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Condition-based maintenance (CBM) has become a central maintenance approach because it performs more efficient diagnoses and prognoses based on equipment health condition compared to time-based methods. CBM models greatly inform maintenance decisions. This research examines three CBM fault prognostics models: logical analysis of data (LAD), artificial neural networks (ANNs) and proportional hazard models (PHM). A methodology, which involves data pre-processing, formulating the models and analyzing model outputs, is developed to apply and compare these models. The methodology is applied on NASA’s Turbofan Engine Degradation data set and the structural health monitoring (SHM) data set from a Nova Scotia Bridge. Results are evaluated using three metrics: error, half-life error and a cost score. This paper concludes that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably, and its predictions show much larger variance than the predictions from the other three methods. Based on these conclusions, the purpose of this paper is to provide recommendations on the appropriate situations in which to apply these three prognostics models.

LAD, ANNs and PHM methods are adopted to perform prognostics and to calculate the mean residual life (MRL) of eqipment using NASA’s Turbofan Engine Degradation data set and the SHM data set from a Nova Scotia Bridge. Statistical testing was used to evaluate the statistical differences between the approaches based on these metrics. By considering the differences in these metrics between the models, it was possible to draw conclusions about how the models perform in specific cases.

Results were evaluated using three metrics: error, half-life error and a cost score. It was concluded that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably and its predictions show much larger variance than the predictions from the other three methods. Overall the models predict failure after it has already occurred (negative error) when the residual life is large and vice versa.

It was concluded that a good CBM prognostics model for practical implications can be determined based on three main considerations: accuracy, run time and data type. When accuracy is a main concern, as in the case where impacts of failure are large, LAD and feedforward neural network are preferred. The preference changes when run time is considered. If data can be easily collected and updating the model is performed often, the ANNs and LAD are preferred. On the other hand, if CM data are not easily obtainable and existing data are not representative of the population’s behavior, data type comes into play. In this case, PHM is preferred.

Previous research in the literature performed reviews of multiple independent studies on CBM techniques performed on different data sets. They concluded that it is typically harder to implement artificial intelligence models, because of difficulties in data procurement, but these approaches offer improved performance as compared to more traditional model-based and statistical approaches. In this research, the authors further investigate and compare the performance and results from two major artificial intelligence models, namely, ANNs and LAD, and one pioneer statistical model, PHM over the same two real life prognostics data sets. Such in-depth comparison and review of major CBM techniques was missing in current literature of CBM field.

The purpose of this paper is to investigate the impact of total quality management (TQM) and supply chain management (SCM) practices on operational performance, and their interlinkage between each other.

Constructs those are critical to pharmaceutical quality and supply chain have been identified with the help of literature and experts from industry. The impact of TQM practices on supply chain practices and on operational performance has been evaluated. Similarly, the impact of supply chain practices on operational performance has been evaluated. Further, alternate models are tested and evaluated through structural equation modeling.

It was observed during testing of alternate models that TQM practices have a direct impact on operational performance. However, TQM practices also directly impact supply chain components, which, in turn, influence overall operational performance. In comparison of alternate models, the model in which TQM practices affect supply chain practices and supply chain practices further affect the operational performance is found most appropriate.

This study provides some useful implications from industry point of view. TQM practices are critical to pharmaceutical industry. TQM practices are the core of attaining a smooth supply chain, which will have greater impact to achieve operational performance. Strategic supplier partnership, procurement management, information sharing, and quality and inventory management practices are driven by TQM practices. This tri-linkage helps to achieve the desired operational performance.

There are very limited studies that have considered both the areas together to achieve better operational performance. In pharmaceutical industry, both TQM and SCM are the critical areas for any organization to drive its growth.