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- Applying performance measures to support informed decision making at an operational levelPublication . Moreira, Madalena; Tjahjono, Benny; Julião, JorgePerformance Measurement Systems (PMS) have commonly been applied to evaluate and reward performances at managerial levels, especially in the context of supply chain management. However, evidence suggests that the effective use of PMS can also positively influence the behaviour and improve performance at an operational level. The purpose of the study described in this paper is to develop a conceptual framework that adopts performance measures for ex-ante decision-making at an operational level within the supply chain. A case study at Coca-Cola Enterprises has been carried out and as a result, a conceptual framework of the PMS has been developed.
- Applicability of a Markov-chain Monte Carlo method for damage detection on data from the Z-24 and Tamar suspension bridgesPublication . Figueiredo, E.; Radu, L.; Westgate, R.; Brownjohn, J.; Cross, E.; Worden, K.; Farrar, C.In the Structural Health Monitoring of bridges, the effects of the operational and environmental variability on the structural responses have posed several challenges for early damage detection. In order to overcome those challenges, in the last decade recourse has been made to the statistical pattern recognition paradigm based on vibration data from long-term monitoring. The use of purely data-based algorithms that do not depend on the physical descriptions of the structures have characterized this paradigm. However, one drawback of this procedure is how to set up the baseline condition for new and existing bridges. Therefore, this paper proposes an algorithm with a Bayesian approach based on a Markov-chain Monte Carlo method to cluster structural responses of the bridges into a reduced number of global state conditions, by taking into account eventual multimodality and heterogeneity of the data distribution. This approach, along with the Mahalanobis squared-distance, permits one to form an algorithm able to detect structural damage based on daily response data even under abnormal events caused by operational and environmental variability. The applicability of this approach is first demonstrated on standard data sets from the Z-24 Bridge, Switzerland. Afterwards, for generalization purposes, it is applied on datasets from a supposed undamaged bridge condition, namely the Tamar Bridge, England. The analysis suggests that this algorithm might be useful for bridge applications, because it permits one to overcome some of the limitations posed by the pattern recognition paradigm, especially when dealing with limited amounts of training data.