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Failures in the operative levels have impact on companies’ profitability as well as it is competitive advantages. Several models have been developed to identify, analyze, prioritize and estimate the impact of failures on companies. For example, Al‐Najjar (2011) presented a model named Maintenance Function Deployment (MFD). It aims to pinpoint, analyze, and prioritize causes behind losses in the working areas belonging to the competitive advantages, such as losses due to bad quality and delayed deliveries. MFD breaks down these losses in a backwards method to approach the root-causes behind the losses which are usually spread in the operative level of different disciplines in a production process. MFD also provides business based production analysis. This is done 10 through quantifying losses (in production time and economy), in accordance with the strategic goals of the company, and identifying causes behind them. Then it breaks down the causes and its costs into their root causes. The author used an example to test the model, and the results showed that the model could be used to identify, analyze and quantify losses in order to make cost effective improving decisions. In Al‐Najjar & Jacobsson (2013), a model that demonstrates the interactions between man-machine-maintenance-economy (MMME) was developed, in order to support cost-effective decisions. The model systematically gathers, categorizes, estimates and quantifies the losses in the production time due to failures in order to identify and prioritize the problem areas in the production process. A software program then was built based on the model, and it was tested in a case study at the automaker FIAT, Italy. The results of the case study showed a possibility to identify the problematic areas. Also, as the model compares the time loss in different time periods, it captures the deviations over time for different categories. Mohideen et al. (2011) presented a model that aims to reduce the breakdown costs and recovering time in construction plant systems. It starts by categorizing and analyzing the breakdown records in order to identify the main breakdowns and the sub-breakdowns using cause effect analysis, and then ranking them using Pareto analysis. The model was tested by a case study on four types of machines in a construction company using four years’ breakdown records. Major contributing failures and their causes were identified and a strategical plan is proposed accordingly. The survey showed that the impact of failures -in particular- on the companies’ competitive advantages is not payed the proper attention.