Indian Journal of Science and Technology

Abstract

Objectives: To explain the development of a questionnaire for the identification of the Critical Success Factors -CSF- of Total Productive Maintenance from the ample list of factors cited in the literature. Methods/Statistical Analysis: The questionnaire is constructed with the factors determined in the literature. The common method bias was discarded and several confirmatory analyses procedures tested the model fit. Other tests were applied for an adequate evaluation of psychometric properties and internal reliability. The main underlying proposal is the construct developed establishing a relationship between the TPM deployment and the improvement of operational performance. Findings: The questionnaire developed was validated psychometrically, was applied in 65 plants, receiving 306 questionnaires back. A ten-factor measuring model of the CSF related to TPM deployment was confirmed with different goodness of fit indexes (χ2=965.69, df=657, p<0.001; NNFI=0.951, CFI=0.957, IFI=0.957, RMSEA: 0.039); well above acceptable thresholds. The instrument shows good internal consistency (CR index ranged from 0.758 to 0.963), good discriminant and convergent validity (AVE´s values for all subscales > 0.572 and the Common Method Bias evaluation indicates results are not biased. Validity of the construct for the operational performance subscale is acceptable, accordingly to Cua et al., 2001; Ma Kone et al., 2001; Konecny and Thun, 2011. Though Ciudad Juarez has a thirty plus years presence of multinational companies with twin plant (maquiladora) world-class operations, some techniques, such as TPM are deployed with noticeable differences among companies. The questionnaire allowed the identification of 34 influencing factors, specifically, the CSF and best practices for the TPM effective application and measures their impact in operational performance. Application/Improvements: This enhancement of the TPM explanatory capabilities allows the companies deploying it to have a better chance of success managing TPM projects under a CSF approach and better practices focus.

Keywords: Classifier, Feature Extraction, Feature Vector, Key Frame, Shot, Training and Testing Data Set