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Purpose – The purpose of this paper is to conceptualize the integration of total productive maintenance (TPM) and ISO 9001 certification by contributing a model named as TPM 9001:2008. Design/methodology/approach – In the beginning of the paper, the origin, growth and capabilities of TPM and ISO 9001 certification in enabling the organizations to perform at world class level are appraised by citing research outcomes reported in literature arena. The information and knowledge thus gathered from literature arena are used to conceptualize TPM 9001:2008 model. Through this conceptualization, the eight TPM pillars are fitted into the five major clauses of ISO 9001:2008 standard. In order to illustrate this conceptualization, the contents of a sub-clause of TPM 9001:2008 model are presented and the rationale behind designating it is appraised. Findings – TPM 9001:2008 model brings out synergy from the two renowned world class strategies namely “TPM” and “ISO 9001 certification”. Practical implications – The paper points out that the practical validity of TPM 9001:2008 model shall be established by conducting real time case studies in various organizations. Originality/value – This paper presents a unique approach for integrating TPM elements with ISO 9001:2008 standard based quality management system, as a single framework benefiting the contemporary organizations.

Recruitment of appropriate employees and their retention are the major concerns towards creating the competitive strength in the knowledge economy. Every year IT companies recruit fresh graduates through their campus selection programs after examining their skills by conducting tests, group discussion and a number of interviews. The recruitment process requires enormous amount of effort and investment. During each phase of the recruitment process, candidates are filtered based on some performance criteria. Intense analysis on the system indicates that a pattern exists among the candidates selected for an industry. The problem domain is complex and the aspects of candidates that impact the recruitment process is not explicit. In this research, the domain knowledge is extracted through knowledge acquisition techniques. Data mining techniques that fit the problem are determined. A study has been made by applying K-means and fuzzy C-means clustering and decision tree classification algorithms to the recruitment data of an industry. Experiments were conducted with the data collected from an IT industry to support their hiring decisions. Pruned and unpruned trees were constructed using ID3, C4.5 and CART algorithms. From the comparative study, it has been observed that the clustering algorithms are not much suitable for the problem and performance of the C4.5 decision tree algorithm is high. Using the constructed decision trees, discussions were made with the domain experts to deduce viable decision rules.

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