Workload analysis
Workload analysis has been defined as a measurement of "a hypothetical construct that represents the cost incurred by a human operator to achieve a particular level of performance" (Hancock, P., 1988, p. 140). The analysis of the workload is thus focused on the person performing an action, not on the task itself (Hancock, P., 1988, p. 140). Arellando offers a different perspective, defining workload "as a complex and multifaceted construct, usually defined as the portion of resource spent to develop a particular activity or task" (2015, p. 2).
Important aspects in workload analysis
Workload should be evaluated taking into account the following aspects (Hancock, P., 1988, p. 140):
- situation in which a task is carried out
- conditions and the requirements connected with a task
- features defining a person performing a task, e.g. their knowledge, skills, personality
- equipment necessary to do a task.
As mentioned by Hancock (1988, p. 139), there is a lot of disagreement about the nature and definition of workload among researchers. However, workload as such can find multiple practical applications because of its measurability. The analysis of the workload can be conducted using various methods, among which "subjective ratings" enjoy the greatest popularity.
Apart from adopting the most effective procedures, an operator needs to manage their expectations connected with the task, utilising the available technology (Hancock, P., 1988, p. 141). In order to measure workload, it is necessary to differentiate between two types of efforts that are made by an operator:
- physical, which is relatively easy to quantify
- mental, which is more challenging to measure (Hancock, P., 1988, p. 141).
In addition, Waard stresses the complexity of the problem of workload analysis by saying that "there is no simple relationship between performance and effort invested" (1996, p. 16). Due to a rapid development of technology and the availability of machines, physical effort seems to be less common in many professions, although the difficulty with measuring mental efforts remains unchanged (Hancock, P., 1988, p. 141).
The analysis of workload is crucial for every type of company and the limitations connected with its proper measurement are significant not only for companies employing physical workers, but also for those taking on white collar workers. Heavy workload is inextricably linked to fatigue, which in turn has a direct impact on safety at work in various industrial environments (Arellano, J., 2015, p. 1).
The importance of understanding the correlation between workload and fatigue is especially important e.g. for manufacturing companies. It has been proved that e.g. sleep deprivation, the number of working hours, the weight carried, etc. can significantly increase workload (Arellano, J., 2015, p. 1). As a consequence of excessive workload in industry, workers can suffer from "blurred vision, paleness of the skin, difficulty in speech, slow response/reaction time, low body temperature, decreased heart rate, headaches and intermittent loss of muscle strength" (Arellano, J., 2015, p. 2). Office workers, who are involved in various strenuous tasks, can also experience these symptoms. Apart from being dangerous for employees, significant and prolonged workload makes workers lose their motivation and engagement at work and can encourage people to leave their company. This is why proper analysis of workload should be of great importance to management.
| Workload analysis — recommended articles |
| Bias for action — Personnel strategy — Internal training — Organizational techniques — Examples of weaknesses — Ergonomics — Internal transfers — Importance of teamwork — Competency modeling |
References
- Arellano, J., (2015), Relationship between Workload and Fatigue among Mexican Assembly Operators, "International Journal of Physical Medicine & Rehabilitation", no. 3
- Hancock, P., (1988), Human Mental Workload, Elsevier Science Publishers B.V., North Holland
- Waard, D., (1996), The Measurement of Drivers' Mental Workload, The Traffic Research Centre VSC, The Netherlands
Author: Małgorzata Goryl
