Knowledge management implementation in Indian automobile ancillary industries

Abstract

Purpose In the past decade, much has been written about knowledge management (KM) in the manufacturing; however, less attention has been paid to the Indian automobile ancillary industries located in Chinchwad, Pune. It is suitable to find out the relationship of the factors of the study. It helps in identifying the hierarchy of factors to be taken, and interlinking of production department with KM improves the productivity of the industries. Categorization of these principles based on their driving power (principles which hold other principles) and dependence (principles which are dependent on other principles) has also been examined for KM implementation to study the driving power and dependence power of these principles. This paper aims to determine the roadmap of KM implementation and categorize KM principles based on their driving power for manufacturing industries with the use of the interpretive structural modeling (ISM)-based model. The results indicate that the principles possessing higher driving power, such as KM, inventory control, quality control, productivity and scheduling and their interlinking. The major contribution of this research lies in the development of contextual relationship among various identified factors of KM and determination of their driving and dependence power through a single systemic framework. Design/methodology/approach In this paper, author find out the suitability ISM for Indian Automobile industries to find the relation among the variables. Findings ISM model has been developed for the hierarchy of the identified KM. As ISM model results a hypothetical hierarchy which needs a proper quantitative analysis to evaluate their percentage effectiveness in the hierarchy. Research limitations/implications It is applied to automobile industries with limited number of variables that will show the dependence variable and driving variables and their interrelations. It can be applied other fields to fine the relationship of variables. Practical implications The ISM may be used in supply chain management and total quality management to find interlinking between the variables. Originality/value The limited data collected from Pimpri Chinchwad industrial area of Pune from Maharashtra state (India).

Enablers Supporting the Implementation of Knowledge Management in the Healthcare of Pakistan

“…The ISM technique is preferred for several reasons, firstly, there are many problems that need to be solved in researches. These problems can be complex and difficult to understand because there may be a presence of many interrelationships that exist between the variables [66]. ISM determines these relationships and interrelationships with the help of experts.…”

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“…Singh et al applied ISM to modeling knowledge management in engineering industries, in this research, the important elements (also referred to as variables) for knowledge management implemented in engineering industries have been analyzed to obtain an interpretive structuring model, which shows the interrelationships of the variables and their levels; these variables also have been categorized according to their driving power and dependencies [Singh et al, 2003]. Patil et al [Patil et al, 2016] have applied the ISM technique in their research to determine a roadmap of knowledge management (KM) implementation and to categorize KM principles based on their driving power for manufacturing industries. In this research, an ISM model was developed for the hierarchy of the identified KM.…”

“…Area in which ISM has been applied [Patil et al, 2016] Knowledge management implementation in Indian automobile ancillary industries [Taghizadeh, Shokri, 2015] Knowledge management from the viewpoint of social capital [Sharma, Singh, 2013] Knowledge sharing barriers: an integrated approach to ISM and AHP [Kanungo, 2009] Evaluating IT effectiveness [Kant, Singh, 2008] Knowledge management implementation -Modeling the barriers [Singh et al, 2003] Knowledge management in engineering industries [Sharma, Gupta, 1995] The objectives of waste management in India [Mandal, Deshmukh, 1994] Vendor selection criteria [Saxena, Vrat, 1992] Hierarchy and classification of program plan elements [Hawthorne, Sage, 1975] Higher education program planning [Back et al, 2005;Cross, Baird, 2000;Jennex, Olfman, 2001 2 Knowledge [Back et al, 2005;Leonard, 2006;Miles, Snow, 1986;Alkhuraiji et al, 2014;Singh et al, 2003] 3 Culture [Alavi, Leidner, 1999;Back et al, 2005;Barna, 2003;Davenport et al, 1998;Jennex, Olfman, 2001;Sage, Rouse, 1999;Yu et al, 2004;Zahra et al, 2007;Alkhuraiji et al, 2016;Alkhuraiji et al, 2014;Holtshouse et al, 2013;Singh et al, 2003] 4 IT Infrastructures [Alavi, Leidner, 1999;Barna, 2003;Cross, Baird, 2000;Davenport et al, 1998;Ginsberg, Kambil, 1999;Jennex, Olfman, 2001;Sage, Rouse, 1999;…”

Modeling the Factors that Affect the Implementation of Knowledge Networks

“…Area in which ISM has been applied [Patil et al, 2016] Knowledge management implementation in Indian automobile ancillary industries [Taghizadeh, Shokri, 2015] Knowledge management from the viewpoint of social capital [Sharma, Singh, 2013] Knowledge sharing barriers: an integrated approach to ISM and AHP [Kanungo, 2009] Evaluating IT effectiveness [Kant, Singh, 2008] Knowledge management implementation -Modeling the barriers [Singh et al, 2003] Knowledge management in engineering industries [Sharma, Gupta, 1995] The objectives of waste management in India [Mandal, Deshmukh, 1994] Vendor selection criteria [Saxena, Vrat, 1992] Hierarchy and classification of program plan elements [Hawthorne, Sage, 1975] Higher education program planning…”

“…Singh et al applied ISM to modeling knowledge management in engineering industries, in this research, the important elements (also referred to as variables) for knowledge management implemented in engineering industries have been analyzed to obtain an interpretive structuring model, which shows the interrelationships of the variables and their levels; these variables also have been categorized according to their driving power and dependencies [Singh et al, 2003]. Patil et al [Patil et al, 2016] have applied the ISM technique in their research to determine a roadmap of knowledge management (KM) implementation and to categorize KM principles based on their driving power for manufacturing industries. In this research, an ISM model was developed for the hierarchy of the identified KM.…”