The Incorporation of Human Performance Improvement Into Systems Design

Author(s):  
Jonathan Corrado

Abstract Although considerable research has been conducted on the human-machine interface, this is a moving target as industry sprints to keep up with technological advances. Conflicts remain between the optimism of technology developers and the real-life operational difficulties that accompany the introduction of these systems. The developers typically claim that the new technology will result in performance improvements. Due to the operational complexities introduced, however, the technology may actually decrease user performance. Unfortunately, the complexities confronting operators are difficult for design teams to predict. Incorporating advances in technology is necessary, but should be properly balanced within the confines of the system. It is easy to forget that humans are a vital part of this system. The human, including the human's inclination for error, should be considered a fundamental aspect of the system, reflected in design and accounted for in the design process. Engaged human involvement is necessary for safe and successful system operation, but like all systems, it has its failure modes. Humans' innate propensity for error in system operation should be addressed from multiple fronts. This article proposes a method to minimize the impact of human error throughout life of a facility via incorporation of a human performance improvement model that institutes human error severity criteria, establishment of a system to capture human error data, and via data trending, a process to predict negative behaviors before potential errors or adverse events can occur.

Author(s):  
Eric Brehm ◽  
Robert Hertle ◽  
Markus Wetzel

In common structural design, random variables, such as material strength or loads, are represented by fixed numbers defined in design codes. This is also referred to as deterministic design. Addressing the random character of these variables directly, the probabilistic design procedure allows the determination of the probability of exceeding a defined limit state. This probability is referred to as failure probability. From there, the structural reliability, representing the survival probability, can be determined. Structural reliability thus is a property of a structure or structural member, depending on the relevant limit states, failure modes and basic variables. This is the basis for the determination of partial safety factors which are, for sake of a simpler design, applied within deterministic design procedures. In addition to the basic variables in terms of material and loads, further basic variables representing the structural model have to be considered. These depend strongly on the experience of the design engineer and the level of detailing of the model. However, in the clear majority of cases [1] failure does not occur due to unexpectedly high or low values of loads or material strength. The most common reasons for failure are human errors in design and execution. This paper will provide practical examples of original designs affected by human error and will assess the impact on structural reliability.


1989 ◽  
Vol 33 (16) ◽  
pp. 1089-1093 ◽  
Author(s):  
James W. Broyles

Fourteen U.S. Navy personnel with Aegis Combat System, Naval Tactical Data System (NTDS), and Non-NTDS operational experience participated in an experiment designed to investigate the impact of proposed workstation designs on operator performance, system usability, and training. Human performance data were collected on a sample of operational procedures typically performed in a Combat Information Center (CIC) for a current Navy Combat System and a prototype workstation. The prototype was developed using specific human factors design principles with the goal of reducing training time, improving operator retention of skills for system operation, reducing errors in system operation, improving operator efficiency (e.g., speed & accuracy of performance), and improving user's satisfaction with the user-computer interface. This paper reports only the preliminary results for data collected from seven subjects who performed procedures using the prototype workstation.


2018 ◽  
Author(s):  
William J. Rothwell ◽  
Carolyn K. Hohne ◽  
Stephen B. King

2014 ◽  
Vol 53 (1) ◽  
pp. 10-23 ◽  
Author(s):  
Anthony Marker ◽  
Steven W. Villachica ◽  
Donald Stepich ◽  
DeAnn Allen ◽  
Lorece Stanton

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