Lateness in production systems - In a nutshell: How to determine the causes of lateness at work systems?

Adherence to customer due dates is the yardstick for the performance of manufacturing companies. In the era of same-day delivery, consumers expect reliable delivery of ordered goods and short delivery times. Also, in the field of business-to-business supply, it is evident that adherence to delivery dates is a fundamental logistical objective for companies. Contract manufacturers, in particular, are confronted with significant challenges: strong fluctuations in customer demand, shorter requested delivery times, and high competitive pressure require appropriate organisation, planning and control of production. However, companies often miss their schedule reliability targets and fail to identify the right causes for these failures. This raises the question of what factors influence the failure to meet schedule reliability targets, how to identify such factors, and what options are available to counteract them. This contribution addresses this issue and focuses on ways to analyse the emerging lateness at work systems in production areas as a deviation of the actual form the planned throughput time. We present existing approaches to analysing the lateness behaviour at work systems and extend the current theory of logistical modelling to determine the three drivers of the so-called relative lateness – planning influences, variance of work-in-process (WIP) and sequence deviations – at work systems systematically. Through this analysis, we enable the practical applicator to initiate target-oriented countermeasures to improve the schedule reliability of their work systems with acceptable analysis expenditure

Supplemental material for Group sequential designs with robust semiparametric recurrent event models

<p>Supplemental material for Group sequential designs with robust semiparametric recurrent event models by Tobias Mütze, Ekkehard Glimm, Heinz Schmidli and Tim Friede in Statistical Methods in Medical Research</p

Insights into permanent pacemaker implantation following TAVR in a real-world cohort.

BACKGROUND:Permanent pacemaker implantation (PPI) following TAVR is a frequent post interventional complication and its management remains controversial. OBJECTIVE:We sought to elucidate the electrophysiological, procedural, and clinical baseline parameters that are associated with and perhaps predict the need for PPI after TAVR in a heterogeneous-valve-type real-world cohort. METHODS:Overall, 494 patients receiving TAVR at our center from April 2009 to August 2015 were screened. ECG analyses and clinical parameters were collected prospectively. RESULTS:Overall, 401 patients in this all-comers real-world TAVR cohort with a PPI rate of 16% were included. The mean age was 82 years, and the mean duration to PPI was 5.5 days. A large proportion of Edwards SAPIEN valves (81%), DirectFlow, CoreValve, and Portico were implanted. The main indications for PPI were atrioventricular (AV) block III, AV-block Mobitz type II, bradycardic atrial fibrillation and persistent sinus bradycardia. Between groups with and without PPI, significant differences were noted in the prevalence of post TAVR balloon dilatation, resting heart rate, QRS interval, PR interval with a cut-off of >178 ms, left anterior fascicular block and RBBB in univariate analyses. In the subsequent multiple regression analysis, post TAVR balloon dilatation and a PR interval with a cut-off of >178 ms were significant predictors of PPI. CONCLUSION:This real-world cohort differs from others in its size and heterogeneous valve selection, and indicates for the first time that patients with post balloon dilatation or prolonged PR interval are at a higher risk for pacemaker dependency after TAVR

Win statistics (win ratio, win odds, and net benefit) can complement one another to show the strength of the treatment effect on time‐to‐event outcomes

Conventional analyses of a composite of multiple time-to-event outcomes use the time to the first event. However, the first event may not be the most important outcome. To address this limitation, generalized pairwise comparisons and win statistics (win ratio, win odds, and net benefit) have become popular and have been applied to clinical trial practice. However, win ratio, win odds, and net benefit have typically been used separately. In this article, we examine the use of these three win statistics jointly for time-to-event outcomes. First, we explain the relation of point estimates and variances among the three win statistics, and the relation between the net benefit and the Mann–Whitney U statistic. Then we explain that the three win statistics are based on the same win proportions, and they test the same null hypothesis of equal win probabilities in two groups. We show theoretically that the Z-values of the corresponding statistical tests are approximately equal; therefore, the three win statistics provide very similar p-values and statistical powers. Finally, using simulation studies and data from a clinical trial, we demonstrate that, when there is no (or little) censoring, the three win statistics can complement one another to show the strength of the treatment effect. However, when the amount of censoring is not small, and without adjustment for censoring, the win odds and the net benefit may have an advantage for interpreting the treatment effect; with adjustment (e.g., IPCW adjustment) for censoring, the three win statistics can complement one another to show the strength of the treatment effect. For calculations we use the R package WINS, available on the CRAN (Comprehensive R Archive Network)