Development and validation of TPM implementation practices in industries: investigation from indian SMEs

This research strives to determine the appropriateness of identified critical success factors (CSFs) towards a successful implementation of TPM. Overall, 08 CSFs with 33 variables were extracted from the diverse literature content and based on discussions with the TPM professionals. The content and construct validation was employed in this research to validate the data obtained from a survey. Validity analysis conducted in the SMEs being grouped as R₁, R₂ and R₃. The content validation was performed to estimate a decision index of the SMEs for the purpose of comparing the organisations on the basis of identified CSFs. The decision index was computed employing the pairwise comparison method of analytical hierarchy process (AHP). The findings of this research indicated that amongst the decision index of studied SMEs, R₃ is the highest among other SMEs groups (R₁ and R₂). The results from this research provided crucial insights to have a better perception towards the TPM implementation in the SMEs, and offered managers with improved guidelines to determine the set of best possible CSFs that will lead to successful implementation of TPM in the SMEs of developing countries. Because this research took into account the manufacturing SMEs as a whole, it has opened up numerous investigation avenues on the interfacial components of TPM. The findings of this study will be helpful to professionals across the world who want to concentrate on manufacturing-focused improvement projects by utilising cutting-edge industrial IoT data management edge platforms. Such validation analysis study will assist professionals in making decisions during actual business-critical scenarios, especially in light of the development of Indian SMEs and integration of Industry 4.0 together with lean, productive maintenance systems. Future asset monitoring systems that issue alerts based on predictive methodologies may benefit from big data analysis systems to monitor assets continuously in real-time.