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978-1-5090-0612-0/16/$31.00 ©2016 IEEE Amrita School of Business , Coimbatore Amrita Vishwa Vidyapeetham University Coimbatore, India rk72821@gmail.com Amrita School of Business , Coimbatore Amrita Vishwa Vidyapeetham University Coimbatore, India m_suresh@cb.amrita.edu, drsureshcontact@gmail.comAbstract— 2016 IEEE International Conference on Computational Intelligence and Computing Researchefficiency, productivity, increasing profit margins and reduced attrition rate. ITERATURE REVIEWThe literature is classified into two parts: First one is safety in Indian manufacturing firms, followed by the literature review for interpretive structural modeling. Literature review on Safety Practices Wu et al. [1] elaborates the complete understanding of safety performance practices and also indicates the overall safety performance level in construction industry. They applied structural equation modelling approach for prospective safety performance evaluation. Azadeh et al. [2] applied neuro-fuzzy algorithm for analysing and improving the factors pertaining to health, safety, environment and ergonomics in a petrochemical plant. Sanz-Calcedo et al. [3] reaps out the economic benefits from the purview of social responsibility of business by analysing the safety associated with the industry with the international standards of ISO 9001:2015, ISO 14001: 2015 and OHSAS 18001: 2007. Mineo and Suzuki [4] proposed a method named, tree analysis for safety, a blend of fault tree analysis and the effectiveness of humans to make up to the limitations of safety index for evaluation purpose. They considered some of the factors includes as factors of physical accidents include, high velocity machinery, high temperature substances. Rui et al. [5] studied the safety capacity of the chemical industry park. They considered the following factors for fixing up the safety capacity of the plant. viz. park site, park master plan, safety and fire planning management. Shi and Shiichiro [6] developed safety culture assessment tool for industrial safety. Alarcon et al. [7] studied about the safety practices put into action by the management to reduce accidents in construction sites. They puts forth that more preventive a strategy is, higher is its capability to reduce the accident rates. Ganesh and Suresh [8] applied fuzzy logic techniques to evaluate safety practice level in manufacturing organisation at India. Literature review on Interpretive Structural Modeling Sarkar and Panchal [9] applied ISM with fuzzy approach to identify the inter relationships in the considered risk components to adopt risk planning and mitigation measures of port construction project. Tripathy et al. [10] applied ISM approach to enhance the performance of R&D in Indian manufacturing firms. Pfohl et al.[11] applied ISM to identify and establish a relationship between various risk factors in the third party logistics. Bolanos et al. [12] applied ISM to strategic group decision making process. Kumar et al. [13] applied ISM for developing a mutual relationship between cost effective and technology transfer in the housing sector of rural areas. Bag [14] applied ISM for identifying an interrelationship between the enablers of flexible manufacturing system. Meena and Thakkar, [15] developed a balance scorecard measurement framework, which in

tegrates both ISM and analytic network p
tegrates both ISM and analytic network process for healthcare system. Azevedo et al. [16] applied ISM approach to identify and ranks a set of performance measures for evaluating the performance of automotive supply chain performance. Srivastava and Sushil [17] used ISM approach for analyzing strategic performance factors for executing the strategies. Nasim and Sushil [18] applied structured modeling approach for strategizing in e-government. Saleeshya et al.[19] applied integrated analytical hierarchy process and ISM approach for supply chain agility assessment of textile Industry in India.III.ANDSOLUTION The urge for the basic crux of this paper is based on the benefits that a company can reap upon implementing safety apart from the basic fact of ensuring the safety levels of the workers. The practice of safety also brings in lot of financial benefits to the table. Major chunk of costs like insurance cost, lost cost and worker’s compensation cost are minimised in the working environment. The indirect cost, when workers turn their attention towards an untoward incident which results in loss of productivity can be reduced. Safety implementation also results in fewer interruptions, thereby enhancing the productivity of the shop floor. Ergonomics, the study of people’s efficiency in the working environment is meant to be boosted in a safe work place, which eventually results in better efficiency, productivity, increasing profit margins and reduced attrition rate. This paper primarily focuses upon identifying the most important drivers that are rudimentary for improvement of safety practices. This paper makes use of interpretive structural modeling whose output elucidates 2 important components. Driving power – the level of prominence that each drivers has got in importance in the safety practice improvement. Dependence power – the level of interdependency between them, i.e. for example - is driver A influencing B or is A getting influenced by B. It makes use of ISM algorithm which identifies the various drivers and also helps in studying the relationship among them. The various drivers that influences safety practice level is identified (Table I) by the study of published works in this TABLE I. DENTIFIED DRIVERS FOR AFETY PRACTICE LEVEL IMPROVEMNET Drivers Definition Reference Management commitment/communication (F1) Management's adherence towards safety in alignment with the formulated [20] Employee involvement and training (F2) safety measures that are intended to make the operations less dangerous by activities such as courses, workshops,seminars, and all kind of safety training for workers [20] Safety practices(F3)Implementing and following the safety norms to ensure the safety and health of the employees within the workplace. [20] procedures(F4) Imparting proper safety pratices corresponding to the operation and [7] 2016 IEEE International Conference on Computational Intelligence and Computing ResearchDrivers Definition Reference briefing about the safety practices to the entire workforce Performance of safety system in case of an accident occurrence(F5) Time during which the system safety system responds to the accidents, starts mitigating the consequences, and the equipment is in safe condition [21] Safety culture(F6) Personnels identifying the flaw of safety

and timely reporting of the same to the
and timely reporting of the same to the management [22] Process hazard(F7) Assessment of safety and performance information for various processes [23] equipment(F8) Equipment's safety degree should fall within the permissible limit [23] and Rewards(F9) all kinds of recognition for good safety [7] Indicators of Safety Capacity(F10) frequency at which an individual may be expected to sustain a given level of harm from the realization of specified [5] ISM model is being used to analyze relationship between the factors. In ISM model each pair ofcompared to find out which factor influences the other. For instance: factor ‘’ and ‘’ are compared on four different options; first influences , second influences , third and j influence each other and fourth are unrelated. Based the overall interaction and the number of connections that one factor has with the remaining factors; the conclusions are drawn whether a factor is a dependent factor or a driving ISM method involves the following steps: 1. Identification of factors from literature review, for safety practice improvement in manufacturing organization (Table-I). 2. Deriving a contextual relationship among the factors. 3. Construction of a self structured interaction matrix (SSIM) derived based on the pair wise comparison of factors (Table-II). 4. Then development of Initial reachability matrix from SSIM (Table III) as shown below: ) V A X O Initial Reachability Matrix () 1 0 1 0 Initial Reachability Matrix () 0 1 1 0 5. Development of Final reachability matrix from initial reachability matrix through transitivity analysis which follows that if A=B, B=C, then A=C (Table IV). 6. The final reachability matrix is then partitioned into different levels (In Table Appendix A1, Appendix A2, Appendix A3 and Appendix A4) and from this ISM rank vector is obtained (In 7. Then ISM model digraph is developed (Fig.1). TABLE II. FOR SAFETY IMPROVEMENT F7 F F5 F4 F3 F2 F1 V O O O O V O V V 1 O A O A X V O V 1 V V V A X V A 1 V V V O V V 1 V O O O A 1 F6 O O O A 1 F7 V V V 1 F7 F F5 F4 F3 F2 F1 F8 V O 1 F9 O 1 F10 1 TABLE III. FIRST REACHABILITY MATRIX F4 F F6 F7 F8 F9 F10 1 1 1 0 1 0 0 0 0 1 0 1 1 0 1 1 0 0 0 0 0 0 1 0 1 1 0 1 1 1 0 0 1 1 1 1 0 1 1 1 0 0 0 0 1 0 0 0 0 1 F6 0 1 1 0 1 1 0 0 0 0 F7 0 1 1 0 0 1 1 1 1 1 F8 0 0 0 0 0 0 0 1 0 1 F9 0 1 0 0 0 0 0 0 1 0 F10 0 0 0 0 0 0 0 0 0 1 TABLE IV. REACHABILITYMATRIX TABLE V. RANK VECTOR OF FACTORS Factors Level F10 I F5, F8 II F2, F3, F6, F9 III F1, F4, F7 IV Fig.1. Digraph of the safety practices drivers for improvement Fig.1 shows that the diagraph depicted for safety practice improvement in manufacturing firms. This diagram explains the level of interdependencies between the drivers and also the level of prominence of each driver towards prioritizing the same for the framework to get implemented. This follows a sequential order, where the drivers at the top level is placed on top the diagram and rest following their order. F4F F6 F7 F8 F9 F10 Driving Power 1 1 1 0 1 1 0 1 1 1 8 0 1 1 0 1 1 0 1 1 1 7 0 1 1 0 1 1 0 1 1 1 7 0 1 1 1 1 1 0 1 1 1 8 0 0 0 0 1 0 0 0 0 1 2 F6 0 1 1 0 1 1 0 1 1 1 7 F7 0 1 1 0 1 1 1 1 1 1 8 F8 0 0 0 0 0 0 0 1 0 1 2 F9 0 1 1 0 1 1 0 0 1 0 5 F10 0 0 0 0 0 0 0 0 0 1 1 Power 1 7 7 1 8 7 1 7 7 9

31 F-10 F-5
31 F-10 F-5 2016 IEEE International Conference on Computational Intelligence and Computing Research10 9 F1,F4,F7 7 F2,F3,F6 6 5 F9 4 3 2 F8 F5 1 F10 1 2 3 4 5 6 7 8 9 10 DEPENDENCE POWER Fig.2. MICMAC analysis of the safety practices drivers for improvement The MICMAC diagram shown in Fig.2 and depicts the categorization of the various drivers identified. It’s been observed from the formulated diagram that, there are no autonomous drivers. i.e. the various drivers identified have great deal of influence with each other and have an enormous role for better There are four drivers that falls under the category of dependent drivers. F8 - Process equipment F9 - Safety Incentives and Rewards F5 - performance of safety system in case of an F10 - Indicators of Safety Capacity These imply a very strong level of interdependency between them. They influence each other unanimously at every stage of any change in imparting safety in the organization. However, these drivers will have to be handled cautiously. The linkage drivers are the ones with high driving as well as dependence power. From the list of drivers the following are the linkage drivers: F2- Employee involvement and training F3- Safety practices F6- Safety culture These are key drivers which have high level of both driving and dependence powers. This implies, all changes from trivial to instrumental in imparting safety in the organisation are being based upon these key drivers. Three drivers come under the purview of independent drivers. They are as follows. F1 - Management commitment/communication F4 - Safety procedures F7 - Process hazard These are group of drivers which have less relevance between them and are absolutely unstable when strategic decisions are framed. ONCLUSIONThis paper aims at giving a comprehensive perspective of implementing safety in a manufacturing firm by identifying those drivers which are prolific by its very nature for serving the purpose. The drivers identified in this paper are immaterial of the nature of the organisation or a manufacturing firm, i.e. irrespective of the products being manufactured or the way in which processes run in a shop floor, these drivers act as pre requisite for imparting safety at the preliminary stage. The sequence of the drivers may vary from one organisation to another, but the narrative of each driver at the initial stage helps firms to embrace every aspect of safety and also assists in avoiding untoward incidents to happen by taking appropriate safety measures. The sub driver in every identified driver helps us to comprehend the nuances of it to get a broader perspective on the outcome that we arrive at from ISM algorithm. ISM’s usage is highly advantageous in this paper, as this framework not just allows us to categories the attained results into various classifications, but also helps us to get deep insights about the direct and indirect relationships between various factors by giving an answer to the following questions. What are the deciding parameters for practising safety in manufacturing firms? What is the level of prominence of each driver in establishing the safety? dent with each other? This enhances the performance of the firms by improving th

e quality of decisions in their function
e quality of decisions in their functional process, by evaluating risk, by assessing the performance metrics, and by providing a systematic approach for the identified factors. EFERENCESENCES Wu, X., Liu, Q., Zhang, L., Skibniewski, M. J., & Wang, Y. (2015). Prospective safety performance evaluation on construction sites. Accident Analysis & Prevention, 58-72. Azadeh, A., Saberi, M., Rouzbahman, M., & Valianpour, F. (2015). A neuro-fuzzy algorithm for assessment of health, safety, environment and ergonomics in a large petrochemical plant. Journal of Loss Prevention in the Process Industries, 100-114. Sanz-Calcedo, J. G., González, A. G., López, O., Salgado, D. R., Cambero, I., & Herrera, J. M. (2015). Analysis on Integrated Management of the Quality, Environment and Safety on the Industrial , 140-145. Mineo, Y., & Suzuki, Y. (1996). Dependability assessment methods for factory automation systems using safety index. Electronics and Communications in Japan (Part III: Fundamental Electronic Science)(6), 23-34. Rui, W., Mingguang, Z., Yinting, C., & Chengjiang, Q. (2014). Study on Safety Capacity of Chemical Industrial Park in Operation Stage. , 213-222. Shi, G., & Shiichiro, I. (2012). Study on the strategies for developing a safety culture in industrial organizations. Procedia Engineering535-541. RIV PO 2016 IEEE International Conference on Computational Intelligence and Computing Research Research Alarcón, L. F., Acuña, D., Diethelm, S., & Pellicer, E. (2016). Strategies for improving safety performance in construction firms. Accident Analysis & Prevention, 107-118. Ganesh, J., & Suresh, M. (2015, December). Safety practice level calculation in Indian manufacturing company using fuzzy logic approach. In 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (pp. 1-4). IEEE. . Sarkar, D., & Panchal, S. (2015). Integrated interpretive structural modeling and fuzzy approach for project risk management of ports. International Journal of Construction Project Management(1), 17. Tripathy, S., Sahu, S., & Ray, P. K. (2013). Interpretive structural modelling for critical success factors of R&D performance in Indian manufacturing firms. Journal of Modelling in Management(2), 212-240. Pfohl, H. C., Gallus, P., & Thomas, D. (2011). Interpretive structural modeling of supply chain risks. International Journal of physical distribution & logistics management(9), 839-859. Bolanos, R., Fontela, E., Nenclares, A., & Pastor, P. (2005). Using interpretive structural modelling in strategic decision-making groups. (6), 877-895. Kumar, N., Prasad, R., Shankar, R., & Iyer, K. C. (2009). Technology transfer for rural housing: An interpretive structural modeling approach. Journal of Advances in Management Research(2), 188-205. Bag, S. (2014). Modeling the Enablers of Flexible Manufacturing Systems using Interpretive Structural Modeling. Journal of Supply (3). Meena, K., & Thakkar, J. (2014). Development of Balanced Scorecard Structural Modeling and Analytic Network Process. Journal of Advances in Management Research(3), 232-256. Azevedo, S., Carvalho, H., & Cruz-Machado, V. (2013). Using interpretive structural modelling to identify and rank performance measures: an application in the automotive supply chain. Baltic Journal (2), 208-2

30. Srivastava, A. K., & Sushil. (2013
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