranjan ◽  
Niladri Das
Keyword(s):  
Environmental Management ◽  
Coal Mining ◽  
Economic Performance ◽  
Corporate Strategy ◽  
Linear Regression Analysis ◽  
Mining Industry ◽  
Primary Data ◽  
Content Type ◽  
Coal Mining Industry ◽  
Key Drivers

Purpose The purpose of this paper is to integrate drivers of economic performance with environmental management aspects and core managerial functions of the Indian coal mining industry. Design/methodology/approach For this research paper, primary and secondary data have been used. The primary data were collected through a questionnaire survey which was distributed in the four subsidiaries of Coal India Limited. The validity and reliability of the questionnaire were tested by appropriate statistical techniques. Further, one-sample t-test and multiple linear regression analysis have been used for data analysis. Findings Testing of hypotheses reveals that there is a high level of integration of environmental management aspects with the seven core managerial functions, namely, production process, distribution process, beneficiation process, quality issues, stakeholders’ interest, health and safety and corporate strategy. Further, the paper identified that there is a positive association between integration of environmental aspects with core functions and the four drivers of economic performance and it is strongly associated with societal-related and risk-related drivers of economic performance. But it is less strongly associated with image-related and efficiency-related drivers of economic performance. Research limitations/implications This paper focuses on integrating the environmental management and core functions with key drivers of economic performance in coal mining industry which is one of the most polluting industries of the world. The limitation of the paper is that it is very specific and limited to the coal mining industry. Originality/value The paper contributes to the existing work by designing a framework which identifies the key drivers of economic performance and integrating it with the environmental management system of the organisation.

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