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Published in Braithwaite Jeffrey, Mannion Russell, Matsuyama Yukihiro, Shekelle Paul, Whittaker Stuart, Al-Adawi Samir, Health Systems Improvement Across the Globe: Success Stories from 60 Countries, 2017
David Vaughan, Mylai Guerrero, Yousuf Khalid Al Maslamani, Charles Pain
An easy-to-use online data system was created that allowed all facilities to input their data and to compare results within and between units and facilities. A large number of secondary measures were defined and developed, which, although not mandatory, were provided to allow facilities to identify and improve areas where their performance might be enhanced.
Bottleneck detection in high-variety make-to-Order shops with complex routings: an assessment by simulation
Published in Production Planning & Control, 2022
Matthias Thürer, Lin Ma, Mark Stevenson, Christoph Roser
A main limitation of our study is that we neglected the actual exploitation of the bottleneck (Step 2 and Step3) and directly jumped to its elevation (Step 4). Future research could explore the link between bottleneck detection and, for example, the Drum-Buffer-Rope approach (e.g. Darlington et al. 2015) or Constant Load (Bagni et al. 2021) that focusses on bottleneck exploitation. Meanwhile, we also did not consider limits on the finished goods inventory, while demand was the bottleneck for a significant amount of time. Specifically, the latter calls for more research, potentially linking bottleneck detection to the job entry or customer enquiry stage where the job acceptance decisions are made and consequently demand is realized. Our focus has been on the actual operational impact of bottleneck detection methods on the shop floor. Finally, future research could also seek to develop new bottleneck detection methods. We saw that bottleneck detection methods can be subdivided according to the measure used, and a bottleneck is necessarily defined in terms of the chosen measure. Thus, a first step is to define the objective of the system and how this is measured. For example, in our make-to-order system the main objective is delivery performance (primary measure) rather than throughput (secondary measure), whereas throughput is the primary measure in most of the previous literature on bottleneck detection. Developing new bottleneck detection methods for so-called lateness bottlenecks (Fang et al. 2020) is a promising avenue for future research.
Mouse Movement Trajectories as an Indicator of Cognitive Workload
Published in International Journal of Human–Computer Interaction, 2022
Alexander Thorpe, Jason Friedman, Sylvia Evans, Keith Nesbitt, Ami Eidels
Another method of assessing the cognitive workload of a computer-based system is by presenting a secondary measure. This can take the form of a physiological measure, such as heart rate tracking (Rajan et al., 2016; Rottger et al., 2009; Ryu & Myung, 2005), or eye tracking (Causse, Imbert et al., 2016; Kim & Wohn, 2011; Kujala & Saariluoma, 2011). These measures are useful as they serve as indicators of physiological arousal, which in turn reflects the user’s stress and attention capacity (Bach et al., 2009). A drawback of these measures is the need for measurement devices, which can be expensive and obtrusive. An alternative secondary measure is a dual task experimental paradigm. Such designs present a secondary task whose demands do not change, alongside a primary task with differing levels of cognitive workload. Any changes in performance on the secondary task are inferred to be the result of changing demands in the primary task. This design has been used to assess the cognitive impact of interacting with synthetic talking head systems (Stevens et al., 2013) and the effects of multitasking on driving ability (Salvucci & Beltowska, 2008). The latter application has also given rise to the development of a standardized dual task measure, the detection response task.
Using timbre to improve performance of larger auditory alarm sets
Published in Ergonomics, 2019
Michael F. Rayo, Emily S. Patterson, Mahmoud Abdel-Rasoul, Susan D. Moffatt-Bruce
Longitudinal logistic regression models were used to determine statistical differences in identifiability. The primary measure was difference in overall alarm identifiability between the two-alarm sets. The secondary measure was the alarm category identifiability between the two-alarm sets. Successful alarm category identification was defined as identifying any alarm in the same alarm category as the alarm being played. Subsequent analyses were performed to determine differences in identifiability of the alarm and alarm category of individual sounds in each set. These models included main effects for the alarm set, the individual sound and participant age, and allowed for repeated measures across participants over time and set. These analyses were conducted using SAS version 9.4. (SAS Institute, Inc., Cary, NC).