Blowing Up Safety Culture: The Lure and Trap of Accident Investigation and Continuous Improvement

Safety culture is clearly recognized as an important element of any organization. This is of particular importance for high-risk industries where complex sociotechnical systems exist. In many industries a great deal of energy, time and money continues to be expended in trying to get the culture right. Active safety programs such as the Voluntary Protection Program, peer observation programs such as behavior-based safety, planned audits and inspections from a variety of bodies both internal and external to the organization, as well as audits by regulatory bodies are regularly employed. And when something bad happens there are standard protocols for investigating accidents leading to corrective actions that seek to prevent another occurrence. This coupled with the fact that for decades there have been countless programs directed at quality and continuous improvement has led to situations where we can become captured by quality and we are led away from understanding the greater situational context. In addition, a specific intervention and step-by-step approach is described that was applied to “blow up” and then reset the safety culture of an operational facility

Retrospective Application of Human Reliability Analysis for Oil and Gas Incidents: A Case Study Using the Petro-HRA Method

Human reliability analysis (HRA) may be performed prospectively for a newly designed system or retrospectively for an as-built system, typically in response to a safety incident. The SPAR-H HRA method was originally developed for retrospective analysis in the U.S. nuclear industry. As HRA has found homes in new safety critical areas, HRA methods developed predominantly for nuclear power applications are being used in novel ways. The Petro-HRA method represents a significant adaptation of the SPAR-H method for petroleum applications. Current guidance on Petro-HRA considers only prospective applications of the method, such as for review of new systems to be installed at offshore installations. In this paper, we review retrospective applications of Petro-HRA and analyze the Macando Oil Well-Deepwater Horizon accident as a case study

The Origins of the SPAR-H Method's Performance Shaping Factor Multipliers

The Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method has proved to be a reliable, easy-to-use method for human reliability analysis. Calculation of human error probability (HEP) rates is especially straightforward, starting with pre-defined nominal error rates for cognitive vs. action oriented tasks, and incorporating performance shaping factor (PSF) multipliers upon those nominal error rates. SPAR-H uses eight PSFs with multipliers typically corresponding to nominal, degraded, and severely degraded human performance for individual PSFs. Additionally, some PSFs feature multipliers to reflect enhanced performance. Although SPAR-H enjoys widespread use among industry and regulators, current source documents on SPAR-H such as NUREG/CR-6883 do not provide a clear account of the origin of these multipliers. The present paper redresses this shortcoming and documents the historic development of the SPAR-H PSF multipliers, from the initial use of nominal error rates, to the selection of the eight PSFs, to the mapping of multipliers to available data sources such as a Technique for Human Error Rate Prediction (THERP). Where error rates were not readily derived from THERP and other sources, expert judgment was used to extrapolate appropriate values. In documenting key background information on the multipliers, this paper provides a much needed cross-reference for human reliability practitioners and researchers of SPAR-H to validate analyses and research findings

HUMAN ERROR QUANTIFICATION USING PERFORMANCE SHAPING FACTORS IN THE SPAR-H METHOD

This paper describes a cognitively based human reliability analysis (HRA) quantification technique for estimating the human error probabilities (HEPs) associated with operator and crew actions at nuclear power plants. The method described here, Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method, was developed to aid in characterizing and quantifying human performance at nuclear power plants. The intent was to develop a defensible method that would consider all factors that may influence performance. In the SPAR-H approach, calculation of HEP rates is especially straightforward, starting with pre-defined nominal error rates for cognitive vs. action-oriented tasks, and incorporating performance shaping factor multipliers upon those nominal error rates

Lessons Learned from Dependency Usage in HERA: Implications for THERP-Related HRA Methods

Dependency occurs when the probability of success or failure on one action changes the probability of success or failure on a subsequent action. Dependency may serve as a modifier on the human error probabilities (HEPs) for successive actions in human reliability analysis (HRA) models. Discretion should be employed when determining whether or not a dependency calculation is warranted: dependency should not be assigned without strongly grounded reasons. Human reliability analysts may sometimes assign dependency in cases where it is unwarranted. This inappropriate assignment is attributed to a lack of clear guidance to encompass the range of scenarios human reliability analysts are addressing. Inappropriate assignment of dependency produces inappropriately elevated HEP values. Lessons learned about dependency usage in the Human Event Repository and Analysis (HERA) system may provide clarification and guidance for analysts using first-generation HRA methods. This paper presents the HERA approach to dependency assessment and discusses considerations for dependency usage in HRA, including the cognitive basis for dependency, direction for determining when dependency should be assessed, considerations for determining the dependency level, temporal issues to consider when assessing dependency, (e.g., considering task sequence versus overall event sequence, and dependency over long periods of time), and diagnosis and action influences on dependency

Assessing Dependency in SPAR-H: Some Practical Considerations

Dependency, the relationship between a series of human errors, is an important factor to consider when conducting a human reliability analysis (HRA). The premise of this paper is that we should not expect dependency to be applied frequently. This paper presents guidance on the application of dependency in the SPAR-H HRA method. Overall guidance is provided to prevent overuse of dependency, and then eight specific insights are provided to ensure dependency is applied correctly. Without proper application of dependency, there is the risk that human error probabilities produced by HRA methods will be inaccurate. This paper seeks to ensure dependency does not lead to spurious quantification of errors

TWO VIEWS OF PUBLIC PARTICIPATION

ABSTRACT Risk perception has been studied extensively over the past several decades. This research has defined the differences that exist between and among various groups as defined by their education, interests, geographic distribution, and beliefs. It has also been repeatedly demonstrated that various public groups can and do have a tremendous impact on decisions made in the public and private sectors. Involved citizens for example, have caused international corporations as well as the Department of Energy to change or even reverse a chosen course of action. A frequent cause of such reversals is attributed to a lack of involvement of the public and other key decision players directly in the decision process itself. Through our research and case studies, we have developed both an "as is" and a "participatory" model of decision-making process. The latter decision model allows the direct involvement of important player groups. The paper presents and discusses these models in theoretical and practical terms taken from case studies of the Brent Spar disposal in the North Atlantic, and the use of incineration as a method of waste treatment at the Idaho National Engineering Laboratory. Results from the case studies are used to demonstrate why the "as is" model accurately describes the current situation, and how the "participatory model" will allow decisions to be made that are publicly supported and can be implemented. The use of such a model will provide users a framework from which to successfully make progress in a wide range of environmental endeavors cooperatively with the public, rather than in spite of the public. THE STATUS QUO In the past, industry as well as DOE has engaged in a variety of technical analyses to support decision-making activities. These analyses have included risk assessments, economic analyses, and a variety of other initiatives to identify needed research and technology development. Unfortunately, involvement of the public in this process has been limited and at best, may be characterized as simply seeking approval of decisions already framed and in many instances, already made. The laws surrounding public involvement do require public meetings for comment and input but fall well short of establishing meaningful dialogue. Much of what may be discussed in public meetings revolves around the technical analyses supporting the decisions and/or a description of the planned activities. Such discussions essentially reduce the role of the public to one of approving a decision that has already been made. Upon presentation of such a decision, coupled with the lack of involvement, the public often objects to the decision and takes action, legal or otherwise, to stop its implementation

Challenges with data for human reliability analysis

A lack of empirical data is often been presented as a large challenge for HRA, which begs the question: why is this so difficult? HRA methods were not developed as objective quantitative test methods, but more as qualitative evaluation methods because objective data did not exist. Since HRA methods include substantial qualitative evaluation of the meaning of the elements in HRA methods, such as definitions of the performance shaping factors as well as their strength, these elements cannot be objective measured. This paper also discusses other challenges with collection data from event reports, literature reviews, experiments and databases. The conclusion in this paper is that a decision should be made about how we should look at HRA methods: as qualitative evaluation methods or objective quantitative test methods. Quantitative and qualitative methods have different approaches to evaluate the quality of the methods making it difficult to be something in between

Lessons Learned from Dependency Usage in HERA: Implications for THERP-Related HRA Methods Joint 8th Annual Conference on Human Factors and Power Plants and the 13th Annual Workshop on Human Performance / Root Cause / Trending / Operating Experience / and

Abstract-Dependency occurs when the probability of success or failure on one action changes the probability of success or failure on a subsequent action. Dependency may serve as a modifier on the human error probabilities (HEPs) for successive actions in human reliability analysis (HRA) models. Discretion should be employed when determining whether or not a dependency calculation is warranted: dependency should not be assigned without strongly grounded reasons. Human reliability analysts may sometimes assign dependency in cases where it is unwarranted. This inappropriate assignment is attributed to a lack of clear guidance to encompass the range of scenarios human reliability analysts are addressing. Inappropriate assignment of dependency produces inappropriately elevated HEP values. Lessons learned about dependency usage in the Human Event Repository and Analysis (HERA) system may provide clarification and guidance for analysts using THERP-based dependency models. This paper presents the HERA approach to dependency assessment and discusses considerations for dependency usage in HRA, including the cognitive basis for dependency, direction for determining when dependency should be assessed, considerations for determining the dependency level, temporal issues to consider when assessing dependency, (e.g., considering task sequence versus overall event sequence, and dependency over long periods of time), and diagnosis and action influences on dependency