Development of a stakeholder identification and analysis method for human factors integration in work system design interventions – Change Agent Infrastructure

In any work system design intervention—for example, a physical workplace re-design, a work process change, or an equipment upgrade—it is often emphasized how important it is to involve stakeholders in the process of analysis and design, to gain their perspectives as input to the development, and ensure their future acceptance of the solution. While the users of an artifact or workplace are most often regarded as being the most important stakeholders in a design intervention, in a work-system context there may be additional influential stakeholders who influence and negotiate the design intervention's outcomes, resource allocation, requirements, and implementation. Literature shows that it is uncommon for empirical ergonomics and human factors (EHF) research to apply and report the use of any structured stakeholder identification method at all, leading to ad-hoc selections of whom to consider important. Conversely, other research fields offer a plethora of stakeholder identification and analysis methods, few of which seem to have been adopted in the EHF context. This article presents the development of a structured method for identification, classification, and qualitative analysis of stakeholders in EHF-related work system design intervention. It describes the method's EHF-related theoretical underpinnings, lessons learned from four use cases, and the incremental development of the method that has resulted in the current method procedure and visualization aids. The method, called Change Agent Infrastructure (abbreviated CHAI), has a mainly macroergonomic purpose, set on increasing the understanding of sociotechnical interactions that create the conditions for work system design intervention, and facilitating participative efforts.     

CCPE: Methodology for a Combined Evaluation of Cognitive and Physical Ergonomics in the Interaction between Human and Machine

Evaluation methods of today often focus on cognitive ergonomics (such as mental workload or usability) or physical ergonomics (such as physical workload or body posture). This article describes an analytical methodology of a joint systematic search for potential deficiencies in the human–machine interaction; such as high physical and mental workload, use errors, usability problems, and physical ergonomic errors. The purpose with the joint search is to achieve a more holistic evaluation approach and make the evaluation cost more effective than when using separate evaluation methods for cognitive and physical ergonomic aspects. The methodology is task‐based, which makes it possible to use both with focus on the device design, as in development projects; as well as with focus on the procedure, in the operative organization. © 2012 Wiley Periodicals, Inc.     

A model for software rework reduction through a combination of anomaly metrics

Analysis of anomalies reported during testing of a project can tell a lot about how well the processes and products work. Still, organizations rarely use anomaly reports for more than progress tracking although projects commonly spend a significant part of the development time on finding and correcting faults. This paper presents an anomaly metrics model that organizations can use for identifying improvements in the development process, i.e. to reduce the cost and lead-time spent on rework-related activities and to improve the quality of the delivered product. The model is the result of a four year research project performed at Ericsson.     

Predictive use error analysis – Development of AEA,SHERPA and PHEA to better predict,identify and present use errors

In health care, the use of technical equipment plays an integral part. To achieve a high level of patient safety, it is important to avoid use errors when handling equipment. Use errors can be mitigated by performing analyses of potential use errors during the design process. One proactive analytical method for use error analysis is Predictive Use Error Analysis (PUEA), which is a further development of the methods Action Error Analysis (AEA), Systematic Human Error Reduction and Prediction Approach (SHERPA) and Predictive Human Error Analysis (PHEA). PUEA employs a detailed process for breaking down the user's tasks into steps and then identifying and investigating potential errors of use for each step. Compared with other methods, it is significant in its use of two question levels, greater inclusion of human cognition theory and that the results of the analysis are presented in matrixes.