A Framework to Guide the Assessment of Human–Machine Systems

Pien

American J Industrial Med

2020

ObjectiveWork automation is increasing worldwide, and the probability of job automation has been associated with workers' adverse health outcomes. This study aimed to examine the association of occupation‐level automation probability with work stress and workers' health.MethodsWe used data from a national survey of 14,948 randomly selected general workers conducted in 2016. Job control and job demand were assessed by the Job Content Questionnaire, and working hours and job insecurity were self‐reported. Health outcomes were measured according to burnout and work‐related injury or disease. We derived automation probabilities for 38 occupational groups and conducted multilevel analyses to examine the associations between occupation‐level automation probability and workers' safety and health after adjusting for psychosocial work conditions.ResultsParticipants working in jobs with a high probability of automation were more likely to have low job control, higher job insecurity, and work‐related injury and disease prevalence; whereas workers in jobs with a low automation probability had higher psychological and physical demands and burnout prevalence. Furthermore, automation probability significantly predicted workers' health after adjustment for demographic characteristics and psychosocial work conditions.ConclusionsWorkers with low automation probability jobs may experience work stress other than that captured by traditional measures of job strain. Organizational approaches to improve employment security and psychosocial conditions are essential for workers' safety and health in the context of increasing job automation.

Section: Discussionmentioning

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Occupation‐level automation probability is associated with psychosocial work conditions and workers' health: A multilevel study

Pien

American J Industrial Med

2020

“…The input phase includes the individual and contextual input components (Stowers et al, ). The individual component is considered to comprise of KNO and the pilot's cognitive capacity—both remaining relatively stable for the duration that the flight deck crew is committed to a task.…”

Section: Flight Deck As a Human‐machine System: Introducing Crm‐dymomentioning

confidence: 99%

Pilot competencies as components of a dynamic human‐machine system

Mansikka

Harris

Virtanen

2019

Hum Ftrs & Erg Mfg Svc

This paper formulates a theoretical model of flight deck team performance as a dynamic human‐machine system, later referred to as CRM‐DYMO (Crew Resources Management‐DYnamic MOdel). The model identifies human and machine subsystems. The human subsystem describes the activities taken by the flight crew and the machine subsystem describes the changes in the aircraft's state. The human subsystem's output forms an input for the machine subsystem, and the machine subsystem's output feeds back to the human system creating a closed loop system. Pilots’ performance are described as competencies, which form part of the elements in the human subsystem. This paper concentrates only on CRM competencies as defined by the International Civil Aviation Authority and the International Air Transport Association. In addition to the detailed conceptual model of the human subsystem and associated competencies, the approach is illustrated with reference to two accident case studies. These demonstrate how CRM‐DYMO can assist in analyzing CRM and help in identifying how the different system components interact. Although the case studies demonstrate the benefits of CRM‐DYMO, they merely serve as a starting point to understand how CRM works and will hopefully encourage supporting quantitative simulation modeling and empirical studies.

“…However, most of the current frameworks in the field of digitization and cyber-physical systems rely heavily on a purely technological approach, neglecting the interdependence of the technology with organizational and human factors in a scenario of digitized work. There are other approaches, such as frameworks on the assessment of human-machine systems [12] or frameworks for human-centred design [13] that include human-related and organizational factors but lack the transfer into a practical instrument and have therefore a limited practical value. An application of these frameworks often requires a deep analysis and understanding of the current (manufacturing) system, a process which can be time and resource intensive.…”

Section: Introductionmentioning

confidence: 99%

Development of a Change Management Instrument for the Implementation of Technologies

et al. 2018

The manufacturing industry is increasingly being dominated by information and communication technology, leading to the development of cyber-physical systems. Most existing frameworks on the assessment of such technological advancements see the technology as a solitary system. However, research has shown that other environmental factors like organizational processes or human factors are also affected. Drawing on the sociotechnical systems approach, future technologies could be evaluated using scenarios of digitized work. These scenarios can help classify new technologies and uncover their advantages and constraints in order to provide guidance for the digital development of organizations. We developed an instrument for evaluating scenarios of digitized work on the relevant dimensions ‘technology’, ‘human’ and ‘organization’ and conducted a quantitative study applying this instrument on three different scenarios (N = 24 subject matter experts). Results show that our instrument is capable of measuring technological, human and organizational aspects of technology implementations and detecting differences in the scenarios under investigation. The instrument’s practical value is significant as it enables the user to compare and quantify scenarios and helps companies to decide which technology they should implement.