Analyzing a fleet solution using scenarios

Transportation is one of the most important logistics activities, accounting for a significant part of the logistics costs and with high level of impact in terms of the service level provided to the customer. To counteract the upward trend in costs, it is fundamental to identify a transport strategy that can reduce costs and, at the same time, does not adversely affect the service levels agreed with customers. The main objective of this research is to propose a methodology for companies to identify, from a set of scenarios proposed and through a comparative analysis of scenarios, a new fleet solution, allowing the Company under study to reduce its transport costs without harming the current service level agreements with its customers. This research is grounded on a case study methodology. The case study used is of a small Portuguese company that produces, imports and distributes a wide range of products. The distribution is conducted based on both its own fleet and subcontracted transportation. The comparative analysis of scenarios allowed identifying the current transportation solution as the most advantageous one for the company. A roadmap to address fleet solution is provided.info:eu-repo/semantics/acceptedVersio

Methods for specifying the target difference in a randomised controlled trial : the Difference ELicitation in TriAls (DELTA) systematic review

Peer reviewedPublisher PD

Validation of a score chart to predict the risk of chronic mesenteric ischemia and development of an updated score chart

Background and objective: The objective of this article is to externally validate and update a recently published score chart for chronic mesenteric ischemia (CMI). Methods: A multicenter prospective cohort analysis was conducted of 666 CMI-suspected patients referred to two Dutch specialized CMI centers. Multidisciplinary consultation resulted in expert-based consensus diagnosis after which CMI consensus patients were treated. A definitive diagnosis of CMI was established if successful treatment resulted in durable symptom relief. The absolute CMI risk was calculated and discriminative ability of the original chart was assessed by the c-statistic in the validation cohort. Thereafter the original score chart was updated based on the performance in the combined original and validation cohort with inclusion of celiac artery (CA) stenosis cause. Results: In 8% of low-risk patients, 39% of intermediate-risk patients and 94% of high-risk patients of the validation cohort, CMI was diagnosed. Discriminative ability of the original model was acceptable (c-statistic 0.79). The total score of the updated chart ranged from 0 to 28 points (low risk 19% absolute CMI risk, intermediate risk 45%, and high risk 92%). The discriminative ability of the updated chart was slightly better (c-statistic 0.80). Conclusion: The CMI prediction model performs and discriminates well in the validation cohort. The updated score chart has excellent discriminative ability and is useful in clinical decision making