Knowledge management implementation in Indian automobile ancillary industries An interpretive structural model for productivity

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).